Orianne Weizman, Kenza Hamzi, Patrick Henry, Guillaume Schurtz, Marie Hauguel-Moreau, Antonin Trimaille, Marc Bedossa, Jean Claude Dib, Sabir Attou, Tanissia Boukertouta, Franck Boccara, Thibaut Pommier, Pascal Lim, Thomas Bochaton, Damien Millischer, Benoit Merat, Fabien Picard, Nissim Grinberg, David Sulman, Bastien Pasdeloup, Yassine El Ouahidi, Treçy Gonçalves, Eric Vicaut, Jean-Guillaume Dillinger, Solenn Toupin, Théo Pezel
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We aimed to build an ML model to predict in-hospital major adverse events (MAE) in patients hospitalized in CICU.</p><p><strong>Methods and results: </strong>In April 2021, a French national prospective multicentre study involving 39 centres included all consecutive patients admitted to CICU. The primary outcome was in-hospital MAE, including death, resuscitated cardiac arrest, or cardiogenic shock. Using 31 randomly assigned centres as an index cohort (divided into training and testing sets), several ML models were evaluated to predict in-hospital MAE. The eight remaining centres were used as an external validation cohort. Among 1499 consecutive patients included (aged 64 ± 15 years, 70% male), 67 had in-hospital MAE (4.3%). Out of 28 clinical, biological, ECG, and echocardiographic variables, seven were selected to predict MAE in the training set (<i>n</i> = 844). Boosted cost-sensitive C5.0 technique showed the best performance compared with other ML methods [receiver operating characteristic area under the curve (AUROC) = 0.90, precision-recall AUC = 0.57, <i>F</i>1 score = 0.5]. Our ML score showed a better performance than existing scores (AUROC: ML score = 0.90 vs. Thrombolysis In Myocardial Infarction (TIMI) score: 0.56, Global Registry of Acute Coronary Events (GRACE) score: 0.52, Acute Heart Failure (ACUTE-HF) score: 0.65; all <i>P</i> < 0.05). Machine learning score also showed excellent performance in the external cohort (AUROC = 0.88).</p><p><strong>Conclusion: </strong>This new ML score is the first to demonstrate improved performance in predicting in-hospital outcomes over existing scores in patients admitted to the intensive care unit based on seven simple and rapid clinical and echocardiographic variables.</p><p><strong>Trial registration: </strong>ClinicalTrials.gov Identifier: NCT05063097.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. 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引用次数: 0
摘要
目的：尽管有一些基于传统统计方法的评分可用于心脏重症监护病房（CICU）住院患者的风险分层，但机器学习（ML）方法在这一领域的风险分层中的作用尚未得到充分证实。我们的目标是建立一个 ML 模型来预测 CICU 住院患者的院内主要不良事件（MAE）：2021 年 4 月，一项涉及 39 个中心的法国全国性前瞻性多中心研究纳入了所有连续入住 CICU 的患者。主要结果是院内MAE，包括死亡、复苏后心脏骤停或心源性休克。以 31 个随机分配的中心为指标队列（分为训练集和测试集），评估了几种预测院内 MAE 的 ML 模型。其余八个中心作为外部验证队列。在纳入的 1499 名连续患者（年龄为 64 ± 15 岁，70% 为男性）中，有 67 名患者出现院内 MAE（4.3%）。在 28 个临床、生物、心电图和超声心动图变量中，选择了 7 个变量来预测训练集中的 MAE（n = 844）。与其他 ML 方法相比，成本敏感型 C5.0 提升技术表现最佳[曲线下接收者操作特征面积 (AUROC) = 0.90，精确度-召回 AUC = 0.57，F1 分数 = 0.5]。我们的 ML 评分显示出比现有评分更好的性能（AUROC：ML 评分 = 0.90 vs. 心肌梗死溶栓治疗（TIMI）评分：0.56；急性冠脉事件全球登记（GRACE）评分：0.52；急性心力衰竭（ACUTE-HF）评分：0.65；所有 P <0.05）。在外部队列中，机器学习评分也表现出色（AUROC = 0.88）：基于七个简单、快速的临床和超声心动图变量，这一新的机器学习评分首次证明，与现有评分相比，它在预测重症监护室住院患者的院内预后方面性能更佳：试验注册：ClinicalTrials.gov Identifier：试验注册：ClinicalTrials.gov Identifier：NCT05063097。
Machine learning score to predict in-hospital outcomes in patients hospitalized in cardiac intensive care unit.
Aims: Although some scores based on traditional statistical methods are available for risk stratification in patients hospitalized in cardiac intensive care units (CICUs), the interest of machine learning (ML) methods for risk stratification in this field is not well established. We aimed to build an ML model to predict in-hospital major adverse events (MAE) in patients hospitalized in CICU.
Methods and results: In April 2021, a French national prospective multicentre study involving 39 centres included all consecutive patients admitted to CICU. The primary outcome was in-hospital MAE, including death, resuscitated cardiac arrest, or cardiogenic shock. Using 31 randomly assigned centres as an index cohort (divided into training and testing sets), several ML models were evaluated to predict in-hospital MAE. The eight remaining centres were used as an external validation cohort. Among 1499 consecutive patients included (aged 64 ± 15 years, 70% male), 67 had in-hospital MAE (4.3%). Out of 28 clinical, biological, ECG, and echocardiographic variables, seven were selected to predict MAE in the training set (n = 844). Boosted cost-sensitive C5.0 technique showed the best performance compared with other ML methods [receiver operating characteristic area under the curve (AUROC) = 0.90, precision-recall AUC = 0.57, F1 score = 0.5]. Our ML score showed a better performance than existing scores (AUROC: ML score = 0.90 vs. Thrombolysis In Myocardial Infarction (TIMI) score: 0.56, Global Registry of Acute Coronary Events (GRACE) score: 0.52, Acute Heart Failure (ACUTE-HF) score: 0.65; all P < 0.05). Machine learning score also showed excellent performance in the external cohort (AUROC = 0.88).
Conclusion: This new ML score is the first to demonstrate improved performance in predicting in-hospital outcomes over existing scores in patients admitted to the intensive care unit based on seven simple and rapid clinical and echocardiographic variables.
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