Data Sheet 2_Machine learning-based early warning system for hemodynamic deterioration in cardiovascular ICU patients: a bidirectional cross-validation study.csv

BackgroundEarly identification of hemodynamic deterioration in cardiovascular intensive care unit (ICU) patients is critical for improving clinical outcomes. Traditional monitoring approaches and scoring systems often fail to capture dynamic multidimensional physiological changes, and existing machine learning models frequently lack robust external validation across diverse healthcare systems.
MethodsWe employed a retrospective multi-center cohort design to develop machine learning prediction models using the MIMIC-IV database (46,007 admissions) and the eICU database (50,949 admissions). To rigorously assess model robustness and generalizability, a novel bidirectional cross-validation framework was implemented: models were trained on MIMIC data and validated on eICU data, and conversely, trained on eICU data and validated on MIMIC data. The study defined a strict composite outcome comprising hemodynamic instability, tissue hypoperfusion, and confirmed cardiac etiology. Multiple machine learning algorithms were evaluated to identify the optimal classifier.
ResultsThe Random Forest model was selected as the optimal classifier. Bidirectional validation demonstrated exceptional cross-database generalizability: the MIMIC-trained model achieved an Area Under the Receiver Operating Characteristic (AUROC) of 0.841 on the eICU cohort, while the eICU-trained model achieved an AUROC of 0.852 on the MIMIC cohort, with performance degradation controlled within a minimal range (<4%). DeLong tests confirmed that the model significantly outperformed traditional clinical scores, including SOFA (AUROC 0.681) and APACHE II (AUROC 0.747). The five-level risk stratification system exhibited a strict monotonic increase in mortality rates, ranging from 0.8% in the very low-risk group to 84.2% in the very high-risk group. SHAP analysis identified hemoglobin, history of acute myocardial infarction, and creatinine as the most significant predictors.
ConclusionsWe successfully developed and validated a machine learning-based early warning system for hemodynamic deterioration in cardiovascular ICU patients. The bidirectional cross-validation approach provides robust evidence for model generalizability, while the multi-level risk stratification system and SHAP-based interpretability offer practical clinical decision support. This system demonstrates significant potential to enhance early identification rates, improve patient outcomes, and optimize healthcare resource utilization efficiency.

背景：早期识别心血管重症监护室患者的血流动力学恶化情况，对改善临床结局至关重要。传统监测手段与评分系统往往无法捕捉动态多维的生理变化，而现有机器学习模型普遍缺乏在不同医疗系统中开展的稳健外部验证。
方法：本研究采用回顾性多中心队列研究设计，依托MIMIC-IV数据库（46007例住院病例）与eICU数据库（50949例住院病例）构建机器学习预测模型。为严格评估模型的稳健性与泛化能力，本研究创新性地采用双向交叉验证框架：分别以MIMIC数据训练模型并在eICU队列中验证，以及以eICU数据训练模型并在MIMIC队列中验证。本研究设定了包含血流动力学不稳定、组织低灌注与明确心脏病因在内的严格复合结局指标。本研究对多种机器学习算法进行了评估，以筛选最优分类器。
结果：本研究最终选定随机森林（Random Forest）作为最优分类器。双向交叉验证结果显示模型具备优异的跨数据库泛化能力：以MIMIC数据训练的模型在eICU队列中受试者工作特征曲线下面积（Area Under the Receiver Operating Characteristic curve, AUROC）达0.841，而以eICU数据训练的模型在MIMIC队列中AUROC达0.852，性能衰减控制在极小范围内（<4%）。DeLong检验证实，本模型的性能显著优于传统临床评分，包括序贯器官衰竭评分（Sequential Organ Failure Assessment, SOFA，AUROC 0.681）与急性生理学与慢性健康状况评分系统Ⅱ（Acute Physiology and Chronic Health Evaluation Ⅱ, APACHE Ⅱ，AUROC 0.747）。本研究构建的五级风险分层系统展现出严格的单调性死亡率递增趋势：极低危组死亡率为0.8%，极高危组死亡率则高达84.2%。SHAP分析（SHapley Additive exPlanations, SHAP）显示，血红蛋白、急性心肌梗死病史与肌酐是最为关键的预测因子。
结论：本研究成功开发并验证了一款基于机器学习的心血管重症监护室患者血流动力学恶化早期预警系统。双向交叉验证方法为模型的泛化能力提供了稳健的证据支持，而多级风险分层系统与基于SHAP的可解释性分析可为临床决策提供实用支撑。本系统在提升早期识别率、改善患者结局以及优化医疗资源利用效率方面具备显著应用潜力。