To improve safety performance, many health care organizations have embarked on the journey to becoming high reliability organizations (HROs). HROs operate in complex, high-risk, constantly changing environments and avoid catastrophic events despite the inherent risks.¹ HROs maintain high levels of safety and reliability by adhering to core principles, foundational practices, rigorous processes, a strong organizational culture, and continuous learning and process improvement.^1-3

Becoming an HRO requires understanding what makes systems safer for patients and staff at all levels by taking ownership of 5 principles: (1) sensitivity to operations (increased awareness of the current status of systems); (2) reluctance to simplify (avoiding oversimplification of the cause[s] of problems); (3) preoccupation with failure (anticipating risks that might be symptomatic of a larger problem); (4) deference to expertise (relying on the most qualified individuals to make decisions); and (5) commitment to resilience (planning for potential failure and being prepared to respond).^1,2,4 In addition to these, the Veterans Health Administration has identified 3 pillars of HROs: leadership commitment (safety and reliability are central to leadership vision, decision-making, and action-oriented behaviors), safety culture (across the organization, safety values are key to preventing harm and learning from mistakes), and continuous process improvement (promoting constant learning and improvement with evidence-based tools and methodologies).⁵

Implementing these principles is not enough to achieve high reliability. This transition requires significant change, which can be met with resistance. Without attending to organizational change, implementation of HRO principles can be superficial, scattered, and isolated.⁶ Large organizations often struggle with change as it conflicts with the fundamental human need for stability and security.⁷ Consequently, the journey to becoming an HRO requires an understanding of the reasons for resistance to change (RtC) as well as evidence-based strategies.

REASONS FOR RESISTANCE TO CHANGE

RtC is the informal and covert behavior of an individual or group to a particular change. RtC is commonly recognized as the failure of employees to do anything requested by managers and is a main reason change initiatives fail.⁸ While some staff see change as opportunities for learning and growth, others resist based on uncertainty about how the changes will impact their current work situation, or fear, frustration, confusion, and distrust.^8,9 Resistance can overtly manifest with some staff publicly expressing their discontent in public without offering solutions, or covertly by ignoring the change or avoiding participation in any aspect of the change process. Both forms of RtC are equally detrimental.⁸

Frequent changes in organizations can also cause cynicism. Employees will view the change as something initially popular, but will only last until another change comes along.^8,9 Resistance can result in the failure to achieve desired objectives, wasted time, effort, and resources, decreased momentum, and loss of confidence and trust in leaders to effectively manage the change process.⁹ To understand RtC, 3 main factors must be considered: individual, interpersonal, and organizational.

Individual

An individual’s personality can be an important indicator for how they will respond to change. Some individuals welcome and thrive on change while others resist in preference for the status quo.^8,10 Individuals will also resist change if they believe their position, power, or prestige within the organization are in jeopardy or that the change is contrary to current personal or organizational values, principles, and objectives.^8-12 Resistance can also be the result of uncertainty about what the change means, lack of information regarding the change, or questioning motives for the change.⁹

Interpersonal

Another influence on RtC is the interpersonal factors of employees. The personal satisfaction individuals receive from their work and the type of interactions they experience with colleagues can impact RtC. When communication with colleagues is lacking before and during change implementation, negative reactions to the change can fuel resistance.¹¹ Cross-functional and bidirectional communication is vital; its absence can leave staff feeling inadequately informed and less supportive of the change.⁸ Employees’ understanding of changes through communication between other members of the organization is critical to success.¹¹

Organizational

How organizational leaders introduce change affects the extent to which staff respond.¹⁰ RtC can emerge if staff feel change is imposed on them. Change is better received when people are actively engaged in the process and adopt a sense of ownership that will ultimately affect them and their role within the organization.^12,13 Organizations are also better equipped to address potential RtC when leadership is respected and have a genuine concern for the overall well-being of staff members. Organizational leaders who mainly focus on the bottom line and have little regard for staff are more likely to be perceived as untrustworthy, which contributes to RtC.^9,13 Lack of proper education and guidance from organizational leaders, as well as poor communication, can lead to RtC.^8,13

MANAGING RESISTANCE TO CHANGE

RtC can be a significant factor in the success or failure of the change process. Poorly managed change can exponentially increase resistance, necessitating a multifaceted approach to managing RtC, while well-managed change can result in a high success rate. Evidence-based strategies to counter RtC focus on communication, employee participation, education and training, and engaging managers.⁸

Communication

Open and effective communication is critical to managing RtC, as uncertainty often exaggerates the negative aspects of change. Effective communication involves active listening, with leadership and management addressing employee concerns in a clear and concise manner. A psychologically safe culture for open dialogue is essential when addressing RtC.^9,14,15 Psychological safety empowers staff to speak up, ask questions, and offer ideas, forming a solid basis for open and effective communication and participation. Leaders and managers should create opportunities for open dialogue for all members of the organization throughout the process. This can be accomplished with one-on-one meetings, open forums, town hall meetings, electronic mail, newsletters, and social media. Topics should cover the reasons for change, details of what is changing, the individual, organizational, and patient risks of not changing, as well as the benefits of changing.⁹ Encouraging staff to ask questions and provide feedback to promote bidirectional and closed-loop communication is essential to avoid misunderstandings.^9,15 While open communication is essential, leaders must carefully plan what information to share, how much to share, and how to avoid information overload. Information about the change should be timely, adequate, applicable, and informative.¹⁵ The HRO practice of leader rounding for high reliability can be instrumental to ensure effective, bidirectional communication and collaboration among all disciplines across a health care organization through improving leadership visibility during times of change and enhancing interactions and communication with staff.³

Employee Participation

Involving staff in the change process significantly reduces RtC. Engagement fosters ownership in the change process, increasing the likelihood employees will support and even champion it. Health care professionals welcome opportunities to be involved in helping with aspects of organizational change, especially when invited to participate in the change early in the process and throughout the course of change.^7,14,15

Leaders should encourage staff to provide feedback to understand the impact the change is having on them and their roles and responsibilities within the organization. This exemplifies the HRO principle of deference to expertise as the employee often has the most in-depth knowledge of their work setting. Employee perspectives can significantly influence the success of change initatives.^7,14 Participation is impactful in providing employees with a sense of agency facilitating acceptance and improving desire to adopt the change.¹⁴

Tiered safety huddles and visual management systems (VMSs) also can engage staff. Tiered safety huddles provide a forum for transparent communication, increasing situational awareness, and improving a health care organization’s ability to appropriately respond to staff questions, suggestions, and concerns. VMSs display the status and progress toward organizational goals during the change process, and are highly effective in creating environments where staff feel empowered to voice concerns related to the change process.³

Education and Training

Educating employees on the value of change is crucial to overcome RtC. RtC often stems from employees not feeling prepared to adapt or adopt new processes. Health care professionals who do not receive information about change are less likely to support it.^7,12,15 Staff are more likely to accept change when they understand why it is needed and how it impacts the organization’s long-term mission.^11,15 Timely, compelling, and informative education on how to adapt to the change will promote more positive appraisal of the change and reduce RtC.^8,15 Employees must feel confident they will receive the appropriate training, resources, and support to successfully adapt to the change. This requires leaders and managers taking time to clarify expectations, conduct a gap analysis to identify the skills and knowledge needed to support the planned change, and provide sufficient educational opportunities to fill those gaps.⁸ For example, the US Department of Veterans Affairs offers classes to employees on the Prosci ADKAR (Awareness, Desire, Knowledge, Ability, and Reinforcement) Model. This training provides individuals with the information and skills needed for change to be successful.¹⁶

Safety forums can be influential and allow leadership to educate staff on updates related to change processes and promote bidirectional communication.³ In safety forums, staff have an opportunity to ask questions, especially as they relate to learning about available resources to become more informed about the organizational changes.

Engaging Managers

Managers are pivotal to the successful implementation of organizational change.⁸ They serve as the bridge between senior leadership and frontline employees and are positioned to influence the adoption and success of change initiatives. Often the first point of contact for employees, managers can effectively communicate the need for change, and act as the liaison to align it with individual employee motivations. Since they are often the first to encounter resistance among employees, managers serve as advocates through the process. Through a coaching role, managers can help employees develop the knowledge and ability to be successful and thrive in the new environment. The Table summarizes the evidence-based strategies.

CONCLUSIONS

Implementing change in health care organizations can be challenging, especially on the journey to high reliability. RtC is the result of factors at the individual, interpersonal, and organizational levels that leaders must address to increase chances for success. Organizational changes in health care are more likely to succeed when staff understand why the change is needed through open and continuous communication, can influence the change by sharing their own perspectives, and have the knowledge, skills, and resources to prepare for and participate in the process.

To improve safety performance, many health care organizations have embarked on the journey to becoming high reliability organizations (HROs). HROs operate in complex, high-risk, constantly changing environments and avoid catastrophic events despite the inherent risks.¹ HROs maintain high levels of safety and reliability by adhering to core principles, foundational practices, rigorous processes, a strong organizational culture, and continuous learning and process improvement.^1-3

Becoming an HRO requires understanding what makes systems safer for patients and staff at all levels by taking ownership of 5 principles: (1) sensitivity to operations (increased awareness of the current status of systems); (2) reluctance to simplify (avoiding oversimplification of the cause[s] of problems); (3) preoccupation with failure (anticipating risks that might be symptomatic of a larger problem); (4) deference to expertise (relying on the most qualified individuals to make decisions); and (5) commitment to resilience (planning for potential failure and being prepared to respond).^1,2,4 In addition to these, the Veterans Health Administration has identified 3 pillars of HROs: leadership commitment (safety and reliability are central to leadership vision, decision-making, and action-oriented behaviors), safety culture (across the organization, safety values are key to preventing harm and learning from mistakes), and continuous process improvement (promoting constant learning and improvement with evidence-based tools and methodologies).⁵

Implementing these principles is not enough to achieve high reliability. This transition requires significant change, which can be met with resistance. Without attending to organizational change, implementation of HRO principles can be superficial, scattered, and isolated.⁶ Large organizations often struggle with change as it conflicts with the fundamental human need for stability and security.⁷ Consequently, the journey to becoming an HRO requires an understanding of the reasons for resistance to change (RtC) as well as evidence-based strategies.

REASONS FOR RESISTANCE TO CHANGE

RtC is the informal and covert behavior of an individual or group to a particular change. RtC is commonly recognized as the failure of employees to do anything requested by managers and is a main reason change initiatives fail.⁸ While some staff see change as opportunities for learning and growth, others resist based on uncertainty about how the changes will impact their current work situation, or fear, frustration, confusion, and distrust.^8,9 Resistance can overtly manifest with some staff publicly expressing their discontent in public without offering solutions, or covertly by ignoring the change or avoiding participation in any aspect of the change process. Both forms of RtC are equally detrimental.⁸

Frequent changes in organizations can also cause cynicism. Employees will view the change as something initially popular, but will only last until another change comes along.^8,9 Resistance can result in the failure to achieve desired objectives, wasted time, effort, and resources, decreased momentum, and loss of confidence and trust in leaders to effectively manage the change process.⁹ To understand RtC, 3 main factors must be considered: individual, interpersonal, and organizational.

Individual

An individual’s personality can be an important indicator for how they will respond to change. Some individuals welcome and thrive on change while others resist in preference for the status quo.^8,10 Individuals will also resist change if they believe their position, power, or prestige within the organization are in jeopardy or that the change is contrary to current personal or organizational values, principles, and objectives.^8-12 Resistance can also be the result of uncertainty about what the change means, lack of information regarding the change, or questioning motives for the change.⁹

Interpersonal

Another influence on RtC is the interpersonal factors of employees. The personal satisfaction individuals receive from their work and the type of interactions they experience with colleagues can impact RtC. When communication with colleagues is lacking before and during change implementation, negative reactions to the change can fuel resistance.¹¹ Cross-functional and bidirectional communication is vital; its absence can leave staff feeling inadequately informed and less supportive of the change.⁸ Employees’ understanding of changes through communication between other members of the organization is critical to success.¹¹

Organizational

How organizational leaders introduce change affects the extent to which staff respond.¹⁰ RtC can emerge if staff feel change is imposed on them. Change is better received when people are actively engaged in the process and adopt a sense of ownership that will ultimately affect them and their role within the organization.^12,13 Organizations are also better equipped to address potential RtC when leadership is respected and have a genuine concern for the overall well-being of staff members. Organizational leaders who mainly focus on the bottom line and have little regard for staff are more likely to be perceived as untrustworthy, which contributes to RtC.^9,13 Lack of proper education and guidance from organizational leaders, as well as poor communication, can lead to RtC.^8,13

MANAGING RESISTANCE TO CHANGE

RtC can be a significant factor in the success or failure of the change process. Poorly managed change can exponentially increase resistance, necessitating a multifaceted approach to managing RtC, while well-managed change can result in a high success rate. Evidence-based strategies to counter RtC focus on communication, employee participation, education and training, and engaging managers.⁸

Communication

Open and effective communication is critical to managing RtC, as uncertainty often exaggerates the negative aspects of change. Effective communication involves active listening, with leadership and management addressing employee concerns in a clear and concise manner. A psychologically safe culture for open dialogue is essential when addressing RtC.^9,14,15 Psychological safety empowers staff to speak up, ask questions, and offer ideas, forming a solid basis for open and effective communication and participation. Leaders and managers should create opportunities for open dialogue for all members of the organization throughout the process. This can be accomplished with one-on-one meetings, open forums, town hall meetings, electronic mail, newsletters, and social media. Topics should cover the reasons for change, details of what is changing, the individual, organizational, and patient risks of not changing, as well as the benefits of changing.⁹ Encouraging staff to ask questions and provide feedback to promote bidirectional and closed-loop communication is essential to avoid misunderstandings.^9,15 While open communication is essential, leaders must carefully plan what information to share, how much to share, and how to avoid information overload. Information about the change should be timely, adequate, applicable, and informative.¹⁵ The HRO practice of leader rounding for high reliability can be instrumental to ensure effective, bidirectional communication and collaboration among all disciplines across a health care organization through improving leadership visibility during times of change and enhancing interactions and communication with staff.³

Employee Participation

Involving staff in the change process significantly reduces RtC. Engagement fosters ownership in the change process, increasing the likelihood employees will support and even champion it. Health care professionals welcome opportunities to be involved in helping with aspects of organizational change, especially when invited to participate in the change early in the process and throughout the course of change.^7,14,15

Leaders should encourage staff to provide feedback to understand the impact the change is having on them and their roles and responsibilities within the organization. This exemplifies the HRO principle of deference to expertise as the employee often has the most in-depth knowledge of their work setting. Employee perspectives can significantly influence the success of change initatives.^7,14 Participation is impactful in providing employees with a sense of agency facilitating acceptance and improving desire to adopt the change.¹⁴

Tiered safety huddles and visual management systems (VMSs) also can engage staff. Tiered safety huddles provide a forum for transparent communication, increasing situational awareness, and improving a health care organization’s ability to appropriately respond to staff questions, suggestions, and concerns. VMSs display the status and progress toward organizational goals during the change process, and are highly effective in creating environments where staff feel empowered to voice concerns related to the change process.³

Education and Training

Educating employees on the value of change is crucial to overcome RtC. RtC often stems from employees not feeling prepared to adapt or adopt new processes. Health care professionals who do not receive information about change are less likely to support it.^7,12,15 Staff are more likely to accept change when they understand why it is needed and how it impacts the organization’s long-term mission.^11,15 Timely, compelling, and informative education on how to adapt to the change will promote more positive appraisal of the change and reduce RtC.^8,15 Employees must feel confident they will receive the appropriate training, resources, and support to successfully adapt to the change. This requires leaders and managers taking time to clarify expectations, conduct a gap analysis to identify the skills and knowledge needed to support the planned change, and provide sufficient educational opportunities to fill those gaps.⁸ For example, the US Department of Veterans Affairs offers classes to employees on the Prosci ADKAR (Awareness, Desire, Knowledge, Ability, and Reinforcement) Model. This training provides individuals with the information and skills needed for change to be successful.¹⁶

Safety forums can be influential and allow leadership to educate staff on updates related to change processes and promote bidirectional communication.³ In safety forums, staff have an opportunity to ask questions, especially as they relate to learning about available resources to become more informed about the organizational changes.

Engaging Managers

Managers are pivotal to the successful implementation of organizational change.⁸ They serve as the bridge between senior leadership and frontline employees and are positioned to influence the adoption and success of change initiatives. Often the first point of contact for employees, managers can effectively communicate the need for change, and act as the liaison to align it with individual employee motivations. Since they are often the first to encounter resistance among employees, managers serve as advocates through the process. Through a coaching role, managers can help employees develop the knowledge and ability to be successful and thrive in the new environment. The Table summarizes the evidence-based strategies.

CONCLUSIONS

Implementing change in health care organizations can be challenging, especially on the journey to high reliability. RtC is the result of factors at the individual, interpersonal, and organizational levels that leaders must address to increase chances for success. Organizational changes in health care are more likely to succeed when staff understand why the change is needed through open and continuous communication, can influence the change by sharing their own perspectives, and have the knowledge, skills, and resources to prepare for and participate in the process.

To improve safety performance, many health care organizations have embarked on the journey to becoming high reliability organizations (HROs). HROs operate in complex, high-risk, constantly changing environments and avoid catastrophic events despite the inherent risks.¹ HROs maintain high levels of safety and reliability by adhering to core principles, foundational practices, rigorous processes, a strong organizational culture, and continuous learning and process improvement.^1-3

Becoming an HRO requires understanding what makes systems safer for patients and staff at all levels by taking ownership of 5 principles: (1) sensitivity to operations (increased awareness of the current status of systems); (2) reluctance to simplify (avoiding oversimplification of the cause[s] of problems); (3) preoccupation with failure (anticipating risks that might be symptomatic of a larger problem); (4) deference to expertise (relying on the most qualified individuals to make decisions); and (5) commitment to resilience (planning for potential failure and being prepared to respond).^1,2,4 In addition to these, the Veterans Health Administration has identified 3 pillars of HROs: leadership commitment (safety and reliability are central to leadership vision, decision-making, and action-oriented behaviors), safety culture (across the organization, safety values are key to preventing harm and learning from mistakes), and continuous process improvement (promoting constant learning and improvement with evidence-based tools and methodologies).⁵

Implementing these principles is not enough to achieve high reliability. This transition requires significant change, which can be met with resistance. Without attending to organizational change, implementation of HRO principles can be superficial, scattered, and isolated.⁶ Large organizations often struggle with change as it conflicts with the fundamental human need for stability and security.⁷ Consequently, the journey to becoming an HRO requires an understanding of the reasons for resistance to change (RtC) as well as evidence-based strategies.

