Development and Validation of a Risk Prediction Model for Atherosclerotic Cardiovascular Disease in Japanese Adults: The Hisayama Study.
Atherosclerosis
Cardiovascular disease
Japanese
Prediction model
Risk factors
Journal
Journal of atherosclerosis and thrombosis
ISSN: 1880-3873
Titre abrégé: J Atheroscler Thromb
Pays: Japan
ID NLM: 9506298
Informations de publication
Date de publication:
01 Mar 2022
01 Mar 2022
Historique:
pubmed:
26
1
2021
medline:
19
3
2022
entrez:
25
1
2021
Statut:
ppublish
Résumé
To develop and validate a new risk prediction model for predicting the 10-year risk of atherosclerotic cardiovascular disease (ASCVD) in Japanese adults. A total of 2,454 participants aged 40-84 years without a history of cardiovascular disease (CVD) were prospectively followed up for 24 years. An incident ASCVD event was defined as the first occurrence of coronary heart disease or atherothrombotic brain infarction. A Cox proportional hazards regression model was used to construct the prediction model. In addition, a simplified scoring system was translated from the developed prediction model. The model performance was evaluated using Harrell's C statistics, a calibration plot with the Greenwood-Nam-D'Agostino test, and a bootstrap validation procedure. During a median of a 24-year follow-up, 270 participants experienced the first ASCVD event. The predictors of the ASCVD events in the multivariable Cox model included age, sex, systolic blood pressure, diabetes, serum high-density lipoprotein cholesterol, serum low-density lipoprotein cholesterol, proteinuria, smoking habits, and regular exercise. The developed models exhibited good discrimination with negligible evidence of overfitting (Harrell's C statistics: 0.786 for the multivariable model and 0.789 for the simplified score) and good calibrations (the Greenwood-Nam-D'Agostino test: P=0.29 for the multivariable model, 0.52 for the simplified score). We constructed a risk prediction model for the development of ASCVD in Japanese adults. This prediction model exhibits great potential as a tool for predicting the risk of ASCVD in clinical practice by enabling the identification of specific risk factors for ASCVD in individual patients.
Identifiants
pubmed: 33487620
doi: 10.5551/jat.61960
pmc: PMC8894117
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
345-361Références
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