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Development and External Validation of a Deep Learning Algorithm for Prognostication of Cardiovascular Outcomes

 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 
 KOREAN CIRCULATION JOURNAL, Vol.50(1) : 72-84, 2020 
Journal Title
Issue Date
Artificial intelligence ; Cardiovascular diseases
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.
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Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Preventive Medicine and Public Health (예방의학교실) > 1. Journal Papers
5. Research Institutes (연구소) > Yonsei Cardiovascular Research Institute (심혈관연구소) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
Yonsei Authors
Kim, Hyeon Chang(김현창) ORCID logo https://orcid.org/0000-0001-7867-1240
Sung, Ji Min(성지민)
Lee, Sang Eun(이상은) ORCID logo https://orcid.org/0000-0001-6645-4038
Chang, Hyuk-Jae(장혁재) ORCID logo https://orcid.org/0000-0002-6139-7545
Cho, In Jeong(조인정)
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