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An updated risk model based on data from patients presenting after acute myocardial infarction to a broad spectrum of U.S. hospitals appears to predict with a high degree of accuracy which patients are at the greatest risk for in-hospital mortality, investigators say.
Created from data on more than 240,000 patients presenting to one of 655 U.S. hospitals in 2012 and 2013 following ST-segment elevation myocardial infarction (STEMI) or non–ST-segment elevation MI (NSTEMI), the model identified the following independent risk factors for in-hospital mortality: age, heart rate, systolic blood pressure, presentation to the hospital after cardiac arrest, presentation in cardiogenic shock, presentation in heart failure, presentation with STEMI, creatinine clearance, and troponin ratio, reported Robert L. McNamara, MD, of Yale University, New Haven, Conn.
The investigators are participants in the ACTION (Acute Coronary Treatment and Intervention Outcomes Network) Registry–GWTG (Get With the Guidelines).
“The new ACTION Registry–GWTG in-hospital mortality risk model and risk score represent robust, parsimonious, and contemporary risk adjustment methodology for use in routine clinical care and hospital quality assessment. The addition of risk adjustment for patients presenting after cardiac arrest is critically important and enables a fairer assessment across hospitals with varied case mix,” they wrote (J Am Coll Cardiol. 2016 Aug 1;68[6]:626-35).
The revised risk model has the potential to facilitate hospital quality assessments and help investigators to identify specific factors that could help clinicians even further lower death rates, the investigators write.
Further mortality reductions?
Although improvements in care of patients with acute MI over the last several decades have driven the in-hospital death rate from 29% in 1969 down to less than 7% today, there are still more than 100,000 AMI-related in-hospital deaths in the United States annually, with wide variations across hospitals, Dr. McNamara and colleagues noted.
A previous risk model published by ACTION Registry–GWTG members included data on patients treated at 306 U.S. hospitals and provided a simple, validated in-hospital mortality and risk score.
Since that model was published, however, the dataset was expanded to include patients presenting after cardiac arrest at the time of AMI presentation.
“Being able to adjust for cardiac arrest is critical because it is a well-documented predictor of mortality. Moreover, continued improvement in AMI care mandates periodic updates to the risk models so that hospitals can assess their quality as contemporary care continues to evolve,” the authors wrote.
To see whether they could develop a new and improved model and risk score, they analyzed data on 243,440 patients treated at one of 655 hospitals in the voluntary network. Data on 145,952 patients (60% of the total), 57,039 of whom presented with STEMI, and 88,913 of whom presented with NSTEMI, were used to for the derivation sample.
Data on the remaining 97,488 (38,060 with STEMI and 59,428 with NSTEMI) were used to create the validation sample.
The authors found that for the total cohort, the in-hospital mortality rate was 4.6%. In multivariate models controlled for demographic and clinical factors, independent risk factors significantly associated with in-hospital mortality (validation cohort) were:
• Presentation after cardiac arrest (odds ratio, 5.15).
• Presentation in cardiogenic shock (OR, 4.22).
• Presentation in heart failure (OR, 1.83).
• STEMI on electrocardiography (OR, 1.81).
• Age, per 5 years (OR, 1.24).
• Systolic BP, per 10 mm Hg decrease (OR, 1.19).
• Creatinine clearance per 5/mL/min/1.73 m2 decrease (OR, 1.11).
• Heart rate per 10 beats/min (OR, 1.09).
• Troponin ratio, per 5 units (OR, 1.05).
The 95% confidence intervals for all of the above factors were significant.
The C-statistic, a standard measure of the predictive accuracy of a logistic regression model, was 0.88, indicating that the final ACTION Registry–GWTG in-hospital mortality model had a high level of discrimination in both the derivation and validation populations, the authors state.
The ACTION Registry–GWTG is a Program of the American College of Cardiology and the American Heart Association, with funding from Schering-Plough and the Bristol-Myers Squibb/Sanofi Pharmaceutical Partnership. Dr. McNamara serves on a clinical trials endpoint adjudication committee for Pfizer. Other coauthors reported multiple financial relationships with pharmaceutical and medical device companies.
