Development and External Validation of a Deep Learning Algorithm for Prognostication of Cardiovascular Outcomes
Korean Circulation Journal
;
: 72-84, 2020.
Artículo
en Inglés
| 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
Texto completo:
Disponible
Índice:
WPRIM (Pacífico Occidental)
Asunto principal:
Inteligencia Artificial
/
Enfermedades Cardiovasculares
/
Tamizaje Masivo
/
Estudios de Cohortes
/
Estudios de Seguimiento
/
Seguro de Salud
/
Aprendizaje
/
Programas Nacionales de Salud
Tipo de estudio:
Estudio de etiología
/
Estudio de incidencia
/
Estudio observacional
/
Estudio pronóstico
/
Factores de riesgo
/
Estudio de tamizaje
Límite:
Adulto
/
Femenino
/
Humanos
/
Masculino
Idioma:
Inglés
Revista:
Korean Circulation Journal
Año:
2020
Tipo del documento:
Artículo
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