Mao Ye, Shi Rui-Xin, Gao Lei-Ming, Xu An-Ying, Li Jia-Ning, Wang Bei, Wu Jun-Yuan
School of Nursing, Nanjing University of Chinese Medicine, Nanjing 210023, Jiangsu Province, China.
School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing 210023, Jiangsu Province, China.
World J Clin Oncol. 2025 Apr 24;16(4):102208. doi: 10.5306/wjco.v16.i4.102208.
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.
To analyze factors influencing depressive symptoms in young/middle-aged BC patients and construct a depression risk predictive model.
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.
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).
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.
乳腺癌（BC）是全球第二常见的恶性肿瘤。中青年患者在诊断、治疗、费用以及自我形象关注、社会障碍和职业挑战等心理问题上面临更多压力。与其他年龄组相比，他们的复发率更高，生存率更低，患抑郁症的风险增加。目前缺乏针对该年龄组影响抑郁症状的因素及预测模型的研究。
分析中青年乳腺癌患者抑郁症状的影响因素并构建抑郁风险预测模型。
选取2023年11月至2024年4月在中国江苏省两家三级医院接受乳腺癌治疗的360例患者纳入研究。采用一般信息问卷、患者健康问卷抑郁量表、视觉模拟疼痛量表、修订后的家庭支持量表和国际体力活动问卷长表对参与者进行调查。进行单因素和多因素分析以确定影响中青年乳腺癌患者抑郁的因素，并基于这些结果建立抑郁风险预测模型。
在360例中青年乳腺癌患者中，抑郁症状的发生率为38.61%（139/360）。多因素分析显示，肿瘤分级、患者月收入、疼痛评分、家庭支持评分和体力活动评分是影响该患者组抑郁的因素（<0.05）。基于这些因素构建的风险预测模型的受试者工作特征曲线下面积为0.852，最大约登指数为0.973，灵敏度为86.80%，特异度为89.50%，诊断比值比为0.552。拟合优度的Hosmer-Lemeshow检验表明模型拟合良好（=0.360，=0.981）。
构建的预测模型具有良好的预测性能，可为医务人员早期识别高危患者并采取相应预防措施以降低该人群抑郁症状的发生率提供参考。