Data analyses for the risk models developed by the ACTION Registry generally showed good accuracy and precision. The calibration information showed that patients with a cardiac arrest experienced much greater risk for mortality than did the other major groups (STEMI, NSTEMI, or no cardiac arrest). Until now, clinicians and researchers have generally used either the TIMI [Thrombolysis in Myocardial Infarction] or GRACE [Global Registry of Acute Coronary Events] score to guide therapeutic decisions. With the advent of the ACTION score, which appears to be most helpful for patients with moderate to severe disease, and the HEART [history, ECG, age, risk factor, troponin] score, which targets care for patients with minimal to mild disease, there are other options. Recently, the DAPT (Dual Antiplatelet Therapy) investigators published a prediction algorithm that provides yet another prognostic score to assess risk of ischemic events and risk of bleeding in patients who have undergone percutaneous coronary intervention. The key variables in the DAPT score are age, cigarette smoking, diabetes, MI at presentation, previous percutaneous coronary intervention or previous MI, use of a paclitaxel-eluting stent, stent diameter of less than 3 mm, heart failure or reduced ejection fraction, and use of a vein graft stent.
A comprehensive cross validation and comparison across at least some of the algorithms – TIMI, GRACE, HEART, DAPT, and ACTION – would help at this point. Interventions and decision points have evolved over the past 15 years, and evaluation of relatively contemporary data would be especially helpful. For example, the HEART score is likely to be used in situations in which the negative predictive capabilities are most important. The ACTION score is likely to be most useful in severely ill patients and to provide guidance for newer interventions. If detailed information concerning stents is available, then the DAPT score should prove helpful.
It is likely that one score does not fit all. Each algorithm provides a useful summary of risk to help guide decision making for patients with ischemic symptoms, depending on the severity of the signs and symptoms at presentation and the duration of the follow-up interval. Consensus building would help to move this field forward for hospital-based management of patients evaluated for cardiac ischemia.
Peter W.F. Wilson, MD, of the Atlanta VAMC and Emory Clinical Cardiovascular Research
Institute, Atlanta; and Ralph B. D’Agostino Sr., PhD, of the department of mathematics and statistics, Boston University, made these comments in an accompanying editorial (J Am Coll Cardiol. 2016 Aug 1;68[6]:636-8). They reported no relevant disclosures.
Data analyses for the risk models developed by the ACTION Registry generally showed good accuracy and precision. The calibration information showed that patients with a cardiac arrest experienced much greater risk for mortality than did the other major groups (STEMI, NSTEMI, or no cardiac arrest). Until now, clinicians and researchers have generally used either the TIMI [Thrombolysis in Myocardial Infarction] or GRACE [Global Registry of Acute Coronary Events] score to guide therapeutic decisions. With the advent of the ACTION score, which appears to be most helpful for patients with moderate to severe disease, and the HEART [history, ECG, age, risk factor, troponin] score, which targets care for patients with minimal to mild disease, there are other options. Recently, the DAPT (Dual Antiplatelet Therapy) investigators published a prediction algorithm that provides yet another prognostic score to assess risk of ischemic events and risk of bleeding in patients who have undergone percutaneous coronary intervention. The key variables in the DAPT score are age, cigarette smoking, diabetes, MI at presentation, previous percutaneous coronary intervention or previous MI, use of a paclitaxel-eluting stent, stent diameter of less than 3 mm, heart failure or reduced ejection fraction, and use of a vein graft stent.
A comprehensive cross validation and comparison across at least some of the algorithms – TIMI, GRACE, HEART, DAPT, and ACTION – would help at this point. Interventions and decision points have evolved over the past 15 years, and evaluation of relatively contemporary data would be especially helpful. For example, the HEART score is likely to be used in situations in which the negative predictive capabilities are most important. The ACTION score is likely to be most useful in severely ill patients and to provide guidance for newer interventions. If detailed information concerning stents is available, then the DAPT score should prove helpful.
