Development and External Validation of a Deep Learning Algorithm for Prognostication of Cardiovascular Outcomes
Korean Circulation Journal
;
: 72-84, 2020.
Article
Dans Anglais
| WPRIM
| ID: wpr-786209
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
Texte intégral:
Disponible
Indice:
WPRIM (Pacifique occidental)
Sujet Principal:
Intelligence artificielle
/
Maladies cardiovasculaires
/
Dépistage de masse
/
Études de cohortes
/
Études de suivi
/
Assurance maladie
/
Apprentissage
/
Programmes nationaux de santé
Type d'étude:
Etude d'étiologie
/
Etude d'incidence
/
Étude observationnelle
/
Étude pronostique
/
Facteurs de risque
/
Étude de dépistage
Limites du sujet:
Adulte
/
Femelle
/
Humains
/
Mâle
langue:
Anglais
Texte intégral:
Korean Circulation Journal
Année:
2020
Type:
Article
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