Article

From the Institute for Healthcare Improvement, Boston, MA (Dr. Abid); Continuous Quality Improvement and Patient Safety Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Abid); Primary and Secondary Healthcare Department, Government of Punjab, Lahore, Pakistan (Dr. Ahmed); Infection Prevention and Control Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Din); Internal Medicine Department, Greater Baltimore Medical Center, Baltimore, MD (Dr. Abid); Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, Houston, TX (Dr. Ratnani).

Diagnostic errors are defined by the National Academies of Sciences, Engineering, and Medicine (NASEM) as the failure to either establish an accurate and timely explanation of the patient’s health problem(s) or communicate that explanation to the patient.¹ According to a report by the Institute of Medicine, diagnostic errors account for a substantial number of adverse events in health care, affecting an estimated 12 million Americans each year.¹ Diagnostic errors are a common and serious issue in health care systems, with studies estimating that 5% to 15% of all diagnoses are incorrect.¹ Such errors can result in unnecessary treatments, delays in necessary treatments, and harm to patients. The high prevalence of diagnostic errors in primary care has been identified as a global issue.² While many factors contribute to diagnostic errors, the complex nature of health care systems, the limited processing capacity of human cognition, and deficiencies in interpersonal patient-clinician communication are primary contributors.^3,4

Discussions around the redesign of health care systems to reduce diagnostic errors have been at the forefront of medical research for years.^2,4 To decrease diagnostic errors in health care, a comprehensive strategy is necessary. This strategy should focus on utilizing both human experience (HX) in health care and artificial intelligence (AI) technologies to transform health care systems into proactive, patient-centered, and safer systems, specifically concerning diagnostic errors.¹

Human Experience and Diagnostic Errors

The role of HX in health care cannot be overstated. The HX in health care integrates the sum of all interactions, every encounter among patients, families and care partners, and the health care workforce.⁵ Patients and their families have a unique perspective on their health care experiences that can provide valuable insight into potential diagnostic errors.⁶ The new definition of diagnostic errors introduced in the 2015 NASEM report emphasized the significance of effective communication during the diagnostic procedure.¹ Engaging patients and their families in the diagnostic process can improve communication, improve diagnostic accuracy, and help to identify errors before they cause harm.⁷ However, many patients and families feel that they are not listened to or taken seriously by health care providers, and may not feel comfortable sharing information that they feel is important.⁸ To address this, health care systems can implement programs that encourage patients and families to be more engaged in the diagnostic process, such as shared decision-making, patient portals, and patient and family advisory councils.⁹ Health care systems must prioritize patient-centered care, teamwork, and communication. Patients and their families must be actively engaged in their care, and health care providers must be willing to work collaboratively and listen to patients’ concerns.^6,10

Health care providers also bring their own valuable experiences and expertise to the diagnostic process, as they are often the ones on the front lines of patient care. However, health care providers may not always feel comfortable reporting errors or near misses, and may not have the time or resources to participate in quality improvement initiatives. To address this, health care systems can implement programs that encourage providers to report errors and near misses, such as anonymous reporting systems, just-culture initiatives, and peer review.¹¹ Creating a culture of teamwork and collaboration among health care providers can improve the accuracy of diagnoses and reduce the risk of errors.¹²

A key factor in utilizing HX to reduce diagnostic errors is effective communication. Communication breakdowns among health care providers, patients, and their families are a common contributing factor resulting in diagnostic errors.² Strategies to improve communication include using clear and concise language, involving patients and their families in the decision-making process, and utilizing electronic health records (EHRs) to ensure that all health care providers have access to relevant, accurate, and up-to-date patient information.^4,13,14

Another important aspect of utilizing HX in health care to reduce diagnostic errors is the need to recognize and address cognitive biases that may influence diagnostic decisions.³ Cognitive biases are common in health care and can lead to errors in diagnosis. For example, confirmation bias, which is the tendency to look for information that confirms preexisting beliefs, can lead providers to overlook important diagnostic information.¹⁵ Biases such as anchoring bias, premature closure, and confirmation bias can lead to incorrect diagnoses and can be difficult to recognize and overcome. Addressing cognitive biases requires a commitment to self-reflection and self-awareness among health care providers as well as structured training of health care providers to improve their diagnostic reasoning skills and reduce the risk of cognitive errors.¹⁵ By implementing these strategies around HX in health care, health care systems can become more patient-centered and reduce the likelihood of diagnostic errors (Figure).

 

 

Artificial Intelligence and Diagnostic Errors

Artificial intelligence has the potential to significantly reduce diagnostic errors in health care (Figure), and its role in health care is rapidly expanding. AI technologies such as machine learning (ML) and natural language processing (NLP) have the potential to significantly reduce diagnostic errors by augmenting human cognition and improving access to relevant patient data.^1,16 Machine learning algorithms can analyze large amounts of patient data sets to identify patterns and risk factors and predict patient outcomes, which can aid health care providers in making accurate diagnoses.¹⁷ Artificial intelligence can also help to address some of the communication breakdowns that contribute to diagnostic errors.¹⁸ Natural language processing can improve the accuracy of EHR documentation and reduce the associated clinician burden, making it easier for providers to access relevant patient information and communicate more effectively with each other.¹⁸

In health care, AI can be used to analyze medical images, laboratory results, genomic data, and EHRs to identify potential diagnoses and flag patients who may be at risk for diagnostic errors. One of the primary benefits of AI in health care is its ability to process large amounts of data quickly and accurately.¹⁹ This can be particularly valuable in diagnosing rare or complex conditions. Machine learning algorithms can analyze patient data to identify subtle patterns that may not be apparent to human providers.¹⁶ This can lead to earlier and more accurate diagnoses, which can reduce diagnostic errors and improve patient outcomes.¹⁷ One example of the application of AI in health care is the use of computer-aided detection (CAD) software to analyze medical images. This software can help radiologists detect abnormalities in medical images that may be missed by the human eye, such as early-stage breast cancer.²⁰ Another example is the use of NLP and ML to analyze unstructured data in EHRs, such as physician notes, to identify potential diagnoses and flag patients who may be at risk for diagnostic errors.²¹ A recent study showed that using NLP on EHRs for screening and detecting individuals at risk for psychosis can considerably enhance the prognostic accuracy of psychosis risk calculators.²² This can help identify patients who require assessment and specialized care, facilitating earlier detection and potentially improving patient outcomes. On the same note, ML-based severe sepsis prediction algorithms have been shown to reduce the average length of stay and in-hospital mortality rate.²³

However, there are also concerns about the use of AI in health care, including the potential for bias and the risk of overreliance on AI. Bias can occur when AI algorithms are trained on data that is not representative of the population being analyzed, leading to inaccurate or unfair results, hence, perpetuating and exacerbating existing biases in health care.²⁴ Over-reliance on AI can occur when health care providers rely too heavily on AI algorithms and fail to consider other important information, such as the lived experience of patients, families, and health care providers. Addressing these concerns will require ongoing efforts to ensure that AI technologies are developed and implemented in an ethical and responsible manner.²⁵

Conclusion

Reducing diagnostic errors is a critical goal for health care systems, and requires a comprehensive approach that utilizes both HX and AI technologies. Engaging patients and their families in the diagnostic process, promoting teamwork and collaboration among health care providers, addressing cognitive biases, and harnessing the power of AI can all contribute to more accurate diagnoses and better patient outcomes. By integrating the lived experience of patients, families, and health care providers with AI technologies, health care systems can be redesigned to become more proactive, safer, and patient-centered in identifying potential health problems and reducing the risk of diagnostic errors, ensuring that patients receive the care they need and deserve.

Corresponding author: Iqbal Ratnani, Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, 6565 Fannin St, Houston, TX 77030; IRatnani@houstonmethodist.org

Disclosures: None reported.

From the Institute for Healthcare Improvement, Boston, MA (Dr. Abid); Continuous Quality Improvement and Patient Safety Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Abid); Primary and Secondary Healthcare Department, Government of Punjab, Lahore, Pakistan (Dr. Ahmed); Infection Prevention and Control Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Din); Internal Medicine Department, Greater Baltimore Medical Center, Baltimore, MD (Dr. Abid); Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, Houston, TX (Dr. Ratnani).

Diagnostic errors are defined by the National Academies of Sciences, Engineering, and Medicine (NASEM) as the failure to either establish an accurate and timely explanation of the patient’s health problem(s) or communicate that explanation to the patient.¹ According to a report by the Institute of Medicine, diagnostic errors account for a substantial number of adverse events in health care, affecting an estimated 12 million Americans each year.¹ Diagnostic errors are a common and serious issue in health care systems, with studies estimating that 5% to 15% of all diagnoses are incorrect.¹ Such errors can result in unnecessary treatments, delays in necessary treatments, and harm to patients. The high prevalence of diagnostic errors in primary care has been identified as a global issue.² While many factors contribute to diagnostic errors, the complex nature of health care systems, the limited processing capacity of human cognition, and deficiencies in interpersonal patient-clinician communication are primary contributors.^3,4

Discussions around the redesign of health care systems to reduce diagnostic errors have been at the forefront of medical research for years.^2,4 To decrease diagnostic errors in health care, a comprehensive strategy is necessary. This strategy should focus on utilizing both human experience (HX) in health care and artificial intelligence (AI) technologies to transform health care systems into proactive, patient-centered, and safer systems, specifically concerning diagnostic errors.¹

Human Experience and Diagnostic Errors

The role of HX in health care cannot be overstated. The HX in health care integrates the sum of all interactions, every encounter among patients, families and care partners, and the health care workforce.⁵ Patients and their families have a unique perspective on their health care experiences that can provide valuable insight into potential diagnostic errors.⁶ The new definition of diagnostic errors introduced in the 2015 NASEM report emphasized the significance of effective communication during the diagnostic procedure.¹ Engaging patients and their families in the diagnostic process can improve communication, improve diagnostic accuracy, and help to identify errors before they cause harm.⁷ However, many patients and families feel that they are not listened to or taken seriously by health care providers, and may not feel comfortable sharing information that they feel is important.⁸ To address this, health care systems can implement programs that encourage patients and families to be more engaged in the diagnostic process, such as shared decision-making, patient portals, and patient and family advisory councils.⁹ Health care systems must prioritize patient-centered care, teamwork, and communication. Patients and their families must be actively engaged in their care, and health care providers must be willing to work collaboratively and listen to patients’ concerns.^6,10

Health care providers also bring their own valuable experiences and expertise to the diagnostic process, as they are often the ones on the front lines of patient care. However, health care providers may not always feel comfortable reporting errors or near misses, and may not have the time or resources to participate in quality improvement initiatives. To address this, health care systems can implement programs that encourage providers to report errors and near misses, such as anonymous reporting systems, just-culture initiatives, and peer review.¹¹ Creating a culture of teamwork and collaboration among health care providers can improve the accuracy of diagnoses and reduce the risk of errors.¹²

A key factor in utilizing HX to reduce diagnostic errors is effective communication. Communication breakdowns among health care providers, patients, and their families are a common contributing factor resulting in diagnostic errors.² Strategies to improve communication include using clear and concise language, involving patients and their families in the decision-making process, and utilizing electronic health records (EHRs) to ensure that all health care providers have access to relevant, accurate, and up-to-date patient information.^4,13,14

Another important aspect of utilizing HX in health care to reduce diagnostic errors is the need to recognize and address cognitive biases that may influence diagnostic decisions.³ Cognitive biases are common in health care and can lead to errors in diagnosis. For example, confirmation bias, which is the tendency to look for information that confirms preexisting beliefs, can lead providers to overlook important diagnostic information.¹⁵ Biases such as anchoring bias, premature closure, and confirmation bias can lead to incorrect diagnoses and can be difficult to recognize and overcome. Addressing cognitive biases requires a commitment to self-reflection and self-awareness among health care providers as well as structured training of health care providers to improve their diagnostic reasoning skills and reduce the risk of cognitive errors.¹⁵ By implementing these strategies around HX in health care, health care systems can become more patient-centered and reduce the likelihood of diagnostic errors (Figure).

 

 

Artificial Intelligence and Diagnostic Errors

Artificial intelligence has the potential to significantly reduce diagnostic errors in health care (Figure), and its role in health care is rapidly expanding. AI technologies such as machine learning (ML) and natural language processing (NLP) have the potential to significantly reduce diagnostic errors by augmenting human cognition and improving access to relevant patient data.^1,16 Machine learning algorithms can analyze large amounts of patient data sets to identify patterns and risk factors and predict patient outcomes, which can aid health care providers in making accurate diagnoses.¹⁷ Artificial intelligence can also help to address some of the communication breakdowns that contribute to diagnostic errors.¹⁸ Natural language processing can improve the accuracy of EHR documentation and reduce the associated clinician burden, making it easier for providers to access relevant patient information and communicate more effectively with each other.¹⁸

In health care, AI can be used to analyze medical images, laboratory results, genomic data, and EHRs to identify potential diagnoses and flag patients who may be at risk for diagnostic errors. One of the primary benefits of AI in health care is its ability to process large amounts of data quickly and accurately.¹⁹ This can be particularly valuable in diagnosing rare or complex conditions. Machine learning algorithms can analyze patient data to identify subtle patterns that may not be apparent to human providers.¹⁶ This can lead to earlier and more accurate diagnoses, which can reduce diagnostic errors and improve patient outcomes.¹⁷ One example of the application of AI in health care is the use of computer-aided detection (CAD) software to analyze medical images. This software can help radiologists detect abnormalities in medical images that may be missed by the human eye, such as early-stage breast cancer.²⁰ Another example is the use of NLP and ML to analyze unstructured data in EHRs, such as physician notes, to identify potential diagnoses and flag patients who may be at risk for diagnostic errors.²¹ A recent study showed that using NLP on EHRs for screening and detecting individuals at risk for psychosis can considerably enhance the prognostic accuracy of psychosis risk calculators.²² This can help identify patients who require assessment and specialized care, facilitating earlier detection and potentially improving patient outcomes. On the same note, ML-based severe sepsis prediction algorithms have been shown to reduce the average length of stay and in-hospital mortality rate.²³

However, there are also concerns about the use of AI in health care, including the potential for bias and the risk of overreliance on AI. Bias can occur when AI algorithms are trained on data that is not representative of the population being analyzed, leading to inaccurate or unfair results, hence, perpetuating and exacerbating existing biases in health care.²⁴ Over-reliance on AI can occur when health care providers rely too heavily on AI algorithms and fail to consider other important information, such as the lived experience of patients, families, and health care providers. Addressing these concerns will require ongoing efforts to ensure that AI technologies are developed and implemented in an ethical and responsible manner.²⁵

Conclusion

Reducing diagnostic errors is a critical goal for health care systems, and requires a comprehensive approach that utilizes both HX and AI technologies. Engaging patients and their families in the diagnostic process, promoting teamwork and collaboration among health care providers, addressing cognitive biases, and harnessing the power of AI can all contribute to more accurate diagnoses and better patient outcomes. By integrating the lived experience of patients, families, and health care providers with AI technologies, health care systems can be redesigned to become more proactive, safer, and patient-centered in identifying potential health problems and reducing the risk of diagnostic errors, ensuring that patients receive the care they need and deserve.

Corresponding author: Iqbal Ratnani, Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, 6565 Fannin St, Houston, TX 77030; IRatnani@houstonmethodist.org

Disclosures: None reported.

From the Institute for Healthcare Improvement, Boston, MA (Dr. Abid); Continuous Quality Improvement and Patient Safety Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Abid); Primary and Secondary Healthcare Department, Government of Punjab, Lahore, Pakistan (Dr. Ahmed); Infection Prevention and Control Department, Armed Forces Hospitals Taif Region, Taif, Saudi Arabia (Dr. Din); Internal Medicine Department, Greater Baltimore Medical Center, Baltimore, MD (Dr. Abid); Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, Houston, TX (Dr. Ratnani).

Diagnostic errors are defined by the National Academies of Sciences, Engineering, and Medicine (NASEM) as the failure to either establish an accurate and timely explanation of the patient’s health problem(s) or communicate that explanation to the patient.¹ According to a report by the Institute of Medicine, diagnostic errors account for a substantial number of adverse events in health care, affecting an estimated 12 million Americans each year.¹ Diagnostic errors are a common and serious issue in health care systems, with studies estimating that 5% to 15% of all diagnoses are incorrect.¹ Such errors can result in unnecessary treatments, delays in necessary treatments, and harm to patients. The high prevalence of diagnostic errors in primary care has been identified as a global issue.² While many factors contribute to diagnostic errors, the complex nature of health care systems, the limited processing capacity of human cognition, and deficiencies in interpersonal patient-clinician communication are primary contributors.^3,4

Discussions around the redesign of health care systems to reduce diagnostic errors have been at the forefront of medical research for years.^2,4 To decrease diagnostic errors in health care, a comprehensive strategy is necessary. This strategy should focus on utilizing both human experience (HX) in health care and artificial intelligence (AI) technologies to transform health care systems into proactive, patient-centered, and safer systems, specifically concerning diagnostic errors.¹

Human Experience and Diagnostic Errors

The role of HX in health care cannot be overstated. The HX in health care integrates the sum of all interactions, every encounter among patients, families and care partners, and the health care workforce.⁵ Patients and their families have a unique perspective on their health care experiences that can provide valuable insight into potential diagnostic errors.⁶ The new definition of diagnostic errors introduced in the 2015 NASEM report emphasized the significance of effective communication during the diagnostic procedure.¹ Engaging patients and their families in the diagnostic process can improve communication, improve diagnostic accuracy, and help to identify errors before they cause harm.⁷ However, many patients and families feel that they are not listened to or taken seriously by health care providers, and may not feel comfortable sharing information that they feel is important.⁸ To address this, health care systems can implement programs that encourage patients and families to be more engaged in the diagnostic process, such as shared decision-making, patient portals, and patient and family advisory councils.⁹ Health care systems must prioritize patient-centered care, teamwork, and communication. Patients and their families must be actively engaged in their care, and health care providers must be willing to work collaboratively and listen to patients’ concerns.^6,10

Health care providers also bring their own valuable experiences and expertise to the diagnostic process, as they are often the ones on the front lines of patient care. However, health care providers may not always feel comfortable reporting errors or near misses, and may not have the time or resources to participate in quality improvement initiatives. To address this, health care systems can implement programs that encourage providers to report errors and near misses, such as anonymous reporting systems, just-culture initiatives, and peer review.¹¹ Creating a culture of teamwork and collaboration among health care providers can improve the accuracy of diagnoses and reduce the risk of errors.¹²

A key factor in utilizing HX to reduce diagnostic errors is effective communication. Communication breakdowns among health care providers, patients, and their families are a common contributing factor resulting in diagnostic errors.² Strategies to improve communication include using clear and concise language, involving patients and their families in the decision-making process, and utilizing electronic health records (EHRs) to ensure that all health care providers have access to relevant, accurate, and up-to-date patient information.^4,13,14

Another important aspect of utilizing HX in health care to reduce diagnostic errors is the need to recognize and address cognitive biases that may influence diagnostic decisions.³ Cognitive biases are common in health care and can lead to errors in diagnosis. For example, confirmation bias, which is the tendency to look for information that confirms preexisting beliefs, can lead providers to overlook important diagnostic information.¹⁵ Biases such as anchoring bias, premature closure, and confirmation bias can lead to incorrect diagnoses and can be difficult to recognize and overcome. Addressing cognitive biases requires a commitment to self-reflection and self-awareness among health care providers as well as structured training of health care providers to improve their diagnostic reasoning skills and reduce the risk of cognitive errors.¹⁵ By implementing these strategies around HX in health care, health care systems can become more patient-centered and reduce the likelihood of diagnostic errors (Figure).

