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Predicting Long-Term Mortality in Patients With Acute Heart Failure by Using Machine Learning
DC Field | Value | Language |
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dc.contributor.author | PARK, JIESUCK | - |
dc.contributor.author | HWANG, IN-CHANG | - |
dc.contributor.author | YOON, YEONYEE E. | - |
dc.contributor.author | PARK, JUN-BEAN | - |
dc.contributor.author | PARK, JAE-HYEONG | - |
dc.contributor.author | CHO, GOO-YEONG | - |
dc.date.accessioned | 2022-09-29T03:19:32Z | - |
dc.date.available | 2022-09-29T03:19:32Z | - |
dc.date.created | 2022-07-18 | - |
dc.date.issued | 2022-07 | - |
dc.identifier.citation | Journal of Cardiac Failure, Vol.28 No.7, pp.1078-1087 | - |
dc.identifier.issn | 1071-9164 | - |
dc.identifier.uri | https://hdl.handle.net/10371/184728 | - |
dc.description.abstract | © 2022 Elsevier Inc.Background: High mortality rates in patients with acute heart failure (AHF) necessitate proper risk stratification. However, risk-assessment tools for long-term mortality are largely lacking. We aimed to develop a machine-learning (ML)-based risk-prediction model for long-term all-cause mortality in patients admitted for AHF. Methods and Results: The ML model, based on boosted a Cox regression algorithm (CoxBoost), was trained with 2704 consecutive patients hospitalized for AHF (median age 73 years, 55% male, and median left ventricular ejection fraction 38%). We selected 27 input variables, including 19 clinical features and 8 echocardiographic parameters, for model development. The best-performing model, along with pre-existing risk scores (BIOSTAT-CHF and AHEAD scores), was validated in an independent test cohort of 1608 patients. During the median 32 months (interquartile range 12–54 months) of the follow-up period, 1050 (38.8%) and 690 (42.9%) deaths occurred in the training and test cohorts, respectively. The area under the receiver operating characteristic curve (AUROC) of the ML model for all-cause mortality at 3 years was 0.761 (95% CI: 0.754–0.767) in the training cohort and 0.760 (95% CI: 0.752–0.768) in the test cohort. The discrimination performance of the ML model significantly outperformed those of the pre-existing risk scores (AUROC 0.714, 95% CI 0.706–0.722 by BIOSTAT-CHF; and 0.681, 95% CI 0.672–0.689 by AHEAD). Risk stratification based on the ML model identified patients at high mortality risk regardless of heart failure phenotypes. Conclusions: The ML-based mortality-prediction model can predict long-term mortality accurately, leading to optimal risk stratification of patients with AHF. | - |
dc.language | 영어 | - |
dc.publisher | Elsevier B.V. | - |
dc.title | Predicting Long-Term Mortality in Patients With Acute Heart Failure by Using Machine Learning | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.cardfail.2022.02.012 | - |
dc.citation.journaltitle | Journal of Cardiac Failure | - |
dc.identifier.wosid | 000830600000006 | - |
dc.identifier.scopusid | 2-s2.0-85129453544 | - |
dc.citation.endpage | 1087 | - |
dc.citation.number | 7 | - |
dc.citation.startpage | 1078 | - |
dc.citation.volume | 28 | - |
dc.description.isOpenAccess | N | - |
dc.contributor.affiliatedAuthor | CHO, GOO-YEONG | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
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