It is likely that one score does not fit all. Each algorithm provides a useful summary of risk to help guide decision making for patients with ischemic symptoms, depending on the severity of the signs and symptoms at presentation and the duration of the follow-up interval. Consensus building would help to move this field forward for hospital-based management of patients evaluated for cardiac ischemia.
Peter W.F. Wilson, MD, of the Atlanta VAMC and Emory Clinical Cardiovascular Research
Institute, Atlanta; and Ralph B. D’Agostino Sr., PhD, of the department of mathematics and statistics, Boston University, made these comments in an accompanying editorial (J Am Coll Cardiol. 2016 Aug 1;68[6]:636-8). They reported no relevant disclosures.
Data analyses for the risk models developed by the ACTION Registry generally showed good accuracy and precision. The calibration information showed that patients with a cardiac arrest experienced much greater risk for mortality than did the other major groups (STEMI, NSTEMI, or no cardiac arrest). Until now, clinicians and researchers have generally used either the TIMI [Thrombolysis in Myocardial Infarction] or GRACE [Global Registry of Acute Coronary Events] score to guide therapeutic decisions. With the advent of the ACTION score, which appears to be most helpful for patients with moderate to severe disease, and the HEART [history, ECG, age, risk factor, troponin] score, which targets care for patients with minimal to mild disease, there are other options. Recently, the DAPT (Dual Antiplatelet Therapy) investigators published a prediction algorithm that provides yet another prognostic score to assess risk of ischemic events and risk of bleeding in patients who have undergone percutaneous coronary intervention. The key variables in the DAPT score are age, cigarette smoking, diabetes, MI at presentation, previous percutaneous coronary intervention or previous MI, use of a paclitaxel-eluting stent, stent diameter of less than 3 mm, heart failure or reduced ejection fraction, and use of a vein graft stent.
A comprehensive cross validation and comparison across at least some of the algorithms – TIMI, GRACE, HEART, DAPT, and ACTION – would help at this point. Interventions and decision points have evolved over the past 15 years, and evaluation of relatively contemporary data would be especially helpful. For example, the HEART score is likely to be used in situations in which the negative predictive capabilities are most important. The ACTION score is likely to be most useful in severely ill patients and to provide guidance for newer interventions. If detailed information concerning stents is available, then the DAPT score should prove helpful.
It is likely that one score does not fit all. Each algorithm provides a useful summary of risk to help guide decision making for patients with ischemic symptoms, depending on the severity of the signs and symptoms at presentation and the duration of the follow-up interval. Consensus building would help to move this field forward for hospital-based management of patients evaluated for cardiac ischemia.
Peter W.F. Wilson, MD, of the Atlanta VAMC and Emory Clinical Cardiovascular Research
Institute, Atlanta; and Ralph B. D’Agostino Sr., PhD, of the department of mathematics and statistics, Boston University, made these comments in an accompanying editorial (J Am Coll Cardiol. 2016 Aug 1;68[6]:636-8). They reported no relevant disclosures.
An updated risk model based on data from patients presenting after acute myocardial infarction to a broad spectrum of U.S. hospitals appears to predict with a high degree of accuracy which patients are at the greatest risk for in-hospital mortality, investigators say.
Created from data on more than 240,000 patients presenting to one of 655 U.S. hospitals in 2012 and 2013 following ST-segment elevation myocardial infarction (STEMI) or non–ST-segment elevation MI (NSTEMI), the model identified the following independent risk factors for in-hospital mortality: age, heart rate, systolic blood pressure, presentation to the hospital after cardiac arrest, presentation in cardiogenic shock, presentation in heart failure, presentation with STEMI, creatinine clearance, and troponin ratio, reported Robert L. McNamara, MD, of Yale University, New Haven, Conn.
The investigators are participants in the ACTION (Acute Coronary Treatment and Intervention Outcomes Network) Registry–GWTG (Get With the Guidelines).
