Heterogeneous treatment effects of coronary artery bypass grafting in ischemic cardiomyopathy: A machine learning causal forest analysis
We aim to evaluate the heterogeneous treatment effects of coronary artery bypass grafting in patients with ischemic cardiomyopathy and to identify a group of patients to have greater benefits from coronary artery bypass grafting compared with medical therapy alone. Machine learning causal forest mod...
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| Veröffentlicht in: | The Journal of thoracic and cardiovascular surgery 2024-11, Vol.168 (5), p.1462-1471.e7 |
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| container_title | The Journal of thoracic and cardiovascular surgery |
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| creator | Zhou, Zhuoming Jian, Bohao Chen, Xuanyu Liu, Menghui Zhang, Shaozhao Fu, Guangguo Li, Gang Liang, Mengya Tian, Ting Wu, Zhongkai |
| description | We aim to evaluate the heterogeneous treatment effects of coronary artery bypass grafting in patients with ischemic cardiomyopathy and to identify a group of patients to have greater benefits from coronary artery bypass grafting compared with medical therapy alone.
Machine learning causal forest modeling was performed to identify the heterogeneous treatment effects of coronary artery bypass grafting in patients with ischemic cardiomyopathy from the Surgical Treatment for Ischemic Heart Failure trial. The risks of death from any cause and death from cardiovascular causes between coronary artery bypass grafting and medical therapy alone were assessed in the identified subgroups.
Among 1212 patients enrolled in the Surgical Treatment for Ischemic Heart Failure trial, left ventricular end-systolic volume index, serum creatinine, and age were identified by the machine learning algorithm to distinguish patients with heterogeneous treatment effects. Among patients with left ventricular end-systolic volume index greater than 84 mL/m2 and age 60.27 years or less, coronary artery bypass grafting was associated with a significantly lower risk of death from any cause (adjusted hazard ratio, 0.61; 95% CI, 0.45-0.84) and death from cardiovascular causes (adjusted hazard ratio, 0.63; 95% CI, 0.45-0.89). By contrast, the survival benefits of coronary artery bypass grafting no longer exist in patients with left ventricular end-systolic volume index 84 mL/m2 or less and serum creatinine 1.04 mg/dL or less, or patients with left ventricular end-systolic volume index greater than 84 mL/m2 and age more than 60.27 years.
The current post hoc analysis of the Surgical Treatment for Ischemic Heart Failure trial identified heterogeneous treatment effects of coronary artery bypass grafting in patients with ischemic cardiomyopathy. Younger patients with severe left ventricular enlargement were more likely to derive greater survival benefits from coronary artery bypass grafting.
[Display omitted] |
| doi_str_mv | 10.1016/j.jtcvs.2023.09.021 |
| format | Article |
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Machine learning causal forest modeling was performed to identify the heterogeneous treatment effects of coronary artery bypass grafting in patients with ischemic cardiomyopathy from the Surgical Treatment for Ischemic Heart Failure trial. The risks of death from any cause and death from cardiovascular causes between coronary artery bypass grafting and medical therapy alone were assessed in the identified subgroups.
Among 1212 patients enrolled in the Surgical Treatment for Ischemic Heart Failure trial, left ventricular end-systolic volume index, serum creatinine, and age were identified by the machine learning algorithm to distinguish patients with heterogeneous treatment effects. Among patients with left ventricular end-systolic volume index greater than 84 mL/m2 and age 60.27 years or less, coronary artery bypass grafting was associated with a significantly lower risk of death from any cause (adjusted hazard ratio, 0.61; 95% CI, 0.45-0.84) and death from cardiovascular causes (adjusted hazard ratio, 0.63; 95% CI, 0.45-0.89). By contrast, the survival benefits of coronary artery bypass grafting no longer exist in patients with left ventricular end-systolic volume index 84 mL/m2 or less and serum creatinine 1.04 mg/dL or less, or patients with left ventricular end-systolic volume index greater than 84 mL/m2 and age more than 60.27 years.
The current post hoc analysis of the Surgical Treatment for Ischemic Heart Failure trial identified heterogeneous treatment effects of coronary artery bypass grafting in patients with ischemic cardiomyopathy. Younger patients with severe left ventricular enlargement were more likely to derive greater survival benefits from coronary artery bypass grafting.
