The Biomarker-Based Prediction Rule for Risk Stratification in Patients with Acute Coronary Syndromes: A Development and Validation Study: Results from BIPass Registry

Abstract

Background: Patients with acute coronary syndromes (ACS), the acute and severe manifestation of coronary artery disease, are highly heterogeneous in their risk for recurrent cardiovascular events. Several biomarkers carry prognostic information for cardiovascular events. However, instruments integrating clinical characteristics and biomarkers to accurately predict the occurrence of cardiovascular events, thus facilitating individualization of ACS care are lacking. Methods: Using data from 4,407 ACS patients enrolled in a prospective and multicenter Chinese cohort, BIomarker-based Prognostic Assessment for Patients with Stable Angina and Acute Coronary Syndromes (BIPass) registry, we developed a risk model to predict the major adverse cardiovascular events (MACE, defined as the composite of cardiac death, myocardial infarction [MI] and ischemic stroke) within 12 months after hospital admission. Validation was performed in 1,409 patients from an independent cohort. Findings: Over 12 months, MACE occurred in 196 patients (incidence rate 5·07 per 100 person- years, 95% CI [4·42-5·81]/100 person-years). Predictors of MACE included N-terminal pro-B-type natriuretic peptide (NT-proBNP) and growth differentiation factor 15 (GDF-15) biomarkers, and clinical variables of age, hypertension, previous MI, previous stroke, Killip class, and heart rate. The developed BIPass risk model displayed excellent discriminative capability (C statistic 0·82, 95% CI 0·78-0·86) and calibration, outperformed GRACE and TIMI risk scores. Similar discrimination, calibration and clinical decision curves were confirmed in the validation cohort. Cumulative rates for MACE demonstrated good separation in BIPass predicted low, intermediate and high-risk groups in both the development and validation cohorts. The BIPass risk model consistently provided enhanced predictions of MACE over a broad-spectrum of ACS and across clinically important subgroups (i.e. age, diagnosis, coronary revascularization, medications). Interpretation: BIPass risk model integrating clinical variables and biomarkers measured at admission is useful to predict risk of 12-month cardiovascular events in ACS, which offers the potential to identify higher-risk patients as candidates for early aggressive treatments. Trial Registration: NCT04044066 Funding Statement: National Key R＆D Program of China (2017YFC0908700, 2017YFC0908703), National S&T Fundamental Resources Investigation Project (2018FY100600, 2018FY100602), Taishan Pandeng Scholar Program of Shandong Province (tspd20181220), Taishan Young Scholar Program of Shandong Province (tsqn20161065, tsqn201812129). Declaration of Interests: None. Ethics Approval Statement: The BIPass study was approved by the research ethics committee of Qilu Hospital, Shandong University which was accepted by all the collaborating hospitals.