Construction of a nomogram-based risk prediction model for depressive symptoms in middle-aged and young breast cancer patients.

BACKGROUND

Breast cancer (BC) is the second most common malignancy globally. Young and middle-aged patients face more pressures from diagnosis, treatment, costs, and psychological issues like self-image concerns, social barriers, and professional challenges. Compared to other age groups, they have higher recurrence rates, lower survival rates, and increased risk of depression. Research is lacking on factors influencing depressive symptoms and predictive models for this age group.

AIM

To analyze factors influencing depressive symptoms in young/middle-aged BC patients and construct a depression risk predictive model.

METHODS

A total of 360 patients undergoing BC treatment at two tertiary hospitals in Jiangsu Province, China from November 2023 to April 2024 were included in the study. Participants were surveyed using a general information questionnaire, the patient health questionnaire depression scale, the visual analog scale for pain, the revised family support scale, and the long form of the international physical activity questionnaire. Univariate and multivariate analyses were conducted to identify the factors affecting depression in middle-aged and young BC patients, and a predictive model for depression risk was developed based on these findings.

RESULTS

Among the 360 middle-aged and young BC patients, the incidence rate of depressive symptoms was 38.61% (139/360). Multivariate analysis revealed that tumor grade, patient's monthly income, pain score, family support score, and physical activity score were factors influencing depression in this patient group ( < 0.05). The risk prediction model constructed based on these factors yielded an area under the receiver operating characteristic curve of 0.852, with a maximum Youden index of 0.973, sensitivity of 86.80%, specificity of 89.50%, and a diagnostic odds ratio of 0.552. The Hosmer-Lemeshow test for goodness of fit indicated an adequate model fit ( = 0.360, = 0.981).

CONCLUSION

The constructed predictive model demonstrates good predictive performance and can serve as a reference for medical professionals to early identify high-risk patients and implement corresponding preventive measures to decrease the incidence of depressive symptoms in this population.