Prognostic value of machine learning for acute heart failure. (4th February 2022)

• Record Type: Journal Article
• Authors: Shimizu, M; Miyazaki, H; Cho, S; Misu, Y; Tateishi, R; Yamaguchi, M; Yamakami, Y; Shimada, H; Manno, T; Isshiki, A; Kimura, S; Fujii, H; Suzuki, M; Nishizaki, M; Sasano, T
• Is Part Of: European heart journal. Volume 43(2022)Supplement 1
• Publication Date: 2022-02-04
• Subjects: Cardiology -- Periodicals; Heart -- Diseases -- Periodicals; 616.12005
• DOI: 10.1093/eurheartj/ehab849.050
• Languages: English
• ISSNs: 0195-668X

Abstract

Abstract: Funding Acknowledgements: Type of funding sources: None. Background: At onset of acute heart failure (AHF), various clinical fundamental parameters including vital sign, laboratory data, or initial treatment were investigated, and we can roughly estimate the prognosis. However, machine learning method for prediction of the prognosis was not studied. Purpose: To elucidate prognostic value of machine learning for AHF comparing conventional statistical model. Methods: We enrolled consecutive 300 patients with AHF (79.5 ± 12.1 years, 158 Males). Patients with acute coronary syndrome, mechanical circulatory support cases, and cardio-pulmonary arrest cases were excluded. The patients were randomly divided into 80% (240 cases) and 20% (60 cases), and the former was used as train data, and the latter as validation data. Objective variable was set as cardiac death in one year. First, logistic regression analysis with Akaike's information criterion (AIC) was performed, and extracted predictive parameters. The predictive model for the cardiac prognosis was constructed by cut-off value of ROC curve analysis of propensity score was calculated. Next, machine learning (random forest method and deep learning) to build predictive model was performed with the predictors. Finally, accuracy of each predictive model was compared. Results: Thirty cases showed cardiac death in one year. Logistic regression with AIC extracted 8 predictors, and the cut off-value of propensity score with the 6 parameters was 0.110. The accuracy was 0.714 and area under ROC (AUROC) was 0.836. Conversely, random forest method demonstrated the accuracy as 0.927, AUROC 0.860. On deep learning, the accuracy was 0.937 and AUROC 0.901. The top 4 high feature importance of random forest were Cl/red blood cell count/pH/Anion Gap. However, accuracy of those predictors was lower than that of machine learning. Conclusion: Machine learning was a powerful tool to predict cardiac prognosis of AHF, comparing with conventional statistical model.