Related Experiment Video
Updated: May 30, 2025
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
Risk prediction models for depression in patients with coronary heart disease: a systematic review and meta-analysis
Jie Zhang1, Yue Zhou1, Linyu Huang1
1School of Nursing, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Insights
Depression risk models for coronary heart disease (CHD) patients show good performance but have high bias and limited use. More robust models are needed for early depression detection in CHD.
Area of Science:
- Cardiology
- Psychiatry
- Medical Informatics
Background:
- Depression is a significant concern in patients with coronary heart disease (CHD).
- Risk prediction models for depression in CHD patients are emerging.
- The clinical utility and quality of these models require systematic evaluation.
Purpose of the Study:
- To systematically evaluate depression risk prediction models specifically for patients with coronary heart disease (CHD).
- To assess the risk of bias and applicability of existing depression risk prediction models in CHD patients.
Main Methods:
- A comprehensive literature search was conducted across multiple databases (PubMed, Web of Science, Embase, etc.) up to September 29, 2024.
- Two independent researchers screened studies, extracted data, and assessed model quality using the Prediction Model Risk of Bias Assessment Tool (PROBAST).
Main Results:
- Eight studies involving 13 models and 8,035 CHD patients (1,971 with depression) were analyzed.
- Models demonstrated good predictive performance (AUC 0.772–0.961), with common predictors including age, education, gender, and cardiac function.
- A high risk of bias was identified, mainly due to issues in data analysis (missing values, univariate selection) and lack of external validation.
Conclusions:
- Current depression risk prediction models for CHD patients exhibit good performance but suffer from significant bias and limited applicability.
- There is a critical need for the development and validation of more robust models.
- Improved models will enhance early identification of high-risk individuals, aiding clinical decision-making.
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).
More Related Videos
06:55An Unpredictable Chronic Mild Stress Protocol for Instigating Depressive Symptoms, Behavioral Changes and Negative Health Outcomes in Rodents
Published on: December 2, 2015
12:18A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Related Concept Videos
01:27Depressive Disorders: Etiology
Biological Factors in Depression
Biological predispositions significantly influence the risk of developing depressive disorders. Genetic studies highlight the role of variations in the serotonin transporter...
01:27Psychoneuroimmunology: Cardiovascular Disease
A key area of focus in PNI is the relationship between stress and coronary...
01:18Depression: Overview