Abstract
BACKGROUND: Breast cancer affects over 2.3 million women worldwide annually, with 30%-50% experiencing psychological morbidity, including anxiety and depression. Family functioning plays a crucial role in patients' psychological adaptation to cancer, yet the specific predictive relationship between family dysfunction dimensions and psychological outcomes remains insufficiently characterized. This study aimed to systematically examine how family dysfunction predicts psychological morbidity in breast cancer patients. AIM: To explore the predictive role of family dysfunction on psychological morbidity in breast cancer patients and develop a clinical risk prediction model. METHODS: A retrospective cohort study was conducted among 285 breast cancer patients from June 2022 to March 2025 at a provincial tertiary hospital. Family functioning was assessed using the Family Assessment Device, and psychological health was evaluated using the Hospital Anxiety and Depression Scale. Univariate and multivariate logistic regression analyses were performed to identify predictive factors. A comprehensive risk prediction model was developed and validated. RESULTS: The overall psychological morbidity rate was 41.5% (n = 118), with anxiety symptoms in 28.1% and depressive symptoms in 31.9% of patients. Family dysfunction was present in 32.8% of patients, with communication dysfunction being most prevalent (41.8%). Pearson correlation analysis revealed significant positive correlations between all family dysfunction dimensions and psychological morbidity indicators, with communication dysfunction showing the strongest correlation (r = 0.542 for anxiety, r = 0.518 for depression, both P < 0.001). Multivariate logistic regression demonstrated that overall family dysfunction was an independent predictor of psychological morbidity (odds ratio = 3.247, 95% confidence interval: 1.832-5.756, P < 0.001), explaining 23.6% of variance (Nagelkerke R (2) = 0.236). CONCLUSION: Family dysfunction significantly predicts psychological morbidity in breast cancer patients, with communication dysfunction being the most critical dimension. The developed risk prediction model demonstrates good accuracy for clinical decision-making.