“The new ACTION Registry–GWTG in-hospital mortality risk model and risk score represent robust, parsimonious, and contemporary risk adjustment methodology for use in routine clinical care and hospital quality assessment. The addition of risk adjustment for patients presenting after cardiac arrest is critically important and enables a fairer assessment across hospitals with varied case mix,” they wrote (J Am Coll Cardiol. 2016 Aug 1;68[6]:626-35).
The revised risk model has the potential to facilitate hospital quality assessments and help investigators to identify specific factors that could help clinicians even further lower death rates, the investigators write.
Further mortality reductions?
Although improvements in care of patients with acute MI over the last several decades have driven the in-hospital death rate from 29% in 1969 down to less than 7% today, there are still more than 100,000 AMI-related in-hospital deaths in the United States annually, with wide variations across hospitals, Dr. McNamara and colleagues noted.
A previous risk model published by ACTION Registry–GWTG members included data on patients treated at 306 U.S. hospitals and provided a simple, validated in-hospital mortality and risk score.
Since that model was published, however, the dataset was expanded to include patients presenting after cardiac arrest at the time of AMI presentation.
“Being able to adjust for cardiac arrest is critical because it is a well-documented predictor of mortality. Moreover, continued improvement in AMI care mandates periodic updates to the risk models so that hospitals can assess their quality as contemporary care continues to evolve,” the authors wrote.
To see whether they could develop a new and improved model and risk score, they analyzed data on 243,440 patients treated at one of 655 hospitals in the voluntary network. Data on 145,952 patients (60% of the total), 57,039 of whom presented with STEMI, and 88,913 of whom presented with NSTEMI, were used to for the derivation sample.
Data on the remaining 97,488 (38,060 with STEMI and 59,428 with NSTEMI) were used to create the validation sample.
The authors found that for the total cohort, the in-hospital mortality rate was 4.6%. In multivariate models controlled for demographic and clinical factors, independent risk factors significantly associated with in-hospital mortality (validation cohort) were:
• Presentation after cardiac arrest (odds ratio, 5.15).
• Presentation in cardiogenic shock (OR, 4.22).
• Presentation in heart failure (OR, 1.83).
• STEMI on electrocardiography (OR, 1.81).
• Age, per 5 years (OR, 1.24).
• Systolic BP, per 10 mm Hg decrease (OR, 1.19).
• Creatinine clearance per 5/mL/min/1.73 m2 decrease (OR, 1.11).
• Heart rate per 10 beats/min (OR, 1.09).
• Troponin ratio, per 5 units (OR, 1.05).
The 95% confidence intervals for all of the above factors were significant.
The C-statistic, a standard measure of the predictive accuracy of a logistic regression model, was 0.88, indicating that the final ACTION Registry–GWTG in-hospital mortality model had a high level of discrimination in both the derivation and validation populations, the authors state.
The ACTION Registry–GWTG is a Program of the American College of Cardiology and the American Heart Association, with funding from Schering-Plough and the Bristol-Myers Squibb/Sanofi Pharmaceutical Partnership. Dr. McNamara serves on a clinical trials endpoint adjudication committee for Pfizer. Other coauthors reported multiple financial relationships with pharmaceutical and medical device companies.
An updated risk model based on data from patients presenting after acute myocardial infarction to a broad spectrum of U.S. hospitals appears to predict with a high degree of accuracy which patients are at the greatest risk for in-hospital mortality, investigators say.
Created from data on more than 240,000 patients presenting to one of 655 U.S. hospitals in 2012 and 2013 following ST-segment elevation myocardial infarction (STEMI) or non–ST-segment elevation MI (NSTEMI), the model identified the following independent risk factors for in-hospital mortality: age, heart rate, systolic blood pressure, presentation to the hospital after cardiac arrest, presentation in cardiogenic shock, presentation in heart failure, presentation with STEMI, creatinine clearance, and troponin ratio, reported Robert L. McNamara, MD, of Yale University, New Haven, Conn.
The investigators are participants in the ACTION (Acute Coronary Treatment and Intervention Outcomes Network) Registry–GWTG (Get With the Guidelines).
