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

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2 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)72-84
Number of pages13
JournalKorean Circulation Journal
Volume50
Issue number1
DOIs
Publication statusPublished - 2020

All Science Journal Classification (ASJC) codes

  • Internal Medicine
  • Cardiology and Cardiovascular Medicine

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    Cho, I. J., Sung, J. M., Kim, H. C., Lee, S. E., Chae, M. H., Kavousi, M., Rueda-Ochoa, O. L., Ikram, M. A., Franco, O. H., Min, J. K., & 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. https://doi.org/10.4070/kcj.2019.0105