Development and External Validation of a Deep Learning Algorithm for Prognostication of Cardiovascular Outcomes
Authors
In-Jeong Cho ; Ji Min Sung ; Hyeon Chang Kim ; Sang-Eun Lee ; Myeong-Hun Chae ; Maryam Kavousi ; Oscar L. Rueda-Ochoa ; M. Arfan Ikram ; Oscar H. Franco ; James K Min ; Hyuk-Jae Chang
Citation
KOREAN CIRCULATION JOURNAL, Vol.50(1) : 72-84, 2020
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.