Construction of a nomogram-based risk prediction model for depressive symptoms in middle-aged and young breast cancer patients
- Ye Mao 1, Rui-Xin Shi 1, Lei-Ming Gao 1, An-Ying Xu 2, Jia-Ning Li 3, Bei Wang 3,4, Jun-Yuan Wu 5
- Ye Mao 1, Rui-Xin Shi 1, Lei-Ming Gao 1
- 1School of Nursing, Nanjing University of Chinese Medicine, Nanjing 210023, Jiangsu Province, China.
- 2School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing 210023, Jiangsu Province, China.
- 3Department of Nursing, Province Academy of Traditional Chinese Medicine, Nanjing 210028, Jiangsu Province, China.
- 4Department of Nursing, Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing 210028, Jiangsu Province, China. wwthk1998@163.com.
- 5Department of Critical Care Medicine, Province Academy of Traditional Chinese Medicine, Nanjing 210028, Jiangsu Province, China.
- 0School of Nursing, Nanjing University of Chinese Medicine, Nanjing 210023, Jiangsu Province, China.
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View abstract on PubMed
Summary
This summary is machine-generated.Young and middle-aged breast cancer patients often experience depression. This study identified key factors like tumor grade and pain, developing a predictive model to help clinicians identify at-risk individuals early.
Area Of Science
- Oncology
- Psychiatry
- Public Health
Background
- Breast cancer (BC) is a leading global malignancy, disproportionately affecting young and middle-aged individuals.
- This demographic faces unique challenges including psychological distress, higher recurrence rates, and lower survival rates.
- Existing research lacks comprehensive analysis of depression predictors and risk models for this specific patient group.
Purpose Of The Study
- To investigate factors associated with depressive symptoms in young and middle-aged breast cancer patients.
- To develop and validate a predictive model for identifying individuals at high risk of depression.
Main Methods
- A cohort of 360 breast cancer patients from two tertiary hospitals in Jiangsu Province, China, were analyzed.
- Data collection involved questionnaires on demographics, depression (PHQ-D), pain (VAS), family support (FRSS), and physical activity (IPAQ-LF).
- Univariate and multivariate analyses were employed to identify significant predictors, followed by predictive model construction.
Main Results
- The incidence of depressive symptoms was 38.61% among the study participants.
- Multivariate analysis identified tumor grade, monthly income, pain score, family support, and physical activity as significant influencing factors (P < 0.05).
- The developed predictive model demonstrated strong performance with an AUC of 0.852 and high sensitivity (86.80%) and specificity (89.50%).
Conclusions
- A validated predictive model for depression risk in young and middle-aged breast cancer patients has been developed.
- This model can aid clinicians in early identification of high-risk individuals.
- Implementing targeted preventive strategies based on this model can help reduce the incidence of depressive symptoms in this vulnerable population.
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