Predictors of Hospital Mortality in the Global Registry of Acute Coronary Events

TLDR

Risk estimation is essential for guiding the management of acute coronary syndromes. The study aimed to create a simple in‑hospital mortality risk model for all ACS patients in routine practice. A multivariable logistic regression was built on 11,389 ACS patients (509 deaths) from GRACE and validated in a 3,972‑patient GRACE cohort and 12,142 GUSTO‑IIb patients. Eight independent predictors—age, Killip class, systolic blood pressure, ST‑segment deviation, cardiac arrest, serum creatinine, initial enzyme positivity, and heart rate—explained 89.9 % of risk, with c‑statistics of 0.83, 0.84, and 0.79 in derivation, confirmation, and GUSTO‑IIb datasets, respectively.

Abstract

<h3>Background</h3> Management of acute coronary syndromes (ACS) should be guided by an estimate of patient risk. <h3>Objective</h3> To develop a simple model to assess the risk for in-hospital mortality for the entire spectrum of ACS treated in general clinical practice. <h3>Methods</h3> A multivariable logistic regression model was developed using 11 389 patients (including 509 in-hospital deaths) with ACS with and without ST-segment elevation enrolled in the Global Registry of Acute Coronary Events (GRACE) from April 1, 1999, through March 31, 2001. Validation data sets included a subsequent cohort of 3972 patients enrolled in GRACE and 12 142 in the Global Use of Strategies to Open Occluded Coronary Arteries IIb (GUSTO-IIb) trial. <h3>Results</h3> The following 8 independent risk factors accounted for 89.9% of the prognostic information: age (odds ratio [OR], 1.7 per 10 years), Killip class (OR, 2.0 per class), systolic blood pressure (OR, 1.4 per 20-mm Hg decrease), ST-segment deviation (OR, 2.4), cardiac arrest during presentation (OR, 4.3), serum creatinine level (OR, 1.2 per 1-mg/dL [88.4-µmol/L] increase), positive initial cardiac enzyme findings (OR, 1.6), and heart rate (OR, 1.3 per 30-beat/min increase). The discrimination ability of the simplified model was excellent with c statistics of 0.83 in the derived database, 0.84 in the confirmation GRACE data set, and 0.79 in the GUSTO-IIb database. <h3>Conclusions</h3> Across the entire spectrum of ACS and in general clinical practice, this model provides excellent ability to assess the risk for death and can be used as a simple nomogram to estimate risk in individual patients.