Development and External Validation of a Deep Learning Algorithm for Prognostication of Cardiovascular Outcomes
- Author:
In Jeong CHO
1
;
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
Author Information
- Publication Type:Original Article
- From:Korean Circulation Journal 2020;50(1):72-84
- CountryRepublic of Korea
- Language:English
-
Abstract:
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.TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02931500
