Wang Jiali, Gao Wei, Chen Guanghui, Chen Ming, Wan Zhi, Zheng Wen, Ma Jingjing, Pang Jiaojiao, Wang Guangmei, Wu Shuo, Wang Shuo, Xu Feng, Chew Derek P, Chen Yuguo
Department of Emergency and Chest Pain Center, Qilu Hospital, Shandong University, Jinan 250012, China.
Shandong Provincal Clinical Research Center for Emergency and Critical Care Medicine, Jinan 250012, China.
Lancet Reg Health West Pac. 2022 May 30;25:100479. doi: 10.1016/j.lanwpc.2022.100479. eCollection 2022 Aug.
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.
Patients ( = 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.
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, = 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.
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.
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.
缺乏整合新生物标志物以预测急性冠状动脉综合征（ACS）心血管事件的风险模型。因此，我们评估了除临床预测指标外生物标志物的预后价值，并建立了一个基于生物标志物的风险模型，用于预测ACS患者入院后12个月内的主要不良心血管事件（MACE）。
将2017年11月至2019年10月连续纳入中国一项前瞻性注册研究（稳定型心绞痛和急性冠状动脉综合征患者基于生物标志物的预后评估，BIPass）的三家医院的患者（n = 4407）指定为风险模型开发队列。在另外两家独立医院纳入的1409例患者中进行验证。采用Cox比例风险回归分析生成风险预测模型，并评估每个生物标志物的增量预后价值。
在12个月期间，196例患者发生了MACE（5.1%/年）。在12种候选生物标志物中，基线时测量的N末端B型利钠肽原（NT-proBNP）显示出与临床预测指标无关的最强预后能力。开发的BIPass风险模型包括年龄、高血压、既往心肌梗死、中风、Killip分级、心率和NT-proBNP。在验证队列中，该模型显示出更好的区分度（C统计量为0.79，95%CI为0.73 - 0.85）、校准度（GOF = 9.82，P = 0.28）和临床决策曲线，优于GRACE和TIMI风险评分。MACE的累积发生率在BIPass预测的低、中、高风险组中显示出良好的区分。
整合临床变量和NT-proBNP的BIPass风险模型可用于预测ACS患者12个月内的MACE。它有效地识别出心血管事件的梯度风险，以辅助个性化治疗。
国家重点研发计划（2017YFC0908700，2020YFC0846600）、国家科技基础资源调查专项（2018FY100600，2018FY100602）、山东省泰山攀登学者计划（tspd20181220）、山东省泰山青年学者计划（tsqn20161065，tsqn201812129）、国家万人计划青年拔尖人才项目和齐鲁青年学者计划。
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