Machine Learning–Based First-Day Mortality Prediction for Venoarterial Extracorporeal Membrane Oxygenation: The Novel RESCUE-24 Score

作者：Jung‐Chi Hsu, Chen‐Hsu Pai, Lian‐Yu Lin, Chih‐Hsien Wang, Ling‐Yi Wei, Jeng‐Wei Chen, Nai‐Hsin Chi, Shu‐Chien Huang, Hsi‐Yu Yu, Nai‐Kuan Chou, Ron‐Bin Hsu, Yih‐Sharng Chen · 发表于：ASAIO Journal · 年份：2025 · DOI：10.1097/mat.0000000000002395 · 被引用次数：5 · 研究领域：Mechanical Circulatory Support Devices、Cardiac Arrest and Resuscitation、Heart Failure Treatment and Management

Extracorporeal membrane oxygenation (ECMO) provides critical cardiac support, but predicting outcomes remains a challenge. We enrolled 1,748 adult venoarterial (VA)-ECMO patients at the National Taiwan University Hospital between 2010 and 2021. The overall mortality rate was 68.2%. Machine learning with the random survival forest (RSF) model demonstrated superior prediction for in-hospital mortality (area under the curve [AUC]: 0.953, 95% confidence interval (CI): 0.925-0.981), outperforming the Sequential Organ Failure Assessment (SOFA; 0.753 [0.689-0.817]), Acute Physiology and Chronic Health Evaluation (APACHE) II (0.737 [0.672-0.802]), Survival after Venoarterial ECMO (SAVE; 0.624 [0.551-0.697]), ENCOURAGE (0.675 [0.606-0.743]), and Simplified Acute Physiology Score (SAPS) III (0.604 [0.533-0.675]) scores. Failure to achieve 25% clearance at 8 hours and 50% at 16 hours significantly increased mortality risk (HR: 1.65, 95% CI: 1.27-2.14, p < 0.001; HR: 1.25, 95% CI: 1.02-1.54, p = 0.035). Based on the RSF-derived variable importance, the RESCUE-24 Score was developed, assigning points for lactic acid clearance (10 for <50% at 16 hours, 6 for <25% at 8 hours), SvO 2 <75% (3 points), oliguria <500 ml (2 points), and age ≥60 years (2 points). Patients were classified into low risk (0-2), medium risk (3-20), and high risk (≥21). The medium- and high-risk groups exhibited significantly higher in-hospital mortality compared with the low-risk group (HR: 1.93 [1.46-2.55] and 5.47 ...