[Display omitted]</description><identifier>ISSN: 0022-5223</identifier><identifier>ISSN: 1097-685X</identifier><identifier>EISSN: 1097-685X</identifier><identifier>DOI: 10.1016/j.jtcvs.2023.09.021</identifier><identifier>PMID: 37716652</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Age Factors ; Aged ; Cardiomyopathies - etiology ; Cardiomyopathies - mortality ; Cardiomyopathies - physiopathology ; Cardiomyopathies - surgery ; causal forest ; Coronary Artery Bypass - adverse effects ; Coronary Artery Bypass - methods ; Coronary Artery Bypass - mortality ; coronary artery bypass surgery ; Female ; heterogeneous treatment effect ; Humans ; ischemic heart failure ; Machine Learning ; Male ; Middle Aged ; Myocardial Ischemia - complications ; Myocardial Ischemia - diagnostic imaging ; Myocardial Ischemia - mortality ; Myocardial Ischemia - surgery ; Risk Assessment ; Risk Factors ; STICH ; Stroke Volume ; Treatment Effect Heterogeneity ; Treatment Outcome ; Ventricular Function, Left</subject><ispartof>The Journal of thoracic and cardiovascular surgery, 2024-11, Vol.168 (5), p.1462-1471.e7</ispartof><rights>2023 The American Association for Thoracic Surgery</rights><rights>Copyright © 2023 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c309t-c81e36df9bc608ec16e9b28cd8924f10d67f99f0a58b76433666e47ad14603313</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0022522323007973$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37716652$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Zhou, Zhuoming</creatorcontrib><creatorcontrib>Jian, Bohao</creatorcontrib><creatorcontrib>Chen, Xuanyu</creatorcontrib><creatorcontrib>Liu, Menghui</creatorcontrib><creatorcontrib>Zhang, Shaozhao</creatorcontrib><creatorcontrib>Fu, Guangguo</creatorcontrib><creatorcontrib>Li, Gang</creatorcontrib><creatorcontrib>Liang, Mengya</creatorcontrib><creatorcontrib>Tian, Ting</creatorcontrib><creatorcontrib>Wu, Zhongkai</creatorcontrib><title>Heterogeneous treatment effects of coronary artery bypass grafting in ischemic cardiomyopathy: A machine learning causal forest analysis</title><title>The Journal of thoracic and cardiovascular surgery</title><addtitle>J Thorac Cardiovasc Surg</addtitle><description>We aim to evaluate the heterogeneous treatment effects of coronary artery bypass grafting in patients with ischemic cardiomyopathy and to identify a group of patients to have greater benefits from coronary artery bypass grafting compared with medical therapy alone.
Machine learning causal forest modeling was performed to identify the heterogeneous treatment effects of coronary artery bypass grafting in patients with ischemic cardiomyopathy from the Surgical Treatment for Ischemic Heart Failure trial. The risks of death from any cause and death from cardiovascular causes between coronary artery bypass grafting and medical therapy alone were assessed in the identified subgroups.
Among 1212 patients enrolled in the Surgical Treatment for Ischemic Heart Failure trial, left ventricular end-systolic volume index, serum creatinine, and age were identified by the machine learning algorithm to distinguish patients with heterogeneous treatment effects. Among patients with left ventricular end-systolic volume index greater than 84 mL/m2 and age 60.27 years or less, coronary artery bypass grafting was associated with a significantly lower risk of death from any cause (adjusted hazard ratio, 0.61; 95% CI, 0.45-0.84) and death from cardiovascular causes (adjusted hazard ratio, 0.63; 95% CI, 0.45-0.89). By contrast, the survival benefits of coronary artery bypass grafting no longer exist in patients with left ventricular end-systolic volume index 84 mL/m2 or less and serum creatinine 1.04 mg/dL or less, or patients with left ventricular end-systolic volume index greater than 84 mL/m2 and age more than 60.27 years.
The current post hoc analysis of the Surgical Treatment for Ischemic Heart Failure trial identified heterogeneous treatment effects of coronary artery bypass grafting in patients with ischemic cardiomyopathy. Younger patients with severe left ventricular enlargement were more likely to derive greater survival benefits from coronary artery bypass grafting.
