可穿戴心电设备预测基层老年心血管代谢疾病患者抑郁发生风险

摘要： 背景 心血管代谢疾病与抑郁密切相关。基层心血管代谢疾病管理工作已初具规模，但对于患者抑郁等心理问题一方面缺乏关注，另一方面缺乏简单、准确、高效的筛查评估工具。 目的 应用单导联可穿戴心电设备构建宁夏基层老年心血管代谢疾病患者抑郁发生风险的列线图预测模型并验证。 方法 选取2022年1月—2023年6月宁夏地区5个市的20家基层医疗卫生机构应用单导联可穿戴心电设备采集并上传至云平台的3 121例65岁以上高血压、糖尿病、冠心病等心血管代谢疾病患者的心电图资料及云平台收集的患者社会人口学、生活行为方式、心理健康等资料。采用简单随机抽样方法按3∶1比例分为训练集（2 341例）和验证集（780例）。通过运行RStudio 4.1.1软件，使用LASSO回归分析以及交叉验证筛选最佳预测因子，采用多因素Logistic回归分析构建预测老年心血管代谢疾病患者抑郁发生风险的列线图预测模型，采用受试者工作特征（ROC）曲线、校正曲线及决策曲线评估模型的效能。 结果 LASSO回归筛选出10个变量：性别、BMI、城乡、运动、焦虑、冠心病、期前收缩、RR间期平均值标准差（SDANN）、正常相邻窦性RR间期差值均方根（rMSSD）、睡眠效率；多因素Logistic回归分析结果显示，性别（OR=1.747，95%CI=1.258~2.434）、BMI（OR=1.073，95%CI=1.024~1.125）、城乡（OR=1.684，95%CI=1.172~2.456）、运动（OR=0.610，95%CI=0.460~0.799）、焦虑（OR=3.041，95%CI=1.597~5.484）、冠心病（OR=2.743，95%CI=1.971~3.815），期前收缩（OR=4.745，95%CI=1.681~19.977）、SDANN（OR=4.745，95%CI=1.681~19.977）、rMSSD（OR=0.986，95%CI=0.972~0.999）、睡眠效率（OR=0.988，95%CI=0.982~0.995）是老年心血管代谢疾病患者抑郁发生风险的影响因素（P<0.05）。Logistic回归方程Logit（P）=4.322+0.558×性别+0.071×BMI+0.521×城乡-0.494×运动+1.112×焦虑+1.009×冠心病+1.557×期前收缩-0.011×SDANN-0.014×rMSSD-0.012×睡眠效率，基于此构建列线图预测模型，在训练集和验证集中预测老年慢性病患者抑郁发生风险的ROC曲线下面积分别为0.748（95%CI=0.707~0.786，P<0.001）、0.751（95%CI=0.692~0.809，P<0.001），灵敏度分别为75.2%、76.7%，特异度分别为63.4%、60.6%。临床决策曲线显示，在训练集和验证集中当抑郁风险阈值概率分别在8%~35%、8%~37%时，预测老年心血管代谢疾病患者抑郁发生风险的净收益更高。 结论 性别、BMI、城乡、运动、焦虑、冠心病、期前收缩、SDANN、rMSSD、睡眠效率均是老年心血管代谢疾病患者抑郁发生风险的影响因素。本研究基于单导联可穿戴心电设备构建基层老年心血管代谢疾病患者抑郁发生风险的列线图模型，有较好的预测效能及临床应用价值，不但有助于在基层医疗卫生机构对患者进行抑郁筛查与个体化干预措施的制订，还可助力基层心血管疾病防控工作。

关键词: 抑郁, 心血管代谢疾病, 基层医疗卫生机构, 老年人, 单导联可穿戴心电设备, 列线图, 预测模型

Abstract:

Background

Cardiovascular metabolic diseases are closely associated with depression. Although the management of cardiovascular metabolic diseases at the community level has been established, psychological issues such as depression in patients have not received sufficient attention. Moreover, there is a lack of simple, accurate, and efficient screening and assessment tools for depression.

Objective

To apply single-lead wearable electrocardiographic devices to predict the risk of depression in elderly patients with cardiovascular metabolic diseases at the community level of Ning Xia Hui Autonomous Region.

