Prediction of depression risk in patients with coronary heart disease based on nomogram for Chinese population: a population-based multi-center study from 2016 to 2018

Abstract

Abstract Background: This study aimed to assess the prevalence and identify risk factors associated with depression among coronary heart disease (CHD) patients at different stages in China. Methods: Conducted as a hospital-based, cross-sectional study across 48 hospitals in 23 provinces, the research spanned from October 2016 to April 2018. A total of 9044 patients were initially recruited, with 8353 deemed eligible for participation. Depression was assessed using the nine-item Patient Health Questionnaire-9 (PHQ-9) Scale. Univariate analysis identified predictors of postoperative depression, and binary logistic regression analysis was employed to ascertain risk factors associated with depressive symptoms. The predictive model was constructed using the "rms" package in R software, demonstrating robust predictive capabilities according to the ROC curve. Results: In general, both the degree and overall score based on the PHQ-9 revealed a trend: as the severity of the disease increased, so did the severity of patient depression. Univariate analysis indicated statistical differences concerning general situations and lifestyles. The binary logistic regression model highlighted the proximity of depression to risk factors such as gender, nationality, marital status, education, drinking, BMI, sleep disturbance, and disease status. Utilizing these findings, a predictive nomogram for depression was developed. The model exhibited excellent predictive ability, with an AUC of 0.768 (95% CI = 0.757–0.780). Conclusions: This study systematically investigated the prevalence of depression among coronary heart disease patients at various stages. As coronary heart disease advanced, the level of depression intensified. The nomogram developed in this study proves valuable in predicting the incidence of depression in coronary heart disease patients.