 

 

Artificial Intelligence and Diagnostic Errors

Artificial intelligence has the potential to significantly reduce diagnostic errors in health care (Figure), and its role in health care is rapidly expanding. AI technologies such as machine learning (ML) and natural language processing (NLP) have the potential to significantly reduce diagnostic errors by augmenting human cognition and improving access to relevant patient data.^1,16 Machine learning algorithms can analyze large amounts of patient data sets to identify patterns and risk factors and predict patient outcomes, which can aid health care providers in making accurate diagnoses.¹⁷ Artificial intelligence can also help to address some of the communication breakdowns that contribute to diagnostic errors.¹⁸ Natural language processing can improve the accuracy of EHR documentation and reduce the associated clinician burden, making it easier for providers to access relevant patient information and communicate more effectively with each other.¹⁸

In health care, AI can be used to analyze medical images, laboratory results, genomic data, and EHRs to identify potential diagnoses and flag patients who may be at risk for diagnostic errors. One of the primary benefits of AI in health care is its ability to process large amounts of data quickly and accurately.¹⁹ This can be particularly valuable in diagnosing rare or complex conditions. Machine learning algorithms can analyze patient data to identify subtle patterns that may not be apparent to human providers.¹⁶ This can lead to earlier and more accurate diagnoses, which can reduce diagnostic errors and improve patient outcomes.¹⁷ One example of the application of AI in health care is the use of computer-aided detection (CAD) software to analyze medical images. This software can help radiologists detect abnormalities in medical images that may be missed by the human eye, such as early-stage breast cancer.²⁰ Another example is the use of NLP and ML to analyze unstructured data in EHRs, such as physician notes, to identify potential diagnoses and flag patients who may be at risk for diagnostic errors.²¹ A recent study showed that using NLP on EHRs for screening and detecting individuals at risk for psychosis can considerably enhance the prognostic accuracy of psychosis risk calculators.²² This can help identify patients who require assessment and specialized care, facilitating earlier detection and potentially improving patient outcomes. On the same note, ML-based severe sepsis prediction algorithms have been shown to reduce the average length of stay and in-hospital mortality rate.²³

However, there are also concerns about the use of AI in health care, including the potential for bias and the risk of overreliance on AI. Bias can occur when AI algorithms are trained on data that is not representative of the population being analyzed, leading to inaccurate or unfair results, hence, perpetuating and exacerbating existing biases in health care.²⁴ Over-reliance on AI can occur when health care providers rely too heavily on AI algorithms and fail to consider other important information, such as the lived experience of patients, families, and health care providers. Addressing these concerns will require ongoing efforts to ensure that AI technologies are developed and implemented in an ethical and responsible manner.²⁵

Conclusion

Reducing diagnostic errors is a critical goal for health care systems, and requires a comprehensive approach that utilizes both HX and AI technologies. Engaging patients and their families in the diagnostic process, promoting teamwork and collaboration among health care providers, addressing cognitive biases, and harnessing the power of AI can all contribute to more accurate diagnoses and better patient outcomes. By integrating the lived experience of patients, families, and health care providers with AI technologies, health care systems can be redesigned to become more proactive, safer, and patient-centered in identifying potential health problems and reducing the risk of diagnostic errors, ensuring that patients receive the care they need and deserve.

Corresponding author: Iqbal Ratnani, Department of Anesthesiology and Critical Care, DeBakey Heart and Vascular Center, Houston Methodist Hospital, 6565 Fannin St, Houston, TX 77030; IRatnani@houstonmethodist.org

Disclosures: None reported.

As the movement to improve quality in health care has evolved over the past several decades, organizations whose missions focus on supporting and promoting quality in health care have defined essential concepts, standards, and measures that comprise quality and that can be used to guide quality improvement (QI) work. The World Health Organization (WHO) defines quality in clinical care as safe, effective, and people-centered service.¹ These 3 pillars of quality form the foundation of a quality system aiming to deliver health care in a timely, equitable, efficient, and integrated manner. The WHO estimates that 5.7 to 8.4 million deaths occur yearly in low- and middle-income countries due to poor quality care. Regarding safety, patient harm from unsafe care is estimated to be among the top 10 causes of death and disability worldwide.² A health care QI plan involves identifying areas for improvement, setting measurable goals, implementing evidence-based strategies and interventions, monitoring progress toward achieving those goals, and continuously evaluating and adjusting the plan as needed to ensure sustained improvement over time. Such a plan can be implemented at various levels of health care organizations, from individual clinical units to entire hospitals or even regional health care systems.

The Institute of Medicine (IOM) identifies 5 domains of quality in health care: effectiveness, efficiency, equity, patient-centeredness, and safety.³ Effectiveness relies on providing care processes supported by scientific evidence and achieving desired outcomes in the IOM recommendations. The primary efficiency aim maximizes the quality of health care delivered or the benefits achieved for a given resource unit. Equity relates to providing health care of equal quality to all individuals, regardless of personal characteristics. Moreover, patient-centeredness relates to meeting patients’ needs and preferences and providing education and support. Safety relates to avoiding actual or potential harm. Timeliness relates to obtaining needed care while minimizing delays. Finally, the IOM defines health care quality as the systematic evaluation and provision of evidence-based and safe care characterized by a culture of continuous improvement, resulting in optimal health outcomes. Taking all these concepts into consideration, 4 key attributes have been identified as essential to the global definition of health care quality: effectiveness, safety, culture of continuous improvement, and desired outcomes. This conceptualization of health care quality encompasses the fundamental components and has the potential to enhance the delivery of care. This definition’s theoretical and practical implications provide a comprehensive and consistent understanding of the elements required to improve health care and maintain public trust.

Health care quality is a dynamic, ever-evolving construct that requires continuous assessment and evaluation to ensure the delivery of care meets the changing needs of society. The National Quality Forum’s National Voluntary Consensus Standards for health care provide measures, guidance, and recommendations on achieving effective outcomes through evidence-based practices.⁴ These standards establish criteria by which health care systems and providers can assess and improve their quality performance.

In the United States, in order to implement and disseminate best practices, the Centers for Medicare & Medicaid Services (CMS) developed Quality Payment Programs that offer incentives to health care providers to improve the quality of care delivery. This CMS program evaluates providers based on their performance in the Merit-Based Incentive Payment System performance categories.⁵ These include measures related to patient experience, cost, clinical quality, improvement activities, and the use of certified electronic health record technology. The scores that providers receive are used to determine their performance-based reimbursements under Medicare’s fee-for-service program.

The concept of health care quality is also applicable in other countries. In the United Kingdom, QI initiatives are led by the Department of Health and Social Care. The National Institute for Health and Care Excellence (NICE) produces guidelines on best practices to ensure that care delivery meets established safety and quality standards, reaching cost-effectiveness excellence.⁶ In Australia, the Australian Commission on Quality and Safety in Health Care is responsible for setting benchmarks for performance in health care systems through a clear, structured agenda.⁷ Ultimately, health care quality is a complex and multifaceted issue that requires a comprehensive approach to ensure the best outcomes for patients. With the implementation of measures such as the CMS Quality Payment Programs and NICE guidelines, health care organizations can take steps to ensure their systems of care delivery reflect evidence-based practices and demonstrate a commitment to providing high-quality care.

Implementing a health care QI plan that encompasses the 4 key attributes of health care quality—effectiveness, safety, culture of continuous improvement, and desired outcomes—requires collaboration among different departments and stakeholders and a data-driven approach to decision-making. Effective communication with patients and their families is critical to ensuring that their needs are being met and that they are active partners in their health care journey. While a health care QI plan is essential for delivering high-quality, safe patient care, it also helps health care organizations comply with regulatory requirements, meet accreditation standards, and stay competitive in the ever-evolving health care landscape.

Corresponding author: Ebrahim Barkoudah, MD, MPH; Ebrahim.Barkoudah@baystatehealth.org

As the movement to improve quality in health care has evolved over the past several decades, organizations whose missions focus on supporting and promoting quality in health care have defined essential concepts, standards, and measures that comprise quality and that can be used to guide quality improvement (QI) work. The World Health Organization (WHO) defines quality in clinical care as safe, effective, and people-centered service.¹ These 3 pillars of quality form the foundation of a quality system aiming to deliver health care in a timely, equitable, efficient, and integrated manner. The WHO estimates that 5.7 to 8.4 million deaths occur yearly in low- and middle-income countries due to poor quality care. Regarding safety, patient harm from unsafe care is estimated to be among the top 10 causes of death and disability worldwide.² A health care QI plan involves identifying areas for improvement, setting measurable goals, implementing evidence-based strategies and interventions, monitoring progress toward achieving those goals, and continuously evaluating and adjusting the plan as needed to ensure sustained improvement over time. Such a plan can be implemented at various levels of health care organizations, from individual clinical units to entire hospitals or even regional health care systems.

The Institute of Medicine (IOM) identifies 5 domains of quality in health care: effectiveness, efficiency, equity, patient-centeredness, and safety.³ Effectiveness relies on providing care processes supported by scientific evidence and achieving desired outcomes in the IOM recommendations. The primary efficiency aim maximizes the quality of health care delivered or the benefits achieved for a given resource unit. Equity relates to providing health care of equal quality to all individuals, regardless of personal characteristics. Moreover, patient-centeredness relates to meeting patients’ needs and preferences and providing education and support. Safety relates to avoiding actual or potential harm. Timeliness relates to obtaining needed care while minimizing delays. Finally, the IOM defines health care quality as the systematic evaluation and provision of evidence-based and safe care characterized by a culture of continuous improvement, resulting in optimal health outcomes. Taking all these concepts into consideration, 4 key attributes have been identified as essential to the global definition of health care quality: effectiveness, safety, culture of continuous improvement, and desired outcomes. This conceptualization of health care quality encompasses the fundamental components and has the potential to enhance the delivery of care. This definition’s theoretical and practical implications provide a comprehensive and consistent understanding of the elements required to improve health care and maintain public trust.

Health care quality is a dynamic, ever-evolving construct that requires continuous assessment and evaluation to ensure the delivery of care meets the changing needs of society. The National Quality Forum’s National Voluntary Consensus Standards for health care provide measures, guidance, and recommendations on achieving effective outcomes through evidence-based practices.⁴ These standards establish criteria by which health care systems and providers can assess and improve their quality performance.

In the United States, in order to implement and disseminate best practices, the Centers for Medicare & Medicaid Services (CMS) developed Quality Payment Programs that offer incentives to health care providers to improve the quality of care delivery. This CMS program evaluates providers based on their performance in the Merit-Based Incentive Payment System performance categories.⁵ These include measures related to patient experience, cost, clinical quality, improvement activities, and the use of certified electronic health record technology. The scores that providers receive are used to determine their performance-based reimbursements under Medicare’s fee-for-service program.

The concept of health care quality is also applicable in other countries. In the United Kingdom, QI initiatives are led by the Department of Health and Social Care. The National Institute for Health and Care Excellence (NICE) produces guidelines on best practices to ensure that care delivery meets established safety and quality standards, reaching cost-effectiveness excellence.⁶ In Australia, the Australian Commission on Quality and Safety in Health Care is responsible for setting benchmarks for performance in health care systems through a clear, structured agenda.⁷ Ultimately, health care quality is a complex and multifaceted issue that requires a comprehensive approach to ensure the best outcomes for patients. With the implementation of measures such as the CMS Quality Payment Programs and NICE guidelines, health care organizations can take steps to ensure their systems of care delivery reflect evidence-based practices and demonstrate a commitment to providing high-quality care.

Implementing a health care QI plan that encompasses the 4 key attributes of health care quality—effectiveness, safety, culture of continuous improvement, and desired outcomes—requires collaboration among different departments and stakeholders and a data-driven approach to decision-making. Effective communication with patients and their families is critical to ensuring that their needs are being met and that they are active partners in their health care journey. While a health care QI plan is essential for delivering high-quality, safe patient care, it also helps health care organizations comply with regulatory requirements, meet accreditation standards, and stay competitive in the ever-evolving health care landscape.

Corresponding author: Ebrahim Barkoudah, MD, MPH; Ebrahim.Barkoudah@baystatehealth.org

As the movement to improve quality in health care has evolved over the past several decades, organizations whose missions focus on supporting and promoting quality in health care have defined essential concepts, standards, and measures that comprise quality and that can be used to guide quality improvement (QI) work. The World Health Organization (WHO) defines quality in clinical care as safe, effective, and people-centered service.¹ These 3 pillars of quality form the foundation of a quality system aiming to deliver health care in a timely, equitable, efficient, and integrated manner. The WHO estimates that 5.7 to 8.4 million deaths occur yearly in low- and middle-income countries due to poor quality care. Regarding safety, patient harm from unsafe care is estimated to be among the top 10 causes of death and disability worldwide.² A health care QI plan involves identifying areas for improvement, setting measurable goals, implementing evidence-based strategies and interventions, monitoring progress toward achieving those goals, and continuously evaluating and adjusting the plan as needed to ensure sustained improvement over time. Such a plan can be implemented at various levels of health care organizations, from individual clinical units to entire hospitals or even regional health care systems.

The Institute of Medicine (IOM) identifies 5 domains of quality in health care: effectiveness, efficiency, equity, patient-centeredness, and safety.³ Effectiveness relies on providing care processes supported by scientific evidence and achieving desired outcomes in the IOM recommendations. The primary efficiency aim maximizes the quality of health care delivered or the benefits achieved for a given resource unit. Equity relates to providing health care of equal quality to all individuals, regardless of personal characteristics. Moreover, patient-centeredness relates to meeting patients’ needs and preferences and providing education and support. Safety relates to avoiding actual or potential harm. Timeliness relates to obtaining needed care while minimizing delays. Finally, the IOM defines health care quality as the systematic evaluation and provision of evidence-based and safe care characterized by a culture of continuous improvement, resulting in optimal health outcomes. Taking all these concepts into consideration, 4 key attributes have been identified as essential to the global definition of health care quality: effectiveness, safety, culture of continuous improvement, and desired outcomes. This conceptualization of health care quality encompasses the fundamental components and has the potential to enhance the delivery of care. This definition’s theoretical and practical implications provide a comprehensive and consistent understanding of the elements required to improve health care and maintain public trust.

Health care quality is a dynamic, ever-evolving construct that requires continuous assessment and evaluation to ensure the delivery of care meets the changing needs of society. The National Quality Forum’s National Voluntary Consensus Standards for health care provide measures, guidance, and recommendations on achieving effective outcomes through evidence-based practices.⁴ These standards establish criteria by which health care systems and providers can assess and improve their quality performance.

In the United States, in order to implement and disseminate best practices, the Centers for Medicare & Medicaid Services (CMS) developed Quality Payment Programs that offer incentives to health care providers to improve the quality of care delivery. This CMS program evaluates providers based on their performance in the Merit-Based Incentive Payment System performance categories.⁵ These include measures related to patient experience, cost, clinical quality, improvement activities, and the use of certified electronic health record technology. The scores that providers receive are used to determine their performance-based reimbursements under Medicare’s fee-for-service program.

The concept of health care quality is also applicable in other countries. In the United Kingdom, QI initiatives are led by the Department of Health and Social Care. The National Institute for Health and Care Excellence (NICE) produces guidelines on best practices to ensure that care delivery meets established safety and quality standards, reaching cost-effectiveness excellence.⁶ In Australia, the Australian Commission on Quality and Safety in Health Care is responsible for setting benchmarks for performance in health care systems through a clear, structured agenda.⁷ Ultimately, health care quality is a complex and multifaceted issue that requires a comprehensive approach to ensure the best outcomes for patients. With the implementation of measures such as the CMS Quality Payment Programs and NICE guidelines, health care organizations can take steps to ensure their systems of care delivery reflect evidence-based practices and demonstrate a commitment to providing high-quality care.

Implementing a health care QI plan that encompasses the 4 key attributes of health care quality—effectiveness, safety, culture of continuous improvement, and desired outcomes—requires collaboration among different departments and stakeholders and a data-driven approach to decision-making. Effective communication with patients and their families is critical to ensuring that their needs are being met and that they are active partners in their health care journey. While a health care QI plan is essential for delivering high-quality, safe patient care, it also helps health care organizations comply with regulatory requirements, meet accreditation standards, and stay competitive in the ever-evolving health care landscape.

Corresponding author: Ebrahim Barkoudah, MD, MPH; Ebrahim.Barkoudah@baystatehealth.org

Study 1 Overview (Park et al)

Objective: To compare rates of adverse events and 30-day readmission among patients with dementia who undergo percutaneous coronary intervention (PCI) with those without dementia.

Design: This cohort study used a national database of hospital readmissions developed by the Agency for Healthcare Research and Quality.

Setting and participants: Data from State Inpatient Databases were used to derive this national readmissions database representing 80% of hospitals from 28 states that contribute data. The study included all individuals aged 18 years and older who were identified to have had a PCI procedure in the years 2017 and 2018. International Classification of Diseases, Tenth Revision (ICD-10) codes were used to identify PCI procedures, including drug-eluting stent placement, bare-metal stent placement, and balloon angioplasty, performed in patients who presented with myocardial infarction and unstable angina and those with stable ischemic heart disease. Patients were stratified into those with or without dementia, also defined using ICD-10 codes. A total of 755,406 index hospitalizations were included; 2.3% of the patients had dementia.

Main outcome measures: The primary study outcome was 30-day all-cause readmission, with the cause classified as cardiovascular or noncardiovascular. Secondary outcome measures examined were delirium, in-hospital mortality, cardiac arrest, blood transfusion, acute kidney injury, fall in hospital, length of hospital stay, and other adverse outcomes. Location at discharge was also examined. Other covariates included in the analysis were age, sex, comorbidities, hospital characteristics, primary payer, and median income. For analysis, a propensity score matching algorithm was applied to match patients with and without dementia. Kaplan-Meier curves were used to examine 30-day readmission rates, and a Cox proportional hazards model was used to calculate hazard ratios (HR) for those with and without dementia. For secondary outcomes, logistic regression models were used to calculate odds ratios (OR) of outcomes between those with and without dementia.

