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Study on the prediction model of atherosclerotic cardiovascular disease in the rural Xinjiang population based on survival analysis
PURPOSE: With 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...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10234013/ https://www.ncbi.nlm.nih.gov/pubmed/37264356 http://dx.doi.org/10.1186/s12889-023-15630-x |
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author | Qian, Xin Keerman, Mulatibieke Zhang, Xianghui Guo, Heng He, Jia Maimaitijiang, Remina Wang, Xinping Ma, Jiaolong Li, Yu Ma, Rulin Guo, Shuxia |
author_facet | Qian, Xin Keerman, Mulatibieke Zhang, Xianghui Guo, Heng He, Jia Maimaitijiang, Remina Wang, Xinping Ma, Jiaolong Li, Yu Ma, Rulin Guo, Shuxia |
author_sort | Qian, Xin |
collection | PubMed |
description | PURPOSE: With 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. METHODS: Baseline 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. RESULTS: After 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. CONCLUSION: The 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-023-15630-x. |
format | Online Article Text |
id | pubmed-10234013 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-102340132023-06-02 Study on the prediction model of atherosclerotic cardiovascular disease in the rural Xinjiang population based on survival analysis Qian, Xin Keerman, Mulatibieke Zhang, Xianghui Guo, Heng He, Jia Maimaitijiang, Remina Wang, Xinping Ma, Jiaolong Li, Yu Ma, Rulin Guo, Shuxia BMC Public Health Research PURPOSE: With 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. METHODS: Baseline 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. RESULTS: After 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. CONCLUSION: The 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-023-15630-x. BioMed Central 2023-06-01 /pmc/articles/PMC10234013/ /pubmed/37264356 http://dx.doi.org/10.1186/s12889-023-15630-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Qian, Xin Keerman, Mulatibieke Zhang, Xianghui Guo, Heng He, Jia Maimaitijiang, Remina Wang, Xinping Ma, Jiaolong Li, Yu Ma, Rulin Guo, Shuxia Study on the prediction model of atherosclerotic cardiovascular disease in the rural Xinjiang population based on survival analysis |
title | Study on the prediction model of atherosclerotic cardiovascular disease in the rural Xinjiang population based on survival analysis |
title_full | Study on the prediction model of atherosclerotic cardiovascular disease in the rural Xinjiang population based on survival analysis |
title_fullStr | Study on the prediction model of atherosclerotic cardiovascular disease in the rural Xinjiang population based on survival analysis |
title_full_unstemmed | Study on the prediction model of atherosclerotic cardiovascular disease in the rural Xinjiang population based on survival analysis |
title_short | Study on the prediction model of atherosclerotic cardiovascular disease in the rural Xinjiang population based on survival analysis |
title_sort | study on the prediction model of atherosclerotic cardiovascular disease in the rural xinjiang population based on survival analysis |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10234013/ https://www.ncbi.nlm.nih.gov/pubmed/37264356 http://dx.doi.org/10.1186/s12889-023-15630-x |
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