Forecasting the Acute Heart Failure Admissions: Development of Deep Learning Prediction Model Incorporating the Climate Information

Abstract

BackgroundClimate is known to influence the incidence of cardiovascular events. However, their prediction with traditional statistical models remain imprecise. Methods and ResultsWe analyzed 27,799 acute heart failure (AHF) admissions within the Tokyo CCU Network Database from January 2014 to December 2019. High-risk AHF (HR-AHF) day was defined as a day with the upper 10th percentile of AHF admission volume. Deep neural network (DNN) and traditional regression models were developed using the admissions in 2014-2018 and tested in 2019. Explanatory variables included 17 meteorological parameters. Shapley additive explanations were used to evaluate their importance. The median number of incidences of AHF was 12 (9-16) per day in 2014–2018 and 11 (9-15) per day in 2019. The predicted AHF admissions correlated well with the observed numbers (DNN: R2 = 0.413, linear regression: R2 = 0.387). The DNN model was superior in predicting HR-AHF days compared with the logistic regression model [c-statistics: 0.888 (95%CI: 0.818-0.958) vs. 0.827 (95%CI: 0.745-0.910): p=0.0013]. Notably, the strongest predictive variable was the 7-day moving average of the lowest ambient temperatures. ConclusionsThe DNN model had good prediction ability for incident AHF using climate information. Forecasting AHF admissions could be useful for the effective management of AHF.