Main results: The average age of those with dementia was 78.8 years vs 64.9 years in those without dementia. Women made up 42.8% of those with dementia and 31.3% of those without dementia. Those with dementia also had higher rates of comorbidities, such as heart failure, renal failure, and depression. After propensity score matching, 17,309 and 17,187 patients with and without dementia, respectively, were included. Covariates were balanced between the 2 groups after matching. For the primary outcome, patients with dementia were more likely to be readmitted at 30 days (HR, 1.11; 95% CI, 1.05-1.18; P < .01) when compared to those without dementia. For other adverse outcomes, delirium was significantly more likely to occur for those with dementia (OR, 4.37; 95% CI, 3.69-5.16; P < .01). Patients with dementia were also more likely to die in hospital (OR, 1.15; 95% CI, 1.01-1.30; P = .03), have cardiac arrest (OR, 1.19; 95% CI, 1.01-1.39; P = .04), receive a blood transfusion (OR, 1.17; 95% CI, 1.00-1.36; P = .05), experience acute kidney injury (OR, 1.30; 95% CI, 1.21-1.39; P < .01), and fall in hospital (OR, 2.51; 95% CI, 2.06-3.07; P < .01). Hospital length of stay was higher for those with dementia, with a mean difference of 1.43 days. For discharge location, patients with dementia were more likely to be sent to a skilled nursing facility (30.1% vs 12.2%) and less likely to be discharged home.

Conclusion: Patients with dementia are more likely to experience adverse events, including delirium, mortality, kidney injury, and falls after PCI, and are more likely to be readmitted to the hospital in 30 days compared to those without dementia.

Study 2 Overview (Gilmore-Bykovskyi et al)

Objective: To examine the association between race and 30-day readmissions in Black and non-Hispanic White Medicare beneficiaries with dementia.

Design: This was a retrospective cohort study that used 100% Medicare fee-for service claims data from all hospitalizations between January 1, 2014, and November 30, 2014, for all enrollees with a dementia diagnosis. The claims data were linked to the patient, hospital stay, and hospital factors. Patients with dementia were identified using a validated algorithm that requires an inpatient, skilled nursing facility, home health, or Part B institutional or noninstitutional claim with a qualifying diagnostic code during a 3-year period. Persons enrolled in a health maintenance organization plan were excluded.

Main outcome measures: The primary outcome examined in this study was 30-day all-cause readmission. Self-reported race and ethnic identity was a baseline covariate. Persons who self-reported Black or non-Hispanic White race were included in the study; other categories of race and ethnicity were excluded because of prior evidence suggesting low accuracy of these categories in Medicare claims data. Other covariates included neighborhood disadvantage, measured using the Area Deprivation Index (ADI), and rurality; hospital-level and hospital stay–level characteristics such as for-profit status and number of annual discharges; and individual demographic characteristics and comorbidities. The ADI is constructed using variables of poverty, education, housing, and employment and is represented as a percentile ranking of level of disadvantage. Unadjusted and adjusted analyses of 30-day hospital readmission were conducted. Models using various levels of adjustment were constructed to examine the contributions of the identified covariates to the estimated association between 30-day readmission and race.

Main results: A total of 1,523,142 index hospital stays among 945,481 beneficiaries were included; 215,815 episodes were among Black beneficiaries and 1,307,327 episodes were among non-Hispanic White beneficiaries. Mean age was 81.5 years, and approximately 61% of beneficiaries were female. Black beneficiaries were younger but had higher rates of dual Medicare/Medicaid eligibility and disability; they were also more likely to reside in disadvantaged neighborhoods. Black beneficiaries had a 30-day readmission rate of 24.1% compared with 18.5% in non-Hispanic White beneficiaries (unadjusted OR, 1.37; 95% CI, 1.35-1.39). The differences in outcomes persisted after adjusting for geographic factors, social factors, hospital characteristics, hospital stay factors, demographics, and comorbidities, suggesting that unmeasured underlying racial disparities not included in this model accounted for the differences. The effects of certain variables, such as neighborhood, differed by race; for example, the protective effect of living in a less disadvantaged neighborhood was observed among White beneficiaries but not Black beneficiaries.

Conclusion: Racial and geographic disparities in 30-day readmission rates were observed among Medicare beneficiaries with dementia. Protective effects associated with neighborhood advantage may confer different levels of benefit for people of different race.

Commentary

Adults living with dementia are at higher risk of adverse outcomes across settings. In the first study, by Park et al, among adults who underwent a cardiac procedure (PCI), those with dementia were more likely to experience adverse events compared to those without dementia. These outcomes include increased rates of 30-day readmissions, delirium, cardiac arrest, and falls. These findings are consistent with other studies that found a similar association among patients who underwent other cardiac procedures, such as transcatheter aortic valve replacement.¹ Because dementia is a strong predisposing factor for delirium, it is not surprising that delirium is observed across patients who underwent different procedures or hospitalization episodes.² Because of the potential hazards for inpatients with dementia, hospitals have developed risk-reduction programs, such as those that promote recognition of dementia, and management strategies that reduce the risk of delirium.³ Delirium prevention may also impact other adverse outcomes, such as falls, discharge to institutional care, and readmissions.

Racial disparities in care outcomes have been documented across settings, including hospital⁴ and hospice care settings.⁵ In study 2, by Gilmore-Bykovskyi et al, the findings of higher rates of hospital readmission among Black patients when compared to non-Hispanic White patients were not surprising. The central finding of this study is that even when accounting for various levels of factors, including hospital-level, hospital stay–level, individual (demographics, comorbidities), and neighborhood characteristics (disadvantage), the observed disparity diminished but persisted, suggesting that while these various levels of factors contributed to the observed disparity, other unmeasured factors also contributed. Another key finding is that the effect of the various factors examined in this study may affect different subgroups in different ways, suggesting underlying factors, and thus potential solutions to reduce disparities in care outcomes, could differ among subgroups.

Applications for Clinical Practice and System Implementation

These 2 studies add to the literature on factors that can affect 30-day hospital readmission rates in patients with dementia. These data could allow for more robust discussions of what to anticipate when adults with dementia undergo specific procedures, and also further build the case that improvements in care, such as delirium prevention programs, could offer benefits. The observation about racial and ethnic disparities in care outcomes among patients with dementia highlights the continued need to better understand the drivers of these disparities so that hospital systems and policy makers can consider and test possible solutions. Future studies should further disentangle the relationships among the various levels of factors and observed disparities in outcomes, especially for this vulnerable population of adults living with dementia.

Practice Points

• Clinicians should be aware of the additional risks for poor outcomes that dementia confers.
• Awareness of this increased risk will inform discussions of risks and benefits for older adults considered for procedures.

–William W. Hung, MD, MPH

Study 1 Overview (Park et al)

Objective: To compare rates of adverse events and 30-day readmission among patients with dementia who undergo percutaneous coronary intervention (PCI) with those without dementia.

Design: This cohort study used a national database of hospital readmissions developed by the Agency for Healthcare Research and Quality.

Setting and participants: Data from State Inpatient Databases were used to derive this national readmissions database representing 80% of hospitals from 28 states that contribute data. The study included all individuals aged 18 years and older who were identified to have had a PCI procedure in the years 2017 and 2018. International Classification of Diseases, Tenth Revision (ICD-10) codes were used to identify PCI procedures, including drug-eluting stent placement, bare-metal stent placement, and balloon angioplasty, performed in patients who presented with myocardial infarction and unstable angina and those with stable ischemic heart disease. Patients were stratified into those with or without dementia, also defined using ICD-10 codes. A total of 755,406 index hospitalizations were included; 2.3% of the patients had dementia.

Main outcome measures: The primary study outcome was 30-day all-cause readmission, with the cause classified as cardiovascular or noncardiovascular. Secondary outcome measures examined were delirium, in-hospital mortality, cardiac arrest, blood transfusion, acute kidney injury, fall in hospital, length of hospital stay, and other adverse outcomes. Location at discharge was also examined. Other covariates included in the analysis were age, sex, comorbidities, hospital characteristics, primary payer, and median income. For analysis, a propensity score matching algorithm was applied to match patients with and without dementia. Kaplan-Meier curves were used to examine 30-day readmission rates, and a Cox proportional hazards model was used to calculate hazard ratios (HR) for those with and without dementia. For secondary outcomes, logistic regression models were used to calculate odds ratios (OR) of outcomes between those with and without dementia.

Main results: The average age of those with dementia was 78.8 years vs 64.9 years in those without dementia. Women made up 42.8% of those with dementia and 31.3% of those without dementia. Those with dementia also had higher rates of comorbidities, such as heart failure, renal failure, and depression. After propensity score matching, 17,309 and 17,187 patients with and without dementia, respectively, were included. Covariates were balanced between the 2 groups after matching. For the primary outcome, patients with dementia were more likely to be readmitted at 30 days (HR, 1.11; 95% CI, 1.05-1.18; P < .01) when compared to those without dementia. For other adverse outcomes, delirium was significantly more likely to occur for those with dementia (OR, 4.37; 95% CI, 3.69-5.16; P < .01). Patients with dementia were also more likely to die in hospital (OR, 1.15; 95% CI, 1.01-1.30; P = .03), have cardiac arrest (OR, 1.19; 95% CI, 1.01-1.39; P = .04), receive a blood transfusion (OR, 1.17; 95% CI, 1.00-1.36; P = .05), experience acute kidney injury (OR, 1.30; 95% CI, 1.21-1.39; P < .01), and fall in hospital (OR, 2.51; 95% CI, 2.06-3.07; P < .01). Hospital length of stay was higher for those with dementia, with a mean difference of 1.43 days. For discharge location, patients with dementia were more likely to be sent to a skilled nursing facility (30.1% vs 12.2%) and less likely to be discharged home.

Conclusion: Patients with dementia are more likely to experience adverse events, including delirium, mortality, kidney injury, and falls after PCI, and are more likely to be readmitted to the hospital in 30 days compared to those without dementia.

Study 2 Overview (Gilmore-Bykovskyi et al)

Objective: To examine the association between race and 30-day readmissions in Black and non-Hispanic White Medicare beneficiaries with dementia.

Design: This was a retrospective cohort study that used 100% Medicare fee-for service claims data from all hospitalizations between January 1, 2014, and November 30, 2014, for all enrollees with a dementia diagnosis. The claims data were linked to the patient, hospital stay, and hospital factors. Patients with dementia were identified using a validated algorithm that requires an inpatient, skilled nursing facility, home health, or Part B institutional or noninstitutional claim with a qualifying diagnostic code during a 3-year period. Persons enrolled in a health maintenance organization plan were excluded.

Main outcome measures: The primary outcome examined in this study was 30-day all-cause readmission. Self-reported race and ethnic identity was a baseline covariate. Persons who self-reported Black or non-Hispanic White race were included in the study; other categories of race and ethnicity were excluded because of prior evidence suggesting low accuracy of these categories in Medicare claims data. Other covariates included neighborhood disadvantage, measured using the Area Deprivation Index (ADI), and rurality; hospital-level and hospital stay–level characteristics such as for-profit status and number of annual discharges; and individual demographic characteristics and comorbidities. The ADI is constructed using variables of poverty, education, housing, and employment and is represented as a percentile ranking of level of disadvantage. Unadjusted and adjusted analyses of 30-day hospital readmission were conducted. Models using various levels of adjustment were constructed to examine the contributions of the identified covariates to the estimated association between 30-day readmission and race.

Main results: A total of 1,523,142 index hospital stays among 945,481 beneficiaries were included; 215,815 episodes were among Black beneficiaries and 1,307,327 episodes were among non-Hispanic White beneficiaries. Mean age was 81.5 years, and approximately 61% of beneficiaries were female. Black beneficiaries were younger but had higher rates of dual Medicare/Medicaid eligibility and disability; they were also more likely to reside in disadvantaged neighborhoods. Black beneficiaries had a 30-day readmission rate of 24.1% compared with 18.5% in non-Hispanic White beneficiaries (unadjusted OR, 1.37; 95% CI, 1.35-1.39). The differences in outcomes persisted after adjusting for geographic factors, social factors, hospital characteristics, hospital stay factors, demographics, and comorbidities, suggesting that unmeasured underlying racial disparities not included in this model accounted for the differences. The effects of certain variables, such as neighborhood, differed by race; for example, the protective effect of living in a less disadvantaged neighborhood was observed among White beneficiaries but not Black beneficiaries.

Conclusion: Racial and geographic disparities in 30-day readmission rates were observed among Medicare beneficiaries with dementia. Protective effects associated with neighborhood advantage may confer different levels of benefit for people of different race.

Commentary

Adults living with dementia are at higher risk of adverse outcomes across settings. In the first study, by Park et al, among adults who underwent a cardiac procedure (PCI), those with dementia were more likely to experience adverse events compared to those without dementia. These outcomes include increased rates of 30-day readmissions, delirium, cardiac arrest, and falls. These findings are consistent with other studies that found a similar association among patients who underwent other cardiac procedures, such as transcatheter aortic valve replacement.¹ Because dementia is a strong predisposing factor for delirium, it is not surprising that delirium is observed across patients who underwent different procedures or hospitalization episodes.² Because of the potential hazards for inpatients with dementia, hospitals have developed risk-reduction programs, such as those that promote recognition of dementia, and management strategies that reduce the risk of delirium.³ Delirium prevention may also impact other adverse outcomes, such as falls, discharge to institutional care, and readmissions.

Racial disparities in care outcomes have been documented across settings, including hospital⁴ and hospice care settings.⁵ In study 2, by Gilmore-Bykovskyi et al, the findings of higher rates of hospital readmission among Black patients when compared to non-Hispanic White patients were not surprising. The central finding of this study is that even when accounting for various levels of factors, including hospital-level, hospital stay–level, individual (demographics, comorbidities), and neighborhood characteristics (disadvantage), the observed disparity diminished but persisted, suggesting that while these various levels of factors contributed to the observed disparity, other unmeasured factors also contributed. Another key finding is that the effect of the various factors examined in this study may affect different subgroups in different ways, suggesting underlying factors, and thus potential solutions to reduce disparities in care outcomes, could differ among subgroups.

Applications for Clinical Practice and System Implementation

These 2 studies add to the literature on factors that can affect 30-day hospital readmission rates in patients with dementia. These data could allow for more robust discussions of what to anticipate when adults with dementia undergo specific procedures, and also further build the case that improvements in care, such as delirium prevention programs, could offer benefits. The observation about racial and ethnic disparities in care outcomes among patients with dementia highlights the continued need to better understand the drivers of these disparities so that hospital systems and policy makers can consider and test possible solutions. Future studies should further disentangle the relationships among the various levels of factors and observed disparities in outcomes, especially for this vulnerable population of adults living with dementia.

Practice Points

• Clinicians should be aware of the additional risks for poor outcomes that dementia confers.
• Awareness of this increased risk will inform discussions of risks and benefits for older adults considered for procedures.

–William W. Hung, MD, MPH

Study 1 Overview (Park et al)

Objective: To compare rates of adverse events and 30-day readmission among patients with dementia who undergo percutaneous coronary intervention (PCI) with those without dementia.

Design: This cohort study used a national database of hospital readmissions developed by the Agency for Healthcare Research and Quality.

Setting and participants: Data from State Inpatient Databases were used to derive this national readmissions database representing 80% of hospitals from 28 states that contribute data. The study included all individuals aged 18 years and older who were identified to have had a PCI procedure in the years 2017 and 2018. International Classification of Diseases, Tenth Revision (ICD-10) codes were used to identify PCI procedures, including drug-eluting stent placement, bare-metal stent placement, and balloon angioplasty, performed in patients who presented with myocardial infarction and unstable angina and those with stable ischemic heart disease. Patients were stratified into those with or without dementia, also defined using ICD-10 codes. A total of 755,406 index hospitalizations were included; 2.3% of the patients had dementia.

Main outcome measures: The primary study outcome was 30-day all-cause readmission, with the cause classified as cardiovascular or noncardiovascular. Secondary outcome measures examined were delirium, in-hospital mortality, cardiac arrest, blood transfusion, acute kidney injury, fall in hospital, length of hospital stay, and other adverse outcomes. Location at discharge was also examined. Other covariates included in the analysis were age, sex, comorbidities, hospital characteristics, primary payer, and median income. For analysis, a propensity score matching algorithm was applied to match patients with and without dementia. Kaplan-Meier curves were used to examine 30-day readmission rates, and a Cox proportional hazards model was used to calculate hazard ratios (HR) for those with and without dementia. For secondary outcomes, logistic regression models were used to calculate odds ratios (OR) of outcomes between those with and without dementia.

Main results: The average age of those with dementia was 78.8 years vs 64.9 years in those without dementia. Women made up 42.8% of those with dementia and 31.3% of those without dementia. Those with dementia also had higher rates of comorbidities, such as heart failure, renal failure, and depression. After propensity score matching, 17,309 and 17,187 patients with and without dementia, respectively, were included. Covariates were balanced between the 2 groups after matching. For the primary outcome, patients with dementia were more likely to be readmitted at 30 days (HR, 1.11; 95% CI, 1.05-1.18; P < .01) when compared to those without dementia. For other adverse outcomes, delirium was significantly more likely to occur for those with dementia (OR, 4.37; 95% CI, 3.69-5.16; P < .01). Patients with dementia were also more likely to die in hospital (OR, 1.15; 95% CI, 1.01-1.30; P = .03), have cardiac arrest (OR, 1.19; 95% CI, 1.01-1.39; P = .04), receive a blood transfusion (OR, 1.17; 95% CI, 1.00-1.36; P = .05), experience acute kidney injury (OR, 1.30; 95% CI, 1.21-1.39; P < .01), and fall in hospital (OR, 2.51; 95% CI, 2.06-3.07; P < .01). Hospital length of stay was higher for those with dementia, with a mean difference of 1.43 days. For discharge location, patients with dementia were more likely to be sent to a skilled nursing facility (30.1% vs 12.2%) and less likely to be discharged home.

Conclusion: Patients with dementia are more likely to experience adverse events, including delirium, mortality, kidney injury, and falls after PCI, and are more likely to be readmitted to the hospital in 30 days compared to those without dementia.

Study 2 Overview (Gilmore-Bykovskyi et al)

Objective: To examine the association between race and 30-day readmissions in Black and non-Hispanic White Medicare beneficiaries with dementia.

Design: This was a retrospective cohort study that used 100% Medicare fee-for service claims data from all hospitalizations between January 1, 2014, and November 30, 2014, for all enrollees with a dementia diagnosis. The claims data were linked to the patient, hospital stay, and hospital factors. Patients with dementia were identified using a validated algorithm that requires an inpatient, skilled nursing facility, home health, or Part B institutional or noninstitutional claim with a qualifying diagnostic code during a 3-year period. Persons enrolled in a health maintenance organization plan were excluded.

Main outcome measures: The primary outcome examined in this study was 30-day all-cause readmission. Self-reported race and ethnic identity was a baseline covariate. Persons who self-reported Black or non-Hispanic White race were included in the study; other categories of race and ethnicity were excluded because of prior evidence suggesting low accuracy of these categories in Medicare claims data. Other covariates included neighborhood disadvantage, measured using the Area Deprivation Index (ADI), and rurality; hospital-level and hospital stay–level characteristics such as for-profit status and number of annual discharges; and individual demographic characteristics and comorbidities. The ADI is constructed using variables of poverty, education, housing, and employment and is represented as a percentile ranking of level of disadvantage. Unadjusted and adjusted analyses of 30-day hospital readmission were conducted. Models using various levels of adjustment were constructed to examine the contributions of the identified covariates to the estimated association between 30-day readmission and race.

