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Diagnostic Prediction Models for Depression in Patients With Breast Cancer: A Systematic Review
Xiaosong Yu1,2, Lin Zhang1, Qianqian Wan2,3
1Department of Nursing, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China.
Aim:
To systematically review the evidence on diagnostic prediction models for depression in patients with breast cancer.
Design:
Systematic review.
Data Sources:
Ten databases were searched from inception to 22 August 2025, with an updated search on 17 December 2025, to identify original studies developing and/or validating diagnostic prediction models for depression in patients with breast cancer.
Methods:
Data were extracted using the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) framework. Two reviewers independently assessed risk of bias and applicability of included studies using the Prediction Model Risk of Bias Assessment Tool (PROBAST).
Results:
Eleven studies were included. Reported area under the curve (AUC) values ranged from 0.784 to 0.890. All included studies were judged to be at high risk of bias, and seven raised high concerns regarding applicability. There was substantial heterogeneity in predictor selection across studies, with age, income level and family support being the most frequently reported predictors.
Conclusion:
Although preliminary research on diagnostic prediction models for depression in patients with breast cancer has been undertaken, their methodological quality remains weak. Reporting of external validation and calibration assessment was limited. Current evidence is therefore insufficient to support their routine use in nursing practice. Future research should standardise model development and validation and strengthen the evaluation of model performance.
Implications For The Profession And/Or Patient Care:
This review suggests that existing diagnostic prediction models for depression in patients with breast cancer are not yet sufficiently robust for routine nursing use, but may provide a reference for future nursing screening research and the optimisation of related tools.
Impact:
This review synthesises the available evidence on diagnostic prediction models for depression in patients with breast cancer and provides a basis for future model development, validation and optimisation.
Reporting Method:
This review was reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement and the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis: Systematic Reviews and Meta-Analyses (TRIPOD-SRMA) checklist.
Patient Or Public Contribution:
No patient or public contribution.
