Predicting In-Hospital Mortality in Patients With Acute Myocardial Infarction

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• 作者：Robert L. McNamara ; MD ; MHS^a ; ^{robert.mcnamara@yale.edu" class="auth_mail" title="E-mail the corresponding author} ; Kevin F. Kennedy ; MS^b ; David J. Cohen ; MD ; MSc^c ; Deborah B. Diercks ; MD ; MSc^d ; Mauro Moscucci ; MD ; MBA^e ; ^f ; Stephen Ramee ; MD^g ; Tracy Y. Wang ; MD ; MHS ; MSc^h ; Traci Connolly ; BSN ; MSⁱ ; John A. Spertus ; MD ; MPH^c
• 关键词：cardiac arrest ; cardiogenic shock ; creatinine clearance ; model ; risk prediction ; systolic blood pressure
• 刊名：Journal of the American College of Cardiology
• 出版年：2016
• 出版时间：9 August 2016
• 年：2016
• 卷：68
• 期：6
• 页码：626-635
• 全文大小：1198 K

文摘

As a foundation for quality improvement, assessing clinical outcomes across hospitals requires appropriate risk adjustment to account for differences in patient case mix, including presentation after cardiac arrest.

Objectives

The aim of this study was to develop and validate a parsimonious patient-level clinical risk model of in-hospital mortality for contemporary patients with acute myocardial infarction.

Methods

Patient characteristics at the time of presentation in the ACTION (Acute Coronary Treatment and Intervention Outcomes Network) Registry–GWTG (Get With the Guidelines) database from January 2012 through December 2013 were used to develop a multivariate hierarchical logistic regression model predicting in-hospital mortality. The population (243,440 patients from 655 hospitals) was divided into a 60% sample for model derivation, with the remaining 40% used for model validation. A simplified risk score was created to enable prospective risk stratification in clinical care.

Results

The in-hospital mortality rate was 4.6%. Age, heart rate, systolic blood pressure, presentation after cardiac arrest, presentation in cardiogenic shock, presentation in heart failure, presentation with ST-segment elevation myocardial infarction, creatinine clearance, and troponin ratio were all independently associated with in-hospital mortality. The C statistic was 0.88, with good calibration. The model performed well in subgroups based on age; sex; race; transfer status; and the presence of diabetes mellitus, renal dysfunction, cardiac arrest, cardiogenic shock, and ST-segment elevation myocardial infarction. Observed mortality rates varied substantially across risk groups, ranging from 0.4% in the lowest risk group (score <30) to 49.5% in the highest risk group (score >59).

Conclusions

This parsimonious risk model for in-hospital mortality is a valid instrument for risk adjustment and risk stratification in contemporary patients with acute myocardial infarction.