Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets
Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets
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DOI：
10.1016/s0140-6736(20)32519-8
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发表时间：
2021-01-16
期刊：
影响因子：
168.9
通讯作者：
De Ferrari, Gaetano Maria
中科院分区：
文献类型：
--
作者：
D'Ascenzo, Fabrizio;De Filippo, Ovidio;De Ferrari, Gaetano Maria
背景：目前急性冠脉综合征（ACS）后缺血和出血事件预测工具的准确性仍不足以用于个体化患者管理策略。我们开发了一个基于机器学习的风险分层模型来预测全因死亡、复发性急性心肌梗死和ACS后大出血。方法不同的机器学习模型用于预测出院后1年全因死亡、心肌梗死、和大出血（定义为出血学术研究联盟3型或5型）对19826例成人ACS患者进行了培训（分为培训队列[80%]和内部验证队列[20%]），来自BleeMACS和RENAMI登记研究，其中包括几个大洲的患者。出院时常规评估的25项临床特征用于告知模型。每个研究结果的最佳模型（PRAISE评分）在一个由3444例ACS患者组成的外部验证队列中进行了测试，这些患者来自一项随机对照试验和三项前瞻性登记研究。模型的性能进行了评估，根据一系列的学习指标，包括面积下的受试者工作特征曲线（AUC）。（95% CI 0.78-0.85），内部验证队列中为0.92 1年全因死亡的外部验证队列中为（0.90-0.93）; AUC为0.74（0.70-0.78），内部验证队列中为0.81在1年心肌梗死的外部验证队列中，（0.76-0.85）; AUC为0.70（0.66-0.75），内部验证队列中为0.86（0.82-0.89）在1年大出血的外部验证队列中。的方法识别ACS后事件的预测因子是可行和有效的。PRAISE评分对全因死亡、心肌梗死和大出血的预测具有准确的区分能力，可能有助于指导临床决策。版权所有（C）2021爱思唯尔有限公司保留所有权利。
Background The accuracy of current prediction tools for ischaemic and bleeding events after an acute coronary syndrome (ACS) remains insufficient for individualised patient management strategies. We developed a machine learning-based risk stratification model to predict all-cause death, recurrent acute myocardial infarction, and major bleeding after ACS.Methods Different machine learning models for the prediction of 1-year post-discharge all-cause death, myocardial infarction, and major bleeding (defined as Bleeding Academic Research Consortium type 3 or 5) were trained on a cohort of 19 826 adult patients with ACS (split into a training cohort [80%] and internal validation cohort [20%]) from the BleeMACS and RENAMI registries, which included patients across several continents. 25 clinical features routinely assessed at discharge were used to inform the models. The best-performing model for each study outcome (the PRAISE score) was tested in an external validation cohort of 3444 patients with ACS pooled from a randomised controlled trial and three prospective registries. Model performance was assessed according to a range of learning metrics including area under the receiver operating characteristic curve (AUC).Findings The PRAISE score showed an AUC of 0.82 (95% CI 0.78-0.85) in the internal validation cohort and 0.92 (0.90-0.93) in the external validation cohort for 1-year all-cause death; an AUC of 0.74 (0.70-0.78) in the internal validation cohort and 0.81 (0.76-0.85) in the external validation cohort for 1-year myocardial infarction; and an AUC of 0.70 (0.66-0.75) in the internal validation cohort and 0.86 (0.82-0.89) in the external validation cohort for 1-year major bleeding.Interpretation A machine learning-based approach for the identification of predictors of events after an ACS is feasible and effective. The PRAISE score showed accurate discriminative capabilities for the prediction of all-cause death, myocardial infarction, and major bleeding, and might be useful to guide clinical decision making. Copyright (C) 2021 Elsevier Ltd. All rights reserved.