Main results: A total of 1,523,142 index hospital stays among 945,481 beneficiaries were included; 215,815 episodes were among Black beneficiaries and 1,307,327 episodes were among non-Hispanic White beneficiaries. Mean age was 81.5 years, and approximately 61% of beneficiaries were female. Black beneficiaries were younger but had higher rates of dual Medicare/Medicaid eligibility and disability; they were also more likely to reside in disadvantaged neighborhoods. Black beneficiaries had a 30-day readmission rate of 24.1% compared with 18.5% in non-Hispanic White beneficiaries (unadjusted OR, 1.37; 95% CI, 1.35-1.39). The differences in outcomes persisted after adjusting for geographic factors, social factors, hospital characteristics, hospital stay factors, demographics, and comorbidities, suggesting that unmeasured underlying racial disparities not included in this model accounted for the differences. The effects of certain variables, such as neighborhood, differed by race; for example, the protective effect of living in a less disadvantaged neighborhood was observed among White beneficiaries but not Black beneficiaries.

Conclusion: Racial and geographic disparities in 30-day readmission rates were observed among Medicare beneficiaries with dementia. Protective effects associated with neighborhood advantage may confer different levels of benefit for people of different race.

Commentary

Adults living with dementia are at higher risk of adverse outcomes across settings. In the first study, by Park et al, among adults who underwent a cardiac procedure (PCI), those with dementia were more likely to experience adverse events compared to those without dementia. These outcomes include increased rates of 30-day readmissions, delirium, cardiac arrest, and falls. These findings are consistent with other studies that found a similar association among patients who underwent other cardiac procedures, such as transcatheter aortic valve replacement.¹ Because dementia is a strong predisposing factor for delirium, it is not surprising that delirium is observed across patients who underwent different procedures or hospitalization episodes.² Because of the potential hazards for inpatients with dementia, hospitals have developed risk-reduction programs, such as those that promote recognition of dementia, and management strategies that reduce the risk of delirium.³ Delirium prevention may also impact other adverse outcomes, such as falls, discharge to institutional care, and readmissions.

Racial disparities in care outcomes have been documented across settings, including hospital⁴ and hospice care settings.⁵ In study 2, by Gilmore-Bykovskyi et al, the findings of higher rates of hospital readmission among Black patients when compared to non-Hispanic White patients were not surprising. The central finding of this study is that even when accounting for various levels of factors, including hospital-level, hospital stay–level, individual (demographics, comorbidities), and neighborhood characteristics (disadvantage), the observed disparity diminished but persisted, suggesting that while these various levels of factors contributed to the observed disparity, other unmeasured factors also contributed. Another key finding is that the effect of the various factors examined in this study may affect different subgroups in different ways, suggesting underlying factors, and thus potential solutions to reduce disparities in care outcomes, could differ among subgroups.

Applications for Clinical Practice and System Implementation

These 2 studies add to the literature on factors that can affect 30-day hospital readmission rates in patients with dementia. These data could allow for more robust discussions of what to anticipate when adults with dementia undergo specific procedures, and also further build the case that improvements in care, such as delirium prevention programs, could offer benefits. The observation about racial and ethnic disparities in care outcomes among patients with dementia highlights the continued need to better understand the drivers of these disparities so that hospital systems and policy makers can consider and test possible solutions. Future studies should further disentangle the relationships among the various levels of factors and observed disparities in outcomes, especially for this vulnerable population of adults living with dementia.

Practice Points

• Clinicians should be aware of the additional risks for poor outcomes that dementia confers.
• Awareness of this increased risk will inform discussions of risks and benefits for older adults considered for procedures.

–William W. Hung, MD, MPH

Study 1 Overview (Trivedi et al)

Objective: This observational quality improvement study aimed to evaluate the discharge communication practices in internal medicine services at 2 urban academic teaching hospitals, specifically focusing on patient education and counseling in 6 key discharge communication domains.

Design: Observations were conducted over a 13-month period from September 2018 through October 2019, following the Standards for Quality Improvement Reporting Excellence (SQUIRE) guidelines.

Setting and participants: The study involved a total of 33 English- and Spanish-speaking patients purposefully selected from the “discharge before noon” list at 2 urban tertiary-care teaching hospitals. A total of 155 observation hours were accumulated, with an average observation time of 4.7 hours per patient on the day of discharge.

Main outcome measures: The study assessed 6 discharge communication domains: (1) the name and function of medication changes, (2) the purpose of postdischarge appointments, (3) disease self-management, (4) red flags or warning signs for complications, (5) teach-back techniques to confirm patient understanding, and (6) staff solicitation of patient questions or concerns.

Main results: The study found several gaps in discharge communication practices. Among the 29 patients with medication changes, 28% were not informed about the name and basic function of the changes, while 59% did not receive counseling on the purpose for the medication change. In terms of postdischarge appointments, 48% of patients were not told the purpose of these appointments. Moreover, 54% of patients did not receive counseling on self-management of their primary discharge diagnosis or other diagnoses, and 73% were not informed about symptom expectations or the expected course of their illness after leaving the hospital. Most patients (82%) were not counseled on red-flag signs and symptoms that should prompt immediate return to care.

Teach-back techniques, which are critical for ensuring patient understanding, were used in only 3% of cases, and 85% of patients were not asked by health care providers if there might be barriers to following the care plan. Less than half (42%) of the patients were asked if they had any questions, with most questions being logistical and often deferred to another team member or met with uncertainty. Of note, among the 33 patients, only 2 patients received extensive information that covered 5 or 6 out of 6 discharge communication domains.

The study found variable roles in who communicated what aspects of discharge education, with most domains being communicated in an ad hoc manner and no clear pattern of responsibility. However, 2 exceptions were observed: nurses were more likely to provide information about new or changed medications and follow-up appointments, and the only example of teach-back was conducted by an attending physician.

Conclusion: The study highlights a significant need for improved discharge techniques to enhance patient safety and quality of care upon leaving the hospital. Interventions should focus on increasing transparency in patient education and understanding, clarifying assumptions of roles among the interprofessional team, and implementing effective communication strategies and system redesigns that foster patient-centered discharge education. Also, the study revealed that some patients received more robust discharge education than others, indicating systemic inequality in the patient experience. Further studies are needed to explore the development and assessment of such interventions to ensure optimal patient outcomes and equal care following hospital discharge.

Study 2 Overview (Marks et al)

Objective: This study aimed to investigate the impact of a nurse-led discharge medication education program, Teaching Important Medication Effects (TIME), on patients’ new medication knowledge at discharge and 48 to 72 hours post discharge. The specific objectives were to identify patients’ priority learning needs, evaluate the influence of TIME on patients’ new medication knowledge before and after discharge, and assess the effect of TIME on patients’ experience and satisfaction with medication education.

Design: The study employed a longitudinal pretest/post-test, 2-group design involving 107 randomly selected medical-surgical patients from an academic hospital. Participants were interviewed before and within 72 hours after discharge following administration of medication instructions. Bivariate analyses were performed to assess demographic and outcome variable differences between groups.

Setting and participants: Conducted on a 24-bed medical-surgical unit at a large Magnet^® hospital over 18 months (2018-2019), the study included patients with at least 1 new medication, aged 18 years or older, able to read and speak English or Spanish, admitted from home with a minimum 1 overnight stay, and planning to return home post discharge. Excluded were cognitively impaired patients, those assigned to a resource pool nurse without TIME training, and those having a research team member assigned. Participants were randomly selected from a computerized list of patients scheduled for discharge.

Main outcome measures: Primary outcome measures included patients’ new medication knowledge before and after discharge and patients’ experience and satisfaction with medication education.

Main results: The usual care (n = 52) and TIME (n = 55) patients had similar baseline demographic characteristics. The study revealed that almost all patients in both usual care and TIME groups were aware of their new medication and its purpose at discharge. However, differences were observed in medication side effect responses, with 72.5% of the usual-care group knowing side effects compared to 94.3% of the TIME group (P = .003). Additionally, 81.5% of the usual-care group understood the medication purpose compared to 100% of the TIME group (P = .02). During the 48- to 72-hour postdischarge calls, consistent responses were found from both groups regarding knowledge of new medication, medication name, and medication purpose. Similar to discharge results, differences in medication side effect responses were observed, with 75.8% of the usual care group correctly identifying at least 1 medication side effect compared to 93.9% of the TIME group (P = .04). TIME was associated with higher satisfaction with medication education compared to usual care (97% vs. 46.9%, P < .001).

Conclusion: The nurse-led discharge medication education program TIME effectively enhanced patients’ new medication knowledge at discharge and 48 to 72 hours after discharge. The program also significantly improved patients’ experience and satisfaction with medication education. These findings indicate that TIME is a valuable tool for augmenting patient education and medication adherence in a hospital setting. By incorporating the teach-back method, TIME offers a structured approach to educating patients about their medications at hospital discharge, leading to improved care transitions.

Commentary

Suboptimal communication between patients, caregivers, and providers upon hospital discharge is a major contributor to patients’ inadequate understanding of postdischarge care plans. This inadequate understanding leads to preventable harms, such as medication errors, adverse events, emergency room visits, and costly hospital readmissions.¹ The issue is further exacerbated by a lack of clarity among health care team members’ respective roles in providing information that optimizes care transitions during the discharge communication process. Moreover, low health literacy, particularly prevalent among seniors, those from disadvantaged backgrouds, and those with lower education attainment or chronic illnesses, create additional barriers to effective discharge communication. A potential solution to this problem is the adoption of effective teaching strategies, specifically the teach-back method. This method employs techniques that ensure patients’ understanding and recall of new information regardless of health literacy, and places accountability on clinicians rather than patients. By closing communication gaps between clinicians and patients, the teach-back method can reduce hospital readmissions, hospital-acquired conditions, and mortality rates, while improving patient satisfaction with health care instructions and the overall hospital experience.²

Study 1, by Trivedi et al, and study 2, by Marks et al, aimed to identify and address problems related to poor communication between patients and health care team members at hospital discharge. Specifically, study 1 examined routine discharge communication practices to determine communication gaps, while study 2 evaluated a nurse-led teach-back intervention program designed to improve patients’ medication knowledge and satisfaction. These distinct objectives and designs reflected the unique ways each study approached the challenges associated with care transitions at the time of hospital discharge.

Study 1 used direct observation of patient-practitioner interactions to evaluate routine discharge communication practices in internal medicine services at 2 urban academic teaching hospitals. In the 33 patients observed, significant gaps in discharge communication practices were identified in the domains of medication changes, postdischarge appointments, disease self-management, and red flags or warning signs. Unsurprisingly, most of these domains were communicated in an ad hoc manner by members of the health care team without a clear pattern of responsibility in reference to patient discharge education, and teach-back was seldom used. These findings underscore the need for improved discharge techniques, effective communication strategies, and clarification of roles among the interprofessional team to enhance the safety, quality of care, and overall patient experience during hospital discharge.

Study 2 aimed to augment the hospital discharge communication process by implementing a nurse-led discharge medication education program (TIME), which targeted patients’ priority learning needs, new medication knowledge, and satisfaction with medication education. In the 107 patients assessed, this teach-back method enhanced patients’ new medication knowledge at discharge and 48 to 72 hours after discharge, as well as improved patients’ experience and satisfaction with medication education. These results suggest that a teach-back method such as the TIME program could be a solution to care transition problems identified in the Trivedi et al study by providing a structured approach to patient education and enhancing communication practices during the hospital discharge process. Thus, by implementing the TIME program, hospitals may improve patient outcomes, safety, and overall quality of care upon leaving the hospital.

Applications for Clinical Practice and System Implementation

Care transition at the time of hospital discharge is a particularly pivotal period in the care of vulnerable individuals. There is growing literature, including studies discussed in this review, to indicate that by focusing on improving patient-practitioner communication during the discharge process and using strategies such as the teach-back method, health care professionals can better prepare patients for self-management in the post-acute period and help them make informed decisions about their care. This emphasis on care-transition communication strategies may lead to a reduction in medication errors, adverse events, and hospital readmissions, ultimately improving patient outcomes and satisfaction. Barriers to system implementation of such strategies may include competing demands and responsibilities of busy practitioners as well as the inherent complexities associated with hospital discharge. Creative solutions, such as the utilization of telehealth and early transition-of-care visits, represent some potential approaches to counter these barriers.

While both studies illustrated barriers and facilitators of hospital discharge communication, each study had limitations that impacted their generalizability to real-world clinical practice. Limitations in study 1 included a small sample size, purposive sampling method, and a focus on planned discharges in a teaching hospital, which may introduce selection bias. The study’s findings may not be generalizable to unplanned discharges, patients who do not speak English or Spanish, or nonteaching hospitals. Additionally, the data were collected before the COVID-19 pandemic, which could have further impacted discharge education practices. The study also revealed that some patients received more robust discharge education than others, which indicated systemic inequality in the patient experience. Further research is required to address this discrepancy. Limitations in study 2 included a relatively small and homogeneous sample, with most participants being younger, non-Hispanic White, English-speaking, and well-educated. This lack of diversity may limit the generalizability of the findings. Furthermore, the study did not evaluate the patients’ knowledge of medication dosage and focused only on new medications. Future studies should examine the effect of teach-back on a broader range of self-management topics in preparation for discharge, while also including a more diverse population to account for factors related to social determinants of health. Taken together, further research is needed to address these limitations and ensure more generalizable results that can more broadly improve discharge education and care transitions that bridge acute and post-acute care.

Practice Points

• There is a significant need for improved discharge strategies to enhance patient safety and quality of care upon leaving the hospital.
• Teach-back method may offer a structured approach to educating patients about their medications at hospital discharge and improve care transitions.

–Yuka Shichijo, MD, and Fred Ko, MD, Mount Sinai Beth Israel Hospital, New York, NY

Study 1 Overview (Trivedi et al)

Objective: This observational quality improvement study aimed to evaluate the discharge communication practices in internal medicine services at 2 urban academic teaching hospitals, specifically focusing on patient education and counseling in 6 key discharge communication domains.

Design: Observations were conducted over a 13-month period from September 2018 through October 2019, following the Standards for Quality Improvement Reporting Excellence (SQUIRE) guidelines.

Setting and participants: The study involved a total of 33 English- and Spanish-speaking patients purposefully selected from the “discharge before noon” list at 2 urban tertiary-care teaching hospitals. A total of 155 observation hours were accumulated, with an average observation time of 4.7 hours per patient on the day of discharge.

Main outcome measures: The study assessed 6 discharge communication domains: (1) the name and function of medication changes, (2) the purpose of postdischarge appointments, (3) disease self-management, (4) red flags or warning signs for complications, (5) teach-back techniques to confirm patient understanding, and (6) staff solicitation of patient questions or concerns.

Main results: The study found several gaps in discharge communication practices. Among the 29 patients with medication changes, 28% were not informed about the name and basic function of the changes, while 59% did not receive counseling on the purpose for the medication change. In terms of postdischarge appointments, 48% of patients were not told the purpose of these appointments. Moreover, 54% of patients did not receive counseling on self-management of their primary discharge diagnosis or other diagnoses, and 73% were not informed about symptom expectations or the expected course of their illness after leaving the hospital. Most patients (82%) were not counseled on red-flag signs and symptoms that should prompt immediate return to care.

Teach-back techniques, which are critical for ensuring patient understanding, were used in only 3% of cases, and 85% of patients were not asked by health care providers if there might be barriers to following the care plan. Less than half (42%) of the patients were asked if they had any questions, with most questions being logistical and often deferred to another team member or met with uncertainty. Of note, among the 33 patients, only 2 patients received extensive information that covered 5 or 6 out of 6 discharge communication domains.

The study found variable roles in who communicated what aspects of discharge education, with most domains being communicated in an ad hoc manner and no clear pattern of responsibility. However, 2 exceptions were observed: nurses were more likely to provide information about new or changed medications and follow-up appointments, and the only example of teach-back was conducted by an attending physician.

Conclusion: The study highlights a significant need for improved discharge techniques to enhance patient safety and quality of care upon leaving the hospital. Interventions should focus on increasing transparency in patient education and understanding, clarifying assumptions of roles among the interprofessional team, and implementing effective communication strategies and system redesigns that foster patient-centered discharge education. Also, the study revealed that some patients received more robust discharge education than others, indicating systemic inequality in the patient experience. Further studies are needed to explore the development and assessment of such interventions to ensure optimal patient outcomes and equal care following hospital discharge.

Study 2 Overview (Marks et al)

Objective: This study aimed to investigate the impact of a nurse-led discharge medication education program, Teaching Important Medication Effects (TIME), on patients’ new medication knowledge at discharge and 48 to 72 hours post discharge. The specific objectives were to identify patients’ priority learning needs, evaluate the influence of TIME on patients’ new medication knowledge before and after discharge, and assess the effect of TIME on patients’ experience and satisfaction with medication education.

Design: The study employed a longitudinal pretest/post-test, 2-group design involving 107 randomly selected medical-surgical patients from an academic hospital. Participants were interviewed before and within 72 hours after discharge following administration of medication instructions. Bivariate analyses were performed to assess demographic and outcome variable differences between groups.

Setting and participants: Conducted on a 24-bed medical-surgical unit at a large Magnet^® hospital over 18 months (2018-2019), the study included patients with at least 1 new medication, aged 18 years or older, able to read and speak English or Spanish, admitted from home with a minimum 1 overnight stay, and planning to return home post discharge. Excluded were cognitively impaired patients, those assigned to a resource pool nurse without TIME training, and those having a research team member assigned. Participants were randomly selected from a computerized list of patients scheduled for discharge.

Main outcome measures: Primary outcome measures included patients’ new medication knowledge before and after discharge and patients’ experience and satisfaction with medication education.

Main results: The usual care (n = 52) and TIME (n = 55) patients had similar baseline demographic characteristics. The study revealed that almost all patients in both usual care and TIME groups were aware of their new medication and its purpose at discharge. However, differences were observed in medication side effect responses, with 72.5% of the usual-care group knowing side effects compared to 94.3% of the TIME group (P = .003). Additionally, 81.5% of the usual-care group understood the medication purpose compared to 100% of the TIME group (P = .02). During the 48- to 72-hour postdischarge calls, consistent responses were found from both groups regarding knowledge of new medication, medication name, and medication purpose. Similar to discharge results, differences in medication side effect responses were observed, with 75.8% of the usual care group correctly identifying at least 1 medication side effect compared to 93.9% of the TIME group (P = .04). TIME was associated with higher satisfaction with medication education compared to usual care (97% vs. 46.9%, P < .001).

Conclusion: The nurse-led discharge medication education program TIME effectively enhanced patients’ new medication knowledge at discharge and 48 to 72 hours after discharge. The program also significantly improved patients’ experience and satisfaction with medication education. These findings indicate that TIME is a valuable tool for augmenting patient education and medication adherence in a hospital setting. By incorporating the teach-back method, TIME offers a structured approach to educating patients about their medications at hospital discharge, leading to improved care transitions.

