{"title":"基于机器学习的静脉体外膜氧合第一天死亡率预测:新的RESCUE-24评分。","authors":"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","doi":"10.1097/MAT.0000000000002395","DOIUrl":null,"url":null,"abstract":"<p><p>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), SvO2 <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.</p>","PeriodicalId":8844,"journal":{"name":"ASAIO Journal","volume":" ","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine Learning-Based First-Day Mortality Prediction for Venoarterial Extracorporeal Membrane Oxygenation: The Novel RESCUE-24 Score.\",\"authors\":\"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\",\"doi\":\"10.1097/MAT.0000000000002395\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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), SvO2 <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.</p>\",\"PeriodicalId\":8844,\"journal\":{\"name\":\"ASAIO Journal\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2025-02-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ASAIO Journal\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1097/MAT.0000000000002395\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ASAIO Journal","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1097/MAT.0000000000002395","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0
摘要
体外膜氧合(ECMO)提供了关键的心脏支持,但预测结果仍然是一个挑战。2010年至2021年间,我们在国立台湾大学医院招募了1748名成人静脉动脉(VA)-ECMO患者。总死亡率为68.2%。使用随机生存森林(RSF)模型的机器学习对住院死亡率的预测优于序贯器官衰竭评估(SOFA)(曲线下面积[AUC]: 0.953, 95%可信区间(CI): 0.925-0.981);0.753[0.689-0.817])、急性生理和慢性健康评估(APACHE) II(0.737[0.672-0.802])、静脉ECMO后生存率(SAVE;0.624[0.551-0.697])、ENCOURAGE(0.675[0.606-0.743])和简化急性生理评分(SAPS) III(0.604[0.533-0.675])得分。8小时清除率达不到25%,16小时清除率达不到50%显著增加了死亡风险(HR: 1.65, 95% CI: 1.27-2.14, p < 0.001;HR: 1.25, 95% CI: 1.02-1.54, p = 0.035)。根据rsf衍生的变量重要性,制定了RESCUE-24评分,为乳酸清除打分(10分)
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), SvO2 <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.
期刊介绍:
ASAIO Journal is in the forefront of artificial organ research and development. On the cutting edge of innovative technology, it features peer-reviewed articles of the highest quality that describe research, development, the most recent advances in the design of artificial organ devices and findings from initial testing. Bimonthly, the ASAIO Journal features state-of-the-art investigations, laboratory and clinical trials, and discussions and opinions from experts around the world.
The official publication of the American Society for Artificial Internal Organs.