Multimodal Data‐Driven Prognostic Model for Predicting Long‐Term Prognosis in Patients With Ischemic Cardiomyopathy and Heart Failure With Preserved Ejection Fraction After Coronary Artery Bypass Grafting: A Multicenter Cohort Study
Background Limited data from the literature are available to assess the efficacy of coronary artery bypass grafting in patients with ischemic cardiomyopathy and heart failure with preserved ejection fraction. Therefore, our objective was to use machine learning techniques integrating clinical featur...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease |
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| Online Access: | https://www.ahajournals.org/doi/10.1161/JAHA.124.036970 |
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| author | Jun Wang Yijun Wang Shoupeng Duan Li Xu Yanan Xu Wenyuan Yin Yi Yang Bing Wu Jinjun Liu |
| author_facet | Jun Wang Yijun Wang Shoupeng Duan Li Xu Yanan Xu Wenyuan Yin Yi Yang Bing Wu Jinjun Liu |
| author_sort | Jun Wang |
| collection | DOAJ |
| description | Background Limited data from the literature are available to assess the efficacy of coronary artery bypass grafting in patients with ischemic cardiomyopathy and heart failure with preserved ejection fraction. Therefore, our objective was to use machine learning techniques integrating clinical features, biomarker data, and echocardiography data to enhance comprehension and risk stratification in patients diagnosed with ischemic cardiomyopathy and heart failure with preserved ejection fraction who have undergone coronary artery bypass grafting surgery. Methods and Results For this study, 294 patients with ischemic cardiomyopathy and heart failure with preserved ejection fraction who underwent coronary artery bypass grafting surgery were assigned to the development cohort (n=176) and the independent validation cohort (n=118). A total of 52 clinical variables were extracted for each patient. The principal clinical end point was the incidence of major adverse cardiovascular events, encompassing cardiac mortality, acute myocardial infarction, acute heart failure, and graft failure. From least absolute shrinkage and selection operator regression, 4 predictors were selected for the final prediction nomogram: diabetes, hypertension, the systemic immune‐inflammation index, and NT‐proBNP (N‐terminal pro‐B‐type natriuretic peptide). The prediction nomogram achieved satisfactory prediction performance in both the development cohort (C index, 0.768 [95% CI, 0.701–0.835]) and independent validation cohort (C index, 0.633 [95% CI, 0.521–0.745]). Adequate calibration was noted for the likelihood of major adverse cardiovascular events in both the development and independent validation cohorts. Decision curve analysis confirmed the clinical usefulness of the established prediction nomogram. Conclusions A clinically feasible prognostic model, based on preoperative multimodal data, was developed for risk stratification of patients with ischemic heart and heart failure with preserved ejection fraction who receive coronary artery bypass grafting surgery. Registration https://www.chictr.org.cn; Unique identifier: ChiCTR2300074439. |
| format | Article |
| id | doaj-art-ae8f0276f5b24eb89054cd526160dd35 |
| institution | Kabale University |
| issn | 2047-9980 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease |
| spelling | doaj-art-ae8f0276f5b24eb89054cd526160dd352024-12-03T10:06:25ZengWileyJournal of the American Heart Association: Cardiovascular and Cerebrovascular Disease2047-99802024-12-01132310.1161/JAHA.124.036970Multimodal Data‐Driven Prognostic Model for Predicting Long‐Term Prognosis in Patients With Ischemic Cardiomyopathy and Heart Failure With Preserved Ejection Fraction After Coronary Artery Bypass Grafting: A Multicenter Cohort StudyJun Wang0Yijun Wang1Shoupeng Duan2Li Xu3Yanan Xu4Wenyuan Yin5Yi Yang6Bing Wu7Jinjun Liu8Department of Cardiology The First Affiliated Hospital of Bengbu Medical University Bengbu Anhui ChinaCenter of Gerontology and Geriatrics, National Clinical Research Center for Geriatrics West China Hospital, Sichuan University Chengdu ChinaDepartment of Cardiology Renmin Hospital of Wuhan University Wuhan ChinaDepartment of Rheumatology and Immunology General Hospital of Central Theater Command Wuhan ChinaPulmonary and Critical Care Medicine The First Affiliated Hospital of Bengbu Medical University Bengbu Anhui ChinaPeople’s Hospital of Xinjiang Uygur Autonomous Region; Electrocardiology Department Urumqi ChinaXinjiang Medical University Urumqi ChinaInstitute of Clinical Medicine and Department of Cardiology Renmin Hospital, Hubei University of Medicine Shiyan