Commentary

Suboptimal communication between patients, caregivers, and providers upon hospital discharge is a major contributor to patients’ inadequate understanding of postdischarge care plans. This inadequate understanding leads to preventable harms, such as medication errors, adverse events, emergency room visits, and costly hospital readmissions.¹ The issue is further exacerbated by a lack of clarity among health care team members’ respective roles in providing information that optimizes care transitions during the discharge communication process. Moreover, low health literacy, particularly prevalent among seniors, those from disadvantaged backgrouds, and those with lower education attainment or chronic illnesses, create additional barriers to effective discharge communication. A potential solution to this problem is the adoption of effective teaching strategies, specifically the teach-back method. This method employs techniques that ensure patients’ understanding and recall of new information regardless of health literacy, and places accountability on clinicians rather than patients. By closing communication gaps between clinicians and patients, the teach-back method can reduce hospital readmissions, hospital-acquired conditions, and mortality rates, while improving patient satisfaction with health care instructions and the overall hospital experience.²

Study 1, by Trivedi et al, and study 2, by Marks et al, aimed to identify and address problems related to poor communication between patients and health care team members at hospital discharge. Specifically, study 1 examined routine discharge communication practices to determine communication gaps, while study 2 evaluated a nurse-led teach-back intervention program designed to improve patients’ medication knowledge and satisfaction. These distinct objectives and designs reflected the unique ways each study approached the challenges associated with care transitions at the time of hospital discharge.

Study 1 used direct observation of patient-practitioner interactions to evaluate routine discharge communication practices in internal medicine services at 2 urban academic teaching hospitals. In the 33 patients observed, significant gaps in discharge communication practices were identified in the domains of medication changes, postdischarge appointments, disease self-management, and red flags or warning signs. Unsurprisingly, most of these domains were communicated in an ad hoc manner by members of the health care team without a clear pattern of responsibility in reference to patient discharge education, and teach-back was seldom used. These findings underscore the need for improved discharge techniques, effective communication strategies, and clarification of roles among the interprofessional team to enhance the safety, quality of care, and overall patient experience during hospital discharge.

Study 2 aimed to augment the hospital discharge communication process by implementing a nurse-led discharge medication education program (TIME), which targeted patients’ priority learning needs, new medication knowledge, and satisfaction with medication education. In the 107 patients assessed, this teach-back method enhanced patients’ new medication knowledge at discharge and 48 to 72 hours after discharge, as well as improved patients’ experience and satisfaction with medication education. These results suggest that a teach-back method such as the TIME program could be a solution to care transition problems identified in the Trivedi et al study by providing a structured approach to patient education and enhancing communication practices during the hospital discharge process. Thus, by implementing the TIME program, hospitals may improve patient outcomes, safety, and overall quality of care upon leaving the hospital.

Applications for Clinical Practice and System Implementation

Care transition at the time of hospital discharge is a particularly pivotal period in the care of vulnerable individuals. There is growing literature, including studies discussed in this review, to indicate that by focusing on improving patient-practitioner communication during the discharge process and using strategies such as the teach-back method, health care professionals can better prepare patients for self-management in the post-acute period and help them make informed decisions about their care. This emphasis on care-transition communication strategies may lead to a reduction in medication errors, adverse events, and hospital readmissions, ultimately improving patient outcomes and satisfaction. Barriers to system implementation of such strategies may include competing demands and responsibilities of busy practitioners as well as the inherent complexities associated with hospital discharge. Creative solutions, such as the utilization of telehealth and early transition-of-care visits, represent some potential approaches to counter these barriers.

While both studies illustrated barriers and facilitators of hospital discharge communication, each study had limitations that impacted their generalizability to real-world clinical practice. Limitations in study 1 included a small sample size, purposive sampling method, and a focus on planned discharges in a teaching hospital, which may introduce selection bias. The study’s findings may not be generalizable to unplanned discharges, patients who do not speak English or Spanish, or nonteaching hospitals. Additionally, the data were collected before the COVID-19 pandemic, which could have further impacted discharge education practices. The study also revealed that some patients received more robust discharge education than others, which indicated systemic inequality in the patient experience. Further research is required to address this discrepancy. Limitations in study 2 included a relatively small and homogeneous sample, with most participants being younger, non-Hispanic White, English-speaking, and well-educated. This lack of diversity may limit the generalizability of the findings. Furthermore, the study did not evaluate the patients’ knowledge of medication dosage and focused only on new medications. Future studies should examine the effect of teach-back on a broader range of self-management topics in preparation for discharge, while also including a more diverse population to account for factors related to social determinants of health. Taken together, further research is needed to address these limitations and ensure more generalizable results that can more broadly improve discharge education and care transitions that bridge acute and post-acute care.

Practice Points

• There is a significant need for improved discharge strategies to enhance patient safety and quality of care upon leaving the hospital.
• Teach-back method may offer a structured approach to educating patients about their medications at hospital discharge and improve care transitions.

–Yuka Shichijo, MD, and Fred Ko, MD, Mount Sinai Beth Israel Hospital, New York, NY

Study 1 Overview (Trivedi et al)

Objective: This observational quality improvement study aimed to evaluate the discharge communication practices in internal medicine services at 2 urban academic teaching hospitals, specifically focusing on patient education and counseling in 6 key discharge communication domains.

Design: Observations were conducted over a 13-month period from September 2018 through October 2019, following the Standards for Quality Improvement Reporting Excellence (SQUIRE) guidelines.

Setting and participants: The study involved a total of 33 English- and Spanish-speaking patients purposefully selected from the “discharge before noon” list at 2 urban tertiary-care teaching hospitals. A total of 155 observation hours were accumulated, with an average observation time of 4.7 hours per patient on the day of discharge.

Main outcome measures: The study assessed 6 discharge communication domains: (1) the name and function of medication changes, (2) the purpose of postdischarge appointments, (3) disease self-management, (4) red flags or warning signs for complications, (5) teach-back techniques to confirm patient understanding, and (6) staff solicitation of patient questions or concerns.

Main results: The study found several gaps in discharge communication practices. Among the 29 patients with medication changes, 28% were not informed about the name and basic function of the changes, while 59% did not receive counseling on the purpose for the medication change. In terms of postdischarge appointments, 48% of patients were not told the purpose of these appointments. Moreover, 54% of patients did not receive counseling on self-management of their primary discharge diagnosis or other diagnoses, and 73% were not informed about symptom expectations or the expected course of their illness after leaving the hospital. Most patients (82%) were not counseled on red-flag signs and symptoms that should prompt immediate return to care.

Teach-back techniques, which are critical for ensuring patient understanding, were used in only 3% of cases, and 85% of patients were not asked by health care providers if there might be barriers to following the care plan. Less than half (42%) of the patients were asked if they had any questions, with most questions being logistical and often deferred to another team member or met with uncertainty. Of note, among the 33 patients, only 2 patients received extensive information that covered 5 or 6 out of 6 discharge communication domains.

The study found variable roles in who communicated what aspects of discharge education, with most domains being communicated in an ad hoc manner and no clear pattern of responsibility. However, 2 exceptions were observed: nurses were more likely to provide information about new or changed medications and follow-up appointments, and the only example of teach-back was conducted by an attending physician.

Conclusion: The study highlights a significant need for improved discharge techniques to enhance patient safety and quality of care upon leaving the hospital. Interventions should focus on increasing transparency in patient education and understanding, clarifying assumptions of roles among the interprofessional team, and implementing effective communication strategies and system redesigns that foster patient-centered discharge education. Also, the study revealed that some patients received more robust discharge education than others, indicating systemic inequality in the patient experience. Further studies are needed to explore the development and assessment of such interventions to ensure optimal patient outcomes and equal care following hospital discharge.

Study 2 Overview (Marks et al)

Objective: This study aimed to investigate the impact of a nurse-led discharge medication education program, Teaching Important Medication Effects (TIME), on patients’ new medication knowledge at discharge and 48 to 72 hours post discharge. The specific objectives were to identify patients’ priority learning needs, evaluate the influence of TIME on patients’ new medication knowledge before and after discharge, and assess the effect of TIME on patients’ experience and satisfaction with medication education.

Design: The study employed a longitudinal pretest/post-test, 2-group design involving 107 randomly selected medical-surgical patients from an academic hospital. Participants were interviewed before and within 72 hours after discharge following administration of medication instructions. Bivariate analyses were performed to assess demographic and outcome variable differences between groups.

Setting and participants: Conducted on a 24-bed medical-surgical unit at a large Magnet^® hospital over 18 months (2018-2019), the study included patients with at least 1 new medication, aged 18 years or older, able to read and speak English or Spanish, admitted from home with a minimum 1 overnight stay, and planning to return home post discharge. Excluded were cognitively impaired patients, those assigned to a resource pool nurse without TIME training, and those having a research team member assigned. Participants were randomly selected from a computerized list of patients scheduled for discharge.

Main outcome measures: Primary outcome measures included patients’ new medication knowledge before and after discharge and patients’ experience and satisfaction with medication education.

Main results: The usual care (n = 52) and TIME (n = 55) patients had similar baseline demographic characteristics. The study revealed that almost all patients in both usual care and TIME groups were aware of their new medication and its purpose at discharge. However, differences were observed in medication side effect responses, with 72.5% of the usual-care group knowing side effects compared to 94.3% of the TIME group (P = .003). Additionally, 81.5% of the usual-care group understood the medication purpose compared to 100% of the TIME group (P = .02). During the 48- to 72-hour postdischarge calls, consistent responses were found from both groups regarding knowledge of new medication, medication name, and medication purpose. Similar to discharge results, differences in medication side effect responses were observed, with 75.8% of the usual care group correctly identifying at least 1 medication side effect compared to 93.9% of the TIME group (P = .04). TIME was associated with higher satisfaction with medication education compared to usual care (97% vs. 46.9%, P < .001).

Conclusion: The nurse-led discharge medication education program TIME effectively enhanced patients’ new medication knowledge at discharge and 48 to 72 hours after discharge. The program also significantly improved patients’ experience and satisfaction with medication education. These findings indicate that TIME is a valuable tool for augmenting patient education and medication adherence in a hospital setting. By incorporating the teach-back method, TIME offers a structured approach to educating patients about their medications at hospital discharge, leading to improved care transitions.

Commentary

Suboptimal communication between patients, caregivers, and providers upon hospital discharge is a major contributor to patients’ inadequate understanding of postdischarge care plans. This inadequate understanding leads to preventable harms, such as medication errors, adverse events, emergency room visits, and costly hospital readmissions.¹ The issue is further exacerbated by a lack of clarity among health care team members’ respective roles in providing information that optimizes care transitions during the discharge communication process. Moreover, low health literacy, particularly prevalent among seniors, those from disadvantaged backgrouds, and those with lower education attainment or chronic illnesses, create additional barriers to effective discharge communication. A potential solution to this problem is the adoption of effective teaching strategies, specifically the teach-back method. This method employs techniques that ensure patients’ understanding and recall of new information regardless of health literacy, and places accountability on clinicians rather than patients. By closing communication gaps between clinicians and patients, the teach-back method can reduce hospital readmissions, hospital-acquired conditions, and mortality rates, while improving patient satisfaction with health care instructions and the overall hospital experience.²

Study 1, by Trivedi et al, and study 2, by Marks et al, aimed to identify and address problems related to poor communication between patients and health care team members at hospital discharge. Specifically, study 1 examined routine discharge communication practices to determine communication gaps, while study 2 evaluated a nurse-led teach-back intervention program designed to improve patients’ medication knowledge and satisfaction. These distinct objectives and designs reflected the unique ways each study approached the challenges associated with care transitions at the time of hospital discharge.

Study 1 used direct observation of patient-practitioner interactions to evaluate routine discharge communication practices in internal medicine services at 2 urban academic teaching hospitals. In the 33 patients observed, significant gaps in discharge communication practices were identified in the domains of medication changes, postdischarge appointments, disease self-management, and red flags or warning signs. Unsurprisingly, most of these domains were communicated in an ad hoc manner by members of the health care team without a clear pattern of responsibility in reference to patient discharge education, and teach-back was seldom used. These findings underscore the need for improved discharge techniques, effective communication strategies, and clarification of roles among the interprofessional team to enhance the safety, quality of care, and overall patient experience during hospital discharge.

Study 2 aimed to augment the hospital discharge communication process by implementing a nurse-led discharge medication education program (TIME), which targeted patients’ priority learning needs, new medication knowledge, and satisfaction with medication education. In the 107 patients assessed, this teach-back method enhanced patients’ new medication knowledge at discharge and 48 to 72 hours after discharge, as well as improved patients’ experience and satisfaction with medication education. These results suggest that a teach-back method such as the TIME program could be a solution to care transition problems identified in the Trivedi et al study by providing a structured approach to patient education and enhancing communication practices during the hospital discharge process. Thus, by implementing the TIME program, hospitals may improve patient outcomes, safety, and overall quality of care upon leaving the hospital.

Applications for Clinical Practice and System Implementation

Care transition at the time of hospital discharge is a particularly pivotal period in the care of vulnerable individuals. There is growing literature, including studies discussed in this review, to indicate that by focusing on improving patient-practitioner communication during the discharge process and using strategies such as the teach-back method, health care professionals can better prepare patients for self-management in the post-acute period and help them make informed decisions about their care. This emphasis on care-transition communication strategies may lead to a reduction in medication errors, adverse events, and hospital readmissions, ultimately improving patient outcomes and satisfaction. Barriers to system implementation of such strategies may include competing demands and responsibilities of busy practitioners as well as the inherent complexities associated with hospital discharge. Creative solutions, such as the utilization of telehealth and early transition-of-care visits, represent some potential approaches to counter these barriers.

While both studies illustrated barriers and facilitators of hospital discharge communication, each study had limitations that impacted their generalizability to real-world clinical practice. Limitations in study 1 included a small sample size, purposive sampling method, and a focus on planned discharges in a teaching hospital, which may introduce selection bias. The study’s findings may not be generalizable to unplanned discharges, patients who do not speak English or Spanish, or nonteaching hospitals. Additionally, the data were collected before the COVID-19 pandemic, which could have further impacted discharge education practices. The study also revealed that some patients received more robust discharge education than others, which indicated systemic inequality in the patient experience. Further research is required to address this discrepancy. Limitations in study 2 included a relatively small and homogeneous sample, with most participants being younger, non-Hispanic White, English-speaking, and well-educated. This lack of diversity may limit the generalizability of the findings. Furthermore, the study did not evaluate the patients’ knowledge of medication dosage and focused only on new medications. Future studies should examine the effect of teach-back on a broader range of self-management topics in preparation for discharge, while also including a more diverse population to account for factors related to social determinants of health. Taken together, further research is needed to address these limitations and ensure more generalizable results that can more broadly improve discharge education and care transitions that bridge acute and post-acute care.

Practice Points

• There is a significant need for improved discharge strategies to enhance patient safety and quality of care upon leaving the hospital.
• Teach-back method may offer a structured approach to educating patients about their medications at hospital discharge and improve care transitions.

–Yuka Shichijo, MD, and Fred Ko, MD, Mount Sinai Beth Israel Hospital, New York, NY

What is your experience with prescribing preventive medication for your patients with migraine?

Roughly 40% of patients living with migraine should be on preventive medication or other treatment, but probably fewer than 15% of patients with migraine are currently receiving therapy. There are several reasons for this: General physicians rarely put patients on preventive medication unless they are interested in or knowledgeable about headache, and the older preventive medicines that neurologists and headache specialists have used for many years have a lot of potential side effects and do not begin to work quickly.

It takes approximately 2 to 3 months for preventive medication to become effective, and many patients need to be slowly titrated up to an effective dose. By the time patients reach a steady state over a few weeks, if it is still not working well, they must slowly taper it and try something else. This is  what often occurs with older preventive migraine medications—especially one of the most commonly used preventives, topiramate (Topamax). This drug was first indicated for epilepsy and then later for mood stabilization. Though it has good efficacy in reducing migraine attacks, it has many possible side effects, some of them troublesome. I often had multiple calls from patients in their first month of taking it complain of memory or word-finding issues and tingling in the extremities. More serious adverse events can be increased pressure in the eyes, such as glaucoma, and kidney stones. I often get referrals from other neurologists and headache specialists regarding patients who have failed multiple preventive medicines; 90% percent of these referrals need to be switched to the newer, more costly calcitonin gene-related peptide (CGRP)-blocking preventative medications, if insurance companies will cover them. 

What categories of migraine preventive drugs do you generally prescribe your patients?

Of the older medications, most are epilepsy medicines, beta blockers, antidepressants, or cardiac medications such as angiotensin receptor blockers (candesartan). Of the newer medications, I use 1 of the 4 injectable monoclonal antibodies (mAbs), or 1 of the 2 gepants.

Older migraine preventive medication

Anticonvulsants (epilepsy medications)

Anticonvulsants are used for the treatment of several conditions, including epilepsy and pain control, but some can help reduce migraine attacks. These medicines, like all drugs, have the potential to cause side effects, especially topiramate; this medicine often causes paresthesia or tingling in the extremities as well as trouble with speech and memory, kidney stones, pancreatitis, and weight loss. The weight loss side effect of this drug has made it more appealing for some patients who had previously gained 10 to 15 pounds taking antidepressant medication to treat their migraine. I personally thought it was the most effective of all the preventive migraine medications if the patient could tolerate it.

Beta Blockers

Beta blockers cause the heart rate to decrease and also lower blood pressure. Most of my migraine patients are healthy females in their 20s and 30s and, when taking a beta blocker, can get short of breath when they exercise. These medications can also cause some depression and gastrointestinal issues and raise cholesterol levels.

Antidepressants

The type of antidepressants that I normally prescribe for migraine prevention are the tricyclic antidepressants. The one that has the best data in the literature and is often prescribed is amitriptyline (Elavil); I prefer a cousin to this medicine, nortriptyline. I prescribe tricyclics because many of my migraine patients have 2 other comorbid problems: depression and trouble staying asleep at night. Amitriptyline tends to cause drowsiness and can help patients sleep. It can also cause dry mouth, trouble urinating (especially in men), constipation, weight gain, and can slow patients down mentally, so it should not be prescribed to elderly patients. These antidepressants should be prescribed in very low doses and taken an hour before bedtime. The dose should be increased gradually over several weeks to help reduce adverse events. The best dose for migraine is often lower than the antidepressant dose, so sometimes a depressed patient needs 2 types of antidepressants. The typical dose for migraine prevention is about 50 to 75 mg. For depression, it is about 150 mg.

The patient would then need to increase their dose gradually for a month and remain on the target dose for at least another month. At the end of 2 months, they would have some idea whether it was working for them. If it was not, I might increase the dose even further. It is important to set expectations with patients at the beginning of treatment and tell them it is going to take 2 to 3 months to see if it works.  If it does not work, I tell them, we will have to try another one, and that is going to take 2 or 3 months as well, until we can switch to the newer medications, which start to work in the first month, often in the first few days. 

Why wouldn’t we just start with the newer preventives? Insurance companies require patients to fail, on average, 2 categories of the older medications before they will pay for the newer ones. Medicare usually only covers the older generic medications.

