Abstract
BACKGROUND: Risk prediction models for depression in patients with coronary heart disease are increasingly being developed. However, the quality and applicability of these models in clinical practice remain uncertain. OBJECTIVE: To systematically evaluate depression risk prediction models in patients with coronary heart disease (CHD). METHODS: Databases including PubMed, Web of Science, Embase, Cochrane Library, CNKI, Wanfang, VIP, and SinoMed were searched for relevant studies from inception to September 29, 2024. Two researchers independently screened the literature, extracted data, and used the Prediction Model Risk of Bias Assessment Tool (PROBAST) to evaluate the models' risk of bias and applicability. RESULTS: Eight studies, encompassing 13 risk prediction models and involving 8,035 CHD patients, were included, with 1,971 patients diagnosed with depression. Common predictors included age, educational level, gender, and cardiac function classification. The area under the curve (AUC) for the models ranged from 0.772 to 0.961, indicating overall good performance; however, risk of bias was high, primarily due to issues in the analysis phase, such as inadequate handling of missing values, univariate analysis for variable selection, and lack of external validation. CONCLUSION: Depression risk prediction models for CHD patients generally perform well, but high risk of bias and limited applicability remain concerns. Future studies should focus on developing and validating more robust models to aid healthcare professionals in early identification of high-risk patients for depression. SYSTEMATIC REVIEW REGISTRATION: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42024625641, identifier (CRD42024625641).