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

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 [4.07-7.35], p < 0.002, respectively). Kaplan-Meier analysis confirmed that improved lactic acid clearance at 8 and 16 hours was associated with better survival (log-rank p < 0.001). The three groups of the RESCUE-24 Score also showed significant survival differences (log-rank p < 0.001). In conclusion, machine learning can help identify high-risk populations for tailored management. Achieving optimal lactic acid clearance within 24 hours is crucial for improving survival outcomes.

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Journal
ASAIO Journal
Published
2025-02-20
DOI
https://doi.org/10.1097/mat.0000000000002395
Primary Topic
Mechanical Circulatory Support Devices
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article
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article

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

Yih‐Sharng Chen, Ling‐Yi Wei, Hsi‐Yu Yu, Shu‐Chien Huang et al.
ASAIO Journal
Mechanical Circulatory Support Devices
article

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

Yih‐Sharng Chen, Ling‐Yi Wei, Hsi‐Yu Yu, Shu‐Chien Huang, Lian‐Yu Lin, Chih‐Hsien Wang, Jung‐Chi Hsu, Nai‐Hsin Chi, Nai‐Kuan Chou, Ron‐Bin Hsu, Jeng‐Wei Chen, Chen‐Hsu Pai
article en

Abstract

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 [4.07-7.35], p < 0.002, respectively). Kaplan-Meier analysis confirmed that improved lactic acid clearance at 8 and 16 hours was associated with better survival (log-rank p < 0.001). The three groups of the RESCUE-24 Score also showed significant survival differences (log-rank p < 0.001). In conclusion, machine learning can help identify high-risk populations for tailored management. Achieving optimal lactic acid clearance within 24 hours is crucial for improving survival outcomes.

ASAIO JournalVol. 72(2)
National Taiwan University Hospital (TW), National Yunlin University of Science and Technology (TW)
Openalex Percentile: Top 97%
Mechanical Circulatory Support Devices
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