New migraine preventive medications

Monoclonal Antibodies

mAbs that block CGRP for the prevention of migraine, such as erenumab, fremanezumab, galcanezumab, and eptinezumab, target either the CGRP ligand itself or block the receptor to CGRP. This class of medication became available about 5 years ago. The first one approved was erenumab (Aimovig). It was tried by a lot of headache specialists, many neurologists, and then some general physicians once it came to market. It is the only one in its class that grabs the ligand CGRP and prevents it from docking on its receptor. Recently, 5-year safety data indicated it is extremely safe with only a few side effects, (it has been shown to cause some constipation and hypertension). It does, however, tend to lower the number of migraine days per month by about 40% to 50%. At the beginning of erenumab’s availability, researchers took patients that had 8 to 22 days of migraine per month and put them in double-blind, placebo-controlled, randomized trials. They found that some patients' migraine days went down gradually to 10 to 12 days from 20 migraine days per month. Erenumab works quickly, and most patients improve within 2 weeks.

Fremanezumab (AJOVY™) was the second mAb approved, followed pretty quickly by the third, galcanezumab (Emgality™). All 3 of these mAbs are administered once a month by a subcutaneous injection from an autoinjector. If a patient takes 3 fremanezumab injections in 1 day, they do not have to repeat that dose for 3 months. The upside of these 3 treatments is that the patient can self-administer the medication at home with few, if any, adverse events; the downside is they are expensive medications, costing about $600 per month. 

Shortly thereafter, a fourth mAb, eptinezumab (VYEPTI™), was brought to market. Unlike the other 3 mAbs, it is administered as an intravenous infusion. The patient must come to an office or infusion center for a 30-minute intravenous infusion, which is not as convenient as treating themselves with an autoinjector at home. Eptinezumab is a strong medication that is often prescribed when other treatments are not effective. Each of the 4 mAbs has its own possible adverse events, but these are few and usually mild. The mAbs have a half-life of about 28 to 32 days; it takes 5 to 6 months after an injection for these mAbs to be metabolized by the reticuloendothelial system. 

Gepants

The gepants are small molecule CGRP receptor blockers with much shorter half-lives than mAbs. They work by blocking the CGRP receptor so the CGRP ligand cannot dock there and cause vasodilation and increased pain transmission. Gepants have half-lives of 6 to 12 hours and can be used to treat a migraine acutely. Several drug companies studied the effects of taking a gepant every day or every other day, showing it can also be used as a migraine preventive medication. Ubrogepant (Ubrelvy®) was the first gepant to receive approval from the US Food and Drug Administration (FDA), but it was authorized only for acute care. Rimegepant (Nurtec®) was the second gepant approved, initially for acute treatment and later becoming the first gepant approved for migraine prevention. The same tablet can be used for acute care or for prevention. Preventive treatment consists of one 75 mg oral disintegrating tablet taken every second day. It works quite well as a preventive and has very few side effects. Nausea and abdominal discomfort occur in < 3% of patients. Some patients prefer to take a pill every other day over having an injection once per month or once every 3 months. It makes more sense for a woman of childbearing potential to take a drug with very short half-life vs one that lasts for 5 to 6 months in case she decides to become pregnant (or unexpectedly becomes pregnant).

A third gepant, atogepant (Qulipta™), was later approved, but only for prevention. It is available in 3 different strengths: 10 mg, 30 mg, and 60 mg. I tend to prescribe the 60-mg strength, and the dose is 1 pill every day. 

If you compare rimegepant, which is taken once every other day, and atogepant, taken once daily, the latter tends to have slightly more side effects of nausea, drowsiness, and constipation, whereas rimegepant has been shown to have fewer side effects in double-blind, randomized studies. Like all gepants, it is quite effective and fast acting. 

The goal of preventive medications is to decrease the frequency, severity, and duration of migraine attacks. Effective treatment can increase responsiveness to acute migraine therapy and improve the quality of life in patients suffering from migraine. Every patient is different and thus the side effects they experience vary. With time and patience, most patients find the relief from migraine they have been desperately seeking through the preventive medicines discussed above. This is a good time to have migraine, if you can get in to see a knowledgeable doctor and your insurance company cooperates. When I started my neurology practice 51 years ago, we had few preventives, and none approved by the FDA. Now we have several older, approved preventives—4 newer mAbs, and 2 newer gepants—as well as several devices, which we will discuss in the future.

What is your experience with prescribing preventive medication for your patients with migraine?

Roughly 40% of patients living with migraine should be on preventive medication or other treatment, but probably fewer than 15% of patients with migraine are currently receiving therapy. There are several reasons for this: General physicians rarely put patients on preventive medication unless they are interested in or knowledgeable about headache, and the older preventive medicines that neurologists and headache specialists have used for many years have a lot of potential side effects and do not begin to work quickly.

It takes approximately 2 to 3 months for preventive medication to become effective, and many patients need to be slowly titrated up to an effective dose. By the time patients reach a steady state over a few weeks, if it is still not working well, they must slowly taper it and try something else. This is  what often occurs with older preventive migraine medications—especially one of the most commonly used preventives, topiramate (Topamax). This drug was first indicated for epilepsy and then later for mood stabilization. Though it has good efficacy in reducing migraine attacks, it has many possible side effects, some of them troublesome. I often had multiple calls from patients in their first month of taking it complain of memory or word-finding issues and tingling in the extremities. More serious adverse events can be increased pressure in the eyes, such as glaucoma, and kidney stones. I often get referrals from other neurologists and headache specialists regarding patients who have failed multiple preventive medicines; 90% percent of these referrals need to be switched to the newer, more costly calcitonin gene-related peptide (CGRP)-blocking preventative medications, if insurance companies will cover them. 

What categories of migraine preventive drugs do you generally prescribe your patients?

Of the older medications, most are epilepsy medicines, beta blockers, antidepressants, or cardiac medications such as angiotensin receptor blockers (candesartan). Of the newer medications, I use 1 of the 4 injectable monoclonal antibodies (mAbs), or 1 of the 2 gepants.

Older migraine preventive medication

Anticonvulsants (epilepsy medications)

Anticonvulsants are used for the treatment of several conditions, including epilepsy and pain control, but some can help reduce migraine attacks. These medicines, like all drugs, have the potential to cause side effects, especially topiramate; this medicine often causes paresthesia or tingling in the extremities as well as trouble with speech and memory, kidney stones, pancreatitis, and weight loss. The weight loss side effect of this drug has made it more appealing for some patients who had previously gained 10 to 15 pounds taking antidepressant medication to treat their migraine. I personally thought it was the most effective of all the preventive migraine medications if the patient could tolerate it.

Beta Blockers

Beta blockers cause the heart rate to decrease and also lower blood pressure. Most of my migraine patients are healthy females in their 20s and 30s and, when taking a beta blocker, can get short of breath when they exercise. These medications can also cause some depression and gastrointestinal issues and raise cholesterol levels.

Antidepressants

The type of antidepressants that I normally prescribe for migraine prevention are the tricyclic antidepressants. The one that has the best data in the literature and is often prescribed is amitriptyline (Elavil); I prefer a cousin to this medicine, nortriptyline. I prescribe tricyclics because many of my migraine patients have 2 other comorbid problems: depression and trouble staying asleep at night. Amitriptyline tends to cause drowsiness and can help patients sleep. It can also cause dry mouth, trouble urinating (especially in men), constipation, weight gain, and can slow patients down mentally, so it should not be prescribed to elderly patients. These antidepressants should be prescribed in very low doses and taken an hour before bedtime. The dose should be increased gradually over several weeks to help reduce adverse events. The best dose for migraine is often lower than the antidepressant dose, so sometimes a depressed patient needs 2 types of antidepressants. The typical dose for migraine prevention is about 50 to 75 mg. For depression, it is about 150 mg.

The patient would then need to increase their dose gradually for a month and remain on the target dose for at least another month. At the end of 2 months, they would have some idea whether it was working for them. If it was not, I might increase the dose even further. It is important to set expectations with patients at the beginning of treatment and tell them it is going to take 2 to 3 months to see if it works.  If it does not work, I tell them, we will have to try another one, and that is going to take 2 or 3 months as well, until we can switch to the newer medications, which start to work in the first month, often in the first few days. 

Why wouldn’t we just start with the newer preventives? Insurance companies require patients to fail, on average, 2 categories of the older medications before they will pay for the newer ones. Medicare usually only covers the older generic medications.

New migraine preventive medications

Monoclonal Antibodies

mAbs that block CGRP for the prevention of migraine, such as erenumab, fremanezumab, galcanezumab, and eptinezumab, target either the CGRP ligand itself or block the receptor to CGRP. This class of medication became available about 5 years ago. The first one approved was erenumab (Aimovig). It was tried by a lot of headache specialists, many neurologists, and then some general physicians once it came to market. It is the only one in its class that grabs the ligand CGRP and prevents it from docking on its receptor. Recently, 5-year safety data indicated it is extremely safe with only a few side effects, (it has been shown to cause some constipation and hypertension). It does, however, tend to lower the number of migraine days per month by about 40% to 50%. At the beginning of erenumab’s availability, researchers took patients that had 8 to 22 days of migraine per month and put them in double-blind, placebo-controlled, randomized trials. They found that some patients' migraine days went down gradually to 10 to 12 days from 20 migraine days per month. Erenumab works quickly, and most patients improve within 2 weeks.

Fremanezumab (AJOVY™) was the second mAb approved, followed pretty quickly by the third, galcanezumab (Emgality™). All 3 of these mAbs are administered once a month by a subcutaneous injection from an autoinjector. If a patient takes 3 fremanezumab injections in 1 day, they do not have to repeat that dose for 3 months. The upside of these 3 treatments is that the patient can self-administer the medication at home with few, if any, adverse events; the downside is they are expensive medications, costing about $600 per month. 

Shortly thereafter, a fourth mAb, eptinezumab (VYEPTI™), was brought to market. Unlike the other 3 mAbs, it is administered as an intravenous infusion. The patient must come to an office or infusion center for a 30-minute intravenous infusion, which is not as convenient as treating themselves with an autoinjector at home. Eptinezumab is a strong medication that is often prescribed when other treatments are not effective. Each of the 4 mAbs has its own possible adverse events, but these are few and usually mild. The mAbs have a half-life of about 28 to 32 days; it takes 5 to 6 months after an injection for these mAbs to be metabolized by the reticuloendothelial system. 

Gepants

The gepants are small molecule CGRP receptor blockers with much shorter half-lives than mAbs. They work by blocking the CGRP receptor so the CGRP ligand cannot dock there and cause vasodilation and increased pain transmission. Gepants have half-lives of 6 to 12 hours and can be used to treat a migraine acutely. Several drug companies studied the effects of taking a gepant every day or every other day, showing it can also be used as a migraine preventive medication. Ubrogepant (Ubrelvy®) was the first gepant to receive approval from the US Food and Drug Administration (FDA), but it was authorized only for acute care. Rimegepant (Nurtec®) was the second gepant approved, initially for acute treatment and later becoming the first gepant approved for migraine prevention. The same tablet can be used for acute care or for prevention. Preventive treatment consists of one 75 mg oral disintegrating tablet taken every second day. It works quite well as a preventive and has very few side effects. Nausea and abdominal discomfort occur in < 3% of patients. Some patients prefer to take a pill every other day over having an injection once per month or once every 3 months. It makes more sense for a woman of childbearing potential to take a drug with very short half-life vs one that lasts for 5 to 6 months in case she decides to become pregnant (or unexpectedly becomes pregnant).

A third gepant, atogepant (Qulipta™), was later approved, but only for prevention. It is available in 3 different strengths: 10 mg, 30 mg, and 60 mg. I tend to prescribe the 60-mg strength, and the dose is 1 pill every day. 

If you compare rimegepant, which is taken once every other day, and atogepant, taken once daily, the latter tends to have slightly more side effects of nausea, drowsiness, and constipation, whereas rimegepant has been shown to have fewer side effects in double-blind, randomized studies. Like all gepants, it is quite effective and fast acting. 

The goal of preventive medications is to decrease the frequency, severity, and duration of migraine attacks. Effective treatment can increase responsiveness to acute migraine therapy and improve the quality of life in patients suffering from migraine. Every patient is different and thus the side effects they experience vary. With time and patience, most patients find the relief from migraine they have been desperately seeking through the preventive medicines discussed above. This is a good time to have migraine, if you can get in to see a knowledgeable doctor and your insurance company cooperates. When I started my neurology practice 51 years ago, we had few preventives, and none approved by the FDA. Now we have several older, approved preventives—4 newer mAbs, and 2 newer gepants—as well as several devices, which we will discuss in the future.

What is your experience with prescribing preventive medication for your patients with migraine?

Roughly 40% of patients living with migraine should be on preventive medication or other treatment, but probably fewer than 15% of patients with migraine are currently receiving therapy. There are several reasons for this: General physicians rarely put patients on preventive medication unless they are interested in or knowledgeable about headache, and the older preventive medicines that neurologists and headache specialists have used for many years have a lot of potential side effects and do not begin to work quickly.

It takes approximately 2 to 3 months for preventive medication to become effective, and many patients need to be slowly titrated up to an effective dose. By the time patients reach a steady state over a few weeks, if it is still not working well, they must slowly taper it and try something else. This is  what often occurs with older preventive migraine medications—especially one of the most commonly used preventives, topiramate (Topamax). This drug was first indicated for epilepsy and then later for mood stabilization. Though it has good efficacy in reducing migraine attacks, it has many possible side effects, some of them troublesome. I often had multiple calls from patients in their first month of taking it complain of memory or word-finding issues and tingling in the extremities. More serious adverse events can be increased pressure in the eyes, such as glaucoma, and kidney stones. I often get referrals from other neurologists and headache specialists regarding patients who have failed multiple preventive medicines; 90% percent of these referrals need to be switched to the newer, more costly calcitonin gene-related peptide (CGRP)-blocking preventative medications, if insurance companies will cover them. 

What categories of migraine preventive drugs do you generally prescribe your patients?

Of the older medications, most are epilepsy medicines, beta blockers, antidepressants, or cardiac medications such as angiotensin receptor blockers (candesartan). Of the newer medications, I use 1 of the 4 injectable monoclonal antibodies (mAbs), or 1 of the 2 gepants.

Older migraine preventive medication

Anticonvulsants (epilepsy medications)

Anticonvulsants are used for the treatment of several conditions, including epilepsy and pain control, but some can help reduce migraine attacks. These medicines, like all drugs, have the potential to cause side effects, especially topiramate; this medicine often causes paresthesia or tingling in the extremities as well as trouble with speech and memory, kidney stones, pancreatitis, and weight loss. The weight loss side effect of this drug has made it more appealing for some patients who had previously gained 10 to 15 pounds taking antidepressant medication to treat their migraine. I personally thought it was the most effective of all the preventive migraine medications if the patient could tolerate it.

Beta Blockers

Beta blockers cause the heart rate to decrease and also lower blood pressure. Most of my migraine patients are healthy females in their 20s and 30s and, when taking a beta blocker, can get short of breath when they exercise. These medications can also cause some depression and gastrointestinal issues and raise cholesterol levels.

Antidepressants

The type of antidepressants that I normally prescribe for migraine prevention are the tricyclic antidepressants. The one that has the best data in the literature and is often prescribed is amitriptyline (Elavil); I prefer a cousin to this medicine, nortriptyline. I prescribe tricyclics because many of my migraine patients have 2 other comorbid problems: depression and trouble staying asleep at night. Amitriptyline tends to cause drowsiness and can help patients sleep. It can also cause dry mouth, trouble urinating (especially in men), constipation, weight gain, and can slow patients down mentally, so it should not be prescribed to elderly patients. These antidepressants should be prescribed in very low doses and taken an hour before bedtime. The dose should be increased gradually over several weeks to help reduce adverse events. The best dose for migraine is often lower than the antidepressant dose, so sometimes a depressed patient needs 2 types of antidepressants. The typical dose for migraine prevention is about 50 to 75 mg. For depression, it is about 150 mg.

The patient would then need to increase their dose gradually for a month and remain on the target dose for at least another month. At the end of 2 months, they would have some idea whether it was working for them. If it was not, I might increase the dose even further. It is important to set expectations with patients at the beginning of treatment and tell them it is going to take 2 to 3 months to see if it works.  If it does not work, I tell them, we will have to try another one, and that is going to take 2 or 3 months as well, until we can switch to the newer medications, which start to work in the first month, often in the first few days. 

Why wouldn’t we just start with the newer preventives? Insurance companies require patients to fail, on average, 2 categories of the older medications before they will pay for the newer ones. Medicare usually only covers the older generic medications.

New migraine preventive medications

Monoclonal Antibodies

mAbs that block CGRP for the prevention of migraine, such as erenumab, fremanezumab, galcanezumab, and eptinezumab, target either the CGRP ligand itself or block the receptor to CGRP. This class of medication became available about 5 years ago. The first one approved was erenumab (Aimovig). It was tried by a lot of headache specialists, many neurologists, and then some general physicians once it came to market. It is the only one in its class that grabs the ligand CGRP and prevents it from docking on its receptor. Recently, 5-year safety data indicated it is extremely safe with only a few side effects, (it has been shown to cause some constipation and hypertension). It does, however, tend to lower the number of migraine days per month by about 40% to 50%. At the beginning of erenumab’s availability, researchers took patients that had 8 to 22 days of migraine per month and put them in double-blind, placebo-controlled, randomized trials. They found that some patients' migraine days went down gradually to 10 to 12 days from 20 migraine days per month. Erenumab works quickly, and most patients improve within 2 weeks.

Fremanezumab (AJOVY™) was the second mAb approved, followed pretty quickly by the third, galcanezumab (Emgality™). All 3 of these mAbs are administered once a month by a subcutaneous injection from an autoinjector. If a patient takes 3 fremanezumab injections in 1 day, they do not have to repeat that dose for 3 months. The upside of these 3 treatments is that the patient can self-administer the medication at home with few, if any, adverse events; the downside is they are expensive medications, costing about $600 per month. 

Shortly thereafter, a fourth mAb, eptinezumab (VYEPTI™), was brought to market. Unlike the other 3 mAbs, it is administered as an intravenous infusion. The patient must come to an office or infusion center for a 30-minute intravenous infusion, which is not as convenient as treating themselves with an autoinjector at home. Eptinezumab is a strong medication that is often prescribed when other treatments are not effective. Each of the 4 mAbs has its own possible adverse events, but these are few and usually mild. The mAbs have a half-life of about 28 to 32 days; it takes 5 to 6 months after an injection for these mAbs to be metabolized by the reticuloendothelial system. 

Gepants

The gepants are small molecule CGRP receptor blockers with much shorter half-lives than mAbs. They work by blocking the CGRP receptor so the CGRP ligand cannot dock there and cause vasodilation and increased pain transmission. Gepants have half-lives of 6 to 12 hours and can be used to treat a migraine acutely. Several drug companies studied the effects of taking a gepant every day or every other day, showing it can also be used as a migraine preventive medication. Ubrogepant (Ubrelvy®) was the first gepant to receive approval from the US Food and Drug Administration (FDA), but it was authorized only for acute care. Rimegepant (Nurtec®) was the second gepant approved, initially for acute treatment and later becoming the first gepant approved for migraine prevention. The same tablet can be used for acute care or for prevention. Preventive treatment consists of one 75 mg oral disintegrating tablet taken every second day. It works quite well as a preventive and has very few side effects. Nausea and abdominal discomfort occur in < 3% of patients. Some patients prefer to take a pill every other day over having an injection once per month or once every 3 months. It makes more sense for a woman of childbearing potential to take a drug with very short half-life vs one that lasts for 5 to 6 months in case she decides to become pregnant (or unexpectedly becomes pregnant).

