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列线图预测冠状动脉粥样硬化性心脏病风险的开发。

Development of a nomogram that predicts the risk for coronary atherosclerotic heart disease.

机构信息

Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou 350122, China.

Department of Cardiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, China.

出版信息

Aging (Albany NY). 2020 May 18;12(10):9427-9439. doi: 10.18632/aging.103216.

DOI:10.18632/aging.103216
PMID:32421687
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7288976/
Abstract

Studies seldom combine biological, behavioral and psychological factors to estimate coronary atherosclerotic heart disease (CHD) risk. Here, we evaluated the associations between these factors and CHD to develop a predictive nomogram to identify those at high risk of CHD. This case-control study included 4392 participants (1578 CHD cases and 2814 controls) in southeast China. Thirty-three biological, behavioral and psychological variables were evaluated. Following multivariate logistic regression analysis, which revealed eight risk factors associated with CHD, a predictive nomogram was developed based on a final model that included the three non-modifiable (sex, age and family history of CHD) and five modifiable (hypertension, hyperlipidemia, diabetes, recent experience of a major traumatic event, and anxiety) variables. The higher total nomogram score, the greater the CHD risk. Final model accuracy (as estimated from the area under the receiver operating characteristic curve) was 0.726 (95% confidence interval: 0.709-0.747). Validation analysis confirmed the high accuracy of the nomogram. High risk of CHD was associated with several biological, behavioral and psychological factors. We have thus developed an intuitive nomogram that could facilitate development of preliminary prevention strategies for CHD.

摘要

研究很少将生物学、行为和心理因素结合起来评估冠状动脉粥样硬化性心脏病(CHD)的风险。在这里,我们评估了这些因素与 CHD 之间的关联,以开发一个预测列线图来识别那些患 CHD 风险高的人。这项病例对照研究包括中国东南部的 4392 名参与者(1578 例 CHD 病例和 2814 例对照)。评估了 33 个生物学、行为和心理变量。经过多变量逻辑回归分析,揭示了与 CHD 相关的八个风险因素,基于包括三个不可改变(性别、年龄和 CHD 家族史)和五个可改变(高血压、高血脂、糖尿病、近期经历重大创伤事件和焦虑)变量的最终模型,开发了一个预测列线图。总列线图评分越高,CHD 风险越大。最终模型的准确性(根据接受者操作特征曲线下的面积估计)为 0.726(95%置信区间:0.709-0.747)。验证分析证实了该列线图的高准确性。CHD 的高风险与几个生物学、行为和心理因素有关。因此,我们开发了一个直观的列线图,可以帮助制定 CHD 的初步预防策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a584/7288976/8420a4631baa/aging-12-103216-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a584/7288976/c238b99ae2ad/aging-12-103216-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a584/7288976/cd4942bbab06/aging-12-103216-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a584/7288976/ae17267ed8ed/aging-12-103216-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a584/7288976/91ff995bc70d/aging-12-103216-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a584/7288976/8420a4631baa/aging-12-103216-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a584/7288976/c238b99ae2ad/aging-12-103216-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a584/7288976/cd4942bbab06/aging-12-103216-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a584/7288976/ae17267ed8ed/aging-12-103216-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a584/7288976/91ff995bc70d/aging-12-103216-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a584/7288976/8420a4631baa/aging-12-103216-g005.jpg

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