[Display omitted]</description><subject>Age Factors</subject><subject>Aged</subject><subject>Cardiomyopathies - etiology</subject><subject>Cardiomyopathies - mortality</subject><subject>Cardiomyopathies - physiopathology</subject><subject>Cardiomyopathies - surgery</subject><subject>causal forest</subject><subject>Coronary Artery Bypass - adverse effects</subject><subject>Coronary Artery Bypass - methods</subject><subject>Coronary Artery Bypass - mortality</subject><subject>coronary artery bypass surgery</subject><subject>Female</subject><subject>heterogeneous treatment effect</subject><subject>Humans</subject><subject>ischemic heart failure</subject><subject>Machine Learning</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Myocardial Ischemia - complications</subject><subject>Myocardial Ischemia - diagnostic imaging</subject><subject>Myocardial Ischemia - mortality</subject><subject>Myocardial Ischemia - surgery</subject><subject>Risk Assessment</subject><subject>Risk Factors</subject><subject>STICH</subject><subject>Stroke Volume</subject><subject>Treatment Effect Heterogeneity</subject><subject>Treatment Outcome</subject><subject>Ventricular Function, Left</subject><issn>0022-5223</issn><issn>1097-685X</issn><issn>1097-685X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kUFr3DAQhUVpaLZpf0Gh6NiL3ZG0lq1CDyG0TSCQSwK9CVke7Wqxra2kDfgf9GdHm0167Gku38x78x4hnxjUDJj8uqt32T6mmgMXNagaOHtDVgxUW8mu-f2WrAA4rxrOxTl5n9IOAFpg6h05F23LpGz4ivy9xowxbHDGcEg0RzR5wjlTdA5tTjQ4akMMs4kLNbGwC-2XvUmJbqJx2c8b6mfqk93i5C21Jg4-TEvYm7xdvtFLOhm79TPSEU2cj7g1h2RG6kLElKmZzbgknz6QM2fGhB9f5gV5-Pnj_uq6ur37dXN1eVtZASpXtmMo5OBUbyV0aJlE1fPODp3ia8dgkK1TyoFpur6VayGklLhuzcDWEoRg4oJ8Od3dx_DnUBzoqZjHcTTPCWjeyabtBJdtQcUJtTGkFNHpffRTCUIz0McK9E4_V6CPFWhQulRQtj6_CBz6CYd_O6-ZF-D7CcDy5qPHqJP1OFscfCyR6yH4_wo8Ae8SnAk</recordid><startdate>20241101</startdate><enddate>20241101</enddate><creator>Zhou, Zhuoming</creator><creator>Jian, Bohao</creator><creator>Chen, Xuanyu</creator><creator>Liu, Menghui</creator><creator>Zhang, Shaozhao</creator><creator>Fu, Guangguo</creator><creator>Li, Gang</creator><creator>Liang, Mengya</creator><creator>Tian, Ting</creator><creator>Wu, Zhongkai</creator><general>Elsevier Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>20241101</creationdate><title>Heterogeneous treatment effects of coronary artery bypass grafting in ischemic cardiomyopathy: A machine learning causal forest analysis</title><author>Zhou, Zhuoming ; 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Machine learning causal forest modeling was performed to identify the heterogeneous treatment effects of coronary artery bypass grafting in patients with ischemic cardiomyopathy from the Surgical Treatment for Ischemic Heart Failure trial. The risks of death from any cause and death from cardiovascular causes between coronary artery bypass grafting and medical therapy alone were assessed in the identified subgroups.
Among 1212 patients enrolled in the Surgical Treatment for Ischemic Heart Failure trial, left ventricular end-systolic volume index, serum creatinine, and age were identified by the machine learning algorithm to distinguish patients with heterogeneous treatment effects. Among patients with left ventricular end-systolic volume index greater than 84 mL/m2 and age 60.27 years or less, coronary artery bypass grafting was associated with a significantly lower risk of death from any cause (adjusted hazard ratio, 0.61; 95% CI, 0.45-0.84) and death from cardiovascular causes (adjusted hazard ratio, 0.63; 95% CI, 0.45-0.89). By contrast, the survival benefits of coronary artery bypass grafting no longer exist in patients with left ventricular end-systolic volume index 84 mL/m2 or less and serum creatinine 1.04 mg/dL or less, or patients with left ventricular end-systolic volume index greater than 84 mL/m2 and age more than 60.27 years.
The current post hoc analysis of the Surgical Treatment for Ischemic Heart Failure trial identified heterogeneous treatment effects of coronary artery bypass grafting in patients with ischemic cardiomyopathy. Younger patients with severe left ventricular enlargement were more likely to derive greater survival benefits from coronary artery bypass grafting.
[Display omitted]</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>37716652</pmid><doi>10.1016/j.jtcvs.2023.09.021</doi></addata></record> |
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| subjects | Age Factors Aged Cardiomyopathies - etiology Cardiomyopathies - mortality Cardiomyopathies - physiopathology Cardiomyopathies - surgery causal forest Coronary Artery Bypass - adverse effects Coronary Artery Bypass - methods Coronary Artery Bypass - mortality coronary artery bypass surgery Female heterogeneous treatment effect Humans ischemic heart failure Machine Learning Male Middle Aged Myocardial Ischemia - complications Myocardial Ischemia - diagnostic imaging Myocardial Ischemia - mortality Myocardial Ischemia - surgery Risk Assessment Risk Factors STICH Stroke Volume Treatment Effect Heterogeneity Treatment Outcome Ventricular Function, Left |
| title | Heterogeneous treatment effects of coronary artery bypass grafting in ischemic cardiomyopathy: A machine learning causal forest analysis |
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