REASONS FOR RESISTANCE TO CHANGE

RtC is the informal and covert behavior of an individual or group to a particular change. RtC is commonly recognized as the failure of employees to do anything requested by managers and is a main reason change initiatives fail.⁸ While some staff see change as opportunities for learning and growth, others resist based on uncertainty about how the changes will impact their current work situation, or fear, frustration, confusion, and distrust.^8,9 Resistance can overtly manifest with some staff publicly expressing their discontent in public without offering solutions, or covertly by ignoring the change or avoiding participation in any aspect of the change process. Both forms of RtC are equally detrimental.⁸

Frequent changes in organizations can also cause cynicism. Employees will view the change as something initially popular, but will only last until another change comes along.^8,9 Resistance can result in the failure to achieve desired objectives, wasted time, effort, and resources, decreased momentum, and loss of confidence and trust in leaders to effectively manage the change process.⁹ To understand RtC, 3 main factors must be considered: individual, interpersonal, and organizational.

Individual

An individual’s personality can be an important indicator for how they will respond to change. Some individuals welcome and thrive on change while others resist in preference for the status quo.^8,10 Individuals will also resist change if they believe their position, power, or prestige within the organization are in jeopardy or that the change is contrary to current personal or organizational values, principles, and objectives.^8-12 Resistance can also be the result of uncertainty about what the change means, lack of information regarding the change, or questioning motives for the change.⁹

Interpersonal

Another influence on RtC is the interpersonal factors of employees. The personal satisfaction individuals receive from their work and the type of interactions they experience with colleagues can impact RtC. When communication with colleagues is lacking before and during change implementation, negative reactions to the change can fuel resistance.¹¹ Cross-functional and bidirectional communication is vital; its absence can leave staff feeling inadequately informed and less supportive of the change.⁸ Employees’ understanding of changes through communication between other members of the organization is critical to success.¹¹

Organizational

How organizational leaders introduce change affects the extent to which staff respond.¹⁰ RtC can emerge if staff feel change is imposed on them. Change is better received when people are actively engaged in the process and adopt a sense of ownership that will ultimately affect them and their role within the organization.^12,13 Organizations are also better equipped to address potential RtC when leadership is respected and have a genuine concern for the overall well-being of staff members. Organizational leaders who mainly focus on the bottom line and have little regard for staff are more likely to be perceived as untrustworthy, which contributes to RtC.^9,13 Lack of proper education and guidance from organizational leaders, as well as poor communication, can lead to RtC.^8,13

MANAGING RESISTANCE TO CHANGE

RtC can be a significant factor in the success or failure of the change process. Poorly managed change can exponentially increase resistance, necessitating a multifaceted approach to managing RtC, while well-managed change can result in a high success rate. Evidence-based strategies to counter RtC focus on communication, employee participation, education and training, and engaging managers.⁸

Communication

Open and effective communication is critical to managing RtC, as uncertainty often exaggerates the negative aspects of change. Effective communication involves active listening, with leadership and management addressing employee concerns in a clear and concise manner. A psychologically safe culture for open dialogue is essential when addressing RtC.^9,14,15 Psychological safety empowers staff to speak up, ask questions, and offer ideas, forming a solid basis for open and effective communication and participation. Leaders and managers should create opportunities for open dialogue for all members of the organization throughout the process. This can be accomplished with one-on-one meetings, open forums, town hall meetings, electronic mail, newsletters, and social media. Topics should cover the reasons for change, details of what is changing, the individual, organizational, and patient risks of not changing, as well as the benefits of changing.⁹ Encouraging staff to ask questions and provide feedback to promote bidirectional and closed-loop communication is essential to avoid misunderstandings.^9,15 While open communication is essential, leaders must carefully plan what information to share, how much to share, and how to avoid information overload. Information about the change should be timely, adequate, applicable, and informative.¹⁵ The HRO practice of leader rounding for high reliability can be instrumental to ensure effective, bidirectional communication and collaboration among all disciplines across a health care organization through improving leadership visibility during times of change and enhancing interactions and communication with staff.³

Employee Participation

Involving staff in the change process significantly reduces RtC. Engagement fosters ownership in the change process, increasing the likelihood employees will support and even champion it. Health care professionals welcome opportunities to be involved in helping with aspects of organizational change, especially when invited to participate in the change early in the process and throughout the course of change.^7,14,15

Leaders should encourage staff to provide feedback to understand the impact the change is having on them and their roles and responsibilities within the organization. This exemplifies the HRO principle of deference to expertise as the employee often has the most in-depth knowledge of their work setting. Employee perspectives can significantly influence the success of change initatives.^7,14 Participation is impactful in providing employees with a sense of agency facilitating acceptance and improving desire to adopt the change.¹⁴

Tiered safety huddles and visual management systems (VMSs) also can engage staff. Tiered safety huddles provide a forum for transparent communication, increasing situational awareness, and improving a health care organization’s ability to appropriately respond to staff questions, suggestions, and concerns. VMSs display the status and progress toward organizational goals during the change process, and are highly effective in creating environments where staff feel empowered to voice concerns related to the change process.³

Education and Training

Educating employees on the value of change is crucial to overcome RtC. RtC often stems from employees not feeling prepared to adapt or adopt new processes. Health care professionals who do not receive information about change are less likely to support it.^7,12,15 Staff are more likely to accept change when they understand why it is needed and how it impacts the organization’s long-term mission.^11,15 Timely, compelling, and informative education on how to adapt to the change will promote more positive appraisal of the change and reduce RtC.^8,15 Employees must feel confident they will receive the appropriate training, resources, and support to successfully adapt to the change. This requires leaders and managers taking time to clarify expectations, conduct a gap analysis to identify the skills and knowledge needed to support the planned change, and provide sufficient educational opportunities to fill those gaps.⁸ For example, the US Department of Veterans Affairs offers classes to employees on the Prosci ADKAR (Awareness, Desire, Knowledge, Ability, and Reinforcement) Model. This training provides individuals with the information and skills needed for change to be successful.¹⁶

Safety forums can be influential and allow leadership to educate staff on updates related to change processes and promote bidirectional communication.³ In safety forums, staff have an opportunity to ask questions, especially as they relate to learning about available resources to become more informed about the organizational changes.

Engaging Managers

Managers are pivotal to the successful implementation of organizational change.⁸ They serve as the bridge between senior leadership and frontline employees and are positioned to influence the adoption and success of change initiatives. Often the first point of contact for employees, managers can effectively communicate the need for change, and act as the liaison to align it with individual employee motivations. Since they are often the first to encounter resistance among employees, managers serve as advocates through the process. Through a coaching role, managers can help employees develop the knowledge and ability to be successful and thrive in the new environment. The Table summarizes the evidence-based strategies.

CONCLUSIONS

Implementing change in health care organizations can be challenging, especially on the journey to high reliability. RtC is the result of factors at the individual, interpersonal, and organizational levels that leaders must address to increase chances for success. Organizational changes in health care are more likely to succeed when staff understand why the change is needed through open and continuous communication, can influence the change by sharing their own perspectives, and have the knowledge, skills, and resources to prepare for and participate in the process.

Epilepsy affects about 4.5 million people in the United States and 150,000 new individuals are diagnosed each year.^1,2 In 2019, epilepsy-attributable health care spending for noninstitutionalized people was around $5.4 billion and total epilepsy-attributable and epilepsy or seizure health care-related costs totaled $54 billion.³

Accurate surveillance of epilepsy in large health care systems can potentially improve health care delivery and resource allocation. A 2012 Institute of Medicine (IOM) report identified 13 recommendations to guide public health action on epilepsy, including validation of standard definitions for case ascertainment, identification of epilepsy through screening programs or protocols, and expansion of surveillance to better understand disease burden.⁴

A systematic review of validation studies concluded that it is reasonable to use administrative data to identify people with epilepsy in epidemiologic research. Combining The International Classification of Diseases (ICD) codes for epilepsy (ICD-10, G40-41; ICD-9, 345) with antiseizure medications (ASMs) could provide high positive predictive values (PPVs) and combining symptoms codes for convulsions (ICD-10, R56; ICD-9, 780.3, 780.39) with ASMs could lead to high sensitivity.⁵ However, identifying individuals with epilepsy from administrative data in large managed health care organizations is challenging.⁶ The IOM report noted that large managed health care organizations presented varying incidence and prevalence estimates due to differing methodology, geographic area, demographics, and definitions of epilepsy.

The Veterans Health Administration (VHA) is the largest integrated US health care system, providing care to > 9.1 million veterans.⁷ To improve the health and well-being of veterans with epilepsy (VWEs), a network of sites was established in 2008 called the US Department of Veterans Affairs (VA) Epilepsy Centers of Excellence (ECoE). Subsequent to the creation of the ECoE, efforts were made to identify VWEs within VHA databases.^8,9 Prior to fiscal year (FY) 2016, the ECoE adopted a modified version of a well-established epilepsy diagnostic algorithm developed by Holden et al for large managed care organizations.¹⁰ The original algorithm identified patients by cross-matching ASMs with ICD-9 codes for an index year. But it failed to capture a considerable number of stable patients with epilepsy in the VHA due to incomplete documentation, and had false positives due to inclusion of patients identified from diagnostic clinics. The modified algorithm the ECoE used prior to FY 2016 considered additional prior years and excluded encounters from diagnostic clinics. The result was an improvement in the sensitivity and specificity of the algorithm. Researchers evaluating 500 patients with epilepsy estimated that the modified algorithm had a PPV of 82.0% (95% CI, 78.6%-85.4%).¹¹

After implementation of ICD-10 codes in the VHA in FY 2016, the task of reliably and efficiently identifying VWE led to a 3-tier algorithm. This article presents a validation of the different tiers of this algorithm after the implementation of ICD-10 diagnosis codes and summarizes the surveillance data collected over the years within the VHA showing the trends of epilepsy.

Methods

The VHA National Neurology office commissioned a Neurology Cube dashboard in FY 2021 in collaboration with VHA Support Service Center (VSSC) for reporting and surveillance of VWEs as a quality improvement initiative. The Neurology Cube uses a 3-tier system for identifying VWE in the VHA databases. VSSC programmers extract data from the VHA Corporate Data Warehouse (CDW) and utilize Microsoft SQL Server and Microsoft Power BI for Neurology Cube reports. The 3-tier system identifies VWE and divides them into distinct groups. The first tier identifies VWE with the highest degree of confidence; Tiers 2 and 3 represent identification with successively lesser degrees of confidence (Figure 1).

Tier 1

Definition. For a given index year and the preceding 2 years, any of following diagnosis codes on ≥ 1 clinical encounter are considered: 345.xx (epilepsy in ICD-9), 780.3x (other convulsions in ICD-9), G40.xxx (epilepsy in ICD-10), R40.4 (transient alteration of awareness), R56.1 (posttraumatic seizures), or R56.9 (unspecified convulsions). To reduce false positive rates, EEG clinic visits, which may include long-term monitoring, are excluded. Patients identified with ICD codes are then evaluated for an ASM prescription for ≥ 30 days during the index year. ASMs are listed in Appendix 1.

Validation. The development and validation of ICD-9 diagnosis codes crossmatched with an ASM prescription in the VHA has been published elsewhere.¹¹ In FY 2017, after implementation of ICD-10 diagnostic codes, Tier 1 development and validation was performed in 2 phases. Even though Tier 1 study phases were conducted and completed during FY 2017, the patients for Tier 1 were identified from evaluation of FY 2016 data (October 1, 2015, to September 30, 2016). After the pilot analysis, the Tier 1 definition was implemented, and a chart review of 625 randomized patients was conducted at 5 sites for validation. Adequate preliminary data was not available to perform a sample size estimation for this study. Therefore, a practical target of 125 patients was set for Tier 1 from each site to obtain a final sample size of 625 patients. This second phase validated that the crossmatch of ICD-10 diagnosis codes with ASMs had a high PPV for identifying VWE.

Tiers 2 and 3

Definitions. For an index year, Tier 2 includes patients with ≥ 1 inpatient encounter documentation of either ICD-9 345.xx or ICD-10 G40.xxx, excluding EEG clinics. Tier 3 Includes patients who have had ≥ 2 outpatient encounters with diagnosis codes 345.xx or G40.xxx on 2 separate days, excluding EEG clinics. Tiers 2 and 3 do not require ASM prescriptions; this helps to identify VWEs who may be getting their medications outside of VHA or those who have received a new diagnosis.

Validations. Tiers 2 and 3 were included in the epilepsy identification algorithm in FY 2021 after validation was performed on a sample of 8 patients in each tier. Five patients were subsequently identified as having epilepsy in Tier 2 and 6 patients were identified in Tier 3. A more comprehensive validation of Tiers 2 and 3 was performed during FY 2022 that included patients at 5 sites seen during FY 2019 to FY 2022. Since yearly trends showed only about 8% of total patients were identified as having epilepsy through Tiers 2 and 3 we sought ≥ 20 patients per tier for the 5 sites for a total of 200 patients to ensure representation across the VHA. The final count was 126 patients for Tier 2 and 174 patients for Tier 3 (n = 300).

Gold Standard Criteria for Epilepsy Diagnosis

We used the International League Against Epilepsy (ILAE) definition of epilepsy for the validation of the 3 algorithm tiers. ILAE defines epilepsy as ≥ 2 unprovoked (or reflex) seizures occurring > 24 hours apart or 1 unprovoked (or reflex) seizure and a probability of further seizures similar to the general recurrence risk (≥ 60%) after 2 unprovoked seizures, occurring over the next 10 years.¹²

A standard protocol was provided to evaluators to identify patients using the VHA Computerized Patient Record System (Appendix 1). After review, evaluators categorized each patient in 1 of 4 ways: (1) Yes, definite: The patient’s health care practitioner (HCP) believes the patient has epilepsy and is treating with medication; (2) Yes, uncertain: The HCP has enough suspicion of epilepsy that a medication is prescribed, but uncertainty is expressed of the diagnosis; (3) No, definite: The HCP does not believe the patient has epilepsy and is therefore not treating with medication for seizure; (4) No, uncertain: The HCP is not treating with medication for epilepsy, because the diagnostic suspicion is not high enough, but there is suspicion for epilepsy.

As a quality improvement operational project, the Epilepsy National Program Office approved this validation project and determined that institutional review board approval was not required.

Statistical Analysis

Counts and percentages were computed for categories of epilepsy status. PPV of each tier was estimated with asymptotic 95% CIs.

Results

ICD-10 codes for 480 patients were evaluated in Tier 1 phase 1; 13.8% were documented with G40.xxx, 27.9% with R56.1, 34.4% with R56.9, and 24.0% with R40.4 (Appendix 2). In total, 68.1% fulfilled the criteria of epilepsy, 19.2% did not, and 12.7% were uncertain). From the validation of Tier 1 phase 2 (n = 625), the PPV of the algorithm for patients presumed to have epilepsy (definite and uncertain) was 85.1% (95% CI, 82.1%-87.8%) (Table).

Of 300 patients evaluated, 126 (42.0%) were evaluated for Tier 2 with a PPV of 61.9% (95% CI, 53.4%-70.4%), and 174 (58.0%) patients were evaluated for Tier 3 with a PPV of 59.8% (95% CI, 52.5%-67.1%. The PPV of the algorithm for patients presumed to have epilepsy (definite and uncertain) were combined to calculate the PPV. Estimates of VHA VWE counts were computed for each tier from FY 2014 to FY 2023 using the VSSC Neurology Cube (Figure 2). For all years, > 92% patients were classified using the Tier 1 definition.

Discussion

The development and validation of the 3-tier diagnostic algorithm represents an important advancement in the surveillance and management of epilepsy among veterans within the VHA. The validation of this algorithm also demonstrates its practical utility in a large, integrated health care system.

Specific challenges were encountered when attempting to use pre-existing algorithms; these challenges included differences in the usage patterns of diagnostic codes and the patterns of ASM use within the VHA. These challenges prompted the need for a tailored approach, which led to the development of this algorithm. The inclusion of additional ICD-10 codes led to further revisions and subsequent validation. While many of the basic concepts of the algorithm, including ICD codes and ASMs, could work in other institutions, it would be wise for health care organizations to develop their own algorithms because of certain variables, including organizational size, patient demographics, common comorbidities, and the specific configurations of electronic health records and administrative data systems.

Studies have shown that ICD-10 codes for epilepsy (G40.* and/or R56.9) perform well in identifying epilepsy whether they are assigned by neurologists (sensitivity, 97.7%; specificity, 44.1%; PPV, 96.2%; negative predictive value, 57.7%), or in emergency department or hospital discharges (PPV, 75.5%).^13,14 The pilot study of the algorithm’s Tier 1 development (phase 1) evaluated whether the selected ICD-10 diagnostic codes accurately included the VWE population within the VHA and revealed that while most codes (eg, epilepsy [G40.xxx]; posttraumatic seizures [R56.1]; and unspecified convulsions [R56.9]), had a low false positive rate (< 16%), the R40.4 code (transient alteration of awareness) had a higher false positivity of 42%. While this is not surprising given the broad spectrum of conditions that can manifest as transient alteration of awareness, it underscores the inherent challenges in diagnosing epilepsy using diagnosis codes.

In phase 2, the Tier 1 algorithm was validated as effective for identifying VWE in the VHA system, as its PPV was determined to be high (85%). In comparison, Tiers 2 and 3, whose criteria did not require data on VHA prescribed ASM use, had lower tiers of epilepsy predictability (PPV about 60% for both). This was thought to be acceptable because Tiers 2 and 3 represent a smaller population of the identified VWEs (about 8%). These VWEs may otherwise have been missed, partly because veterans are not required to get ASMs from the VHA.

Upon VHA implementation in FY 2021, this diagnostic algorithm exhibited significant clinical utility when integrated within the VSSC Neurology Cube. It facilitated an efficient approach to identifying VWEs using readily available databases. This led to better tracking of real-time epilepsy cases, which facilitated improving current resource allocation and targeted intervention strategies such as identification of drug-resistant epilepsy patients, optimizing strategies for telehealth and patient outreach for awareness of epilepsy care resources within VHA. Meanwhile, data acquired by the algorithm over the decade since its development (FY 2014 to FY 2023) contributed to more accurate epidemiologic information and identification of historic trends. Development of the algorithm represents one of the ways ECoEs have led to improved care for VWEs. ECoEs have been shown to improve health care for veterans in several metrics.¹⁵

A strength of this study is the rigorous multitiered validation process to confirm the diagnostic accuracy of ICD-10 codes against the gold standard ILAE definition of epilepsy to identify “definite” epilepsy cases within the VHA. The use of specific ICD codes further enhances the precision of epilepsy diagnoses. The inclusion of ASMs, which are sometimes prescribed for conditions other than epilepsy, could potentially inflate false positive rates.¹⁶

This study focused exclusively on the identification and validation of definite epilepsy cases within the VHA VSSC database, employing more stringent diagnostic criteria to ensure the highest level of certainty in ascertaining epilepsy. It is important to note there is a separate category of probable epilepsy, which involves a broader set of diagnostic criteria. While not covered in this study, probable epilepsy would be subject to future research and validation, which could provide insights into a wider spectrum of epilepsy diagnoses. Such future research could help refine the algorithm’s applicability and accuracy and potentially lead to more comprehensive surveillance and management strategies in clinical practice.

