Li Zhao, Zhao Yu, Kang Hyunsik
College of Sport Science, Sungkyunkwan University, Suwon 16419, Republic of Korea.
Healthcare (Basel). 2025 May 29;13(11):1287. doi: 10.3390/healthcare13111287.
Approximately one-third of patients with cardiovascular disease (CVD) experience depression. This study aimed to develop and validate a nomogram for assessing the risk of depression in patients with CVD. : In a cross-sectional study design, we analyzed data obtained from 6702 patients with CVD who participated in the 2007-2018 National Health and Nutrition Examination Survey. The dataset was randomly split into training and validation cohorts at a 0.75 to 0.25 ratio. Univariate and multivariate logistic regression analyses were applied to the training cohort to identify predictors for a web-based dynamic nomogram, which was then validated in the validation cohort. : Blood Cd concentration, sedentary time, eosinophil count, marital status, work limitations, sleep disorders, asthma, stomach or intestinal illness, confusion or memory problems, ethnicity, and cotinine were identified as risk factors for depression in patients with CVD, and these 11 risk factors were incorporated into the nomogram. The area under the curve (AUC) of the nomogram was 0.852 (95% CI: 0.842-0.862) in the training cohort, with a sensitivity of 83.28% and specificity of 72.95%. The AUC was 0.856 (95% CI: 0.838-0.872) in the validation cohort, with a sensitivity of 79.14% and a specificity of 76.65%. The C-index of the nomogram was 0.852 in the training cohort, with a mean absolute error of 0.012 based on 1000 bootstrap replicates. The C-index of the nomogram model was 0.863 in the validation cohort, with a mean absolute error of 0.017. : Our nomogram model demonstrates potential clinical utility for the early screening of depression risk in patients with CVD.
约三分之一的心血管疾病（CVD）患者患有抑郁症。本研究旨在开发并验证一种用于评估CVD患者抑郁风险的列线图。：在一项横断面研究设计中，我们分析了参与2007 - 2018年国家健康与营养检查调查的6702例CVD患者的数据。数据集以0.75比0.25的比例随机分为训练队列和验证队列。对训练队列进行单因素和多因素逻辑回归分析，以确定基于网络的动态列线图的预测因素，然后在验证队列中进行验证。：血镉浓度、久坐时间、嗜酸性粒细胞计数、婚姻状况、工作限制、睡眠障碍、哮喘、胃肠疾病、意识模糊或记忆问题、种族和可替宁被确定为CVD患者抑郁的危险因素，这11个危险因素被纳入列线图。列线图在训练队列中的曲线下面积（AUC）为0.852（95%CI：0.842 - 0.862），灵敏度为83.28%，特异度为72.95%。在验证队列中，AUC为0.856（95%CI：0.838 - 0.872），灵敏度为79.14%，特异度为76.65%。列线图在训练队列中的C指数为0.852，基于1000次自抽样重复的平均绝对误差为0.012。列线图模型在验证队列中的C指数为0.863，平均绝对误差为0.017。：我们的列线图模型显示出在早期筛查CVD患者抑郁风险方面的潜在临床应用价值。
