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.