This study highlights the inherent challenges in leveraging administrative data for disease identification, particularly for conditions such as epilepsy, where diagnostic clarity can be complex. However, other conditions such as multiple sclerosis have noted similar success with the use of VHA administrative data for categorizing disease.¹⁷

Limitations

The algorithm discussed in this article is, in and of itself, generalizable. However, the validation process was unique to the VHA patient population, limiting the generalizability of the findings. Documentation practices and HCP attitudes within the VHA may differ from those in other health care settings. Identifying people with epilepsy can be challenging because of changing definitions of epilepsy over time. In addition to clinical evaluation, EEG and magnetic resonance imaging results, response to ASM treatment, and video-EEG monitoring of habitual events all can help establish the diagnosis. Therefore, studies may vary in how inclusive or exclusive the criteria are. ASMs such as gabapentin, pregabalin, carbamazepine, lamotrigine, topiramate, and valproate are used to treat other conditions, including headaches, generalized pain, and mood disorders. Consequently, including these ASMs in the Tier 1 definition may have increased the false positive rate. Additional research is needed to evaluate whether excluding these ASMs from the algorithm based on specific criteria (eg, dose of ASM used) can further refine the algorithm to identify patients with epilepsy.

Further refinement of this algorithm may also occur as technology changes. Future electronic health records may allow better tracking of different epilepsy factors, the integration of additional diagnostic criteria, and the use of natural language processing or other forms of artificial intelligence.

Conclusions

This study presents a significant step forward in epilepsy surveillance within the VHA. The algorithm offers a robust tool for identifying VWEs with good PPVs, facilitating better resource allocation and targeted care. Despite its limitations, this research lays a foundation for future advancements in the management and understanding of epilepsy within large health care systems. Since this VHA algorithm is based on ASMs and ICD diagnosis codes from patient records, other large managed health care systems also may be able to adapt this algorithm to their data specifications.

Epilepsy affects about 4.5 million people in the United States and 150,000 new individuals are diagnosed each year.^1,2 In 2019, epilepsy-attributable health care spending for noninstitutionalized people was around $5.4 billion and total epilepsy-attributable and epilepsy or seizure health care-related costs totaled $54 billion.³

Accurate surveillance of epilepsy in large health care systems can potentially improve health care delivery and resource allocation. A 2012 Institute of Medicine (IOM) report identified 13 recommendations to guide public health action on epilepsy, including validation of standard definitions for case ascertainment, identification of epilepsy through screening programs or protocols, and expansion of surveillance to better understand disease burden.⁴

A systematic review of validation studies concluded that it is reasonable to use administrative data to identify people with epilepsy in epidemiologic research. Combining The International Classification of Diseases (ICD) codes for epilepsy (ICD-10, G40-41; ICD-9, 345) with antiseizure medications (ASMs) could provide high positive predictive values (PPVs) and combining symptoms codes for convulsions (ICD-10, R56; ICD-9, 780.3, 780.39) with ASMs could lead to high sensitivity.⁵ However, identifying individuals with epilepsy from administrative data in large managed health care organizations is challenging.⁶ The IOM report noted that large managed health care organizations presented varying incidence and prevalence estimates due to differing methodology, geographic area, demographics, and definitions of epilepsy.

The Veterans Health Administration (VHA) is the largest integrated US health care system, providing care to > 9.1 million veterans.⁷ To improve the health and well-being of veterans with epilepsy (VWEs), a network of sites was established in 2008 called the US Department of Veterans Affairs (VA) Epilepsy Centers of Excellence (ECoE). Subsequent to the creation of the ECoE, efforts were made to identify VWEs within VHA databases.^8,9 Prior to fiscal year (FY) 2016, the ECoE adopted a modified version of a well-established epilepsy diagnostic algorithm developed by Holden et al for large managed care organizations.¹⁰ The original algorithm identified patients by cross-matching ASMs with ICD-9 codes for an index year. But it failed to capture a considerable number of stable patients with epilepsy in the VHA due to incomplete documentation, and had false positives due to inclusion of patients identified from diagnostic clinics. The modified algorithm the ECoE used prior to FY 2016 considered additional prior years and excluded encounters from diagnostic clinics. The result was an improvement in the sensitivity and specificity of the algorithm. Researchers evaluating 500 patients with epilepsy estimated that the modified algorithm had a PPV of 82.0% (95% CI, 78.6%-85.4%).¹¹

After implementation of ICD-10 codes in the VHA in FY 2016, the task of reliably and efficiently identifying VWE led to a 3-tier algorithm. This article presents a validation of the different tiers of this algorithm after the implementation of ICD-10 diagnosis codes and summarizes the surveillance data collected over the years within the VHA showing the trends of epilepsy.

Methods

The VHA National Neurology office commissioned a Neurology Cube dashboard in FY 2021 in collaboration with VHA Support Service Center (VSSC) for reporting and surveillance of VWEs as a quality improvement initiative. The Neurology Cube uses a 3-tier system for identifying VWE in the VHA databases. VSSC programmers extract data from the VHA Corporate Data Warehouse (CDW) and utilize Microsoft SQL Server and Microsoft Power BI for Neurology Cube reports. The 3-tier system identifies VWE and divides them into distinct groups. The first tier identifies VWE with the highest degree of confidence; Tiers 2 and 3 represent identification with successively lesser degrees of confidence (Figure 1).

Tier 1

Definition. For a given index year and the preceding 2 years, any of following diagnosis codes on ≥ 1 clinical encounter are considered: 345.xx (epilepsy in ICD-9), 780.3x (other convulsions in ICD-9), G40.xxx (epilepsy in ICD-10), R40.4 (transient alteration of awareness), R56.1 (posttraumatic seizures), or R56.9 (unspecified convulsions). To reduce false positive rates, EEG clinic visits, which may include long-term monitoring, are excluded. Patients identified with ICD codes are then evaluated for an ASM prescription for ≥ 30 days during the index year. ASMs are listed in Appendix 1.

Validation. The development and validation of ICD-9 diagnosis codes crossmatched with an ASM prescription in the VHA has been published elsewhere.¹¹ In FY 2017, after implementation of ICD-10 diagnostic codes, Tier 1 development and validation was performed in 2 phases. Even though Tier 1 study phases were conducted and completed during FY 2017, the patients for Tier 1 were identified from evaluation of FY 2016 data (October 1, 2015, to September 30, 2016). After the pilot analysis, the Tier 1 definition was implemented, and a chart review of 625 randomized patients was conducted at 5 sites for validation. Adequate preliminary data was not available to perform a sample size estimation for this study. Therefore, a practical target of 125 patients was set for Tier 1 from each site to obtain a final sample size of 625 patients. This second phase validated that the crossmatch of ICD-10 diagnosis codes with ASMs had a high PPV for identifying VWE.

Tiers 2 and 3

Definitions. For an index year, Tier 2 includes patients with ≥ 1 inpatient encounter documentation of either ICD-9 345.xx or ICD-10 G40.xxx, excluding EEG clinics. Tier 3 Includes patients who have had ≥ 2 outpatient encounters with diagnosis codes 345.xx or G40.xxx on 2 separate days, excluding EEG clinics. Tiers 2 and 3 do not require ASM prescriptions; this helps to identify VWEs who may be getting their medications outside of VHA or those who have received a new diagnosis.

Validations. Tiers 2 and 3 were included in the epilepsy identification algorithm in FY 2021 after validation was performed on a sample of 8 patients in each tier. Five patients were subsequently identified as having epilepsy in Tier 2 and 6 patients were identified in Tier 3. A more comprehensive validation of Tiers 2 and 3 was performed during FY 2022 that included patients at 5 sites seen during FY 2019 to FY 2022. Since yearly trends showed only about 8% of total patients were identified as having epilepsy through Tiers 2 and 3 we sought ≥ 20 patients per tier for the 5 sites for a total of 200 patients to ensure representation across the VHA. The final count was 126 patients for Tier 2 and 174 patients for Tier 3 (n = 300).

Gold Standard Criteria for Epilepsy Diagnosis

We used the International League Against Epilepsy (ILAE) definition of epilepsy for the validation of the 3 algorithm tiers. ILAE defines epilepsy as ≥ 2 unprovoked (or reflex) seizures occurring > 24 hours apart or 1 unprovoked (or reflex) seizure and a probability of further seizures similar to the general recurrence risk (≥ 60%) after 2 unprovoked seizures, occurring over the next 10 years.¹²

A standard protocol was provided to evaluators to identify patients using the VHA Computerized Patient Record System (Appendix 1). After review, evaluators categorized each patient in 1 of 4 ways: (1) Yes, definite: The patient’s health care practitioner (HCP) believes the patient has epilepsy and is treating with medication; (2) Yes, uncertain: The HCP has enough suspicion of epilepsy that a medication is prescribed, but uncertainty is expressed of the diagnosis; (3) No, definite: The HCP does not believe the patient has epilepsy and is therefore not treating with medication for seizure; (4) No, uncertain: The HCP is not treating with medication for epilepsy, because the diagnostic suspicion is not high enough, but there is suspicion for epilepsy.

As a quality improvement operational project, the Epilepsy National Program Office approved this validation project and determined that institutional review board approval was not required.

Statistical Analysis

Counts and percentages were computed for categories of epilepsy status. PPV of each tier was estimated with asymptotic 95% CIs.

Results

ICD-10 codes for 480 patients were evaluated in Tier 1 phase 1; 13.8% were documented with G40.xxx, 27.9% with R56.1, 34.4% with R56.9, and 24.0% with R40.4 (Appendix 2). In total, 68.1% fulfilled the criteria of epilepsy, 19.2% did not, and 12.7% were uncertain). From the validation of Tier 1 phase 2 (n = 625), the PPV of the algorithm for patients presumed to have epilepsy (definite and uncertain) was 85.1% (95% CI, 82.1%-87.8%) (Table).

Of 300 patients evaluated, 126 (42.0%) were evaluated for Tier 2 with a PPV of 61.9% (95% CI, 53.4%-70.4%), and 174 (58.0%) patients were evaluated for Tier 3 with a PPV of 59.8% (95% CI, 52.5%-67.1%. The PPV of the algorithm for patients presumed to have epilepsy (definite and uncertain) were combined to calculate the PPV. Estimates of VHA VWE counts were computed for each tier from FY 2014 to FY 2023 using the VSSC Neurology Cube (Figure 2). For all years, > 92% patients were classified using the Tier 1 definition.

Discussion

The development and validation of the 3-tier diagnostic algorithm represents an important advancement in the surveillance and management of epilepsy among veterans within the VHA. The validation of this algorithm also demonstrates its practical utility in a large, integrated health care system.

Specific challenges were encountered when attempting to use pre-existing algorithms; these challenges included differences in the usage patterns of diagnostic codes and the patterns of ASM use within the VHA. These challenges prompted the need for a tailored approach, which led to the development of this algorithm. The inclusion of additional ICD-10 codes led to further revisions and subsequent validation. While many of the basic concepts of the algorithm, including ICD codes and ASMs, could work in other institutions, it would be wise for health care organizations to develop their own algorithms because of certain variables, including organizational size, patient demographics, common comorbidities, and the specific configurations of electronic health records and administrative data systems.

Studies have shown that ICD-10 codes for epilepsy (G40.* and/or R56.9) perform well in identifying epilepsy whether they are assigned by neurologists (sensitivity, 97.7%; specificity, 44.1%; PPV, 96.2%; negative predictive value, 57.7%), or in emergency department or hospital discharges (PPV, 75.5%).^13,14 The pilot study of the algorithm’s Tier 1 development (phase 1) evaluated whether the selected ICD-10 diagnostic codes accurately included the VWE population within the VHA and revealed that while most codes (eg, epilepsy [G40.xxx]; posttraumatic seizures [R56.1]; and unspecified convulsions [R56.9]), had a low false positive rate (< 16%), the R40.4 code (transient alteration of awareness) had a higher false positivity of 42%. While this is not surprising given the broad spectrum of conditions that can manifest as transient alteration of awareness, it underscores the inherent challenges in diagnosing epilepsy using diagnosis codes.

In phase 2, the Tier 1 algorithm was validated as effective for identifying VWE in the VHA system, as its PPV was determined to be high (85%). In comparison, Tiers 2 and 3, whose criteria did not require data on VHA prescribed ASM use, had lower tiers of epilepsy predictability (PPV about 60% for both). This was thought to be acceptable because Tiers 2 and 3 represent a smaller population of the identified VWEs (about 8%). These VWEs may otherwise have been missed, partly because veterans are not required to get ASMs from the VHA.

Upon VHA implementation in FY 2021, this diagnostic algorithm exhibited significant clinical utility when integrated within the VSSC Neurology Cube. It facilitated an efficient approach to identifying VWEs using readily available databases. This led to better tracking of real-time epilepsy cases, which facilitated improving current resource allocation and targeted intervention strategies such as identification of drug-resistant epilepsy patients, optimizing strategies for telehealth and patient outreach for awareness of epilepsy care resources within VHA. Meanwhile, data acquired by the algorithm over the decade since its development (FY 2014 to FY 2023) contributed to more accurate epidemiologic information and identification of historic trends. Development of the algorithm represents one of the ways ECoEs have led to improved care for VWEs. ECoEs have been shown to improve health care for veterans in several metrics.¹⁵

A strength of this study is the rigorous multitiered validation process to confirm the diagnostic accuracy of ICD-10 codes against the gold standard ILAE definition of epilepsy to identify “definite” epilepsy cases within the VHA. The use of specific ICD codes further enhances the precision of epilepsy diagnoses. The inclusion of ASMs, which are sometimes prescribed for conditions other than epilepsy, could potentially inflate false positive rates.¹⁶

This study focused exclusively on the identification and validation of definite epilepsy cases within the VHA VSSC database, employing more stringent diagnostic criteria to ensure the highest level of certainty in ascertaining epilepsy. It is important to note there is a separate category of probable epilepsy, which involves a broader set of diagnostic criteria. While not covered in this study, probable epilepsy would be subject to future research and validation, which could provide insights into a wider spectrum of epilepsy diagnoses. Such future research could help refine the algorithm’s applicability and accuracy and potentially lead to more comprehensive surveillance and management strategies in clinical practice.

This study highlights the inherent challenges in leveraging administrative data for disease identification, particularly for conditions such as epilepsy, where diagnostic clarity can be complex. However, other conditions such as multiple sclerosis have noted similar success with the use of VHA administrative data for categorizing disease.¹⁷

Limitations

The algorithm discussed in this article is, in and of itself, generalizable. However, the validation process was unique to the VHA patient population, limiting the generalizability of the findings. Documentation practices and HCP attitudes within the VHA may differ from those in other health care settings. Identifying people with epilepsy can be challenging because of changing definitions of epilepsy over time. In addition to clinical evaluation, EEG and magnetic resonance imaging results, response to ASM treatment, and video-EEG monitoring of habitual events all can help establish the diagnosis. Therefore, studies may vary in how inclusive or exclusive the criteria are. ASMs such as gabapentin, pregabalin, carbamazepine, lamotrigine, topiramate, and valproate are used to treat other conditions, including headaches, generalized pain, and mood disorders. Consequently, including these ASMs in the Tier 1 definition may have increased the false positive rate. Additional research is needed to evaluate whether excluding these ASMs from the algorithm based on specific criteria (eg, dose of ASM used) can further refine the algorithm to identify patients with epilepsy.

Further refinement of this algorithm may also occur as technology changes. Future electronic health records may allow better tracking of different epilepsy factors, the integration of additional diagnostic criteria, and the use of natural language processing or other forms of artificial intelligence.

Conclusions

This study presents a significant step forward in epilepsy surveillance within the VHA. The algorithm offers a robust tool for identifying VWEs with good PPVs, facilitating better resource allocation and targeted care. Despite its limitations, this research lays a foundation for future advancements in the management and understanding of epilepsy within large health care systems. Since this VHA algorithm is based on ASMs and ICD diagnosis codes from patient records, other large managed health care systems also may be able to adapt this algorithm to their data specifications.

Epilepsy affects about 4.5 million people in the United States and 150,000 new individuals are diagnosed each year.^1,2 In 2019, epilepsy-attributable health care spending for noninstitutionalized people was around $5.4 billion and total epilepsy-attributable and epilepsy or seizure health care-related costs totaled $54 billion.³

Accurate surveillance of epilepsy in large health care systems can potentially improve health care delivery and resource allocation. A 2012 Institute of Medicine (IOM) report identified 13 recommendations to guide public health action on epilepsy, including validation of standard definitions for case ascertainment, identification of epilepsy through screening programs or protocols, and expansion of surveillance to better understand disease burden.⁴

A systematic review of validation studies concluded that it is reasonable to use administrative data to identify people with epilepsy in epidemiologic research. Combining The International Classification of Diseases (ICD) codes for epilepsy (ICD-10, G40-41; ICD-9, 345) with antiseizure medications (ASMs) could provide high positive predictive values (PPVs) and combining symptoms codes for convulsions (ICD-10, R56; ICD-9, 780.3, 780.39) with ASMs could lead to high sensitivity.⁵ However, identifying individuals with epilepsy from administrative data in large managed health care organizations is challenging.⁶ The IOM report noted that large managed health care organizations presented varying incidence and prevalence estimates due to differing methodology, geographic area, demographics, and definitions of epilepsy.

The Veterans Health Administration (VHA) is the largest integrated US health care system, providing care to > 9.1 million veterans.⁷ To improve the health and well-being of veterans with epilepsy (VWEs), a network of sites was established in 2008 called the US Department of Veterans Affairs (VA) Epilepsy Centers of Excellence (ECoE). Subsequent to the creation of the ECoE, efforts were made to identify VWEs within VHA databases.^8,9 Prior to fiscal year (FY) 2016, the ECoE adopted a modified version of a well-established epilepsy diagnostic algorithm developed by Holden et al for large managed care organizations.¹⁰ The original algorithm identified patients by cross-matching ASMs with ICD-9 codes for an index year. But it failed to capture a considerable number of stable patients with epilepsy in the VHA due to incomplete documentation, and had false positives due to inclusion of patients identified from diagnostic clinics. The modified algorithm the ECoE used prior to FY 2016 considered additional prior years and excluded encounters from diagnostic clinics. The result was an improvement in the sensitivity and specificity of the algorithm. Researchers evaluating 500 patients with epilepsy estimated that the modified algorithm had a PPV of 82.0% (95% CI, 78.6%-85.4%).¹¹

After implementation of ICD-10 codes in the VHA in FY 2016, the task of reliably and efficiently identifying VWE led to a 3-tier algorithm. This article presents a validation of the different tiers of this algorithm after the implementation of ICD-10 diagnosis codes and summarizes the surveillance data collected over the years within the VHA showing the trends of epilepsy.

Methods

The VHA National Neurology office commissioned a Neurology Cube dashboard in FY 2021 in collaboration with VHA Support Service Center (VSSC) for reporting and surveillance of VWEs as a quality improvement initiative. The Neurology Cube uses a 3-tier system for identifying VWE in the VHA databases. VSSC programmers extract data from the VHA Corporate Data Warehouse (CDW) and utilize Microsoft SQL Server and Microsoft Power BI for Neurology Cube reports. The 3-tier system identifies VWE and divides them into distinct groups. The first tier identifies VWE with the highest degree of confidence; Tiers 2 and 3 represent identification with successively lesser degrees of confidence (Figure 1).

