Key result
Risk prediction models for depression in patients with coronary heart disease demonstrated good overall predictive performance with a pooled area under the curve of 0.84, though they carry a high risk of bias.
Why the study?
Risk prediction models for depression in coronary heart disease are increasingly developed, but their quality and clinical applicability remain uncertain.
Do risk prediction models improve the identification of depression in patients with coronary heart disease?
Meta-Analysis (n=8,035)
Do risk prediction models improve the identification of depression in patients with coronary heart disease?
Effect estimate: AUC 0.84 (95% CI 0.80-0.88)
While depression risk prediction models for CHD patients show good discriminative performance (AUC 0.772-0.961), their clinical utility is currently limited by high risk of bias and a lack of external validation.
Current CHD depression prediction models warrant caution due to bias despite good performance; challenges routine adoption and calls for rigorous external validation.
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).
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Zhang et al. (2025) conducted a meta-analysis in Coronary heart disease and depression (n=8,035). Depression risk prediction models was evaluated on Predictive performance (Area Under the Curve) for depression (AUC 0.84, 95% CI 0.80-0.88). Risk prediction models for depression in patients with coronary heart disease demonstrated good overall predictive performance with a pooled area under the curve of 0.84, though they carry a high risk of bias.
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