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

  • 주제(키워드) Cardiovascular diseases , Artificial intelligence
  • 주제(기타) Cardiac & Cardiovascular Systems
  • 설명문(일반) [Cho, In-Jeong] Ewha Womans Univ, Coll Med, Seoul Hosp, Div Cardiol,Dept Internal Med, Seoul, South Korea; [Cho, In-Jeong] Ewha Womans Univ, Grad Sch, Seoul, South Korea; [Cho, In-Jeong; Sung, Ji Min; Kim, Hyeon Chang; Lee, Sang-Eun; Chang, Hyuk-Jae] Yonsei Univ, Coll Med, Severance Cardiovasc Hosp, Div Cardiol, 50 Yonsei Ro, Seoul 03722, South Korea; [Kim, Hyeon Chang] Yonsei Univ, Coll Med, Dept Prevent Med, Seoul, South Korea; [Chae, Myeong-Hun] Selvas AI Inc, AI R&D Lab, Seoul, South Korea; [Kavousi, Maryam; Rueda-Ochoa, Oscar L.; Ikram, M. Arfan; Franco, Oscar H.] Erasmus MC, Dept Epidemiol, Rotterdam, Netherlands; [Rueda-Ochoa, Oscar L.] UIS, Fac Hlth, Sch Med, Bucaramanga, Colombia; [Ikram, M. Arfan] Erasmus MC, Dept Radiol, Rotterdam, Netherlands; [Min, James K.] New York Presbyterian Hosp, Dalio Inst Cardiovasc Imaging, Weill Cornell Med Coll, Dept Radiol & Med, New York, NY USA; [Chang, Hyuk-Jae] Yonsei Univ, Coll Med, Severance Biomed Sci Inst, Seoul, South Korea
  • 등재 SCIE, SCOPUS, KCI등재
  • OA유형 Green Published
  • 발행기관 KOREAN SOC CARDIOLOGY
  • 발행년도 2020
  • 총서유형 Journal
  • URI http://www.dcollection.net/handler/ewha/000000171948
  • 본문언어 영어
  • Published As https://dx.doi.org/10.4070/kcj.2019.0105
  • PubMed https://pubmed.ncbi.nlm.nih.gov/31456363

초록/요약

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 [NM], 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.

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