Tier 1

Definition. For a given index year and the preceding 2 years, any of following diagnosis codes on ≥ 1 clinical encounter are considered: 345.xx (epilepsy in ICD-9), 780.3x (other convulsions in ICD-9), G40.xxx (epilepsy in ICD-10), R40.4 (transient alteration of awareness), R56.1 (posttraumatic seizures), or R56.9 (unspecified convulsions). To reduce false positive rates, EEG clinic visits, which may include long-term monitoring, are excluded. Patients identified with ICD codes are then evaluated for an ASM prescription for ≥ 30 days during the index year. ASMs are listed in Appendix 1.

Validation. The development and validation of ICD-9 diagnosis codes crossmatched with an ASM prescription in the VHA has been published elsewhere.¹¹ In FY 2017, after implementation of ICD-10 diagnostic codes, Tier 1 development and validation was performed in 2 phases. Even though Tier 1 study phases were conducted and completed during FY 2017, the patients for Tier 1 were identified from evaluation of FY 2016 data (October 1, 2015, to September 30, 2016). After the pilot analysis, the Tier 1 definition was implemented, and a chart review of 625 randomized patients was conducted at 5 sites for validation. Adequate preliminary data was not available to perform a sample size estimation for this study. Therefore, a practical target of 125 patients was set for Tier 1 from each site to obtain a final sample size of 625 patients. This second phase validated that the crossmatch of ICD-10 diagnosis codes with ASMs had a high PPV for identifying VWE.

Tiers 2 and 3

Definitions. For an index year, Tier 2 includes patients with ≥ 1 inpatient encounter documentation of either ICD-9 345.xx or ICD-10 G40.xxx, excluding EEG clinics. Tier 3 Includes patients who have had ≥ 2 outpatient encounters with diagnosis codes 345.xx or G40.xxx on 2 separate days, excluding EEG clinics. Tiers 2 and 3 do not require ASM prescriptions; this helps to identify VWEs who may be getting their medications outside of VHA or those who have received a new diagnosis.

Validations. Tiers 2 and 3 were included in the epilepsy identification algorithm in FY 2021 after validation was performed on a sample of 8 patients in each tier. Five patients were subsequently identified as having epilepsy in Tier 2 and 6 patients were identified in Tier 3. A more comprehensive validation of Tiers 2 and 3 was performed during FY 2022 that included patients at 5 sites seen during FY 2019 to FY 2022. Since yearly trends showed only about 8% of total patients were identified as having epilepsy through Tiers 2 and 3 we sought ≥ 20 patients per tier for the 5 sites for a total of 200 patients to ensure representation across the VHA. The final count was 126 patients for Tier 2 and 174 patients for Tier 3 (n = 300).

Gold Standard Criteria for Epilepsy Diagnosis

We used the International League Against Epilepsy (ILAE) definition of epilepsy for the validation of the 3 algorithm tiers. ILAE defines epilepsy as ≥ 2 unprovoked (or reflex) seizures occurring > 24 hours apart or 1 unprovoked (or reflex) seizure and a probability of further seizures similar to the general recurrence risk (≥ 60%) after 2 unprovoked seizures, occurring over the next 10 years.¹²

A standard protocol was provided to evaluators to identify patients using the VHA Computerized Patient Record System (Appendix 1). After review, evaluators categorized each patient in 1 of 4 ways: (1) Yes, definite: The patient’s health care practitioner (HCP) believes the patient has epilepsy and is treating with medication; (2) Yes, uncertain: The HCP has enough suspicion of epilepsy that a medication is prescribed, but uncertainty is expressed of the diagnosis; (3) No, definite: The HCP does not believe the patient has epilepsy and is therefore not treating with medication for seizure; (4) No, uncertain: The HCP is not treating with medication for epilepsy, because the diagnostic suspicion is not high enough, but there is suspicion for epilepsy.

As a quality improvement operational project, the Epilepsy National Program Office approved this validation project and determined that institutional review board approval was not required.

Statistical Analysis

Counts and percentages were computed for categories of epilepsy status. PPV of each tier was estimated with asymptotic 95% CIs.

Results

ICD-10 codes for 480 patients were evaluated in Tier 1 phase 1; 13.8% were documented with G40.xxx, 27.9% with R56.1, 34.4% with R56.9, and 24.0% with R40.4 (Appendix 2). In total, 68.1% fulfilled the criteria of epilepsy, 19.2% did not, and 12.7% were uncertain). From the validation of Tier 1 phase 2 (n = 625), the PPV of the algorithm for patients presumed to have epilepsy (definite and uncertain) was 85.1% (95% CI, 82.1%-87.8%) (Table).

Of 300 patients evaluated, 126 (42.0%) were evaluated for Tier 2 with a PPV of 61.9% (95% CI, 53.4%-70.4%), and 174 (58.0%) patients were evaluated for Tier 3 with a PPV of 59.8% (95% CI, 52.5%-67.1%. The PPV of the algorithm for patients presumed to have epilepsy (definite and uncertain) were combined to calculate the PPV. Estimates of VHA VWE counts were computed for each tier from FY 2014 to FY 2023 using the VSSC Neurology Cube (Figure 2). For all years, > 92% patients were classified using the Tier 1 definition.

Discussion

The development and validation of the 3-tier diagnostic algorithm represents an important advancement in the surveillance and management of epilepsy among veterans within the VHA. The validation of this algorithm also demonstrates its practical utility in a large, integrated health care system.

Specific challenges were encountered when attempting to use pre-existing algorithms; these challenges included differences in the usage patterns of diagnostic codes and the patterns of ASM use within the VHA. These challenges prompted the need for a tailored approach, which led to the development of this algorithm. The inclusion of additional ICD-10 codes led to further revisions and subsequent validation. While many of the basic concepts of the algorithm, including ICD codes and ASMs, could work in other institutions, it would be wise for health care organizations to develop their own algorithms because of certain variables, including organizational size, patient demographics, common comorbidities, and the specific configurations of electronic health records and administrative data systems.

Studies have shown that ICD-10 codes for epilepsy (G40.* and/or R56.9) perform well in identifying epilepsy whether they are assigned by neurologists (sensitivity, 97.7%; specificity, 44.1%; PPV, 96.2%; negative predictive value, 57.7%), or in emergency department or hospital discharges (PPV, 75.5%).^13,14 The pilot study of the algorithm’s Tier 1 development (phase 1) evaluated whether the selected ICD-10 diagnostic codes accurately included the VWE population within the VHA and revealed that while most codes (eg, epilepsy [G40.xxx]; posttraumatic seizures [R56.1]; and unspecified convulsions [R56.9]), had a low false positive rate (< 16%), the R40.4 code (transient alteration of awareness) had a higher false positivity of 42%. While this is not surprising given the broad spectrum of conditions that can manifest as transient alteration of awareness, it underscores the inherent challenges in diagnosing epilepsy using diagnosis codes.

In phase 2, the Tier 1 algorithm was validated as effective for identifying VWE in the VHA system, as its PPV was determined to be high (85%). In comparison, Tiers 2 and 3, whose criteria did not require data on VHA prescribed ASM use, had lower tiers of epilepsy predictability (PPV about 60% for both). This was thought to be acceptable because Tiers 2 and 3 represent a smaller population of the identified VWEs (about 8%). These VWEs may otherwise have been missed, partly because veterans are not required to get ASMs from the VHA.

Upon VHA implementation in FY 2021, this diagnostic algorithm exhibited significant clinical utility when integrated within the VSSC Neurology Cube. It facilitated an efficient approach to identifying VWEs using readily available databases. This led to better tracking of real-time epilepsy cases, which facilitated improving current resource allocation and targeted intervention strategies such as identification of drug-resistant epilepsy patients, optimizing strategies for telehealth and patient outreach for awareness of epilepsy care resources within VHA. Meanwhile, data acquired by the algorithm over the decade since its development (FY 2014 to FY 2023) contributed to more accurate epidemiologic information and identification of historic trends. Development of the algorithm represents one of the ways ECoEs have led to improved care for VWEs. ECoEs have been shown to improve health care for veterans in several metrics.¹⁵

A strength of this study is the rigorous multitiered validation process to confirm the diagnostic accuracy of ICD-10 codes against the gold standard ILAE definition of epilepsy to identify “definite” epilepsy cases within the VHA. The use of specific ICD codes further enhances the precision of epilepsy diagnoses. The inclusion of ASMs, which are sometimes prescribed for conditions other than epilepsy, could potentially inflate false positive rates.¹⁶

This study focused exclusively on the identification and validation of definite epilepsy cases within the VHA VSSC database, employing more stringent diagnostic criteria to ensure the highest level of certainty in ascertaining epilepsy. It is important to note there is a separate category of probable epilepsy, which involves a broader set of diagnostic criteria. While not covered in this study, probable epilepsy would be subject to future research and validation, which could provide insights into a wider spectrum of epilepsy diagnoses. Such future research could help refine the algorithm’s applicability and accuracy and potentially lead to more comprehensive surveillance and management strategies in clinical practice.

This study highlights the inherent challenges in leveraging administrative data for disease identification, particularly for conditions such as epilepsy, where diagnostic clarity can be complex. However, other conditions such as multiple sclerosis have noted similar success with the use of VHA administrative data for categorizing disease.¹⁷

Limitations

The algorithm discussed in this article is, in and of itself, generalizable. However, the validation process was unique to the VHA patient population, limiting the generalizability of the findings. Documentation practices and HCP attitudes within the VHA may differ from those in other health care settings. Identifying people with epilepsy can be challenging because of changing definitions of epilepsy over time. In addition to clinical evaluation, EEG and magnetic resonance imaging results, response to ASM treatment, and video-EEG monitoring of habitual events all can help establish the diagnosis. Therefore, studies may vary in how inclusive or exclusive the criteria are. ASMs such as gabapentin, pregabalin, carbamazepine, lamotrigine, topiramate, and valproate are used to treat other conditions, including headaches, generalized pain, and mood disorders. Consequently, including these ASMs in the Tier 1 definition may have increased the false positive rate. Additional research is needed to evaluate whether excluding these ASMs from the algorithm based on specific criteria (eg, dose of ASM used) can further refine the algorithm to identify patients with epilepsy.

Further refinement of this algorithm may also occur as technology changes. Future electronic health records may allow better tracking of different epilepsy factors, the integration of additional diagnostic criteria, and the use of natural language processing or other forms of artificial intelligence.

Conclusions

This study presents a significant step forward in epilepsy surveillance within the VHA. The algorithm offers a robust tool for identifying VWEs with good PPVs, facilitating better resource allocation and targeted care. Despite its limitations, this research lays a foundation for future advancements in the management and understanding of epilepsy within large health care systems. Since this VHA algorithm is based on ASMs and ICD diagnosis codes from patient records, other large managed health care systems also may be able to adapt this algorithm to their data specifications.

Total hip arthroplasty (THA) is among the most common elective orthopedic procedures performed annually in the United States, with an estimated 635,000 to 909,000 THAs expected each year by 2030.¹ Consequently, complication rates and revision surgeries related to THA have been increasing, along with the financial burden on the health care system.^2-4 Optimizing outcomes for patients undergoing THA and identifying risk factors for treatment failure have become areas of focus.

Over the last decade, there has been a renewed interest in the effect of previous lumbar spine fusion (LSF) surgery on THA outcomes. Studies have explored the rates of complications, postoperative mobility, and THA implant impingement.^5-8 However, the outcome receiving the most attention in recent literature is the rate and effect of dislocation in patients with lumbar fusion surgery. Large Medicare database analyses have discovered an association with increased rates of dislocations in patients with lumbar fusion surgeries compared with those without.^9,10 Prosthetic hip dislocation is an expensive complication of THA and is projected to have greater impact through 2035 due to a growing number of THA procedures.¹¹ Identifying risk factors associated with hip dislocation is paramount to mitigating its effect on patients who have undergone THA.

Recent research has found increased rates of THA dislocation and revision surgery in patients with LSF, with some studies showing previous LSF as the strongest independent predictor.^6-16 However, controversy surrounds this relationship, including the sequence of procedures (LSF before or after THA), the time between procedures, and involvement of the sacrum in LSF. One study found that patients had a 106% increased risk of dislocation when LSF was performed before THA compared with patients who underwent LSF 5 years after undergoing THA, while another study showed no significant difference in dislocations pre- vs post-LSF.^16,17 An additional study showed no significant difference in the rate of dislocation in patients without sacral involvement in the LSF, while also showing significantly higher rates of dislocation in LSF with sacral involvement.¹² The researchers also found a trend toward more dislocations in longer lumbosacral fusions. Recent studies have also examined dislocation rates with lumbar fusion in patients treated with dual-mobility liners.^18-20 The consensus from these studies is that dual-mobility liners significantly decrease the rate of dislocation in primary THAs with lumbar fusion.

The present study sought to determine the rates of hip dislocations in a US Department of Veterans Affairs (VA) hospital setting. To the authors’ knowledge, no retrospective study focusing on THAs in the veteran population has been performed. This study benefits from controlling for various surgeon techniques and surgical preferences when compared to large Medicare database studies because the orthopedic surgeon (ABK) only performed the posterior approach for all patients during the study period.

The primary objective of this study was to determine whether the rates of hip dislocation would, in fact, be higher in patients with lumbar fusion surgery, as recent database studies suggest. Secondary objectives included determining whether patient characteristics, comorbidities, number of levels fused, or inclusion of the sacrum in the fusion construct influenced dislocation rates. Furthermore, VA Dayton Healthcare System (VADHS) began routine use of dual-mobility liners for lumbar fusion patients in 2018, allowing for examination of these patients.

Methods

The Wright State University and VADHS Institutional Review Board approved this study design. A retrospective review of all primary THAs at VADHS was performed to investigate the relationship between previous lumbar spine fusion and the incidence of THA revision. Manual chart review was performed for patients who underwent primary THA between January 2003, and December 2022. One surgeon performed all surgeries using only the posterior approach. Patients were not excluded if they had bilateral procedures and all eligible hips were included. Patients with a concomitant diagnosis of fracture of the femoral head or femoral neck at the time of surgery were excluded. Additionally, only patients with ≥ 12 months of follow-up data were included.

The primary outcome was dislocation within 12 months of THA; the primary independent variable was LSF prior to THA. Covariates included patient demographics (age, sex, body mass index [BMI]) and Charlson Comorbidity Index (CCI) score, with additional data collected on the number of levels fused, sacral spine involvement, revision rates, and use of dual-mobility liners. Year of surgery was also included in analyses to account for any changes that may have occurred during the study period.

Statistical Analysis

Statistical analyses were performed in SAS 9.4. Patients were grouped into 2 cohorts, depending on whether they had received LSF prior to THA. Analyses were adjusted for repeated measures to account for the small percentage of patients with bilateral procedures.

Univariate comparisons between cohorts for covariates, as well as rates of dislocation and revision, were performed using the independent samples t test for continuous variables and the Fisher exact test for dichotomous categorical variables. Significant comorbidities, as well as age, sex, BMI, liner type, LSF cohort, and surgery year, were included in a logistic regression model to determine what effect, if any, they had on the likelihood of dislocation. Variables were removed using a backward stepwise approach, starting with the nonsignificant variable effect with the lowest χ² value, and continuing until reaching a final model where all remaining variable effects were significant. For the variables retained in the final model, odds ratios (ORs) with 95% CIs were derived, with dislocation designated as the event. Individual comorbidity subcomponents of the CCI were also analyzed for their effects on dislocation using backward stepwise logistic regression. A secondary analysis among patients with LSF tested for the influence of the number of vertebral levels fused, the presence or absence of sacral involvement in the fusion, and the use of dual-mobility liners on the likelihood of hip dislocation.

Results

The LSF cohort included 39 patients with THA and prior LSF, 3 of whom had bilateral procedures, for a total of 42 hips. The non-LSF cohort included 813 patients with THA, 112 of whom had bilateral procedures, for a total of 925 hips. The LSF and non-LSF cohorts did not differ significantly in age, sex, BMI, CCI, or revision rates (Table). The LSF cohort included a significantly higher percentage of hips receiving dual-mobility liners than did the non-LSF cohort (23.8% vs 0.6%; P < .001) and had more than twice the rate of dislocation (4 of 42 hips [9.5%] vs 35 of 925 hips [3.8%]), although this difference was not statistically significant (P = .08).

The final logistic regression model with dislocation as the outcome was statistically significant (χ², 17.47; P < .001) and retained 2 significant predictor variables: LSF cohort (χ², 4.63; P = .03), and sex (χ², 18.27; P < .001). Females were more likely than males to experience dislocation (OR, 5.84; 95% CI, 2.60-13.13; P < .001) as were patients who had LSF prior to THA (OR, 3.42; 95% CI, 1.12-10.47; P = .03) (Figure). None of the CCI subcomponent comorbidities significantly affected the probability of dislocation (myocardial infarction, P = .46; congestive heart failure, P = .47; peripheral vascular disease, P = .97; stroke, P = .51; dementia, P = .99; chronic obstructive pulmonary disease, P = .95; connective tissue disease, P = .25; peptic ulcer, P = .41; liver disease, P = .30; diabetes, P = .06; hemiplegia, P = .99; chronic kidney disease, P = .82; solid tumor, P = .90; leukemia, P = .99; lymphoma, P = .99; AIDS, P = .99). Within the LSF cohort, neither the number of levels fused (P = .83) nor sacral involvement (P = .42), significantly affected the probability of hip dislocation. None of the patients in either cohort who received dual-mobility liners subsequently dislocated their hips, nor did any of them require revision surgery.

Discussion

Spinopelvic biomechanics have been an area of increasing interest and research. Spinal fusion has been shown to alter the mobility of the pelvis and has been associated with decreased stability of THA implants.²¹ For example, in the setting of a fused spine, the lack of compensatory changes in pelvic tilt or acetabular anteversion when adjusting to a seated or standing position may predispose patients to impingement because the acetabular component is not properly positioned. Dual-mobility constructs mitigate this risk by providing an additional articulation, which increases jump distance and range of motion prior to impingement, thereby enhancing stability.

The use of dual-mobility liners in patients with LSF has also been examined.^18-20 These studies demonstrate a reduced risk of postoperative THA dislocation in patients with previous LSF. The rate of postoperative complications and revisions for LSF patients with dual-mobility liners was also found to be similar to that of THAs without dual-mobility in patients without prior LSF. This study focused on a veteran population to demonstrate the efficacy of dual-mobility liners in patients with LSF. The results indicate that LSF prior to THA and female sex were predictors for prosthetic hip dislocations in the 12-month postoperative period in this patient population, which aligns with the current literature.

The dislocation rate in the LSF-THA group (9.5%) was higher than the dislocation rate in the control group (3.8%). Although not statistically significant in the univariate analysis, LSF was shown to be a significant risk factor after controlling for patient sex. Other studies have found the dislocation rate to be 3% to 7%, which is lower than the dislocation rate observed in this study.^8,10,16

The reasons for this higher rate of dislocation are not entirely clear. A veteran population has poorer overall health than the general population, which may contribute to the higher than previously reported dislocation rates.²² These results can be applied to the management of veterans seeking THA.

