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

Background: Climate is known to influence the incidence of cardiovascular events. However, their prediction with traditional statistical models remains imprecise.

Methods And Results: We 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: R = 0.413, linear regression: R = 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 = .0013]. Notably, the strongest predictive variable was the 7-day moving average of the lowest ambient temperatures.

Conclusions: The DNN model had good prediction ability for incident AHF using climate information. Forecasting AHF admissions could be useful for the effective management of AHF.