Shuo Wang, Liangshan Wang, Zhongtao Du, Feng Yang, Xing Hao, Xiaomeng Wang, Chengcheng Shao, Jin Li, Hong Wang, Chenglong Li, Xiaotong Hou
{"title":"利用机器学习算法对接受静脉-动脉体外膜氧合治疗的难治性心源性休克患者进行表型分析","authors":"Shuo Wang, Liangshan Wang, Zhongtao Du, Feng Yang, Xing Hao, Xiaomeng Wang, Chengcheng Shao, Jin Li, Hong Wang, Chenglong Li, Xiaotong Hou","doi":"10.31083/j.rcm2508303","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>This study used machine learning to categorize cardiogenic shock (CS) patients treated with venous-arterial extracorporeal membrane oxygenation (VA-ECMO) into distinct phenotypes. Subsequently, it aimed to clarify the wide mortality variance observed in refractory CS, attributing it to the condition's inherent heterogeneity.</p><p><strong>Methods: </strong>This study enrolled a cohort of CS patients who received VA-ECMO support. By employing rigorous machine learning (ML) techniques, we generated and validated clusters based on determinants identified through algorithmic analysis. These clusters, characterized by distinct clinical outcomes, facilitated the examination of clinical and laboratory profiles to enhance the understanding of patient responses to VA-ECMO treatment.</p><p><strong>Results: </strong>In a study of 210 CS patients undergoing VA-ECMO treatment, 70.5% were male with a median age of 62, ranging from 53 to 67 years. Survival rates were 67.6% during VA-ECMO and 49.5% post-discharge. Patients were classified into three phenotypes based on the clinical and laboratory findings: \"platelet preserved (I)\", those with stable platelet counts, \"hyperinflammatory (II)\", those indicating significant inflammation, and \"hepatic-renal (III)\", those showing compromised liver and kidney functions. Mortality rates (25.0%, 52.8%, and 55.9% for phenotypes I, Ⅱ, and Ⅲ, respectively (<i>p</i> = 0.005)) varied significantly among these groups, highlighting the importance of phenotype identification in patient management.</p><p><strong>Conclusions: </strong>This study identified three distinct phenotypes among refractory CS patients treated using VA-ECMO, each with unique clinical characteristics and mortality risks. Thus, highlighting the importance of early detection and targeted intervention, these findings suggest that proactive management could improve outcomes for those showing critical signs.</p>","PeriodicalId":20989,"journal":{"name":"Reviews in cardiovascular medicine","volume":"25 8","pages":"303"},"PeriodicalIF":1.9000,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11367001/pdf/","citationCount":"0","resultStr":"{\"title\":\"Phenotyping Refractory Cardiogenic Shock Patients Receiving Venous-Arterial Extracorporeal Membrane Oxygenation Using Machine Learning Algorithms.\",\"authors\":\"Shuo Wang, Liangshan Wang, Zhongtao Du, Feng Yang, Xing Hao, Xiaomeng Wang, Chengcheng Shao, Jin Li, Hong Wang, Chenglong Li, Xiaotong Hou\",\"doi\":\"10.31083/j.rcm2508303\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>This study used machine learning to categorize cardiogenic shock (CS) patients treated with venous-arterial extracorporeal membrane oxygenation (VA-ECMO) into distinct phenotypes. Subsequently, it aimed to clarify the wide mortality variance observed in refractory CS, attributing it to the condition's inherent heterogeneity.</p><p><strong>Methods: </strong>This study enrolled a cohort of CS patients who received VA-ECMO support. By employing rigorous machine learning (ML) techniques, we generated and validated clusters based on determinants identified through algorithmic analysis. These clusters, characterized by distinct clinical outcomes, facilitated the examination of clinical and laboratory profiles to enhance the understanding of patient responses to VA-ECMO treatment.</p><p><strong>Results: </strong>In a study of 210 CS patients undergoing VA-ECMO treatment, 70.5% were male with a median age of 62, ranging from 53 to 67 years. Survival rates were 67.6% during VA-ECMO and 49.5% post-discharge. Patients were classified into three phenotypes based on the clinical and laboratory findings: \\\"platelet preserved (I)\\\", those with stable platelet counts, \\\"hyperinflammatory (II)\\\", those indicating significant inflammation, and \\\"hepatic-renal (III)\\\", those showing compromised liver and kidney functions. Mortality rates (25.0%, 52.8%, and 55.9% for phenotypes I, Ⅱ, and Ⅲ, respectively (<i>p</i> = 0.005)) varied significantly among these groups, highlighting the importance of phenotype identification in patient management.</p><p><strong>Conclusions: </strong>This study identified three distinct phenotypes among refractory CS patients treated using VA-ECMO, each with unique clinical characteristics and mortality risks. Thus, highlighting the importance of early detection and targeted intervention, these findings suggest that proactive management could improve outcomes for those showing critical signs.</p>\",\"PeriodicalId\":20989,\"journal\":{\"name\":\"Reviews in cardiovascular medicine\",\"volume\":\"25 8\",\"pages\":\"303\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2024-08-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11367001/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Reviews in cardiovascular medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.31083/j.rcm2508303\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/8/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q3\",\"JCRName\":\"CARDIAC & CARDIOVASCULAR SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Reviews in cardiovascular medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.31083/j.rcm2508303","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/8/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}
Background: This study used machine learning to categorize cardiogenic shock (CS) patients treated with venous-arterial extracorporeal membrane oxygenation (VA-ECMO) into distinct phenotypes. Subsequently, it aimed to clarify the wide mortality variance observed in refractory CS, attributing it to the condition's inherent heterogeneity.
Methods: This study enrolled a cohort of CS patients who received VA-ECMO support. By employing rigorous machine learning (ML) techniques, we generated and validated clusters based on determinants identified through algorithmic analysis. These clusters, characterized by distinct clinical outcomes, facilitated the examination of clinical and laboratory profiles to enhance the understanding of patient responses to VA-ECMO treatment.
Results: In a study of 210 CS patients undergoing VA-ECMO treatment, 70.5% were male with a median age of 62, ranging from 53 to 67 years. Survival rates were 67.6% during VA-ECMO and 49.5% post-discharge. Patients were classified into three phenotypes based on the clinical and laboratory findings: "platelet preserved (I)", those with stable platelet counts, "hyperinflammatory (II)", those indicating significant inflammation, and "hepatic-renal (III)", those showing compromised liver and kidney functions. Mortality rates (25.0%, 52.8%, and 55.9% for phenotypes I, Ⅱ, and Ⅲ, respectively (p = 0.005)) varied significantly among these groups, highlighting the importance of phenotype identification in patient management.
Conclusions: This study identified three distinct phenotypes among refractory CS patients treated using VA-ECMO, each with unique clinical characteristics and mortality risks. Thus, highlighting the importance of early detection and targeted intervention, these findings suggest that proactive management could improve outcomes for those showing critical signs.
期刊介绍:
RCM is an international, peer-reviewed, open access journal. RCM publishes research articles, review papers and short communications on cardiovascular medicine as well as research on cardiovascular disease. We aim to provide a forum for publishing papers which explore the pathogenesis and promote the progression of cardiac and vascular diseases. We also seek to establish an interdisciplinary platform, focusing on translational issues, to facilitate the advancement of research, clinical treatment and diagnostic procedures. Heart surgery, cardiovascular imaging, risk factors and various clinical cardiac & vascular research will be considered.