There have been conflicting reports regarding the impact a patient’s sex has on THA outcomes in the general population.^23-26 This study found that female patients had higher rates of dislocation within 1 year of THA than male patients. This difference, which could be due to differences in baseline anatomic hip morphology between the sexes; females tend to have smaller femoral head sizes and less offset compared with males.^27,28 However, this finding could have been confounded by the small number of female veterans in the study cohort.

A type 2 diabetes mellitus (T2DM) diagnosis, which is a component of CCI, trended toward increased risk of prosthetic hip dislocation. Multiple studies have also discussed the increased risk of postoperative infections and revisions following THA in patients with T2DM.^29-31 One study found T2DM to be an independent risk factor for immediate in-hospital postoperative complications following hip arthroplasty.³²

Another factor that may influence postoperative dislocation risk is surgical approach. The posterior approach has historically been associated with higher rates of instability when compared to anterior or lateral THA.³³ Researchers have also looked at the role that surgical approach plays in patients with prior LSF. Huebschmann et al confirmed that not only is LSF a significant risk factor for dislocation following THA, but anterior and laterally based surgical approaches may mitigate this risk.³⁴

Limitations

As a retrospective cohort study, the reliability of the data hinges on complete documentation. Documentation of all encounters for dislocations was obtained from the VA Computerized Patient Record System, which may have led to some dislocation events being missed. However, as long as there was adequate postoperative follow-up, it was assumed all events outside the VA were included. Another limitation of this study was that male patients greatly outnumbered female patients, and this fact could limit the generalizability of findings to the population as a whole.

Conclusions

This study in a veteran population found that prior LSF and female sex were significant predictors for postoperative dislocation within 1 year of THA surgery. Additionally, the use of a dual-mobility liner was found to be protective against postoperative dislocation events. These data allow clinicians to better counsel veterans on the risk factors associated with postoperative dislocation and strategies to mitigate this risk.

Total hip arthroplasty (THA) is among the most common elective orthopedic procedures performed annually in the United States, with an estimated 635,000 to 909,000 THAs expected each year by 2030.¹ Consequently, complication rates and revision surgeries related to THA have been increasing, along with the financial burden on the health care system.^2-4 Optimizing outcomes for patients undergoing THA and identifying risk factors for treatment failure have become areas of focus.

Over the last decade, there has been a renewed interest in the effect of previous lumbar spine fusion (LSF) surgery on THA outcomes. Studies have explored the rates of complications, postoperative mobility, and THA implant impingement.^5-8 However, the outcome receiving the most attention in recent literature is the rate and effect of dislocation in patients with lumbar fusion surgery. Large Medicare database analyses have discovered an association with increased rates of dislocations in patients with lumbar fusion surgeries compared with those without.^9,10 Prosthetic hip dislocation is an expensive complication of THA and is projected to have greater impact through 2035 due to a growing number of THA procedures.¹¹ Identifying risk factors associated with hip dislocation is paramount to mitigating its effect on patients who have undergone THA.

Recent research has found increased rates of THA dislocation and revision surgery in patients with LSF, with some studies showing previous LSF as the strongest independent predictor.^6-16 However, controversy surrounds this relationship, including the sequence of procedures (LSF before or after THA), the time between procedures, and involvement of the sacrum in LSF. One study found that patients had a 106% increased risk of dislocation when LSF was performed before THA compared with patients who underwent LSF 5 years after undergoing THA, while another study showed no significant difference in dislocations pre- vs post-LSF.^16,17 An additional study showed no significant difference in the rate of dislocation in patients without sacral involvement in the LSF, while also showing significantly higher rates of dislocation in LSF with sacral involvement.¹² The researchers also found a trend toward more dislocations in longer lumbosacral fusions. Recent studies have also examined dislocation rates with lumbar fusion in patients treated with dual-mobility liners.^18-20 The consensus from these studies is that dual-mobility liners significantly decrease the rate of dislocation in primary THAs with lumbar fusion.

The present study sought to determine the rates of hip dislocations in a US Department of Veterans Affairs (VA) hospital setting. To the authors’ knowledge, no retrospective study focusing on THAs in the veteran population has been performed. This study benefits from controlling for various surgeon techniques and surgical preferences when compared to large Medicare database studies because the orthopedic surgeon (ABK) only performed the posterior approach for all patients during the study period.

The primary objective of this study was to determine whether the rates of hip dislocation would, in fact, be higher in patients with lumbar fusion surgery, as recent database studies suggest. Secondary objectives included determining whether patient characteristics, comorbidities, number of levels fused, or inclusion of the sacrum in the fusion construct influenced dislocation rates. Furthermore, VA Dayton Healthcare System (VADHS) began routine use of dual-mobility liners for lumbar fusion patients in 2018, allowing for examination of these patients.

Methods

The Wright State University and VADHS Institutional Review Board approved this study design. A retrospective review of all primary THAs at VADHS was performed to investigate the relationship between previous lumbar spine fusion and the incidence of THA revision. Manual chart review was performed for patients who underwent primary THA between January 2003, and December 2022. One surgeon performed all surgeries using only the posterior approach. Patients were not excluded if they had bilateral procedures and all eligible hips were included. Patients with a concomitant diagnosis of fracture of the femoral head or femoral neck at the time of surgery were excluded. Additionally, only patients with ≥ 12 months of follow-up data were included.

The primary outcome was dislocation within 12 months of THA; the primary independent variable was LSF prior to THA. Covariates included patient demographics (age, sex, body mass index [BMI]) and Charlson Comorbidity Index (CCI) score, with additional data collected on the number of levels fused, sacral spine involvement, revision rates, and use of dual-mobility liners. Year of surgery was also included in analyses to account for any changes that may have occurred during the study period.

Statistical Analysis

Statistical analyses were performed in SAS 9.4. Patients were grouped into 2 cohorts, depending on whether they had received LSF prior to THA. Analyses were adjusted for repeated measures to account for the small percentage of patients with bilateral procedures.

Univariate comparisons between cohorts for covariates, as well as rates of dislocation and revision, were performed using the independent samples t test for continuous variables and the Fisher exact test for dichotomous categorical variables. Significant comorbidities, as well as age, sex, BMI, liner type, LSF cohort, and surgery year, were included in a logistic regression model to determine what effect, if any, they had on the likelihood of dislocation. Variables were removed using a backward stepwise approach, starting with the nonsignificant variable effect with the lowest χ² value, and continuing until reaching a final model where all remaining variable effects were significant. For the variables retained in the final model, odds ratios (ORs) with 95% CIs were derived, with dislocation designated as the event. Individual comorbidity subcomponents of the CCI were also analyzed for their effects on dislocation using backward stepwise logistic regression. A secondary analysis among patients with LSF tested for the influence of the number of vertebral levels fused, the presence or absence of sacral involvement in the fusion, and the use of dual-mobility liners on the likelihood of hip dislocation.

Results

The LSF cohort included 39 patients with THA and prior LSF, 3 of whom had bilateral procedures, for a total of 42 hips. The non-LSF cohort included 813 patients with THA, 112 of whom had bilateral procedures, for a total of 925 hips. The LSF and non-LSF cohorts did not differ significantly in age, sex, BMI, CCI, or revision rates (Table). The LSF cohort included a significantly higher percentage of hips receiving dual-mobility liners than did the non-LSF cohort (23.8% vs 0.6%; P < .001) and had more than twice the rate of dislocation (4 of 42 hips [9.5%] vs 35 of 925 hips [3.8%]), although this difference was not statistically significant (P = .08).

The final logistic regression model with dislocation as the outcome was statistically significant (χ², 17.47; P < .001) and retained 2 significant predictor variables: LSF cohort (χ², 4.63; P = .03), and sex (χ², 18.27; P < .001). Females were more likely than males to experience dislocation (OR, 5.84; 95% CI, 2.60-13.13; P < .001) as were patients who had LSF prior to THA (OR, 3.42; 95% CI, 1.12-10.47; P = .03) (Figure). None of the CCI subcomponent comorbidities significantly affected the probability of dislocation (myocardial infarction, P = .46; congestive heart failure, P = .47; peripheral vascular disease, P = .97; stroke, P = .51; dementia, P = .99; chronic obstructive pulmonary disease, P = .95; connective tissue disease, P = .25; peptic ulcer, P = .41; liver disease, P = .30; diabetes, P = .06; hemiplegia, P = .99; chronic kidney disease, P = .82; solid tumor, P = .90; leukemia, P = .99; lymphoma, P = .99; AIDS, P = .99). Within the LSF cohort, neither the number of levels fused (P = .83) nor sacral involvement (P = .42), significantly affected the probability of hip dislocation. None of the patients in either cohort who received dual-mobility liners subsequently dislocated their hips, nor did any of them require revision surgery.

Discussion

Spinopelvic biomechanics have been an area of increasing interest and research. Spinal fusion has been shown to alter the mobility of the pelvis and has been associated with decreased stability of THA implants.²¹ For example, in the setting of a fused spine, the lack of compensatory changes in pelvic tilt or acetabular anteversion when adjusting to a seated or standing position may predispose patients to impingement because the acetabular component is not properly positioned. Dual-mobility constructs mitigate this risk by providing an additional articulation, which increases jump distance and range of motion prior to impingement, thereby enhancing stability.

The use of dual-mobility liners in patients with LSF has also been examined.^18-20 These studies demonstrate a reduced risk of postoperative THA dislocation in patients with previous LSF. The rate of postoperative complications and revisions for LSF patients with dual-mobility liners was also found to be similar to that of THAs without dual-mobility in patients without prior LSF. This study focused on a veteran population to demonstrate the efficacy of dual-mobility liners in patients with LSF. The results indicate that LSF prior to THA and female sex were predictors for prosthetic hip dislocations in the 12-month postoperative period in this patient population, which aligns with the current literature.

The dislocation rate in the LSF-THA group (9.5%) was higher than the dislocation rate in the control group (3.8%). Although not statistically significant in the univariate analysis, LSF was shown to be a significant risk factor after controlling for patient sex. Other studies have found the dislocation rate to be 3% to 7%, which is lower than the dislocation rate observed in this study.^8,10,16

The reasons for this higher rate of dislocation are not entirely clear. A veteran population has poorer overall health than the general population, which may contribute to the higher than previously reported dislocation rates.²² These results can be applied to the management of veterans seeking THA.

There have been conflicting reports regarding the impact a patient’s sex has on THA outcomes in the general population.^23-26 This study found that female patients had higher rates of dislocation within 1 year of THA than male patients. This difference, which could be due to differences in baseline anatomic hip morphology between the sexes; females tend to have smaller femoral head sizes and less offset compared with males.^27,28 However, this finding could have been confounded by the small number of female veterans in the study cohort.

A type 2 diabetes mellitus (T2DM) diagnosis, which is a component of CCI, trended toward increased risk of prosthetic hip dislocation. Multiple studies have also discussed the increased risk of postoperative infections and revisions following THA in patients with T2DM.^29-31 One study found T2DM to be an independent risk factor for immediate in-hospital postoperative complications following hip arthroplasty.³²

Another factor that may influence postoperative dislocation risk is surgical approach. The posterior approach has historically been associated with higher rates of instability when compared to anterior or lateral THA.³³ Researchers have also looked at the role that surgical approach plays in patients with prior LSF. Huebschmann et al confirmed that not only is LSF a significant risk factor for dislocation following THA, but anterior and laterally based surgical approaches may mitigate this risk.³⁴

Limitations

As a retrospective cohort study, the reliability of the data hinges on complete documentation. Documentation of all encounters for dislocations was obtained from the VA Computerized Patient Record System, which may have led to some dislocation events being missed. However, as long as there was adequate postoperative follow-up, it was assumed all events outside the VA were included. Another limitation of this study was that male patients greatly outnumbered female patients, and this fact could limit the generalizability of findings to the population as a whole.

Conclusions

This study in a veteran population found that prior LSF and female sex were significant predictors for postoperative dislocation within 1 year of THA surgery. Additionally, the use of a dual-mobility liner was found to be protective against postoperative dislocation events. These data allow clinicians to better counsel veterans on the risk factors associated with postoperative dislocation and strategies to mitigate this risk.

Total hip arthroplasty (THA) is among the most common elective orthopedic procedures performed annually in the United States, with an estimated 635,000 to 909,000 THAs expected each year by 2030.¹ Consequently, complication rates and revision surgeries related to THA have been increasing, along with the financial burden on the health care system.^2-4 Optimizing outcomes for patients undergoing THA and identifying risk factors for treatment failure have become areas of focus.

Over the last decade, there has been a renewed interest in the effect of previous lumbar spine fusion (LSF) surgery on THA outcomes. Studies have explored the rates of complications, postoperative mobility, and THA implant impingement.^5-8 However, the outcome receiving the most attention in recent literature is the rate and effect of dislocation in patients with lumbar fusion surgery. Large Medicare database analyses have discovered an association with increased rates of dislocations in patients with lumbar fusion surgeries compared with those without.^9,10 Prosthetic hip dislocation is an expensive complication of THA and is projected to have greater impact through 2035 due to a growing number of THA procedures.¹¹ Identifying risk factors associated with hip dislocation is paramount to mitigating its effect on patients who have undergone THA.

Recent research has found increased rates of THA dislocation and revision surgery in patients with LSF, with some studies showing previous LSF as the strongest independent predictor.^6-16 However, controversy surrounds this relationship, including the sequence of procedures (LSF before or after THA), the time between procedures, and involvement of the sacrum in LSF. One study found that patients had a 106% increased risk of dislocation when LSF was performed before THA compared with patients who underwent LSF 5 years after undergoing THA, while another study showed no significant difference in dislocations pre- vs post-LSF.^16,17 An additional study showed no significant difference in the rate of dislocation in patients without sacral involvement in the LSF, while also showing significantly higher rates of dislocation in LSF with sacral involvement.¹² The researchers also found a trend toward more dislocations in longer lumbosacral fusions. Recent studies have also examined dislocation rates with lumbar fusion in patients treated with dual-mobility liners.^18-20 The consensus from these studies is that dual-mobility liners significantly decrease the rate of dislocation in primary THAs with lumbar fusion.

The present study sought to determine the rates of hip dislocations in a US Department of Veterans Affairs (VA) hospital setting. To the authors’ knowledge, no retrospective study focusing on THAs in the veteran population has been performed. This study benefits from controlling for various surgeon techniques and surgical preferences when compared to large Medicare database studies because the orthopedic surgeon (ABK) only performed the posterior approach for all patients during the study period.

The primary objective of this study was to determine whether the rates of hip dislocation would, in fact, be higher in patients with lumbar fusion surgery, as recent database studies suggest. Secondary objectives included determining whether patient characteristics, comorbidities, number of levels fused, or inclusion of the sacrum in the fusion construct influenced dislocation rates. Furthermore, VA Dayton Healthcare System (VADHS) began routine use of dual-mobility liners for lumbar fusion patients in 2018, allowing for examination of these patients.

Methods

The Wright State University and VADHS Institutional Review Board approved this study design. A retrospective review of all primary THAs at VADHS was performed to investigate the relationship between previous lumbar spine fusion and the incidence of THA revision. Manual chart review was performed for patients who underwent primary THA between January 2003, and December 2022. One surgeon performed all surgeries using only the posterior approach. Patients were not excluded if they had bilateral procedures and all eligible hips were included. Patients with a concomitant diagnosis of fracture of the femoral head or femoral neck at the time of surgery were excluded. Additionally, only patients with ≥ 12 months of follow-up data were included.

The primary outcome was dislocation within 12 months of THA; the primary independent variable was LSF prior to THA. Covariates included patient demographics (age, sex, body mass index [BMI]) and Charlson Comorbidity Index (CCI) score, with additional data collected on the number of levels fused, sacral spine involvement, revision rates, and use of dual-mobility liners. Year of surgery was also included in analyses to account for any changes that may have occurred during the study period.

Statistical Analysis

Statistical analyses were performed in SAS 9.4. Patients were grouped into 2 cohorts, depending on whether they had received LSF prior to THA. Analyses were adjusted for repeated measures to account for the small percentage of patients with bilateral procedures.

Univariate comparisons between cohorts for covariates, as well as rates of dislocation and revision, were performed using the independent samples t test for continuous variables and the Fisher exact test for dichotomous categorical variables. Significant comorbidities, as well as age, sex, BMI, liner type, LSF cohort, and surgery year, were included in a logistic regression model to determine what effect, if any, they had on the likelihood of dislocation. Variables were removed using a backward stepwise approach, starting with the nonsignificant variable effect with the lowest χ² value, and continuing until reaching a final model where all remaining variable effects were significant. For the variables retained in the final model, odds ratios (ORs) with 95% CIs were derived, with dislocation designated as the event. Individual comorbidity subcomponents of the CCI were also analyzed for their effects on dislocation using backward stepwise logistic regression. A secondary analysis among patients with LSF tested for the influence of the number of vertebral levels fused, the presence or absence of sacral involvement in the fusion, and the use of dual-mobility liners on the likelihood of hip dislocation.

Results

The LSF cohort included 39 patients with THA and prior LSF, 3 of whom had bilateral procedures, for a total of 42 hips. The non-LSF cohort included 813 patients with THA, 112 of whom had bilateral procedures, for a total of 925 hips. The LSF and non-LSF cohorts did not differ significantly in age, sex, BMI, CCI, or revision rates (Table). The LSF cohort included a significantly higher percentage of hips receiving dual-mobility liners than did the non-LSF cohort (23.8% vs 0.6%; P < .001) and had more than twice the rate of dislocation (4 of 42 hips [9.5%] vs 35 of 925 hips [3.8%]), although this difference was not statistically significant (P = .08).

The final logistic regression model with dislocation as the outcome was statistically significant (χ², 17.47; P < .001) and retained 2 significant predictor variables: LSF cohort (χ², 4.63; P = .03), and sex (χ², 18.27; P < .001). Females were more likely than males to experience dislocation (OR, 5.84; 95% CI, 2.60-13.13; P < .001) as were patients who had LSF prior to THA (OR, 3.42; 95% CI, 1.12-10.47; P = .03) (Figure). None of the CCI subcomponent comorbidities significantly affected the probability of dislocation (myocardial infarction, P = .46; congestive heart failure, P = .47; peripheral vascular disease, P = .97; stroke, P = .51; dementia, P = .99; chronic obstructive pulmonary disease, P = .95; connective tissue disease, P = .25; peptic ulcer, P = .41; liver disease, P = .30; diabetes, P = .06; hemiplegia, P = .99; chronic kidney disease, P = .82; solid tumor, P = .90; leukemia, P = .99; lymphoma, P = .99; AIDS, P = .99). Within the LSF cohort, neither the number of levels fused (P = .83) nor sacral involvement (P = .42), significantly affected the probability of hip dislocation. None of the patients in either cohort who received dual-mobility liners subsequently dislocated their hips, nor did any of them require revision surgery.