Methods

A total of 3 121 elderly patients (aged over 65) with hypertension, diabetes, coronary heart disease, and other cardiovascular metabolic diseases were selected from 20 primary medical and health care institutions in Ningxia between January 2022 and June 2023. Electrocardiographic data collected via single-lead wearable electrocardiographic devices were uploaded to a cloud platform. Additionally, sociodemographic, lifestyle, and mental health data were collected from the same platform. The data were divided into a training set (2 341 cases) and a validation set (780 cases) using a simple random sampling method at a 3∶1 ratio. LASSO regression analysis and cross-validation were performed using RStudio 4.1.1 software to identify the best predictors. A multivariable Logistic regression model was then established using the predictors selected by LASSO regression. A nomogram model for predicting the risk of depression in elderly patients with cardiovascular metabolic diseases was constructed. The model's efficacy was evaluated using the receiver operating characteristic (ROC) curve, calibration, and decision curve analysis.

Results

In the training set, LASSO regression combined with Logistic regression analysis identified several significant factors associated with depression in elderly patients with cardiovascular metabolic diseases: gender (OR=1.747, 95%CI=1.258-2.434) , BMI (OR=1.073, 95%CI=1.024-1.125) , urban and rural areas (OR=1.684, 95%CI=1.172-2.456) , exercise (OR=0.610, 95%CI=0.460-0.799) , anxiety (OR=3.041, 95%CI=1.597-5.484) , coronary heart disease (OR=2.743, 95%CI=1.971-3.815), premature beats (OR=4.745, 95%CI=1.681-19.977) , standard deviation of average normal-to-normal Intervals (SDANN) (OR=4.745, 95%CI=1.681-19.977) , root mean square deviation (rMSSD) (OR=0.986, 95%CI=0.972-0.999) , and sleep efficiency (OR=0.988, 95%CI=0.982-0.995) . The differences were statistically significant (P<0.05) . The Logistic regression equation Logit (P) =4.322+0.558×gender+0.071×BMI+0.521×urban and rural areas-0.494×exercise+1.112×anxiety+1.009×coronary heart disease+1.557×premature beat-0.011×SDANN-0.014×rMSSD-0.012×sleep efficiency was used to construct a column chart prediction model. The area under the curve for predicting the risk of depression in elderly chronic disease patients in the training and validation sets were 0.748 (95%CI=0.707-0.786, P<0.001) , 75.2%, 63.4% and 0.751 (95%CI=0.692-0.809) , 76.7%, 60.6%, respectively. The clinical decision curve analysis showed that when the probability threshold for depression risk was between 8% and 35% in the training set and between 8% and 37% in the validation set, the net benefit of predicting the risk of depression in elderly patients with cardiovascular metabolic diseases was higher.

Conclusion

Gender, BMI, urban and rural areas, exercise, anxiety, coronary heart disease, premature beats, SDANN, rMSSD, sleep efficiency are contributing factors to the risk of depression in elderly patients with cardiovascular metabolic diseases. This study successfully constructed a nomogram model for predicting the risk of depression in elderly patients with cardiovascular metabolic diseases at the community level, based on single-lead wearable electrocardiographic devices. The model demonstrated good predictive efficacy and clinical application value. It can assist primary medical and health care institutions in conducting depression screening and formulating individualized intervention measures for patients, thereby aiding in the prevention and control of cardiovascular diseases at the community level.

Key words: Depression, Cardiovascular metabolic diseases, Primary medical and health care institution, Aged, Single-lead wearable electrocardiographic device, Nomogram, Prediction model

中图分类号: R 749.42

引用本文

余新艳,马忠,曹凡,等. 基于单导联可穿戴心电设备预测基层老年心血管代谢疾病患者抑郁发生风险的研究[J]. 中国全科医学, 2026, 29(10): 1300-1310. DOI: 10.12114/j.issn.1007-9572.2025.0064.

YU Xinyan,MA Zhong,CAO Fan, et al. Predicting the Risk of Depression in Elderly Patients with Cardiovascular Metabolic Diseases Using Single-lead Wearable Electrocardiography at the Community Level[J]. Chinese General Practice, 2026, 29(10): 1300-1310. DOI: 10.12114/j.issn.1007-9572.2025.0064.