title: Predicting In-Hospital Mortality in Patients With Acute Myocardial Infarction.
abstract: 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.
  
  
  
  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.
  
  
  
  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.
  
  
  
  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).
  
  
  
  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.
authors: Yale University School of Medicine, New Haven, Connecticut. Electronic address: robert.mcnamara@yale.edu.
authors: Robert L
authors: RL
authors: McNamara
authors: Mid America Heart Institute, Kansas City, Missouri.
authors: Kevin F
authors: KF
authors: Kennedy
authors: Saint-Luke's Mid America Heart Institute and University of Missouri-Kansas City School of Medicine, Kansas City, Missouri.
authors: David J
authors: DJ
authors: Cohen
authors: University of Texas Southwestern Medical Center, Dallas, Texas.
authors: Deborah B
authors: DB
authors: Diercks
authors: Sinai Hospital of Baltimore, Baltimore, Maryland; University of Michigan Health System, Ann Arbor, Michigan.
authors: Mauro
authors: M
authors: Moscucci
authors: Ochsner Medical Center, New Orleans, Louisiana.
authors: Stephen
authors: S
authors: Ramee
authors: Duke University Medical Center and Duke Clinical Research Institute, Durham, North Carolina.
authors: Tracy Y
authors: TY
authors: Wang
authors: American College of Cardiology, Washington, DC.
authors: Traci
authors: T
authors: Connolly
authors: Saint-Luke's Mid America Heart Institute and University of Missouri-Kansas City School of Medicine, Kansas City, Missouri.
authors: John A
authors: JA
authors: Spertus
mesh_terms: D000368:Aged; D005260:Female; D017052:Hospital Mortality / Q000639:trends; D006801:Humans; D008297:Male; D008875:Middle Aged; D009203:Myocardial Infarction / Q000401:mortality*; D011379:Prognosis; D012042:Registries; D012189:Retrospective Studies; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D014481:United States / Q000453:epidemiology
keywords: cardiac arrest; cardiogenic shock; creatinine clearance; model; risk prediction; systolic blood pressure