Discussion

Spinopelvic biomechanics have been an area of increasing interest and research. Spinal fusion has been shown to alter the mobility of the pelvis and has been associated with decreased stability of THA implants.²¹ For example, in the setting of a fused spine, the lack of compensatory changes in pelvic tilt or acetabular anteversion when adjusting to a seated or standing position may predispose patients to impingement because the acetabular component is not properly positioned. Dual-mobility constructs mitigate this risk by providing an additional articulation, which increases jump distance and range of motion prior to impingement, thereby enhancing stability.

The use of dual-mobility liners in patients with LSF has also been examined.^18-20 These studies demonstrate a reduced risk of postoperative THA dislocation in patients with previous LSF. The rate of postoperative complications and revisions for LSF patients with dual-mobility liners was also found to be similar to that of THAs without dual-mobility in patients without prior LSF. This study focused on a veteran population to demonstrate the efficacy of dual-mobility liners in patients with LSF. The results indicate that LSF prior to THA and female sex were predictors for prosthetic hip dislocations in the 12-month postoperative period in this patient population, which aligns with the current literature.

The dislocation rate in the LSF-THA group (9.5%) was higher than the dislocation rate in the control group (3.8%). Although not statistically significant in the univariate analysis, LSF was shown to be a significant risk factor after controlling for patient sex. Other studies have found the dislocation rate to be 3% to 7%, which is lower than the dislocation rate observed in this study.^8,10,16

The reasons for this higher rate of dislocation are not entirely clear. A veteran population has poorer overall health than the general population, which may contribute to the higher than previously reported dislocation rates.²² These results can be applied to the management of veterans seeking THA.

There have been conflicting reports regarding the impact a patient’s sex has on THA outcomes in the general population.^23-26 This study found that female patients had higher rates of dislocation within 1 year of THA than male patients. This difference, which could be due to differences in baseline anatomic hip morphology between the sexes; females tend to have smaller femoral head sizes and less offset compared with males.^27,28 However, this finding could have been confounded by the small number of female veterans in the study cohort.

A type 2 diabetes mellitus (T2DM) diagnosis, which is a component of CCI, trended toward increased risk of prosthetic hip dislocation. Multiple studies have also discussed the increased risk of postoperative infections and revisions following THA in patients with T2DM.^29-31 One study found T2DM to be an independent risk factor for immediate in-hospital postoperative complications following hip arthroplasty.³²

Another factor that may influence postoperative dislocation risk is surgical approach. The posterior approach has historically been associated with higher rates of instability when compared to anterior or lateral THA.³³ Researchers have also looked at the role that surgical approach plays in patients with prior LSF. Huebschmann et al confirmed that not only is LSF a significant risk factor for dislocation following THA, but anterior and laterally based surgical approaches may mitigate this risk.³⁴

Limitations

As a retrospective cohort study, the reliability of the data hinges on complete documentation. Documentation of all encounters for dislocations was obtained from the VA Computerized Patient Record System, which may have led to some dislocation events being missed. However, as long as there was adequate postoperative follow-up, it was assumed all events outside the VA were included. Another limitation of this study was that male patients greatly outnumbered female patients, and this fact could limit the generalizability of findings to the population as a whole.

Conclusions

This study in a veteran population found that prior LSF and female sex were significant predictors for postoperative dislocation within 1 year of THA surgery. Additionally, the use of a dual-mobility liner was found to be protective against postoperative dislocation events. These data allow clinicians to better counsel veterans on the risk factors associated with postoperative dislocation and strategies to mitigate this risk.

Discussion

A diagnosis of dural arteriovenous fistula (dAVF) was made. Lesions involving the spinal cord are traditionally classified by location as extradural, intradural/extramedullary, or intramedullary. Intramedullary spinal cord abnormalities pose considerable diagnostic and management challenges because of the risks of biopsy in this location and the added potential for morbidity and mortality from improperly treated lesions. Although MRI is the preferred imaging modality, PET/CT and magnetic resonance angiography (MRA) may also help narrow the differential diagnosis and potentially avoid complications from an invasive biopsy.¹ This patient’s intramedullary lesion, which represented a dAVF, posed a diagnostic challenge; after diagnosis, it was successfully managed conservatively with dexamethasone and physical therapy.

Intradural tumors account for 2% to 4% of all primary central nervous system (CNS) tumors.² Ependymomas account for 50% to 60% of intramedullary tumors in adults, while astrocytomas account for about 60% of all lesions in children and adolescents.^3,4 The differential diagnosis for intramedullary tumors also includes hemangioblastoma, metastases, primary CNS lymphoma, germ cell tumors, and gangliogliomas.^5,6

Intramedullary metastases remain rare, although the incidence is rising with improvements in oncologic and supportive treatments. Autopsy studies conducted decades ago demonstrated that about 0.9% to 2.1% of patients with systemic cancer have intramedullary metastases at death.^7,8 In patients with an established history of malignancy, a metastatic intramedullary tumor should be placed higher on the differential diagnosis. Intramedullary metastases most often occur in the setting of widespread metastatic disease. A systematic review of the literature on patients with lung cancer (small cell and non-small cell lung carcinomas) and ≥ 1 intramedullary spinal cord metastasis demonstrated that 55.8% of patients had concurrent brain metastases, 20.0% had leptomeningeal carcinomatosis, and 19.5% had vertebral metastases.⁹ While about half of all intramedullary metastases are associated with lung cancer, other common malignancies that metastasize to this area include colorectal, breast, and renal cell carcinoma, as well as lymphoma and melanoma primaries.^10,11

On imaging, intramedullary metastases often appear as several short, studded segments with surrounding edema, typically out of proportion to the size of the lesion.¹ By contrast, astrocytomas and ependymomas often span multiple segments, and enhancement patterns can vary depending on the subtype and grade. Glioblastoma multiforme, or grade 4 IDH wild-type astrocytomas, demonstrate an irregular, heterogeneous pattern of enhancement. Hemangioblastomas vary in size and are classically hypointense to isointense on T1-weighted sequences, isointense to hyperintense on T2-weighted sequences, and demonstrate avid enhancement on T1- postcontrast images. In large hemangioblastomas, flow voids due to prominent vasculature may be visualized.

Numerous nonneoplastic tumor mimics can obscure the differential diagnosis. Vascular malformations, including cavernomas and dAVFs, can also present with enhancement and edema. dAVFs are the most common type of spinal vascular malformation, accounting for about 70% of cases.¹² They are supplied by the radiculomeningeal arteries, whereas pial arteriovenous malformations (AVMs) are supplied by the radiculomedullary and radiculopial arteries. On MRI, dAVFs usually have venous congestion with intramedullary edema, which appears as an ill-defined centromedullary hyperintensity on T2-weighted imaging over multiple segments. The spinal cord may appear swollen with atrophic changes in chronic cases. Spinal cord AVMs are rarer and have an intramedullary nidus. They usually demonstrate mixed heterogeneous signal on T1- and T2-weighted imaging due to blood products, while the nidus demonstrates a variable degree of enhancement. Serpiginous flow voids are seen both within the nidus and at the cord surface.

Demyelinating lesions of the spine may be seen in neuroinflammatory conditions such as multiple sclerosis, neuromyelitis optica spectrum disorder, acute transverse myelitis, and acute disseminated encephalomyelitis. In multiple sclerosis, lesions typically extend ≤ 2 vertebral segments in length, cover less than half of the vertebral cross-sectional area, and have a dorsolateral predilection.¹³ Active lesions may demonstrate enhancement along the rim or in a patchy pattern. In the presence of demyelinating lesions, there may occasionally appear to be an expansile mass with a syrinx.¹⁴

Infections such as tuberculosis and neurosarcoidosis should also remain on the differential diagnosis. On MRI, tuberculosis usually involves the thoracic cord and is typically rim-enhancing.¹⁵ If there are caseating granulomas, T2-weighted images may also demonstrate rim enhancement.¹⁶ Spinal sarcoidosis is unusual without intracranial involvement, and its appearance may include leptomeningeal enhancement, cord expansion, and hyperintense signal on T2- weighted imaging.¹⁷

Finally, iatrogenic causes are also possible, including radiation myelopathy and mechanical spinal cord injury. For radiation myelopathy, it is important to ascertain whether a patient has undergone prior radiotherapy in the region and to obtain the pertinent dosimetry. Spinal cord injury may cause a focal signal abnormality within the cord, with T2 hyperintensity; these foci may or may not present with enhancement, edema, or hematoma and therefore may resemble tumors.¹³

This patient presented with progressive right-sided lower extremity weakness and hypoesthesia and a history of a low-grade right renal/pelvic ureteral tumor. The immediate impression was that the thoracic intramedullary lesion represented a metastatic lesion. However, in the absence of any systemic or intracranial metastases, this progression was much less likely. An extensive interdisciplinary workup was conducted that included medical oncology, neurology, neuroradiology, neuro-oncology, neurosurgery, nuclear medicine, and radiation oncology. Neuroradiology and nuclear medicine identified a slightly hypermetabolic focus on the PET/CT from 1.5 years prior that correlated exactly with the same location as the lesion on the recent spinal MRI. This finding, along with the MRA, confirmed the diagnosis of a dAVF, which was successfully managed conservatively with dexamethasone and physical therapy, rather than through oncologic treatments such as radiotherapy

There remains debate regarding the utility of steroids in treating patients with dAVF. Although there are some case reports documenting that the edema associated with the dAVF responds to steroids, other case series have found that steroids may worsen outcomes in patients with dAVF, possibly due to increased venous hydrostatic pressure.

This case demonstrates the importance of an interdisciplinary workup when evaluating an intramedullary lesion, as well as maintaining a wide differential diagnosis, particularly in the absence of a history of polymetastatic cancer. All the clues (such as the slightly hypermetabolic focus on a PET/CT from 1.5 years prior) need to be obtained to comfortably reach a diagnosis in the absence of pathologic confirmation. These cases can be especially challenging due to the lack of pathologic confirmation, but by understanding the main differentiating features among the various etiologies and obtaining all available information, a correct diagnosis can be made without unnecessary interventions.

Discussion

A diagnosis of dural arteriovenous fistula (dAVF) was made. Lesions involving the spinal cord are traditionally classified by location as extradural, intradural/extramedullary, or intramedullary. Intramedullary spinal cord abnormalities pose considerable diagnostic and management challenges because of the risks of biopsy in this location and the added potential for morbidity and mortality from improperly treated lesions. Although MRI is the preferred imaging modality, PET/CT and magnetic resonance angiography (MRA) may also help narrow the differential diagnosis and potentially avoid complications from an invasive biopsy.¹ This patient’s intramedullary lesion, which represented a dAVF, posed a diagnostic challenge; after diagnosis, it was successfully managed conservatively with dexamethasone and physical therapy.

Intradural tumors account for 2% to 4% of all primary central nervous system (CNS) tumors.² Ependymomas account for 50% to 60% of intramedullary tumors in adults, while astrocytomas account for about 60% of all lesions in children and adolescents.^3,4 The differential diagnosis for intramedullary tumors also includes hemangioblastoma, metastases, primary CNS lymphoma, germ cell tumors, and gangliogliomas.^5,6

Intramedullary metastases remain rare, although the incidence is rising with improvements in oncologic and supportive treatments. Autopsy studies conducted decades ago demonstrated that about 0.9% to 2.1% of patients with systemic cancer have intramedullary metastases at death.^7,8 In patients with an established history of malignancy, a metastatic intramedullary tumor should be placed higher on the differential diagnosis. Intramedullary metastases most often occur in the setting of widespread metastatic disease. A systematic review of the literature on patients with lung cancer (small cell and non-small cell lung carcinomas) and ≥ 1 intramedullary spinal cord metastasis demonstrated that 55.8% of patients had concurrent brain metastases, 20.0% had leptomeningeal carcinomatosis, and 19.5% had vertebral metastases.⁹ While about half of all intramedullary metastases are associated with lung cancer, other common malignancies that metastasize to this area include colorectal, breast, and renal cell carcinoma, as well as lymphoma and melanoma primaries.^10,11

On imaging, intramedullary metastases often appear as several short, studded segments with surrounding edema, typically out of proportion to the size of the lesion.¹ By contrast, astrocytomas and ependymomas often span multiple segments, and enhancement patterns can vary depending on the subtype and grade. Glioblastoma multiforme, or grade 4 IDH wild-type astrocytomas, demonstrate an irregular, heterogeneous pattern of enhancement. Hemangioblastomas vary in size and are classically hypointense to isointense on T1-weighted sequences, isointense to hyperintense on T2-weighted sequences, and demonstrate avid enhancement on T1- postcontrast images. In large hemangioblastomas, flow voids due to prominent vasculature may be visualized.

Numerous nonneoplastic tumor mimics can obscure the differential diagnosis. Vascular malformations, including cavernomas and dAVFs, can also present with enhancement and edema. dAVFs are the most common type of spinal vascular malformation, accounting for about 70% of cases.¹² They are supplied by the radiculomeningeal arteries, whereas pial arteriovenous malformations (AVMs) are supplied by the radiculomedullary and radiculopial arteries. On MRI, dAVFs usually have venous congestion with intramedullary edema, which appears as an ill-defined centromedullary hyperintensity on T2-weighted imaging over multiple segments. The spinal cord may appear swollen with atrophic changes in chronic cases. Spinal cord AVMs are rarer and have an intramedullary nidus. They usually demonstrate mixed heterogeneous signal on T1- and T2-weighted imaging due to blood products, while the nidus demonstrates a variable degree of enhancement. Serpiginous flow voids are seen both within the nidus and at the cord surface.

Demyelinating lesions of the spine may be seen in neuroinflammatory conditions such as multiple sclerosis, neuromyelitis optica spectrum disorder, acute transverse myelitis, and acute disseminated encephalomyelitis. In multiple sclerosis, lesions typically extend ≤ 2 vertebral segments in length, cover less than half of the vertebral cross-sectional area, and have a dorsolateral predilection.¹³ Active lesions may demonstrate enhancement along the rim or in a patchy pattern. In the presence of demyelinating lesions, there may occasionally appear to be an expansile mass with a syrinx.¹⁴

Infections such as tuberculosis and neurosarcoidosis should also remain on the differential diagnosis. On MRI, tuberculosis usually involves the thoracic cord and is typically rim-enhancing.¹⁵ If there are caseating granulomas, T2-weighted images may also demonstrate rim enhancement.¹⁶ Spinal sarcoidosis is unusual without intracranial involvement, and its appearance may include leptomeningeal enhancement, cord expansion, and hyperintense signal on T2- weighted imaging.¹⁷

Finally, iatrogenic causes are also possible, including radiation myelopathy and mechanical spinal cord injury. For radiation myelopathy, it is important to ascertain whether a patient has undergone prior radiotherapy in the region and to obtain the pertinent dosimetry. Spinal cord injury may cause a focal signal abnormality within the cord, with T2 hyperintensity; these foci may or may not present with enhancement, edema, or hematoma and therefore may resemble tumors.¹³

This patient presented with progressive right-sided lower extremity weakness and hypoesthesia and a history of a low-grade right renal/pelvic ureteral tumor. The immediate impression was that the thoracic intramedullary lesion represented a metastatic lesion. However, in the absence of any systemic or intracranial metastases, this progression was much less likely. An extensive interdisciplinary workup was conducted that included medical oncology, neurology, neuroradiology, neuro-oncology, neurosurgery, nuclear medicine, and radiation oncology. Neuroradiology and nuclear medicine identified a slightly hypermetabolic focus on the PET/CT from 1.5 years prior that correlated exactly with the same location as the lesion on the recent spinal MRI. This finding, along with the MRA, confirmed the diagnosis of a dAVF, which was successfully managed conservatively with dexamethasone and physical therapy, rather than through oncologic treatments such as radiotherapy

There remains debate regarding the utility of steroids in treating patients with dAVF. Although there are some case reports documenting that the edema associated with the dAVF responds to steroids, other case series have found that steroids may worsen outcomes in patients with dAVF, possibly due to increased venous hydrostatic pressure.

This case demonstrates the importance of an interdisciplinary workup when evaluating an intramedullary lesion, as well as maintaining a wide differential diagnosis, particularly in the absence of a history of polymetastatic cancer. All the clues (such as the slightly hypermetabolic focus on a PET/CT from 1.5 years prior) need to be obtained to comfortably reach a diagnosis in the absence of pathologic confirmation. These cases can be especially challenging due to the lack of pathologic confirmation, but by understanding the main differentiating features among the various etiologies and obtaining all available information, a correct diagnosis can be made without unnecessary interventions.

Discussion

A diagnosis of dural arteriovenous fistula (dAVF) was made. Lesions involving the spinal cord are traditionally classified by location as extradural, intradural/extramedullary, or intramedullary. Intramedullary spinal cord abnormalities pose considerable diagnostic and management challenges because of the risks of biopsy in this location and the added potential for morbidity and mortality from improperly treated lesions. Although MRI is the preferred imaging modality, PET/CT and magnetic resonance angiography (MRA) may also help narrow the differential diagnosis and potentially avoid complications from an invasive biopsy.¹ This patient’s intramedullary lesion, which represented a dAVF, posed a diagnostic challenge; after diagnosis, it was successfully managed conservatively with dexamethasone and physical therapy.

Intradural tumors account for 2% to 4% of all primary central nervous system (CNS) tumors.² Ependymomas account for 50% to 60% of intramedullary tumors in adults, while astrocytomas account for about 60% of all lesions in children and adolescents.^3,4 The differential diagnosis for intramedullary tumors also includes hemangioblastoma, metastases, primary CNS lymphoma, germ cell tumors, and gangliogliomas.^5,6

Intramedullary metastases remain rare, although the incidence is rising with improvements in oncologic and supportive treatments. Autopsy studies conducted decades ago demonstrated that about 0.9% to 2.1% of patients with systemic cancer have intramedullary metastases at death.^7,8 In patients with an established history of malignancy, a metastatic intramedullary tumor should be placed higher on the differential diagnosis. Intramedullary metastases most often occur in the setting of widespread metastatic disease. A systematic review of the literature on patients with lung cancer (small cell and non-small cell lung carcinomas) and ≥ 1 intramedullary spinal cord metastasis demonstrated that 55.8% of patients had concurrent brain metastases, 20.0% had leptomeningeal carcinomatosis, and 19.5% had vertebral metastases.⁹ While about half of all intramedullary metastases are associated with lung cancer, other common malignancies that metastasize to this area include colorectal, breast, and renal cell carcinoma, as well as lymphoma and melanoma primaries.^10,11

On imaging, intramedullary metastases often appear as several short, studded segments with surrounding edema, typically out of proportion to the size of the lesion.¹ By contrast, astrocytomas and ependymomas often span multiple segments, and enhancement patterns can vary depending on the subtype and grade. Glioblastoma multiforme, or grade 4 IDH wild-type astrocytomas, demonstrate an irregular, heterogeneous pattern of enhancement. Hemangioblastomas vary in size and are classically hypointense to isointense on T1-weighted sequences, isointense to hyperintense on T2-weighted sequences, and demonstrate avid enhancement on T1- postcontrast images. In large hemangioblastomas, flow voids due to prominent vasculature may be visualized.

