Biomarker-based risk model to predict cardiovascular events in patients with acute coronary syndromes – Results from BIPass registry

The Lancet Regional Health. Western Pacific

Feng Xu¹  Ming Chen¹  Shuo Wang²  Guanghui Chen²  Wei Gao³  Yuguo Chen³  Shuo Wu³  Wen Zheng⁴  Jingjing Ma⁵  Zhi Wan⁶  Derek P. Chew⁷  Guangmei Wang⁷  Jiali Wang⁷  Jiaojiao Pang⁸ 

[1] Key Laboratory of Cardiovascular Remodeling and Function Research, Chinese Ministry of Education and Chinese Ministry of Public Health, Qilu Hospital, Shandong University, Jinan 250012, China;Key Laboratory of Emergency and Critical Care Medicine of Shandong Province, Jinan, 250012, China;Shandong Provincal Clinical Research Center for Emergency and Critical Care Medicine, Jinan 250012, China;Department of Cardiology, Chinese PLA General Hospital, Beijing 100853, China;Department of Cardiology, Peking University First Hospital, Beijing 100034, China;Department of Cardiology, Peking University Third Hospital, Beijing 100191, China;Department of Emergency and Chest Pain Center, Qilu Hospital, Shandong University, Jinan 250012, China;Department of Emergency, Huaxi Hospital, Chengdu 610041, China;

关键词: Acute coronary syndromes;    Prognosis;    Biomarker;    Risk prediction model;   

DOI  :  

来源: DOAJ

【 摘 要 】

Summary:Background: Risk models integrating new biomarkers to predict cardiovascular events in acute coronary syndromes (ACS) are lacking. Therefore, we evaluated the prognostic value of biomarkers in addition to clinical predictors and developed a biomarker-based risk model for major adverse cardiovascular events (MACE) within 12 months after hospital admission with ACS. Methods: Patients (n = 4407) consecutively enrolled from November, 2017 to October, 2019 in three hospitals of a prospective Chinese registry (BIomarker-based Prognostic Assessment for Patients with Stable Angina and Acute Coronary Syndromes, BIPass) were designated as the risk model development cohort. Validation was performed in 1409 patients enrolled in two independent hospitals. Cox proportional hazards regression analysis was used to generate a risk prediction model and evaluate the incremental prognostic value of each biomarker. Findings: Over 12 months, 196 patients experienced MACE (5.1%/year). Among twelve candidate biomarkers, N-terminal pro-B-type natriuretic peptide (NT-proBNP) measured at baseline showed the most prognostic capability independent of clinical predictors. The developed BIPass risk model included age, hypertension, previous myocardial infarction, stroke, Killip class, heart rate, and NT-proBNP. It displayed improved discrimination (C-statistic 0.79, 95% CI 0.73-0.85), calibration (GOF = 9.82, p = 0.28) and clinical decision curve in the validation cohort, outperforming the GRACE and TIMI risk scores. Cumulative rates for MACE demonstrated good separation in the BIPass predicted low, intermediate, and high-risk groups. Interpretation: The BIPass risk model, integrating clinical variables and NT-proBNP, is useful for predicting 12-month MACE in ACS. It effectively identifies a gradient risk of cardiovascular events to aid personalized care. Funding: National Key R&D Program of China (2017YFC0908700, 2020YFC0846600), 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), Youth Top-Talent Project of National Ten Thousand Talents Plan and Qilu Young Scholar Program.

【 授权许可】

Unknown