Predicting In-Hospital Mortality in Patients With Acute Myocardial Infarction

被引:183
作者
McNamara, Robert L. [1 ]
Kennedy, Kevin F. [2 ]
Cohen, David J. [3 ,4 ]
Diercks, Deborah B. [5 ]
Moscucci, Mauro [6 ,7 ]
Ramee, Stephen [8 ]
Wang, Tracy Y. [9 ,10 ]
Connolly, Traci [11 ]
Spertus, John A. [3 ,4 ]
机构
[1] Yale Univ, Sch Med, New Haven, CT USA
[2] Mid Amer Heart Inst, Kansas City, MO USA
[3] St Lukes Mid Amer Heart Inst, Kansas City, MO USA
[4] Univ Missouri, Sch Med, Kansas City, MO 64108 USA
[5] Univ Texas Southwestern Med Ctr, Dallas, TX USA
[6] Sinai Hosp Baltimore, Baltimore, MD USA
[7] Univ Michigan Hlth Syst, Ann Arbor, MI USA
[8] Ochsner Med Ctr, New Orleans, LA USA
[9] Duke Univ, Med Ctr, Durham, NC USA
[10] Duke Clin Res Inst, Durham, NC USA
[11] Amer Coll Radiol, Washington, DC USA
关键词
cardiac arrest; cardiogenic shock; creatinine clearance; model; risk prediction; systolic blood pressure; PERCUTANEOUS CORONARY INTERVENTION; CARDIOVASCULAR DATA REGISTRY; AMERICAN-HEART-ASSOCIATION; TIMI RISK SCORE; OUTCOMES NETWORK; QUALITY; ADJUSTMENT; PROGNOSIS; DISEASE; FIBRILLATION;
D O I
10.1016/j.jacc.2016.05.049
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
BACKGROUND 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. (C) 2016 by the American College of Cardiology Foundation.
引用
收藏
页码:626 / 635
页数:10
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