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Incorporating longitudinal history of risk factors into atherosclerotic cardiovascular disease risk prediction using deep learning

التفاصيل البيبلوغرافية
العنوان: Incorporating longitudinal history of risk factors into atherosclerotic cardiovascular disease risk prediction using deep learning
المؤلفون: Jingzhi Yu, Xiaoyun Yang, Yu Deng, Amy E. Krefman, Lindsay R. Pool, Lihui Zhao, Xinlei Mi, Hongyan Ning, John Wilkins, Donald M. Lloyd-Jones, Lucia C. Petito, Norrina B. Allen
المصدر: Scientific Reports, Vol 14, Iss 1, Pp 1-10 (2024)
بيانات النشر: Nature Portfolio, 2024.
سنة النشر: 2024
المجموعة: LCC:Medicine
LCC:Science
مصطلحات موضوعية: Medicine, Science
الوصف: Abstract It is increasingly clear that longitudinal risk factor levels and trajectories are related to risk for atherosclerotic cardiovascular disease (ASCVD) above and beyond single measures. Currently used in clinical care, the Pooled Cohort Equations (PCE) are based on regression methods that predict ASCVD risk based on cross-sectional risk factor levels. Deep learning (DL) models have been developed to incorporate longitudinal data for risk prediction but its benefit for ASCVD risk prediction relative to the traditional Pooled Cohort Equations (PCE) remain unknown. Our study included 15,565 participants from four cardiovascular disease cohorts free of baseline ASCVD who were followed for adjudicated ASCVD. Ten-year ASCVD risk was calculated in the training set using our benchmark, the PCE, and a longitudinal DL model, Dynamic-DeepHit. Predictors included those incorporated in the PCE: sex, race, age, total cholesterol, high density lipid cholesterol, systolic and diastolic blood pressure, diabetes, hypertension treatment and smoking. The discrimination and calibration performance of the two models were evaluated in an overall hold-out testing dataset. Of the 15,565 participants in our dataset, 2170 (13.9%) developed ASCVD. The performance of the longitudinal DL model that incorporated 8 years of longitudinal risk factor data improved upon that of the PCE [AUROC: 0.815 (CI 0.782–0.844) vs 0.792 (CI 0.760–0.825)] and the net reclassification index was 0.385. The brier score for the DL model was 0.0514 compared with 0.0542 in the PCE. Incorporating longitudinal risk factors in ASCVD risk prediction using DL can improve model discrimination and calibration.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2045-2322
Relation: https://doaj.org/toc/2045-2322
DOI: 10.1038/s41598-024-51685-5
URL الوصول: https://doaj.org/article/633a0dc2bb474405b392fa018b5e503a
رقم الأكسشن: edsdoj.633a0dc2bb474405b392fa018b5e503a
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20452322
DOI:10.1038/s41598-024-51685-5