Risk prediction models for depression in patients with coronary heart disease: a systematic review and meta-analysis
BackgroundRisk 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.ObjectiveTo systematically evaluate depression risk prediction models in patients wit...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Cardiovascular Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcvm.2024.1522619/full |
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| author | Jie Zhang Yue Zhou Linyu Huang Xingling Zhang Long Li Chongcheng Xi |
| author_facet | Jie Zhang Yue Zhou Linyu Huang Xingling Zhang Long Li Chongcheng Xi |
| author_sort | Jie Zhang |
| collection | DOAJ |
| description | BackgroundRisk 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.ObjectiveTo systematically evaluate depression risk prediction models in patients with coronary heart disease (CHD).MethodsDatabases 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.ResultsEight 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.ConclusionDepression 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 Registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42024625641, identifier (CRD42024625641). |
| format | Article |
| id | doaj-art-cafd6abdbdbd4ac28ec99ccabbe07783 |
| institution | Kabale University |
| issn | 2297-055X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cardiovascular Medicine |
| spelling | doaj-art-cafd6abdbdbd4ac28ec99ccabbe077832025-01-15T06:10:33ZengFrontiers Media S.A.Frontiers in Cardiovascular Medicine2297-055X2025-01-011110.3389/fcvm.2024.15226191522619Risk prediction models for depression in patients with coronary heart disease: a systematic review and meta-analysisJie Zhang0Yue Zhou1Linyu Huang2Xingling Zhang3Long Li4Chongcheng Xi5School of Nursing, Chengdu University of Traditional Chinese Medicine, Chengdu, ChinaSchool of Nursing, Chengdu University of Traditional Chinese Medicine, Chengdu, ChinaSchool of Nursing, Chengdu University of Traditional Chinese Medicine, Chengdu, ChinaSchool of Nursing, Chengdu University of Traditional Chinese Medicine, Chengdu, ChinaSchool of Nursing, Chengdu University of Traditional Chinese Medicine, Chengdu, ChinaSchool of Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, ChinaBackgroundRisk 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.ObjectiveTo systematically evaluate depression risk prediction models in patients with coronary heart disease (CHD).MethodsDatabases 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.ResultsEight 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.ConclusionDepression 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 Registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42024625641, identifier (CRD42024625641).https://www.frontiersin.org/articles/10.3389/fcvm.2024.1522619/fullcoronary heart diseasedepressionprediction modelssystematic reviewmeta-analysis |
| spellingShingle | Jie Zhang Yue Zhou Linyu Huang Xingling Zhang Long Li Chongcheng Xi Risk prediction models for depression in patients with coronary heart disease: a systematic review and meta-analysis Frontiers in Cardiovascular Medicine coronary heart disease depression prediction models systematic review meta-analysis |
| title | Risk prediction models for depression in patients with coronary heart disease: a systematic review and meta-analysis |
| title_full | Risk prediction models for depression in patients with coronary heart disease: a systematic review and meta-analysis |
| title_fullStr | Risk prediction models for depression in patients with coronary heart disease: a systematic review and meta-analysis |
| title_full_unstemmed | Risk prediction models for depression in patients with coronary heart disease: a systematic review and meta-analysis |
| title_short | Risk prediction models for depression in patients with coronary heart disease: a systematic review and meta-analysis |
| title_sort | risk prediction models for depression in patients with coronary heart disease a systematic review and meta analysis |
| topic | coronary heart disease depression prediction models systematic review meta-analysis |
| url | https://www.frontiersin.org/articles/10.3389/fcvm.2024.1522619/full |
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