A third gepant, atogepant (Qulipta™), was later approved, but only for prevention. It is available in 3 different strengths: 10 mg, 30 mg, and 60 mg. I tend to prescribe the 60-mg strength, and the dose is 1 pill every day. 

If you compare rimegepant, which is taken once every other day, and atogepant, taken once daily, the latter tends to have slightly more side effects of nausea, drowsiness, and constipation, whereas rimegepant has been shown to have fewer side effects in double-blind, randomized studies. Like all gepants, it is quite effective and fast acting. 

The goal of preventive medications is to decrease the frequency, severity, and duration of migraine attacks. Effective treatment can increase responsiveness to acute migraine therapy and improve the quality of life in patients suffering from migraine. Every patient is different and thus the side effects they experience vary. With time and patience, most patients find the relief from migraine they have been desperately seeking through the preventive medicines discussed above. This is a good time to have migraine, if you can get in to see a knowledgeable doctor and your insurance company cooperates. When I started my neurology practice 51 years ago, we had few preventives, and none approved by the FDA. Now we have several older, approved preventives—4 newer mAbs, and 2 newer gepants—as well as several devices, which we will discuss in the future.

Introduction

The first Janus kinase (JAK) inhibitor received regulatory approval for the treatment of psoriatic arthritis (PsA) more than 5 years ago. Although there are limited comparative data between this and other JAK inhibitors approved or in development for the treatment of PsA, it is reasonable to anticipate variability in therapeutic effect and the risk of adverse events between different JAK inhibitors. So far, there have been considerable differences in the relative selectivity of each agent on the 4 JAK isoform enzymes, JAK1, JAK2, JAK3, and TYK2. This selectivity determines the downstream signal transducers and activators of transcription proteins (JAK-STAT [signal transducer and activator of transcription] pathway) that ultimately mediate both anti-inflammatory and off-target effects. In this review of JAK inhibitors in PsA, differences between JAK inhibitors will be explored for their potential impact on benefit-to-risk ratio while treating PsA.  

Background

Data from the National Psoriasis Foundation (NPF) estimates that 8 million individuals in the United States have psoriasis.¹ PsA, an inflammatory spondyloarthritis associated with psoriasis, develops in about 30% of these individuals, but precise epidemiology on this subset of psoriasis patients is complicated by missed and delayed diagnoses. Of patients with psoriasis, only about 15% of patients with PsA have joint inflammation at the time or in advance of skin lesions.² This might explain delays in diagnosis. In one study, 15% of patients treated for psoriasis were found to have concomitant but unrecognized PsA.³ 

PsA was first classified as a distinct pathologic condition only about 50 years ago, even though skeletal remains indicate that this disease existed in early civilizations.² Based on consensus that PsA deserved definition as a distinct entity, the Classification Criteria for Psoriatic Arthritis (CASPAR) were published in 2006.⁴ By these criteria, cumulative points are allotted for clinical signs of skin, nail, and joint involvement, as well as radiographic signs in patients judged to have inflammatory disease in the joints, spine, or entheses to classify them as having PsA. 

There are numerous recommendations for the treatment of PsA, including those issued by the American College of Rheumatology (ACR),⁵ the European Alliance of Associations for Rheumatology (EULAR),⁶ and the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA).⁷ Although generally compatible with the others, the GRAPPA recommendations, which are the most recent, have addressed the heterogeneity of PsA by recommending therapies for specific disease domains, such as the skin, nail, and joint manifestations.

For treatment of PsA, the available drug classes for moderate-to-severe disease include immunomodulators, such as methotrexate, biologics that inhibit cytokines, such as tumor necrosis factor (TNF) and the interleukin (IL) cytokines IL-17, 1L-23, and IL12/IL-23, phosphodiesterase-4 (PDE4) inhibitors, and JAK inhibitors. In the GRAPPA recommendations, JAK inhibitors are listed along with other targeted therapies as first-line choices for peripheral arthritis, axial disease, enthesitis, dactylitis, and plaque psoriasis. 

JAK Inhibitors and PsA

There are multiple ways to classify JAK inhibitors. Tofacitinib, the first JAK inhibitor approved for PsA, is labeled a first-generation agent because it is relatively nonselective for the 4 JAK isoforms.⁸ Second-generation agents, such as upadacitinib, have been distinguished from tofacitinib, baricitinib, and other first-generation drugs by greater relative selectivity on the JAK1 enzyme. Other drugs in development for PsA target different JAK isoforms. Deucravacitinib, for example, which was approved for psoriasis after a favorable phase 3 trial⁹ and has shown promise for PsA in a phase 2 trial, is selective for the TYK2 isoform.¹⁰ A rapidly growing list of JAK inhibitors with different selectivity profiles, including dual JAK inhibitory effects, are being explored in a host of inflammatory diseases.

The relationship between selectivity on specific JAK isoforms, anti-inflammatory effects, and off-target effects is not fully understood.⁸ In addition, characteristics beyond JAK selectivity have potential pharmacologic importance. For example, JAK inhibitors can be classified as ATP competitive inhibitors and allosteric inhibitors, both of which are reversible binding modes.⁸ Within each of these subcategories, the site of kinase binding has the potential to influence clinical activity.⁸

JAK Inhibitors: Clinical Experience in PsA 

Tofacitinib, a first-generation JAK inhibitor, initially licensed for use in the treatment of rheumatoid arthritis (RA), received regulatory approval for PsA on the basis of the OPAL Beyond trial.¹¹ Approval of upadacitinib for PsA followed about 4 years later on the basis of the SELECT PsA-1 trial.¹² The primary endpoint in both of these studies was proportion of patients with an ACR response, signifying degree of improvement from baseline, of ≥20%. For the JAK inhibitors, the ACR20 rates were about 50% and 70% in the tofacitinib and upadacitinib phase 3 trials, respectively. Other JAK inhibitors have been evaluated in PsA but none so far are approved in the United States.

Despite experimental evidence supporting the hypothesis that JAK1 selectivity is clinically relevant to the treatment of PsA and other spondyloarthritides,¹³ there is no level 1 evidence of an efficacy or safety advantage for second- relative to first-generation JAK inhibitors. A small number of indirect comparisons, such as one employing a network Bayesian analysis to compare these drugs for the treatment of RA,¹⁴ have supported a clinical advantage for JAK1 selectivity, but head-to-head comparisons are needed to confirm differences. 

Prescribing information for both tofacitinib and upadacitinib in PsA and other indications include a black box warning for risk of serious adverse events, including major adverse cardiac events (MACE) and thromboembolism. The warning is based on the placebo-controlled ORAL trial with tofacitinib in RA.¹⁵ The study population was enhanced for risk with eligibility that required older age and the presence of cardiovascular risk factors. In this high-risk RA population, tofacitinib was associated with modest increases in serious adverse events, including MACE and thromboembolism, relative to placebo over several years of follow-up. A similar trial has not been conducted with upadacitinib or in patients with PsA.  

In a phase 3 trial with the TYK2-selective deucravacitinib in psoriasis, there was no increase in the rate of MACE or thromboembolism.⁹ When granted regulatory approval for psoriasis, the product information did not include a black box warning, differentiating it from other currently available JAK inhibitors. It has not yet been proven whether the absence of serious adverse events in the phase 3 psoriasis and phase 2 PsA trials with deucravacitinib are related to TYK2 JAK enzyme selectivity. 

Although TYK2 is closely associated with upregulation of IL-23 and other inflammatory cytokines implicated in the pathophysiology of PSA, the JAK-STAT signaling pathway is incompletely understood.⁸ Moreover, all of the JAK inhibitors synthesized so far have relative rather than absolute selectivity for any specific JAK isoform. This complicates the ability to attribute benefits and risks to the inhibition of any single JAK enzyme isoform and amplifies the need for comparative studies. 

While other JAK inhibitors have reached late stages of development for the treatment of PsA, such as filgotinib (a JAK1 selective drug) and brepocitinib (which is selective for both JAK1 and TYK2),^16,17 it is appropriate to emphasize that currently available JAK inhibitors are effective and acceptably safe for PsA. The goal of continued drug development is the potential to develop agents with even greater efficacy but with a lower risk of off-target effects. Currently, the black box warnings included in the labeling of tofacitinib and upadacitinib give pause, leading many clinicians to move to these agents after an inadequate response to biologics. Newer therapies in the JAK inhibitor class free of serious adverse effects might reverse the order, given the preference of many patients for oral agents. 

The JAK inhibitor development program is rich not just for inflammatory diseases and autoimmune diseases, but for myeloproliferative diseases and neoplasms. JAK inhibitors are already identified in the GRAPPA recommendations as appropriate first-line options for most manifestations of PsA, including joint and skin involvement, but newer drugs with a more favorable JAK selectivity or other pharmacologic characteristics and decreased adverse risks might make these a more dominant treatment choice. 

Summary

Relative selectivity for JAK isoforms promises therapies that are both more effective and safer for PsA as well as other inflammatory diseases. This promise is now being explored in experimental trials testing therapies with variable degrees of selectivity in the context of other characteristics, such as kinase binding, with the potential to influence clinical effects. However, the promise will not be fulfilled until large clinical trials, particularly comparative trials, can confirm the importance of JAK isoform selectivity. If specific types of selectivity prove relevant to the benefit-to-risk ratio of JAK inhibitors in PsA, it may alter the current order of treatment preferences for this disease.

Introduction

The first Janus kinase (JAK) inhibitor received regulatory approval for the treatment of psoriatic arthritis (PsA) more than 5 years ago. Although there are limited comparative data between this and other JAK inhibitors approved or in development for the treatment of PsA, it is reasonable to anticipate variability in therapeutic effect and the risk of adverse events between different JAK inhibitors. So far, there have been considerable differences in the relative selectivity of each agent on the 4 JAK isoform enzymes, JAK1, JAK2, JAK3, and TYK2. This selectivity determines the downstream signal transducers and activators of transcription proteins (JAK-STAT [signal transducer and activator of transcription] pathway) that ultimately mediate both anti-inflammatory and off-target effects. In this review of JAK inhibitors in PsA, differences between JAK inhibitors will be explored for their potential impact on benefit-to-risk ratio while treating PsA.  

Background

Data from the National Psoriasis Foundation (NPF) estimates that 8 million individuals in the United States have psoriasis.¹ PsA, an inflammatory spondyloarthritis associated with psoriasis, develops in about 30% of these individuals, but precise epidemiology on this subset of psoriasis patients is complicated by missed and delayed diagnoses. Of patients with psoriasis, only about 15% of patients with PsA have joint inflammation at the time or in advance of skin lesions.² This might explain delays in diagnosis. In one study, 15% of patients treated for psoriasis were found to have concomitant but unrecognized PsA.³ 

PsA was first classified as a distinct pathologic condition only about 50 years ago, even though skeletal remains indicate that this disease existed in early civilizations.² Based on consensus that PsA deserved definition as a distinct entity, the Classification Criteria for Psoriatic Arthritis (CASPAR) were published in 2006.⁴ By these criteria, cumulative points are allotted for clinical signs of skin, nail, and joint involvement, as well as radiographic signs in patients judged to have inflammatory disease in the joints, spine, or entheses to classify them as having PsA. 

There are numerous recommendations for the treatment of PsA, including those issued by the American College of Rheumatology (ACR),⁵ the European Alliance of Associations for Rheumatology (EULAR),⁶ and the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA).⁷ Although generally compatible with the others, the GRAPPA recommendations, which are the most recent, have addressed the heterogeneity of PsA by recommending therapies for specific disease domains, such as the skin, nail, and joint manifestations.

For treatment of PsA, the available drug classes for moderate-to-severe disease include immunomodulators, such as methotrexate, biologics that inhibit cytokines, such as tumor necrosis factor (TNF) and the interleukin (IL) cytokines IL-17, 1L-23, and IL12/IL-23, phosphodiesterase-4 (PDE4) inhibitors, and JAK inhibitors. In the GRAPPA recommendations, JAK inhibitors are listed along with other targeted therapies as first-line choices for peripheral arthritis, axial disease, enthesitis, dactylitis, and plaque psoriasis. 

JAK Inhibitors and PsA

There are multiple ways to classify JAK inhibitors. Tofacitinib, the first JAK inhibitor approved for PsA, is labeled a first-generation agent because it is relatively nonselective for the 4 JAK isoforms.⁸ Second-generation agents, such as upadacitinib, have been distinguished from tofacitinib, baricitinib, and other first-generation drugs by greater relative selectivity on the JAK1 enzyme. Other drugs in development for PsA target different JAK isoforms. Deucravacitinib, for example, which was approved for psoriasis after a favorable phase 3 trial⁹ and has shown promise for PsA in a phase 2 trial, is selective for the TYK2 isoform.¹⁰ A rapidly growing list of JAK inhibitors with different selectivity profiles, including dual JAK inhibitory effects, are being explored in a host of inflammatory diseases.

The relationship between selectivity on specific JAK isoforms, anti-inflammatory effects, and off-target effects is not fully understood.⁸ In addition, characteristics beyond JAK selectivity have potential pharmacologic importance. For example, JAK inhibitors can be classified as ATP competitive inhibitors and allosteric inhibitors, both of which are reversible binding modes.⁸ Within each of these subcategories, the site of kinase binding has the potential to influence clinical activity.⁸

JAK Inhibitors: Clinical Experience in PsA 

Tofacitinib, a first-generation JAK inhibitor, initially licensed for use in the treatment of rheumatoid arthritis (RA), received regulatory approval for PsA on the basis of the OPAL Beyond trial.¹¹ Approval of upadacitinib for PsA followed about 4 years later on the basis of the SELECT PsA-1 trial.¹² The primary endpoint in both of these studies was proportion of patients with an ACR response, signifying degree of improvement from baseline, of ≥20%. For the JAK inhibitors, the ACR20 rates were about 50% and 70% in the tofacitinib and upadacitinib phase 3 trials, respectively. Other JAK inhibitors have been evaluated in PsA but none so far are approved in the United States.

Despite experimental evidence supporting the hypothesis that JAK1 selectivity is clinically relevant to the treatment of PsA and other spondyloarthritides,¹³ there is no level 1 evidence of an efficacy or safety advantage for second- relative to first-generation JAK inhibitors. A small number of indirect comparisons, such as one employing a network Bayesian analysis to compare these drugs for the treatment of RA,¹⁴ have supported a clinical advantage for JAK1 selectivity, but head-to-head comparisons are needed to confirm differences. 

Prescribing information for both tofacitinib and upadacitinib in PsA and other indications include a black box warning for risk of serious adverse events, including major adverse cardiac events (MACE) and thromboembolism. The warning is based on the placebo-controlled ORAL trial with tofacitinib in RA.¹⁵ The study population was enhanced for risk with eligibility that required older age and the presence of cardiovascular risk factors. In this high-risk RA population, tofacitinib was associated with modest increases in serious adverse events, including MACE and thromboembolism, relative to placebo over several years of follow-up. A similar trial has not been conducted with upadacitinib or in patients with PsA.  

In a phase 3 trial with the TYK2-selective deucravacitinib in psoriasis, there was no increase in the rate of MACE or thromboembolism.⁹ When granted regulatory approval for psoriasis, the product information did not include a black box warning, differentiating it from other currently available JAK inhibitors. It has not yet been proven whether the absence of serious adverse events in the phase 3 psoriasis and phase 2 PsA trials with deucravacitinib are related to TYK2 JAK enzyme selectivity. 

Although TYK2 is closely associated with upregulation of IL-23 and other inflammatory cytokines implicated in the pathophysiology of PSA, the JAK-STAT signaling pathway is incompletely understood.⁸ Moreover, all of the JAK inhibitors synthesized so far have relative rather than absolute selectivity for any specific JAK isoform. This complicates the ability to attribute benefits and risks to the inhibition of any single JAK enzyme isoform and amplifies the need for comparative studies. 

While other JAK inhibitors have reached late stages of development for the treatment of PsA, such as filgotinib (a JAK1 selective drug) and brepocitinib (which is selective for both JAK1 and TYK2),^16,17 it is appropriate to emphasize that currently available JAK inhibitors are effective and acceptably safe for PsA. The goal of continued drug development is the potential to develop agents with even greater efficacy but with a lower risk of off-target effects. Currently, the black box warnings included in the labeling of tofacitinib and upadacitinib give pause, leading many clinicians to move to these agents after an inadequate response to biologics. Newer therapies in the JAK inhibitor class free of serious adverse effects might reverse the order, given the preference of many patients for oral agents. 

The JAK inhibitor development program is rich not just for inflammatory diseases and autoimmune diseases, but for myeloproliferative diseases and neoplasms. JAK inhibitors are already identified in the GRAPPA recommendations as appropriate first-line options for most manifestations of PsA, including joint and skin involvement, but newer drugs with a more favorable JAK selectivity or other pharmacologic characteristics and decreased adverse risks might make these a more dominant treatment choice. 

Summary

Relative selectivity for JAK isoforms promises therapies that are both more effective and safer for PsA as well as other inflammatory diseases. This promise is now being explored in experimental trials testing therapies with variable degrees of selectivity in the context of other characteristics, such as kinase binding, with the potential to influence clinical effects. However, the promise will not be fulfilled until large clinical trials, particularly comparative trials, can confirm the importance of JAK isoform selectivity. If specific types of selectivity prove relevant to the benefit-to-risk ratio of JAK inhibitors in PsA, it may alter the current order of treatment preferences for this disease.

Introduction

The first Janus kinase (JAK) inhibitor received regulatory approval for the treatment of psoriatic arthritis (PsA) more than 5 years ago. Although there are limited comparative data between this and other JAK inhibitors approved or in development for the treatment of PsA, it is reasonable to anticipate variability in therapeutic effect and the risk of adverse events between different JAK inhibitors. So far, there have been considerable differences in the relative selectivity of each agent on the 4 JAK isoform enzymes, JAK1, JAK2, JAK3, and TYK2. This selectivity determines the downstream signal transducers and activators of transcription proteins (JAK-STAT [signal transducer and activator of transcription] pathway) that ultimately mediate both anti-inflammatory and off-target effects. In this review of JAK inhibitors in PsA, differences between JAK inhibitors will be explored for their potential impact on benefit-to-risk ratio while treating PsA.  

Background

Data from the National Psoriasis Foundation (NPF) estimates that 8 million individuals in the United States have psoriasis.¹ PsA, an inflammatory spondyloarthritis associated with psoriasis, develops in about 30% of these individuals, but precise epidemiology on this subset of psoriasis patients is complicated by missed and delayed diagnoses. Of patients with psoriasis, only about 15% of patients with PsA have joint inflammation at the time or in advance of skin lesions.² This might explain delays in diagnosis. In one study, 15% of patients treated for psoriasis were found to have concomitant but unrecognized PsA.³ 

PsA was first classified as a distinct pathologic condition only about 50 years ago, even though skeletal remains indicate that this disease existed in early civilizations.² Based on consensus that PsA deserved definition as a distinct entity, the Classification Criteria for Psoriatic Arthritis (CASPAR) were published in 2006.⁴ By these criteria, cumulative points are allotted for clinical signs of skin, nail, and joint involvement, as well as radiographic signs in patients judged to have inflammatory disease in the joints, spine, or entheses to classify them as having PsA. 