Hubei ChinaDepartment of Cardiology The First Affiliated Hospital of Bengbu Medical University Bengbu Anhui ChinaBackground Limited data from the literature are available to assess the efficacy of coronary artery bypass grafting in patients with ischemic cardiomyopathy and heart failure with preserved ejection fraction. Therefore, our objective was to use machine learning techniques integrating clinical features, biomarker data, and echocardiography data to enhance comprehension and risk stratification in patients diagnosed with ischemic cardiomyopathy and heart failure with preserved ejection fraction who have undergone coronary artery bypass grafting surgery. Methods and Results For this study, 294 patients with ischemic cardiomyopathy and heart failure with preserved ejection fraction who underwent coronary artery bypass grafting surgery were assigned to the development cohort (n=176) and the independent validation cohort (n=118). A total of 52 clinical variables were extracted for each patient. The principal clinical end point was the incidence of major adverse cardiovascular events, encompassing cardiac mortality, acute myocardial infarction, acute heart failure, and graft failure. From least absolute shrinkage and selection operator regression, 4 predictors were selected for the final prediction nomogram: diabetes, hypertension, the systemic immune‐inflammation index, and NT‐proBNP (N‐terminal pro‐B‐type natriuretic peptide). The prediction nomogram achieved satisfactory prediction performance in both the development cohort (C index, 0.768 [95% CI, 0.701–0.835]) and independent validation cohort (C index, 0.633 [95% CI, 0.521–0.745]). Adequate calibration was noted for the likelihood of major adverse cardiovascular events in both the development and independent validation cohorts. Decision curve analysis confirmed the clinical usefulness of the established prediction nomogram. Conclusions A clinically feasible prognostic model, based on preoperative multimodal data, was developed for risk stratification of patients with ischemic heart and heart failure with preserved ejection fraction who receive coronary artery bypass grafting surgery. Registration https://www.chictr.org.cn; Unique identifier: ChiCTR2300074439.https://www.ahajournals.org/doi/10.1161/JAHA.124.036970coronary artery bypass graftingheart failure with preserved ejection fractionmultimodal dataprediction nomogram |
| spellingShingle | Jun Wang Yijun Wang Shoupeng Duan Li Xu Yanan Xu Wenyuan Yin Yi Yang Bing Wu Jinjun Liu Multimodal Data‐Driven Prognostic Model for Predicting Long‐Term Prognosis in Patients With Ischemic Cardiomyopathy and Heart Failure With Preserved Ejection Fraction After Coronary Artery Bypass Grafting: A Multicenter Cohort Study Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease coronary artery bypass grafting heart failure with preserved ejection fraction multimodal data prediction nomogram |
| title | Multimodal Data‐Driven Prognostic Model for Predicting Long‐Term Prognosis in Patients With Ischemic Cardiomyopathy and Heart Failure With Preserved Ejection Fraction After Coronary Artery Bypass Grafting: A Multicenter Cohort Study |
| title_full | Multimodal Data‐Driven Prognostic Model for Predicting Long‐Term Prognosis in Patients With Ischemic Cardiomyopathy and Heart Failure With Preserved Ejection Fraction After Coronary Artery Bypass Grafting: A Multicenter Cohort Study |
| title_fullStr | Multimodal Data‐Driven Prognostic Model for Predicting Long‐Term Prognosis in Patients With Ischemic Cardiomyopathy and Heart Failure With Preserved Ejection Fraction After Coronary Artery Bypass Grafting: A Multicenter Cohort Study |
| title_full_unstemmed | Multimodal Data‐Driven Prognostic Model for Predicting Long‐Term Prognosis in Patients With Ischemic Cardiomyopathy and Heart Failure With Preserved Ejection Fraction After Coronary Artery Bypass Grafting: A Multicenter Cohort Study |
| title_short | Multimodal Data‐Driven Prognostic Model for Predicting Long‐Term Prognosis in Patients With Ischemic Cardiomyopathy and Heart Failure With Preserved Ejection Fraction After Coronary Artery Bypass Grafting: A Multicenter Cohort Study |
| title_sort | multimodal data driven prognostic model for predicting long term prognosis in patients with ischemic cardiomyopathy and heart failure with preserved ejection fraction after coronary artery bypass grafting a multicenter cohort study |
| topic | coronary artery bypass grafting heart failure with preserved ejection fraction multimodal data prediction nomogram |
| url | https://www.ahajournals.org/doi/10.1161/JAHA.124.036970 |
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