Prognostic values of 4 risk scores in Chinese patients with chest pain: Prospective 2-centre cohort study

Abstract

Copyright © 2016 the Author(s). Published by Wolters Kluwer Health, Inc. Four risk scores for stratifying patients with chest pain presenting to emergency departments (EDs) (namely Thrombolysis in myocardial infarction [TIMI], Global registry for acute coronary events [GRACE], Banach and HEART) have been developed in Western settings but have never been compared and validated in Chinese patients. We aimed to find out to the number of MACE within 7 days, 30 days, and 6 months after initial ED presentation, and also to compare the prognostic performance of these scores in Chinese patients with suspected cardiac chest pain (CCP) to predict 7-day, 30-day, and 6-month major adverse cardiac events (MACE). A prospective 2-center observational cohort study of consecutive patients presenting with chest pain to the EDs of 2 university hospitals in Guangdong and Hong Kong from 17 March 2012 to 14 August 2013 was conducted. Patients aged ≥18 years with suspected CCP but without ST-segment elevation myocardial infarction (STEMI) were recruited. Of 833 enrolled patients (mean age 65.1 years, SD14.5; 55.6% males), 121 (14.5%) experienced MACE within 6 months (4.8% with safety outcomes and 10.3% with effectiveness outcomes). The HEART score had the largest area under the receiver operating characteristic (ROC) curve for predicting MACE at 7-day, 30-day, and 6-month follow-up [area under curve (AUC)=0.731, 0.726, and 0.747, respectively. The HEART score also had the largest AUC for predicting effectiveness outcome (AUC=0.715, 0.704, and 0.721, respectively). However, there was no significant difference in AUC between HEART and TIMI scores. Banach had the largest AUC for predicting safety outcome (AUC=0.856, 0.837, and 0.850, respectively). The HEART score performed better than the GRACE and Banach scores to predict total MACE and effectiveness outcome in Chinese patients with suspected CCP, whereas the Banach score best predicted safety outcomes.