There are numerous recommendations for the treatment of PsA, including those issued by the American College of Rheumatology (ACR),⁵ the European Alliance of Associations for Rheumatology (EULAR),⁶ and the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA).⁷ Although generally compatible with the others, the GRAPPA recommendations, which are the most recent, have addressed the heterogeneity of PsA by recommending therapies for specific disease domains, such as the skin, nail, and joint manifestations.

For treatment of PsA, the available drug classes for moderate-to-severe disease include immunomodulators, such as methotrexate, biologics that inhibit cytokines, such as tumor necrosis factor (TNF) and the interleukin (IL) cytokines IL-17, 1L-23, and IL12/IL-23, phosphodiesterase-4 (PDE4) inhibitors, and JAK inhibitors. In the GRAPPA recommendations, JAK inhibitors are listed along with other targeted therapies as first-line choices for peripheral arthritis, axial disease, enthesitis, dactylitis, and plaque psoriasis. 

JAK Inhibitors and PsA

There are multiple ways to classify JAK inhibitors. Tofacitinib, the first JAK inhibitor approved for PsA, is labeled a first-generation agent because it is relatively nonselective for the 4 JAK isoforms.⁸ Second-generation agents, such as upadacitinib, have been distinguished from tofacitinib, baricitinib, and other first-generation drugs by greater relative selectivity on the JAK1 enzyme. Other drugs in development for PsA target different JAK isoforms. Deucravacitinib, for example, which was approved for psoriasis after a favorable phase 3 trial⁹ and has shown promise for PsA in a phase 2 trial, is selective for the TYK2 isoform.¹⁰ A rapidly growing list of JAK inhibitors with different selectivity profiles, including dual JAK inhibitory effects, are being explored in a host of inflammatory diseases.

The relationship between selectivity on specific JAK isoforms, anti-inflammatory effects, and off-target effects is not fully understood.⁸ In addition, characteristics beyond JAK selectivity have potential pharmacologic importance. For example, JAK inhibitors can be classified as ATP competitive inhibitors and allosteric inhibitors, both of which are reversible binding modes.⁸ Within each of these subcategories, the site of kinase binding has the potential to influence clinical activity.⁸

JAK Inhibitors: Clinical Experience in PsA 

Tofacitinib, a first-generation JAK inhibitor, initially licensed for use in the treatment of rheumatoid arthritis (RA), received regulatory approval for PsA on the basis of the OPAL Beyond trial.¹¹ Approval of upadacitinib for PsA followed about 4 years later on the basis of the SELECT PsA-1 trial.¹² The primary endpoint in both of these studies was proportion of patients with an ACR response, signifying degree of improvement from baseline, of ≥20%. For the JAK inhibitors, the ACR20 rates were about 50% and 70% in the tofacitinib and upadacitinib phase 3 trials, respectively. Other JAK inhibitors have been evaluated in PsA but none so far are approved in the United States.

Despite experimental evidence supporting the hypothesis that JAK1 selectivity is clinically relevant to the treatment of PsA and other spondyloarthritides,¹³ there is no level 1 evidence of an efficacy or safety advantage for second- relative to first-generation JAK inhibitors. A small number of indirect comparisons, such as one employing a network Bayesian analysis to compare these drugs for the treatment of RA,¹⁴ have supported a clinical advantage for JAK1 selectivity, but head-to-head comparisons are needed to confirm differences. 

Prescribing information for both tofacitinib and upadacitinib in PsA and other indications include a black box warning for risk of serious adverse events, including major adverse cardiac events (MACE) and thromboembolism. The warning is based on the placebo-controlled ORAL trial with tofacitinib in RA.¹⁵ The study population was enhanced for risk with eligibility that required older age and the presence of cardiovascular risk factors. In this high-risk RA population, tofacitinib was associated with modest increases in serious adverse events, including MACE and thromboembolism, relative to placebo over several years of follow-up. A similar trial has not been conducted with upadacitinib or in patients with PsA.  

In a phase 3 trial with the TYK2-selective deucravacitinib in psoriasis, there was no increase in the rate of MACE or thromboembolism.⁹ When granted regulatory approval for psoriasis, the product information did not include a black box warning, differentiating it from other currently available JAK inhibitors. It has not yet been proven whether the absence of serious adverse events in the phase 3 psoriasis and phase 2 PsA trials with deucravacitinib are related to TYK2 JAK enzyme selectivity. 

Although TYK2 is closely associated with upregulation of IL-23 and other inflammatory cytokines implicated in the pathophysiology of PSA, the JAK-STAT signaling pathway is incompletely understood.⁸ Moreover, all of the JAK inhibitors synthesized so far have relative rather than absolute selectivity for any specific JAK isoform. This complicates the ability to attribute benefits and risks to the inhibition of any single JAK enzyme isoform and amplifies the need for comparative studies. 

While other JAK inhibitors have reached late stages of development for the treatment of PsA, such as filgotinib (a JAK1 selective drug) and brepocitinib (which is selective for both JAK1 and TYK2),^16,17 it is appropriate to emphasize that currently available JAK inhibitors are effective and acceptably safe for PsA. The goal of continued drug development is the potential to develop agents with even greater efficacy but with a lower risk of off-target effects. Currently, the black box warnings included in the labeling of tofacitinib and upadacitinib give pause, leading many clinicians to move to these agents after an inadequate response to biologics. Newer therapies in the JAK inhibitor class free of serious adverse effects might reverse the order, given the preference of many patients for oral agents. 

The JAK inhibitor development program is rich not just for inflammatory diseases and autoimmune diseases, but for myeloproliferative diseases and neoplasms. JAK inhibitors are already identified in the GRAPPA recommendations as appropriate first-line options for most manifestations of PsA, including joint and skin involvement, but newer drugs with a more favorable JAK selectivity or other pharmacologic characteristics and decreased adverse risks might make these a more dominant treatment choice. 

Summary

Relative selectivity for JAK isoforms promises therapies that are both more effective and safer for PsA as well as other inflammatory diseases. This promise is now being explored in experimental trials testing therapies with variable degrees of selectivity in the context of other characteristics, such as kinase binding, with the potential to influence clinical effects. However, the promise will not be fulfilled until large clinical trials, particularly comparative trials, can confirm the importance of JAK isoform selectivity. If specific types of selectivity prove relevant to the benefit-to-risk ratio of JAK inhibitors in PsA, it may alter the current order of treatment preferences for this disease.

Key clinical point: Two months of supplementation with omega-3 fatty acids had favorable effects on inflammatory and anti-inflammatory markers in patients with episodic migraine.

Major finding: After 2 months of treatment, the serum concentration of anti-inflammatory interleukin-4 (IL-4) was significantly increased (P = .010) whereas that of proinflammatory interferon gamma was significantly decreased (P = .001) in the omega-3 supplementation vs placebo group. The serum concentration of transforming growth factor beta or IL-17 was not significantly different between the groups.

Study details: The data come from a randomized controlled trial including 40 patients with episodic migraine who were randomly assigned to receive omega-3 supplementation (2 capsules/day; each capsule containing 600 mg eicosapentaenoic acid and 300 mg docosahexaenoic acid; n = 20) or placebo (paraffin oil capsules; n = 20) for 2 months.

Disclosures: This study did not receive any funding. The authors declared no potential conflicts of interest.

Source: Djalali M et al. The effect of omega-3 fatty acids supplementation on inflammatory biomarkers in subjects with migraine: A randomized, double-blind, placebo-controlled trial. Immunopharmacol Immunotoxicol. 2023 (Apr 26). doi: 10.1080/08923973.2023.2196600

Key clinical point: Two months of supplementation with omega-3 fatty acids had favorable effects on inflammatory and anti-inflammatory markers in patients with episodic migraine.

Major finding: After 2 months of treatment, the serum concentration of anti-inflammatory interleukin-4 (IL-4) was significantly increased (P = .010) whereas that of proinflammatory interferon gamma was significantly decreased (P = .001) in the omega-3 supplementation vs placebo group. The serum concentration of transforming growth factor beta or IL-17 was not significantly different between the groups.

Study details: The data come from a randomized controlled trial including 40 patients with episodic migraine who were randomly assigned to receive omega-3 supplementation (2 capsules/day; each capsule containing 600 mg eicosapentaenoic acid and 300 mg docosahexaenoic acid; n = 20) or placebo (paraffin oil capsules; n = 20) for 2 months.

Disclosures: This study did not receive any funding. The authors declared no potential conflicts of interest.

Source: Djalali M et al. The effect of omega-3 fatty acids supplementation on inflammatory biomarkers in subjects with migraine: A randomized, double-blind, placebo-controlled trial. Immunopharmacol Immunotoxicol. 2023 (Apr 26). doi: 10.1080/08923973.2023.2196600

Key clinical point: Two months of supplementation with omega-3 fatty acids had favorable effects on inflammatory and anti-inflammatory markers in patients with episodic migraine.

Major finding: After 2 months of treatment, the serum concentration of anti-inflammatory interleukin-4 (IL-4) was significantly increased (P = .010) whereas that of proinflammatory interferon gamma was significantly decreased (P = .001) in the omega-3 supplementation vs placebo group. The serum concentration of transforming growth factor beta or IL-17 was not significantly different between the groups.

Study details: The data come from a randomized controlled trial including 40 patients with episodic migraine who were randomly assigned to receive omega-3 supplementation (2 capsules/day; each capsule containing 600 mg eicosapentaenoic acid and 300 mg docosahexaenoic acid; n = 20) or placebo (paraffin oil capsules; n = 20) for 2 months.

Disclosures: This study did not receive any funding. The authors declared no potential conflicts of interest.

Source: Djalali M et al. The effect of omega-3 fatty acids supplementation on inflammatory biomarkers in subjects with migraine: A randomized, double-blind, placebo-controlled trial. Immunopharmacol Immunotoxicol. 2023 (Apr 26). doi: 10.1080/08923973.2023.2196600

Key clinical point: Maternal migraine diagnosis is associated with a higher risk for several childhood cancers in offspring.

Major finding: A significant positive association was observed between maternal migraine and the risk for non-Hodgkin lymphoma (odds ratio [OR] 1.70; 95% CI 1.01-2.86), central nervous system tumors (OR 1.31; 95% CI 1.02-1.68; particularly glioma: OR 1.64; 95% CI 1.12-2.40), neuroblastoma (OR 1.75; 95% CI 1.00-3.08), and osteosarcoma (OR 2.60; 95% CI 1.18-5.76).

Study details: This study included children age < 20 years with cancers (cases) and birth year- and sex-matched (25:1) children without cancers (control individuals).

Disclosures: This study was supported by the US National Institutes of Health. The authors declared no conflicts of interest.

Source: Orimoloye HT et al. Maternal migraine and risk of pediatric cancers. Pediatr Blood Cancer. 2023 (Apr 26). doi: 10.1002/pbc.30385

Key clinical point: Maternal migraine diagnosis is associated with a higher risk for several childhood cancers in offspring.

Major finding: A significant positive association was observed between maternal migraine and the risk for non-Hodgkin lymphoma (odds ratio [OR] 1.70; 95% CI 1.01-2.86), central nervous system tumors (OR 1.31; 95% CI 1.02-1.68; particularly glioma: OR 1.64; 95% CI 1.12-2.40), neuroblastoma (OR 1.75; 95% CI 1.00-3.08), and osteosarcoma (OR 2.60; 95% CI 1.18-5.76).

Study details: This study included children age < 20 years with cancers (cases) and birth year- and sex-matched (25:1) children without cancers (control individuals).

Disclosures: This study was supported by the US National Institutes of Health. The authors declared no conflicts of interest.

Source: Orimoloye HT et al. Maternal migraine and risk of pediatric cancers. Pediatr Blood Cancer. 2023 (Apr 26). doi: 10.1002/pbc.30385

Key clinical point: Maternal migraine diagnosis is associated with a higher risk for several childhood cancers in offspring.

Major finding: A significant positive association was observed between maternal migraine and the risk for non-Hodgkin lymphoma (odds ratio [OR] 1.70; 95% CI 1.01-2.86), central nervous system tumors (OR 1.31; 95% CI 1.02-1.68; particularly glioma: OR 1.64; 95% CI 1.12-2.40), neuroblastoma (OR 1.75; 95% CI 1.00-3.08), and osteosarcoma (OR 2.60; 95% CI 1.18-5.76).

Study details: This study included children age < 20 years with cancers (cases) and birth year- and sex-matched (25:1) children without cancers (control individuals).

Disclosures: This study was supported by the US National Institutes of Health. The authors declared no conflicts of interest.

Source: Orimoloye HT et al. Maternal migraine and risk of pediatric cancers. Pediatr Blood Cancer. 2023 (Apr 26). doi: 10.1002/pbc.30385

Key clinical point: This meta-analysis demonstrated a significant bidirectional association between psoriasis and migraine, with greater severity of psoriasis being associated with an increasingly higher risk of developing migraine.

Major finding: Presence vs absence of psoriasis was associated with 1.69-fold higher odds of prevalent migraine (pooled odds ratio [OR] 1.69; 95% CI 1.26-2.28), with the risk for incident migraine being significantly higher in patients with mild (incidence rate ratio [IRR] 1.37; 95% CI 1.30-1.44) and severe (IRR 1.55; 95% CI 1.29-1.86) psoriasis and psoriatic arthritis (IRR 1.92; 95% CI 1.65-2.23). Moreover, presence vs absence of migraine was associated with 1.88-fold higher odds of prevalent psoriasis (OR 1.88; 95% CI 1.32-3.67).

Study details: Findings are from a systematic review and meta-analysis of 10 studies including 6,745,968 participants.

Disclosures: This study did not declare the funding source. The authors declared no conflicts of interest.

Source: Huang IH et al. Bidirectional associations between psoriasis and migraine: A systematic review and meta-analysis. J Dtsch Dermatol Ges. 2023;21(5):493-502 (Apr 17). doi: 10.1111/ddg.14994

Key clinical point: This meta-analysis demonstrated a significant bidirectional association between psoriasis and migraine, with greater severity of psoriasis being associated with an increasingly higher risk of developing migraine.

Major finding: Presence vs absence of psoriasis was associated with 1.69-fold higher odds of prevalent migraine (pooled odds ratio [OR] 1.69; 95% CI 1.26-2.28), with the risk for incident migraine being significantly higher in patients with mild (incidence rate ratio [IRR] 1.37; 95% CI 1.30-1.44) and severe (IRR 1.55; 95% CI 1.29-1.86) psoriasis and psoriatic arthritis (IRR 1.92; 95% CI 1.65-2.23). Moreover, presence vs absence of migraine was associated with 1.88-fold higher odds of prevalent psoriasis (OR 1.88; 95% CI 1.32-3.67).

Study details: Findings are from a systematic review and meta-analysis of 10 studies including 6,745,968 participants.

Disclosures: This study did not declare the funding source. The authors declared no conflicts of interest.

Source: Huang IH et al. Bidirectional associations between psoriasis and migraine: A systematic review and meta-analysis. J Dtsch Dermatol Ges. 2023;21(5):493-502 (Apr 17). doi: 10.1111/ddg.14994

Key clinical point: This meta-analysis demonstrated a significant bidirectional association between psoriasis and migraine, with greater severity of psoriasis being associated with an increasingly higher risk of developing migraine.

Major finding: Presence vs absence of psoriasis was associated with 1.69-fold higher odds of prevalent migraine (pooled odds ratio [OR] 1.69; 95% CI 1.26-2.28), with the risk for incident migraine being significantly higher in patients with mild (incidence rate ratio [IRR] 1.37; 95% CI 1.30-1.44) and severe (IRR 1.55; 95% CI 1.29-1.86) psoriasis and psoriatic arthritis (IRR 1.92; 95% CI 1.65-2.23). Moreover, presence vs absence of migraine was associated with 1.88-fold higher odds of prevalent psoriasis (OR 1.88; 95% CI 1.32-3.67).

Study details: Findings are from a systematic review and meta-analysis of 10 studies including 6,745,968 participants.

Disclosures: This study did not declare the funding source. The authors declared no conflicts of interest.

Source: Huang IH et al. Bidirectional associations between psoriasis and migraine: A systematic review and meta-analysis. J Dtsch Dermatol Ges. 2023;21(5):493-502 (Apr 17). doi: 10.1111/ddg.14994

Key clinical point: Cross-sectional study confirms a significant association of migraine history with the severity of vasomotor symptoms (VMS) and hypertension in midlife women, potentially helping to identify those at risk for severe menopause symptoms.

Major finding: The likelihood of severe or very severe vs no hot flashes (adjusted odds ratio [aOR] 1.34; P = .007) and risk for hypertension (aOR 1.31; P = .002) were significantly higher among women with vs without a history of migraine.

Study details: Findings are from a cross-sectional study including 5708 women aged between 45 and 60 years, of whom 23.7% had a history of migraine.

Disclosures: This study was partially supported by a grant from the National Institute on Aging. Dr. Kling and Dr. Kapoor declared serving as consultants for various sources.

Source: Faubion SS et al. Association of migraine and vasomotor symptoms. Mayo Clin Proc. 2023;98(5):701-712 (May 1). doi: 10.1016/j.mayocp.2023.01.010.

Key clinical point: Cross-sectional study confirms a significant association of migraine history with the severity of vasomotor symptoms (VMS) and hypertension in midlife women, potentially helping to identify those at risk for severe menopause symptoms.

Major finding: The likelihood of severe or very severe vs no hot flashes (adjusted odds ratio [aOR] 1.34; P = .007) and risk for hypertension (aOR 1.31; P = .002) were significantly higher among women with vs without a history of migraine.

Study details: Findings are from a cross-sectional study including 5708 women aged between 45 and 60 years, of whom 23.7% had a history of migraine.

Disclosures: This study was partially supported by a grant from the National Institute on Aging. Dr. Kling and Dr. Kapoor declared serving as consultants for various sources.

Source: Faubion SS et al. Association of migraine and vasomotor symptoms. Mayo Clin Proc. 2023;98(5):701-712 (May 1). doi: 10.1016/j.mayocp.2023.01.010.

Key clinical point: Cross-sectional study confirms a significant association of migraine history with the severity of vasomotor symptoms (VMS) and hypertension in midlife women, potentially helping to identify those at risk for severe menopause symptoms.

Major finding: The likelihood of severe or very severe vs no hot flashes (adjusted odds ratio [aOR] 1.34; P = .007) and risk for hypertension (aOR 1.31; P = .002) were significantly higher among women with vs without a history of migraine.

Study details: Findings are from a cross-sectional study including 5708 women aged between 45 and 60 years, of whom 23.7% had a history of migraine.

Disclosures: This study was partially supported by a grant from the National Institute on Aging. Dr. Kling and Dr. Kapoor declared serving as consultants for various sources.

Source: Faubion SS et al. Association of migraine and vasomotor symptoms. Mayo Clin Proc. 2023;98(5):701-712 (May 1). doi: 10.1016/j.mayocp.2023.01.010.