ArticleBMC public health2023
Study on the prediction model of atherosclerotic cardiovascular disease in the rural Xinjiang population based on survival analysis.
Article in BMC public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 3 of them syntheses that pooled it.
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Who cites it
7 citing papers in PubMed, 3 syntheses or guidelines pooled it, 8 citations in OpenAlex.
- Future promise, current clinical ambiguity: a systematic review of machine learning algorithm outputs predicting risk of cardiovascular disease.Open heart · 2026Pooled it
- A Systematic Review of Artificial Intelligence Models for Time-to-Event Outcome Applied in Cardiovascular Disease Risk Prediction.Journal of medical systems · 2024Pooled it
- Survival prediction landscape: an in-depth systematic literature review on activities, methods, tools, diseases, and databases.Frontiers in artificial intelligence · 2024Pooled it
- Machine learning and SHAP values for predicting coronary artery disease risk in Xinjiang, China.European journal of medical research · 2026Article
- Prognostic stratification of familial hypercholesterolaemia patients using AI algorithms: a gender-specific approach.European heart journal. Digital health · 2025Article
- Development and validation of a prospective study to predict the risk of readmission within 365 days of respiratory failure: based on a random survival forest algorithm combined with COX regression modeling.BMC pulmonary medicine · 2024Article
- Association of adiposity indicators with cardiometabolic multimorbidity risk in hypertensive patients: a large cross-sectional study.Frontiers in endocrinology · 2024Article
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Authors and funding
11 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
purposeWith the increase in aging and cardiovascular risk factors, the morbidity and mortality of atherosclerotic cardiovascular disease (ASCVD), represented by ischemic heart disease and stroke, continue to rise in China. For better prevention and intervention, relevant guidelines recommend using predictive models for early detection of ASCVD high-risk groups. Therefore, this study aims to establish a population ASCVD prediction model in rural areas of Xinjiang using survival analysis.
methodsBaseline cohort data were collected from September to December 2016 and followed up till June 2022. A total of 7975 residents (4054 males and 3920 females) aged 30-74 years were included in the analysis. The data set was divided according to different genders, and the training and test sets ratio was 7:3 for different genders. A Cox regression, Lasso-Cox regression, and random survival forest (RSF) model were established in the training set. The model parameters were determined by cross-validation and parameter tuning and then verified in the training set. Traditional ASCVD prediction models (Framingham and China-PAR models) were constructed in the test set. Different models' discrimination and calibration degrees were compared to find the optimal prediction model for this population according to different genders and further analyze the risk factors of ASCVD.
resultsAfter 5.79 years of follow-up, 873 ASCVD events with a cumulative incidence of 10.19% were found (7.57% in men and 14.44% in women). By comparing the discrimination and calibration degrees of each model, the RSF showed the best prediction performance in males and females (male: Area Under Curve (AUC) 0.791 (95%CI 0.767,0.813), C statistic 0.780 (95%CI 0.730,0.829), Brier Score (BS):0.060, female: AUC 0.759 (95%CI 0.734,0.783) C statistic was 0.737 (95%CI 0.702,0.771), BS:0.110). Age, systolic blood pressure (SBP), apolipoprotein B (APOB), Visceral Adiposity Index (VAI), hip circumference (HC), and plasma arteriosclerosis index (AIP) are important predictors of ASCVD in the rural population of Xinjiang.
conclusionThe performance of the ASCVD prediction model based on the RSF algorithm is better than that based on Cox regression, Lasso-Cox, and the traditional ASCVD prediction model in the rural population of Xinjiang.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.