Abstract 9495: Prognostic Value of Machine Learning on Clinical Parameters for Cardiac Prognosis in Patinets with Acute Congestive Heart Failure

• Published in: Circulation (New York, N.Y.) N.Y.), 2021-11, Vol.144 (Suppl_1), p.A9495-A9495
• Main Authors: Shimizu, Masato, Miyazaki, Hiroko, cho, shummo, Misu, Yoshiki, Tateishi, Ryo, Yamaguchi, Masao, Yamakami, Yosuke, Shimada, Hiroshi, Manno, Tomoko, Isshiki, Ami, Kimura, Shigeki, Fujii, Hiroyuki, Suzuki, Makoto, Nishizaki, Mitsuhiro, Sasano, Tetsuo
• Format: Article
• Language: English
• ISSN: 0009-7322; 1524-4539
• DOI: 10.1161/circ.144.suppl_1.9495

IntroductionCongestive heart failure (CHF) in acute phase, many clinical fundamental parameters including vital sign, laboratory data, or initial treatment were investigated, and we can roughly estimate the prognosis with the parameters. However, machine learning method for prediction of the prognosis is still challenging. HypothesisMachine learning methods have higher performance for predicting cardiac prognosis in patients with CHF than conventional statistical model. MethodsWe enrolled consecutive 300 patients with CHF (79.5±12.1 years, 158 Males). Patients with acute coronary syndrome, mechanical circulatory support cases, and cardio-pulmonary arrest cases were excluded. Objective variable was set as cardiac death in one year. First, multivariate stepwise logistic regression analysis with Akaike’s information criterion (AIC) was performed, and extracted parameters which had the most versatile predictive value. Propensity score (PS) was calculated with the extracted parameters, and the prediction model constructed by cut-off value of ROC curve analysis of PS. 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. ResultsThirty cases showed cardiac death in one year. Logistic regression with AIC extracted 8 predictors, and the cut off-value of propensity score with the 8 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. ConclusionsMachine learning was a powerful tool to predict cardiac prognosis of CHF, comparing with conventional statistical model.