Development and Validation of a Cardiovascular Disease Risk Prediction Model for the Japanese Working Population: The Japan Epidemiology Collaboration on Occupational Health Study.

IF 3 2区 医学 Q2 PERIPHERAL VASCULAR DISEASE Journal of atherosclerosis and thrombosis Pub Date : 2024-09-19 DOI:10.5551/jat.64919
Huan Hu, Tohru Nakagawa, Toru Honda, Shuichiro Yamamoto, Takeshi Kochi, Hiroko Okazaki, Toshiaki Miyamoto, Takayuki Ogasawara, Naoki Gommori, Makoto Yamamoto, Maki Konishi, Yosuke Inoue, Isamu Kabe, Seitaro Dohi, Tetsuya Mizoue
{"title":"Development and Validation of a Cardiovascular Disease Risk Prediction Model for the Japanese Working Population: The Japan Epidemiology Collaboration on Occupational Health Study.","authors":"Huan Hu, Tohru Nakagawa, Toru Honda, Shuichiro Yamamoto, Takeshi Kochi, Hiroko Okazaki, Toshiaki Miyamoto, Takayuki Ogasawara, Naoki Gommori, Makoto Yamamoto, Maki Konishi, Yosuke Inoue, Isamu Kabe, Seitaro Dohi, Tetsuya Mizoue","doi":"10.5551/jat.64919","DOIUrl":null,"url":null,"abstract":"<p><strong>Aims: </strong>This study aimed to develop a cardiovascular disease (CVD) risk model using data from a large occupational cohort.</p><p><strong>Methods: </strong>A risk prediction model was developed using the routine health checkup data of 96,117 Japanese employees (84.0% men) who were 30-64 years of age and had no CVD at baseline. Cox proportional hazards regression models were employed to develop a risk model for assessing the 10-year CVD risk. Measures of discrimination and calibration were used to assess the predictive performance of the model and internal validation was used to examine potential overfitting.</p><p><strong>Results: </strong>During a mean follow-up period of 6.7 years (range, 0.1-11.0 years), 422 cases of incident CVD were confirmed. The final model, which included predictor variables of age, smoking, diabetes, systolic blood pressure, and low- and high-density lipoprotein cholesterol levels, demonstrated a good predictive ability (Harrell's C-statistic, 0.796; 95% confidence interval, 0.775-0.817) with excellent calibration between observed and predicted values. Internal validation revealed minimal overfitting.</p><p><strong>Conclusions: </strong>The developed model can accurately predict the 10-year CVD risk. Because it is based on routine health checkup data, the prediction model can be easily implemented in the workplace. Further studies are required to assess the external validity and transferability of the proposed CVD risk model.</p>","PeriodicalId":15128,"journal":{"name":"Journal of atherosclerosis and thrombosis","volume":" ","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of atherosclerosis and thrombosis","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.5551/jat.64919","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PERIPHERAL VASCULAR DISEASE","Score":null,"Total":0}
引用次数: 0

Abstract

Aims: This study aimed to develop a cardiovascular disease (CVD) risk model using data from a large occupational cohort.

Methods: A risk prediction model was developed using the routine health checkup data of 96,117 Japanese employees (84.0% men) who were 30-64 years of age and had no CVD at baseline. Cox proportional hazards regression models were employed to develop a risk model for assessing the 10-year CVD risk. Measures of discrimination and calibration were used to assess the predictive performance of the model and internal validation was used to examine potential overfitting.

Results: During a mean follow-up period of 6.7 years (range, 0.1-11.0 years), 422 cases of incident CVD were confirmed. The final model, which included predictor variables of age, smoking, diabetes, systolic blood pressure, and low- and high-density lipoprotein cholesterol levels, demonstrated a good predictive ability (Harrell's C-statistic, 0.796; 95% confidence interval, 0.775-0.817) with excellent calibration between observed and predicted values. Internal validation revealed minimal overfitting.

Conclusions: The developed model can accurately predict the 10-year CVD risk. Because it is based on routine health checkup data, the prediction model can be easily implemented in the workplace. Further studies are required to assess the external validity and transferability of the proposed CVD risk model.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
日本职业人群心血管疾病风险预测模型的开发与验证:日本职业健康流行病学合作研究》。
目的:本研究旨在利用一个大型职业队列的数据建立心血管疾病(CVD)风险模型:方法:利用 96,117 名日本员工(84.0% 为男性)的常规健康检查数据建立了一个风险预测模型,这些员工的年龄在 30-64 岁之间,基线时没有心血管疾病。采用 Cox 比例危险回归模型建立了评估 10 年心血管疾病风险的风险模型。使用辨别度和校准度来评估模型的预测性能,并使用内部验证来检查潜在的过度拟合:结果:在平均 6.7 年(0.1-11.0 年)的随访期间,共确诊 422 例心血管疾病。最终模型包括年龄、吸烟、糖尿病、收缩压、低密度和高密度脂蛋白胆固醇水平等预测变量,显示出良好的预测能力(哈雷尔 C 统计量,0.796;95% 置信区间,0.775-0.817),观察值和预测值之间的校准效果极佳。内部验证显示过拟合程度极低:结论:所开发的模型可准确预测 10 年心血管疾病风险。结论:所开发的模型可准确预测 10 年心血管疾病风险,由于该模型基于常规健康检查数据,因此可在工作场所轻松实施。还需要进一步的研究来评估所提出的心血管疾病风险模型的外部有效性和可转移性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
6.60
自引率
15.90%
发文量
271
审稿时长
1 months
期刊介绍: JAT publishes articles focused on all aspects of research on atherosclerosis, vascular biology, thrombosis, lipid and metabolism.
期刊最新文献
Action Required to Maximize Protection against Ischemic Stroke among Patients with Atrial Fibrillation. Can Large Language Models Help Healthcare? Impact of Clonal Hematopoiesis of Indeterminate Potential on the Long-Term Risk of Recurrent Stroke in Patients with a High Atherosclerotic Burden. Atherosclerotic Diseases in Chronic Kidney Disease. Non-high-density Lipoprotein Cholesterol for Secondary Prevention after Minor Stroke.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1