Development and external validation of a deep learning algorithm for prognostication of cardiovascular outcomes
Korean Circulation Journal , Volume 50 - Issue 1 p. 72- 84
Background and Objectives: We aim to explore the additional discriminative accuracy of a deep learning (DL) algorithm using repeated-measures data for identifying people at high risk for cardiovascular disease (CVD), compared to Cox hazard regression. Methods: Two CVD prediction models were developed from National Health Insurance Service-Health Screening Cohort (NHIS-HEALS): A Cox regression model and a DL model. Performance of each model was assessed in the internal and 2 external validation cohorts in Koreans (National Health Insurance Service-National Sample Cohort; NHIS-NSC) and in Europeans (Rotterdam Study). A total of 412,030 adults in the NHIS-HEALS; 178,875 adults in the NHIS-NSC; and the 4,296 adults in Rotterdam Study were included. Results: Mean ages was 52 years (46% women) and there were 25,777 events (6.3%) in NHIS-HEALS during the follow-up. In internal validation, the DL approach demonstrated a C-statistic of 0.896 (95% confidence interval, 0.886-0.907) in men and 0.921 (0.908-0.934) in women and improved reclassification compared with Cox regression (net reclassification index [NRI], 24.8% in men, 29.0% in women). In external validation with NHIS-NSC, DL demonstrated a C-statistic of 0.868 (0.860-0.876) in men and 0.889 (0.876-0.898) in women, and improved reclassification compared with Cox regression (NRI, 24.9% in men, 26.2% in women). In external validation applied to the Rotterdam Study, DL demonstrated a C-statistic of 0.860 (0.824-0.897) in men and 0.867 (0.830-0.903) in women, and improved reclassification compared with Cox regression (NRI, 36.9% in men, 31.8% in women). Conclusions: A DL algorithm exhibited greater discriminative accuracy than Cox model approaches.
|Artificial intelligence, Cardiovascular diseases|
|Korean Circulation Journal|
|Organisation||Department of Epidemiology|
Cho, I.-J. (In-Jeong), Sung, J.M, Kim, H.C. (Hyeon Chang), Lee, S.-E. (Sang-Eun), Chae, M.-H. (Myeong-Hun), Kavousi, M, … Chang, H.-J. (2020). Development and external validation of a deep learning algorithm for prognostication of cardiovascular outcomes. Korean Circulation Journal, 50(1), 72–84. doi:10.4070/kcj.2019.0105