Numerous nonneoplastic tumor mimics can obscure the differential diagnosis. Vascular malformations, including cavernomas and dAVFs, can also present with enhancement and edema. dAVFs are the most common type of spinal vascular malformation, accounting for about 70% of cases.¹² They are supplied by the radiculomeningeal arteries, whereas pial arteriovenous malformations (AVMs) are supplied by the radiculomedullary and radiculopial arteries. On MRI, dAVFs usually have venous congestion with intramedullary edema, which appears as an ill-defined centromedullary hyperintensity on T2-weighted imaging over multiple segments. The spinal cord may appear swollen with atrophic changes in chronic cases. Spinal cord AVMs are rarer and have an intramedullary nidus. They usually demonstrate mixed heterogeneous signal on T1- and T2-weighted imaging due to blood products, while the nidus demonstrates a variable degree of enhancement. Serpiginous flow voids are seen both within the nidus and at the cord surface.

Demyelinating lesions of the spine may be seen in neuroinflammatory conditions such as multiple sclerosis, neuromyelitis optica spectrum disorder, acute transverse myelitis, and acute disseminated encephalomyelitis. In multiple sclerosis, lesions typically extend ≤ 2 vertebral segments in length, cover less than half of the vertebral cross-sectional area, and have a dorsolateral predilection.¹³ Active lesions may demonstrate enhancement along the rim or in a patchy pattern. In the presence of demyelinating lesions, there may occasionally appear to be an expansile mass with a syrinx.¹⁴

Infections such as tuberculosis and neurosarcoidosis should also remain on the differential diagnosis. On MRI, tuberculosis usually involves the thoracic cord and is typically rim-enhancing.¹⁵ If there are caseating granulomas, T2-weighted images may also demonstrate rim enhancement.¹⁶ Spinal sarcoidosis is unusual without intracranial involvement, and its appearance may include leptomeningeal enhancement, cord expansion, and hyperintense signal on T2- weighted imaging.¹⁷

Finally, iatrogenic causes are also possible, including radiation myelopathy and mechanical spinal cord injury. For radiation myelopathy, it is important to ascertain whether a patient has undergone prior radiotherapy in the region and to obtain the pertinent dosimetry. Spinal cord injury may cause a focal signal abnormality within the cord, with T2 hyperintensity; these foci may or may not present with enhancement, edema, or hematoma and therefore may resemble tumors.¹³

This patient presented with progressive right-sided lower extremity weakness and hypoesthesia and a history of a low-grade right renal/pelvic ureteral tumor. The immediate impression was that the thoracic intramedullary lesion represented a metastatic lesion. However, in the absence of any systemic or intracranial metastases, this progression was much less likely. An extensive interdisciplinary workup was conducted that included medical oncology, neurology, neuroradiology, neuro-oncology, neurosurgery, nuclear medicine, and radiation oncology. Neuroradiology and nuclear medicine identified a slightly hypermetabolic focus on the PET/CT from 1.5 years prior that correlated exactly with the same location as the lesion on the recent spinal MRI. This finding, along with the MRA, confirmed the diagnosis of a dAVF, which was successfully managed conservatively with dexamethasone and physical therapy, rather than through oncologic treatments such as radiotherapy

There remains debate regarding the utility of steroids in treating patients with dAVF. Although there are some case reports documenting that the edema associated with the dAVF responds to steroids, other case series have found that steroids may worsen outcomes in patients with dAVF, possibly due to increased venous hydrostatic pressure.

This case demonstrates the importance of an interdisciplinary workup when evaluating an intramedullary lesion, as well as maintaining a wide differential diagnosis, particularly in the absence of a history of polymetastatic cancer. All the clues (such as the slightly hypermetabolic focus on a PET/CT from 1.5 years prior) need to be obtained to comfortably reach a diagnosis in the absence of pathologic confirmation. These cases can be especially challenging due to the lack of pathologic confirmation, but by understanding the main differentiating features among the various etiologies and obtaining all available information, a correct diagnosis can be made without unnecessary interventions.

Hyperkalemia involves elevated serum potassium levels (> 5.0 mEq/L) and represents an important electrolyte disturbance due to its potentially severe consequences, including cardiac effects that can lead to dysrhythmia and even asystole and death.^1,2 In a US Medicare population, the prevalence of hyperkalemia has been estimated at 2.7% and is associated with substantial health care costs.³ The prevalence is even more marked in patients with preexisting conditions such as chronic kidney disease (CKD) and heart failure.^4,5

Hyperkalemia can result from multiple factors, including impaired renal function, adrenal disease, adverse drug reactions of angiotensin-converting enzyme inhibitors (ACEIs) and other medications, and heritable mutations.⁶ Hyperkalemia poses a considerable clinical risk, associated with adverse outcomes such as myocardial infarction and increased mortality in patients with CKD.^5,7,8 Electrocardiographic (ECG) changes associated with hyperkalemia play a vital role in guiding clinical decisions and treatment strategies.⁹ Understanding the pathophysiology, risk factors, and consequences of hyperkalemia, as well as the significance of ECG changes in its management, is essential for health care practitioners.

Case Presentation

An 81-year-old Hispanic man with a history of hypertension, hypothyroidism, gout, and CKD stage 3B presented to the emergency department with progressive weakness resulting in falls and culminating in an inability to ambulate independently. Additional symptoms included nausea, diarrhea, and myalgia. His vital signs were notable for a pulse of 41 beats/min. The physical examination was remarkable for significant weakness of the bilateral upper extremities, inability to bear his own weight, and bilateral lower extremity edema. His initial ECG upon arrival showed bradycardia with wide QRS, absent P waves, and peaked T waves (Figure 1a). These findings differed from his baseline ECG taken 1 year earlier, which showed sinus rhythm with premature atrial complexes and an old right bundle branch block (Figure 1b).

Medication review revealed that the patient was currently prescribed 100 mg allopurinol daily, 2.5 mg amlodipine daily, 10 mg atorvastatin at bedtime, 4 mg doxazosin daily, 112 mcg levothyroxine daily, 100 mg losartan daily, 25 mg metoprolol daily, and 0.4 mg tamsulosin daily. The patient had also been taking over-the-counter indomethacin for knee pain.

Based on the ECG results, he was treated with 0.083%/6 mL nebulized albuterol, 4.65 Mq/250 mL saline solution intravenous (IV) calcium gluconate, 10 units IV insulin with concomitant 50%/25 mL IV dextrose and 8.4 g of oral patiromer suspension. IV furosemide was held due to concern for renal function. The decision to proceed with hemodialysis was made. Repeat laboratory tests were performed, and an ECG obtained after treatment initiation but prior to hemodialysis demonstrated improvement of rate and T wave shortening (Figure 1c). The serum potassium level dropped from 9.8 mEq/L to 7.9 mEq/L (reference range, 3.5-5.0 mEq/L) (Table 1).

In addition to hemodialysis, sodium zirconium 10 g orally 3 times daily was added. Laboratory test results and an ECG was performed after dialysis continued to demonstrate improvement (Figure 1d). The patient’s potassium level decreased to 5.8 mEq/L, with the ECG demonstrating stability of heart rate and further improvement of the PR interval, QRS complex, and T waves.

Despite the established treatment regimen, potassium levels again rose to 6.7 mEq/L, but there were no significant changes in the ECG, and thus no medication changes were made (Figure 1e). Subsequent monitoring demonstrated a further increase in potassium to 7.4 mEq/L, with an ECG demonstrating a return to the baseline of 1 year prior. The patient underwent hemodialysis again and was given oral furosemide 60 mg every 12 hours. The potassium concentration after dialysis decreased to 4.7 mEq/L and remained stable, not going above 5.0 mEq/L on subsequent monitoring. The patient had resolution of all symptoms and was discharged.

Discussion

We have described in detail the presentation of each pathology and mechanisms of each treatment, starting with the patient’s initial condition that brought him to the emergency room—muscle weakness. Skeletal muscle weakness is a common manifestation of hyperkalemia, occurring in 20% to 40% of cases, and is more prevalent in severe elevations of potassium. Rarely, the weakness can progress to flaccid paralysis of the patient’s extremities and, in extreme cases, the diaphragm.

Muscle weakness progression occurs in a manner that resembles Guillain-Barré syndrome, starting in the lower extremities and ascending toward the upper extremities.¹⁰ This is known as secondary hyperkalemic periodic paralysis. Hyperkalemia lowers the transmembrane gradient in neurons, leading to neuronal depolarization independent of the degree of hyperkalemia. If the degree of hyperkalemia is large enough, this depolarization inactivates voltage-gated sodium channels, making neurons refractory to excitation. Electromyographical studies have shown reduction in the compounded muscle action potential.¹¹ The transient nature of this paralysis is reflected by rapid correction of weakness and paralysis when the electrolyte disorder is corrected.

The patient in this case also presented with bradycardia. The ECG manifestations of hyperkalemia can include atrial asystole, intraventricular conduction disturbances, peaked T waves, and widened QRS complexes. However, some patients with renal insufficiency may not exhibit ECG changes despite significantly elevated serum potassium levels.¹²

The severity of hyperkalemia is crucial in determining the associated ECG changes, with levels > 6.0 mEq/L presenting with abnormalities.¹³ ECG findings alone may not always accurately reflect the severity of hyperkalemia, as up to 60% of patients with potassium levels > 6.0 mEq/L may not show ECG changes.¹⁴ Additionally, extreme hyperkalemia can lead to inconsistent ECG findings, making it challenging to rely solely on ECG for diagnosis and monitoring.⁸ The level of potassium that causes these effects varies widely through patient populations.

The main mechanism by which hyperkalemia affects the heart’s conduction system is through voltage differences across the conduction fibers and eventual steady-state inactivation of sodium channels. This combination of mechanisms shortens the action potential duration, allowing more cardiomyocytes to undergo synchronized depolarization. This amalgamation of cardiomyocytes repolarizing can be reflected on ECGs as peaked T waves. As the action potential decreases, there is a period during which cardiomyocytes are prone to tachyarrhythmias and ventricular fibrillation.

A reduced action potential may lead to increased rates of depolarization and thus conduction, which in some scenarios may increase heart rate. As the levels of potassium rise, intracellular accumulation impedes the entry of sodium by decreasing the cation gradient across the cell membrane. This effectively slows the sinus nodes and prolongs the QRS by slowing the overall propagation of action potentials. By this mechanism, conduction delays, blocks, or asystole are manifested. The patient in this case showed conduction delays, peaked T waves, and disappearance of P waves when he first arrived.

Hyperkalemia Treatment

Hyperkalemia develops most commonly due to acute or chronic kidney diseases, as was the case with this patient. The patient’s hyperkalemia was also augmented by the use of nonsteroidal anti-inflammatory drugs (NSAIDs), which can directly affect renal function. A properly functioning kidney is responsible for excretion of up to 90% of ingested potassium, while the remainder is excreted through the gastrointestinal (GI) tract. Definitive treatment of hyperkalemia is mitigated primarily through these 2 organ systems. The treatment also includes transitory mechanisms of potassium reduction. The goal of each method is to preserve the action potential of cardiomyocytes and myocytes. This patient presented with acute symptomatic hyperkalemia and received various medications to acutely, transitorily, and definitively treat it.

Initial therapy included calcium gluconate, which functions to stabilize the myocardial cell membrane. Hyperkalemia decreases the resting membrane action potential of excitable cells and predisposes them to early depolarization and thus dysrhythmias. Calcium decreases the threshold potential across cells and offsets the overall gradient back to near normal levels.¹⁵ Calcium can be delivered through calcium gluconate or calcium chloride. Calcium chloride is not preferred because extravasation can cause pain, blistering and tissue ischemia. Central venous access is required, potentially delaying prompt treatment. Calcium acts rapidly after administration—within 1 to 3 minutes—but only lasts 30 to 60 minutes.¹⁶ Administration of calcium gluconate can be repeated as often as necessary, but patients must be monitored for adverse effects of calcium such as nausea, abdominal pain, polydipsia, polyuria, muscle weakness, and paresthesia. Care must be taken when patients are taking digoxin, because calcium may potentiate toxicity.¹⁷ Although calcium provides immediate benefits it does little to correct the underlying cause; other medications are required to remove potassium from the body.

Two medication classes have been proven to shift potassium intracellularly. The first are β-2 agonists, such as albuterol/levalbuterol, and the second is insulin. Both work through sodium-potassium-ATPase in a direct manner. β-2 agonists stimulate sodium-potassium-ATPase to move more potassium intracellularly, but these effects have been seen only with high doses of albuterol, typically 4× the standard dose of 0.5 mg in nebulized solutions to achieve decreases in potassium of 0.3 to 0.6 mEq/L, although some trials have reported decreases of 0.62 to 0.98 mEq/L.^15,18 These potassium-lowering effects of β-2 agonist are modest, but can be seen 20 to 30 minutes after administration and persist up to 1 to 2 hours. β-2 agonists are also readily affected by β blockers, which may reduce or negate the desired effect in hyperkalemia. For these reasons, a β-2 agonist should not be given as monotherapy and should be provided as an adjuvant to more independent therapies such as insulin. Insulin binds to receptors on muscle cells and increases the quantity of sodium-potassium-ATPase and glucose transporters. With this increase in influx pumps, surrounding tissues with higher resting membrane potentials can absorb the potassium load, thereby protecting cardiomyocytes.

Potassium Removal

Three methods are currently available to remove potassium from the body: GI excretion, renal excretion, and direct removal from the bloodstream. Under normal physiologic conditions, the kidneys account for about 90% of the body’s ability to remove potassium. Loop diuretics facilitate the removal of potassium by increasing urine production and have an additional potassium-wasting effect. Although the onset of action of loop diuretics is typically 30 to 60 minutes after oral administration, their effect can last for several hours. In this patient, furosemide was introduced later in the treatment plan to manage recurring hyperkalemia by enhancing renal potassium excretion.

Potassium binders such as patiromer act in the GI tract, effectively reducing serum potassium levels although with a slower onset of action than furosemide, generally taking hours to days to exert its effect. Both medications illustrate a tailored approach to managing potassium levels, adapted to the evolving needs and renal function of the patient. The last method is using hemodialysis—by far the most rapid method to remove potassium, but also the most invasive. The different methods of treating hyperkalemia are summarized in Table 2. This patient required multiple days of hemodialysis to completely correct the electrolyte disorder. Upon discharge, the patient continued oral furosemide 40 mg daily and eventually discontinued hemodialysis due to stable renal function.

Often, after correcting an inciting event, potassium stores in the body eventually stabilize and do not require additional follow-up. Patients prone to hyperkalemia should be thoroughly educated on medications to avoid (NSAIDs, ACEIs/ARBs, trimethoprim), an adequate low potassium diet, and symptoms that may warrant medical attention.¹⁹

Conclusions

This case illustrates the importance of recognizing the spectrum of manifestations of hyperkalemia, which ranged from muscle weakness to cardiac dysrhythmias. Management strategies for the patient included stabilization of cardiac membranes, potassium shifting, and potassium removal, each tailored to the patient’s individual clinical findings.

The case further illustrates the critical role of continuous monitoring and dynamic adjustment of therapeutic strategies in response to evolving clinical and laboratory findings. The initial and subsequent ECGs, alongside laboratory tests, were instrumental in guiding the adjustments needed in the treatment regimen, ensuring both the efficacy and safety of the interventions. This proactive approach can mitigate the risk of recurrent hyperkalemia and its complications.

Hyperkalemia involves elevated serum potassium levels (> 5.0 mEq/L) and represents an important electrolyte disturbance due to its potentially severe consequences, including cardiac effects that can lead to dysrhythmia and even asystole and death.^1,2 In a US Medicare population, the prevalence of hyperkalemia has been estimated at 2.7% and is associated with substantial health care costs.³ The prevalence is even more marked in patients with preexisting conditions such as chronic kidney disease (CKD) and heart failure.^4,5

Hyperkalemia can result from multiple factors, including impaired renal function, adrenal disease, adverse drug reactions of angiotensin-converting enzyme inhibitors (ACEIs) and other medications, and heritable mutations.⁶ Hyperkalemia poses a considerable clinical risk, associated with adverse outcomes such as myocardial infarction and increased mortality in patients with CKD.^5,7,8 Electrocardiographic (ECG) changes associated with hyperkalemia play a vital role in guiding clinical decisions and treatment strategies.⁹ Understanding the pathophysiology, risk factors, and consequences of hyperkalemia, as well as the significance of ECG changes in its management, is essential for health care practitioners.

Case Presentation

An 81-year-old Hispanic man with a history of hypertension, hypothyroidism, gout, and CKD stage 3B presented to the emergency department with progressive weakness resulting in falls and culminating in an inability to ambulate independently. Additional symptoms included nausea, diarrhea, and myalgia. His vital signs were notable for a pulse of 41 beats/min. The physical examination was remarkable for significant weakness of the bilateral upper extremities, inability to bear his own weight, and bilateral lower extremity edema. His initial ECG upon arrival showed bradycardia with wide QRS, absent P waves, and peaked T waves (Figure 1a). These findings differed from his baseline ECG taken 1 year earlier, which showed sinus rhythm with premature atrial complexes and an old right bundle branch block (Figure 1b).

Medication review revealed that the patient was currently prescribed 100 mg allopurinol daily, 2.5 mg amlodipine daily, 10 mg atorvastatin at bedtime, 4 mg doxazosin daily, 112 mcg levothyroxine daily, 100 mg losartan daily, 25 mg metoprolol daily, and 0.4 mg tamsulosin daily. The patient had also been taking over-the-counter indomethacin for knee pain.

Based on the ECG results, he was treated with 0.083%/6 mL nebulized albuterol, 4.65 Mq/250 mL saline solution intravenous (IV) calcium gluconate, 10 units IV insulin with concomitant 50%/25 mL IV dextrose and 8.4 g of oral patiromer suspension. IV furosemide was held due to concern for renal function. The decision to proceed with hemodialysis was made. Repeat laboratory tests were performed, and an ECG obtained after treatment initiation but prior to hemodialysis demonstrated improvement of rate and T wave shortening (Figure 1c). The serum potassium level dropped from 9.8 mEq/L to 7.9 mEq/L (reference range, 3.5-5.0 mEq/L) (Table 1).

In addition to hemodialysis, sodium zirconium 10 g orally 3 times daily was added. Laboratory test results and an ECG was performed after dialysis continued to demonstrate improvement (Figure 1d). The patient’s potassium level decreased to 5.8 mEq/L, with the ECG demonstrating stability of heart rate and further improvement of the PR interval, QRS complex, and T waves.

Despite the established treatment regimen, potassium levels again rose to 6.7 mEq/L, but there were no significant changes in the ECG, and thus no medication changes were made (Figure 1e). Subsequent monitoring demonstrated a further increase in potassium to 7.4 mEq/L, with an ECG demonstrating a return to the baseline of 1 year prior. The patient underwent hemodialysis again and was given oral furosemide 60 mg every 12 hours. The potassium concentration after dialysis decreased to 4.7 mEq/L and remained stable, not going above 5.0 mEq/L on subsequent monitoring. The patient had resolution of all symptoms and was discharged.

