Yujun Lee ^1,2* Xianghong Ding ³ Zijuan Shi ¹ Liping Xiang ¹ Qin Liu ¹ Li Wu ¹ Qingwen Long ¹

• ¹ Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan Province, China
• ² Xi'an International Studies University, Xi'an, Shaanxi Province, China
• ³ Mental Health Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, Shanxi Province, China

Background: Comorbid depression, frequently observed in heart disease patients, has detrimental effects on mental health and may exacerbate cardiac conditions. The objective of this study was to create and validate a risk prediction nomogram specifically for comorbid depression in elderly patients suffering from heart disease. Methods: The 2018 data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) was analyzed and 2,110 elderly patients with heart disease aged 60 and above were included in the study. They were randomly divided in a 7:3 ratio into a training set (n=1,477) and a validation set (n=633). Depression symptoms were assessed using the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10) and the participants were categorized into depressed (n=687) and non-depressed (n=1423) groups. We collected information regarding general demographics, lifestyle habits, and medical history of the included patients. LASSO regression and binary logistic regression analyses were performed to identify independent risk factors and construct the depression prediction nomogram. Receiver operating characteristic curve analysis and the Hosmer-Lemeshow test were used to assess the model's discrimination and calibration. Decision curve analysis helped evaluate the clinical utility of the predictive nomogram. Results: Based on the LASSO and multivariable regression analyses, education level, quality of life, sleep quality, frequency of watching TV, and history of arthritis were identified as independent risk factors for comorbid depression in the elderly heart disease patients. A nomogram model was constructed with these five independent risk factors. The nomogram showed good clinical performance with an area under the curve (AUC) value of 0.816 (95% CI: 0.793 to 0.839). The calibration curves and Hosmer-Lemeshow goodness-of-fit test (training set χt2=4.796, p=0.760; validation set χv2=7.236, p=0.511) showed its satisfactory. Clinical usefulness of the nomogram was confirmed by decision curve analysis. Conclusions: A five-parameter nomogram for predicting depression in elderly heart disease patients was developed and validated. The nomogram demonstrated high accuracy, discrimination ability, and clinical utility in assessing the risk of depression in the elderly patients with heart disease.