“The new ACTION Registry–GWTG in-hospital mortality risk model and risk score represent robust, parsimonious, and contemporary risk adjustment methodology for use in routine clinical care and hospital quality assessment. The addition of risk adjustment for patients presenting after cardiac arrest is critically important and enables a fairer assessment across hospitals with varied case mix,” they wrote (J Am Coll Cardiol. 2016 Aug 1;68[6]:626-35).
The revised risk model has the potential to facilitate hospital quality assessments and help investigators to identify specific factors that could help clinicians even further lower death rates, the investigators write.
Further mortality reductions?
Although improvements in care of patients with acute MI over the last several decades have driven the in-hospital death rate from 29% in 1969 down to less than 7% today, there are still more than 100,000 AMI-related in-hospital deaths in the United States annually, with wide variations across hospitals, Dr. McNamara and colleagues noted.
A previous risk model published by ACTION Registry–GWTG members included data on patients treated at 306 U.S. hospitals and provided a simple, validated in-hospital mortality and risk score.
Since that model was published, however, the dataset was expanded to include patients presenting after cardiac arrest at the time of AMI presentation.
“Being able to adjust for cardiac arrest is critical because it is a well-documented predictor of mortality. Moreover, continued improvement in AMI care mandates periodic updates to the risk models so that hospitals can assess their quality as contemporary care continues to evolve,” the authors wrote.
To see whether they could develop a new and improved model and risk score, they analyzed data on 243,440 patients treated at one of 655 hospitals in the voluntary network. Data on 145,952 patients (60% of the total), 57,039 of whom presented with STEMI, and 88,913 of whom presented with NSTEMI, were used to for the derivation sample.
Data on the remaining 97,488 (38,060 with STEMI and 59,428 with NSTEMI) were used to create the validation sample.
The authors found that for the total cohort, the in-hospital mortality rate was 4.6%. In multivariate models controlled for demographic and clinical factors, independent risk factors significantly associated with in-hospital mortality (validation cohort) were:
• Presentation after cardiac arrest (odds ratio, 5.15).
• Presentation in cardiogenic shock (OR, 4.22).
• Presentation in heart failure (OR, 1.83).
• STEMI on electrocardiography (OR, 1.81).
• Age, per 5 years (OR, 1.24).
• Systolic BP, per 10 mm Hg decrease (OR, 1.19).
• Creatinine clearance per 5/mL/min/1.73 m2 decrease (OR, 1.11).
• Heart rate per 10 beats/min (OR, 1.09).
• Troponin ratio, per 5 units (OR, 1.05).
The 95% confidence intervals for all of the above factors were significant.
The C-statistic, a standard measure of the predictive accuracy of a logistic regression model, was 0.88, indicating that the final ACTION Registry–GWTG in-hospital mortality model had a high level of discrimination in both the derivation and validation populations, the authors state.
The ACTION Registry–GWTG is a Program of the American College of Cardiology and the American Heart Association, with funding from Schering-Plough and the Bristol-Myers Squibb/Sanofi Pharmaceutical Partnership. Dr. McNamara serves on a clinical trials endpoint adjudication committee for Pfizer. Other coauthors reported multiple financial relationships with pharmaceutical and medical device companies.
FROM JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY
Key clinical point: An updated cardiac mortality risk model may help to further reduce in-hospital deaths following acute myocardial infarction.
Major finding: The C-statistic for the model, a measure of predictive accuracy, was 0.88.
Data source: Updated risk model and in-hospital mortality score based on data from 243,440 patients following an AMI in 655 U.S. hospitals.
Disclosures: The ACTION Registry-GWTG is a Program of the American College of Cardiology and the American Heart Association, with funding from Schering-Plough and the Bristol-Myers Squibb/Sanofi Pharmaceutical Partnership. Dr. McNamara serves on a clinical trials endpoint adjudication committee for Pfizer. Other coauthors reported multiple financial relationships with pharmaceutical and medical device companies.