Discussion

We have described in detail the presentation of each pathology and mechanisms of each treatment, starting with the patient’s initial condition that brought him to the emergency room—muscle weakness. Skeletal muscle weakness is a common manifestation of hyperkalemia, occurring in 20% to 40% of cases, and is more prevalent in severe elevations of potassium. Rarely, the weakness can progress to flaccid paralysis of the patient’s extremities and, in extreme cases, the diaphragm.

Muscle weakness progression occurs in a manner that resembles Guillain-Barré syndrome, starting in the lower extremities and ascending toward the upper extremities.¹⁰ This is known as secondary hyperkalemic periodic paralysis. Hyperkalemia lowers the transmembrane gradient in neurons, leading to neuronal depolarization independent of the degree of hyperkalemia. If the degree of hyperkalemia is large enough, this depolarization inactivates voltage-gated sodium channels, making neurons refractory to excitation. Electromyographical studies have shown reduction in the compounded muscle action potential.¹¹ The transient nature of this paralysis is reflected by rapid correction of weakness and paralysis when the electrolyte disorder is corrected.

The patient in this case also presented with bradycardia. The ECG manifestations of hyperkalemia can include atrial asystole, intraventricular conduction disturbances, peaked T waves, and widened QRS complexes. However, some patients with renal insufficiency may not exhibit ECG changes despite significantly elevated serum potassium levels.¹²

The severity of hyperkalemia is crucial in determining the associated ECG changes, with levels > 6.0 mEq/L presenting with abnormalities.¹³ ECG findings alone may not always accurately reflect the severity of hyperkalemia, as up to 60% of patients with potassium levels > 6.0 mEq/L may not show ECG changes.¹⁴ Additionally, extreme hyperkalemia can lead to inconsistent ECG findings, making it challenging to rely solely on ECG for diagnosis and monitoring.⁸ The level of potassium that causes these effects varies widely through patient populations.

The main mechanism by which hyperkalemia affects the heart’s conduction system is through voltage differences across the conduction fibers and eventual steady-state inactivation of sodium channels. This combination of mechanisms shortens the action potential duration, allowing more cardiomyocytes to undergo synchronized depolarization. This amalgamation of cardiomyocytes repolarizing can be reflected on ECGs as peaked T waves. As the action potential decreases, there is a period during which cardiomyocytes are prone to tachyarrhythmias and ventricular fibrillation.

A reduced action potential may lead to increased rates of depolarization and thus conduction, which in some scenarios may increase heart rate. As the levels of potassium rise, intracellular accumulation impedes the entry of sodium by decreasing the cation gradient across the cell membrane. This effectively slows the sinus nodes and prolongs the QRS by slowing the overall propagation of action potentials. By this mechanism, conduction delays, blocks, or asystole are manifested. The patient in this case showed conduction delays, peaked T waves, and disappearance of P waves when he first arrived.

Hyperkalemia Treatment

Hyperkalemia develops most commonly due to acute or chronic kidney diseases, as was the case with this patient. The patient’s hyperkalemia was also augmented by the use of nonsteroidal anti-inflammatory drugs (NSAIDs), which can directly affect renal function. A properly functioning kidney is responsible for excretion of up to 90% of ingested potassium, while the remainder is excreted through the gastrointestinal (GI) tract. Definitive treatment of hyperkalemia is mitigated primarily through these 2 organ systems. The treatment also includes transitory mechanisms of potassium reduction. The goal of each method is to preserve the action potential of cardiomyocytes and myocytes. This patient presented with acute symptomatic hyperkalemia and received various medications to acutely, transitorily, and definitively treat it.

Initial therapy included calcium gluconate, which functions to stabilize the myocardial cell membrane. Hyperkalemia decreases the resting membrane action potential of excitable cells and predisposes them to early depolarization and thus dysrhythmias. Calcium decreases the threshold potential across cells and offsets the overall gradient back to near normal levels.¹⁵ Calcium can be delivered through calcium gluconate or calcium chloride. Calcium chloride is not preferred because extravasation can cause pain, blistering and tissue ischemia. Central venous access is required, potentially delaying prompt treatment. Calcium acts rapidly after administration—within 1 to 3 minutes—but only lasts 30 to 60 minutes.¹⁶ Administration of calcium gluconate can be repeated as often as necessary, but patients must be monitored for adverse effects of calcium such as nausea, abdominal pain, polydipsia, polyuria, muscle weakness, and paresthesia. Care must be taken when patients are taking digoxin, because calcium may potentiate toxicity.¹⁷ Although calcium provides immediate benefits it does little to correct the underlying cause; other medications are required to remove potassium from the body.

Two medication classes have been proven to shift potassium intracellularly. The first are β-2 agonists, such as albuterol/levalbuterol, and the second is insulin. Both work through sodium-potassium-ATPase in a direct manner. β-2 agonists stimulate sodium-potassium-ATPase to move more potassium intracellularly, but these effects have been seen only with high doses of albuterol, typically 4× the standard dose of 0.5 mg in nebulized solutions to achieve decreases in potassium of 0.3 to 0.6 mEq/L, although some trials have reported decreases of 0.62 to 0.98 mEq/L.^15,18 These potassium-lowering effects of β-2 agonist are modest, but can be seen 20 to 30 minutes after administration and persist up to 1 to 2 hours. β-2 agonists are also readily affected by β blockers, which may reduce or negate the desired effect in hyperkalemia. For these reasons, a β-2 agonist should not be given as monotherapy and should be provided as an adjuvant to more independent therapies such as insulin. Insulin binds to receptors on muscle cells and increases the quantity of sodium-potassium-ATPase and glucose transporters. With this increase in influx pumps, surrounding tissues with higher resting membrane potentials can absorb the potassium load, thereby protecting cardiomyocytes.

Potassium Removal

Three methods are currently available to remove potassium from the body: GI excretion, renal excretion, and direct removal from the bloodstream. Under normal physiologic conditions, the kidneys account for about 90% of the body’s ability to remove potassium. Loop diuretics facilitate the removal of potassium by increasing urine production and have an additional potassium-wasting effect. Although the onset of action of loop diuretics is typically 30 to 60 minutes after oral administration, their effect can last for several hours. In this patient, furosemide was introduced later in the treatment plan to manage recurring hyperkalemia by enhancing renal potassium excretion.

Potassium binders such as patiromer act in the GI tract, effectively reducing serum potassium levels although with a slower onset of action than furosemide, generally taking hours to days to exert its effect. Both medications illustrate a tailored approach to managing potassium levels, adapted to the evolving needs and renal function of the patient. The last method is using hemodialysis—by far the most rapid method to remove potassium, but also the most invasive. The different methods of treating hyperkalemia are summarized in Table 2. This patient required multiple days of hemodialysis to completely correct the electrolyte disorder. Upon discharge, the patient continued oral furosemide 40 mg daily and eventually discontinued hemodialysis due to stable renal function.

Often, after correcting an inciting event, potassium stores in the body eventually stabilize and do not require additional follow-up. Patients prone to hyperkalemia should be thoroughly educated on medications to avoid (NSAIDs, ACEIs/ARBs, trimethoprim), an adequate low potassium diet, and symptoms that may warrant medical attention.¹⁹

Conclusions

This case illustrates the importance of recognizing the spectrum of manifestations of hyperkalemia, which ranged from muscle weakness to cardiac dysrhythmias. Management strategies for the patient included stabilization of cardiac membranes, potassium shifting, and potassium removal, each tailored to the patient’s individual clinical findings.

The case further illustrates the critical role of continuous monitoring and dynamic adjustment of therapeutic strategies in response to evolving clinical and laboratory findings. The initial and subsequent ECGs, alongside laboratory tests, were instrumental in guiding the adjustments needed in the treatment regimen, ensuring both the efficacy and safety of the interventions. This proactive approach can mitigate the risk of recurrent hyperkalemia and its complications.

Hyperkalemia involves elevated serum potassium levels (> 5.0 mEq/L) and represents an important electrolyte disturbance due to its potentially severe consequences, including cardiac effects that can lead to dysrhythmia and even asystole and death.^1,2 In a US Medicare population, the prevalence of hyperkalemia has been estimated at 2.7% and is associated with substantial health care costs.³ The prevalence is even more marked in patients with preexisting conditions such as chronic kidney disease (CKD) and heart failure.^4,5

Hyperkalemia can result from multiple factors, including impaired renal function, adrenal disease, adverse drug reactions of angiotensin-converting enzyme inhibitors (ACEIs) and other medications, and heritable mutations.⁶ Hyperkalemia poses a considerable clinical risk, associated with adverse outcomes such as myocardial infarction and increased mortality in patients with CKD.^5,7,8 Electrocardiographic (ECG) changes associated with hyperkalemia play a vital role in guiding clinical decisions and treatment strategies.⁹ Understanding the pathophysiology, risk factors, and consequences of hyperkalemia, as well as the significance of ECG changes in its management, is essential for health care practitioners.

Case Presentation

An 81-year-old Hispanic man with a history of hypertension, hypothyroidism, gout, and CKD stage 3B presented to the emergency department with progressive weakness resulting in falls and culminating in an inability to ambulate independently. Additional symptoms included nausea, diarrhea, and myalgia. His vital signs were notable for a pulse of 41 beats/min. The physical examination was remarkable for significant weakness of the bilateral upper extremities, inability to bear his own weight, and bilateral lower extremity edema. His initial ECG upon arrival showed bradycardia with wide QRS, absent P waves, and peaked T waves (Figure 1a). These findings differed from his baseline ECG taken 1 year earlier, which showed sinus rhythm with premature atrial complexes and an old right bundle branch block (Figure 1b).

Medication review revealed that the patient was currently prescribed 100 mg allopurinol daily, 2.5 mg amlodipine daily, 10 mg atorvastatin at bedtime, 4 mg doxazosin daily, 112 mcg levothyroxine daily, 100 mg losartan daily, 25 mg metoprolol daily, and 0.4 mg tamsulosin daily. The patient had also been taking over-the-counter indomethacin for knee pain.

Based on the ECG results, he was treated with 0.083%/6 mL nebulized albuterol, 4.65 Mq/250 mL saline solution intravenous (IV) calcium gluconate, 10 units IV insulin with concomitant 50%/25 mL IV dextrose and 8.4 g of oral patiromer suspension. IV furosemide was held due to concern for renal function. The decision to proceed with hemodialysis was made. Repeat laboratory tests were performed, and an ECG obtained after treatment initiation but prior to hemodialysis demonstrated improvement of rate and T wave shortening (Figure 1c). The serum potassium level dropped from 9.8 mEq/L to 7.9 mEq/L (reference range, 3.5-5.0 mEq/L) (Table 1).

In addition to hemodialysis, sodium zirconium 10 g orally 3 times daily was added. Laboratory test results and an ECG was performed after dialysis continued to demonstrate improvement (Figure 1d). The patient’s potassium level decreased to 5.8 mEq/L, with the ECG demonstrating stability of heart rate and further improvement of the PR interval, QRS complex, and T waves.

Despite the established treatment regimen, potassium levels again rose to 6.7 mEq/L, but there were no significant changes in the ECG, and thus no medication changes were made (Figure 1e). Subsequent monitoring demonstrated a further increase in potassium to 7.4 mEq/L, with an ECG demonstrating a return to the baseline of 1 year prior. The patient underwent hemodialysis again and was given oral furosemide 60 mg every 12 hours. The potassium concentration after dialysis decreased to 4.7 mEq/L and remained stable, not going above 5.0 mEq/L on subsequent monitoring. The patient had resolution of all symptoms and was discharged.

Discussion

We have described in detail the presentation of each pathology and mechanisms of each treatment, starting with the patient’s initial condition that brought him to the emergency room—muscle weakness. Skeletal muscle weakness is a common manifestation of hyperkalemia, occurring in 20% to 40% of cases, and is more prevalent in severe elevations of potassium. Rarely, the weakness can progress to flaccid paralysis of the patient’s extremities and, in extreme cases, the diaphragm.

Muscle weakness progression occurs in a manner that resembles Guillain-Barré syndrome, starting in the lower extremities and ascending toward the upper extremities.¹⁰ This is known as secondary hyperkalemic periodic paralysis. Hyperkalemia lowers the transmembrane gradient in neurons, leading to neuronal depolarization independent of the degree of hyperkalemia. If the degree of hyperkalemia is large enough, this depolarization inactivates voltage-gated sodium channels, making neurons refractory to excitation. Electromyographical studies have shown reduction in the compounded muscle action potential.¹¹ The transient nature of this paralysis is reflected by rapid correction of weakness and paralysis when the electrolyte disorder is corrected.

The patient in this case also presented with bradycardia. The ECG manifestations of hyperkalemia can include atrial asystole, intraventricular conduction disturbances, peaked T waves, and widened QRS complexes. However, some patients with renal insufficiency may not exhibit ECG changes despite significantly elevated serum potassium levels.¹²

The severity of hyperkalemia is crucial in determining the associated ECG changes, with levels > 6.0 mEq/L presenting with abnormalities.¹³ ECG findings alone may not always accurately reflect the severity of hyperkalemia, as up to 60% of patients with potassium levels > 6.0 mEq/L may not show ECG changes.¹⁴ Additionally, extreme hyperkalemia can lead to inconsistent ECG findings, making it challenging to rely solely on ECG for diagnosis and monitoring.⁸ The level of potassium that causes these effects varies widely through patient populations.

The main mechanism by which hyperkalemia affects the heart’s conduction system is through voltage differences across the conduction fibers and eventual steady-state inactivation of sodium channels. This combination of mechanisms shortens the action potential duration, allowing more cardiomyocytes to undergo synchronized depolarization. This amalgamation of cardiomyocytes repolarizing can be reflected on ECGs as peaked T waves. As the action potential decreases, there is a period during which cardiomyocytes are prone to tachyarrhythmias and ventricular fibrillation.

A reduced action potential may lead to increased rates of depolarization and thus conduction, which in some scenarios may increase heart rate. As the levels of potassium rise, intracellular accumulation impedes the entry of sodium by decreasing the cation gradient across the cell membrane. This effectively slows the sinus nodes and prolongs the QRS by slowing the overall propagation of action potentials. By this mechanism, conduction delays, blocks, or asystole are manifested. The patient in this case showed conduction delays, peaked T waves, and disappearance of P waves when he first arrived.

Hyperkalemia Treatment

Hyperkalemia develops most commonly due to acute or chronic kidney diseases, as was the case with this patient. The patient’s hyperkalemia was also augmented by the use of nonsteroidal anti-inflammatory drugs (NSAIDs), which can directly affect renal function. A properly functioning kidney is responsible for excretion of up to 90% of ingested potassium, while the remainder is excreted through the gastrointestinal (GI) tract. Definitive treatment of hyperkalemia is mitigated primarily through these 2 organ systems. The treatment also includes transitory mechanisms of potassium reduction. The goal of each method is to preserve the action potential of cardiomyocytes and myocytes. This patient presented with acute symptomatic hyperkalemia and received various medications to acutely, transitorily, and definitively treat it.

Initial therapy included calcium gluconate, which functions to stabilize the myocardial cell membrane. Hyperkalemia decreases the resting membrane action potential of excitable cells and predisposes them to early depolarization and thus dysrhythmias. Calcium decreases the threshold potential across cells and offsets the overall gradient back to near normal levels.¹⁵ Calcium can be delivered through calcium gluconate or calcium chloride. Calcium chloride is not preferred because extravasation can cause pain, blistering and tissue ischemia. Central venous access is required, potentially delaying prompt treatment. Calcium acts rapidly after administration—within 1 to 3 minutes—but only lasts 30 to 60 minutes.¹⁶ Administration of calcium gluconate can be repeated as often as necessary, but patients must be monitored for adverse effects of calcium such as nausea, abdominal pain, polydipsia, polyuria, muscle weakness, and paresthesia. Care must be taken when patients are taking digoxin, because calcium may potentiate toxicity.¹⁷ Although calcium provides immediate benefits it does little to correct the underlying cause; other medications are required to remove potassium from the body.

Two medication classes have been proven to shift potassium intracellularly. The first are β-2 agonists, such as albuterol/levalbuterol, and the second is insulin. Both work through sodium-potassium-ATPase in a direct manner. β-2 agonists stimulate sodium-potassium-ATPase to move more potassium intracellularly, but these effects have been seen only with high doses of albuterol, typically 4× the standard dose of 0.5 mg in nebulized solutions to achieve decreases in potassium of 0.3 to 0.6 mEq/L, although some trials have reported decreases of 0.62 to 0.98 mEq/L.^15,18 These potassium-lowering effects of β-2 agonist are modest, but can be seen 20 to 30 minutes after administration and persist up to 1 to 2 hours. β-2 agonists are also readily affected by β blockers, which may reduce or negate the desired effect in hyperkalemia. For these reasons, a β-2 agonist should not be given as monotherapy and should be provided as an adjuvant to more independent therapies such as insulin. Insulin binds to receptors on muscle cells and increases the quantity of sodium-potassium-ATPase and glucose transporters. With this increase in influx pumps, surrounding tissues with higher resting membrane potentials can absorb the potassium load, thereby protecting cardiomyocytes.

Potassium Removal

Three methods are currently available to remove potassium from the body: GI excretion, renal excretion, and direct removal from the bloodstream. Under normal physiologic conditions, the kidneys account for about 90% of the body’s ability to remove potassium. Loop diuretics facilitate the removal of potassium by increasing urine production and have an additional potassium-wasting effect. Although the onset of action of loop diuretics is typically 30 to 60 minutes after oral administration, their effect can last for several hours. In this patient, furosemide was introduced later in the treatment plan to manage recurring hyperkalemia by enhancing renal potassium excretion.

Potassium binders such as patiromer act in the GI tract, effectively reducing serum potassium levels although with a slower onset of action than furosemide, generally taking hours to days to exert its effect. Both medications illustrate a tailored approach to managing potassium levels, adapted to the evolving needs and renal function of the patient. The last method is using hemodialysis—by far the most rapid method to remove potassium, but also the most invasive. The different methods of treating hyperkalemia are summarized in Table 2. This patient required multiple days of hemodialysis to completely correct the electrolyte disorder. Upon discharge, the patient continued oral furosemide 40 mg daily and eventually discontinued hemodialysis due to stable renal function.

Often, after correcting an inciting event, potassium stores in the body eventually stabilize and do not require additional follow-up. Patients prone to hyperkalemia should be thoroughly educated on medications to avoid (NSAIDs, ACEIs/ARBs, trimethoprim), an adequate low potassium diet, and symptoms that may warrant medical attention.¹⁹

Conclusions

This case illustrates the importance of recognizing the spectrum of manifestations of hyperkalemia, which ranged from muscle weakness to cardiac dysrhythmias. Management strategies for the patient included stabilization of cardiac membranes, potassium shifting, and potassium removal, each tailored to the patient’s individual clinical findings.

The case further illustrates the critical role of continuous monitoring and dynamic adjustment of therapeutic strategies in response to evolving clinical and laboratory findings. The initial and subsequent ECGs, alongside laboratory tests, were instrumental in guiding the adjustments needed in the treatment regimen, ensuring both the efficacy and safety of the interventions. This proactive approach can mitigate the risk of recurrent hyperkalemia and its complications.