Development and validation of a model for predicting the risk of suicide in patients with cancer. Issue 2 (3rd April 2023)

• Record Type: Journal Article
• Authors: Du, Lin; Shi, Hai-Yan; Qian, Yan-; Jin, Xiao-Hong; Yu, Hai-Rong; Fu, Xue-Lei; Wu, Hua; Chen, Hong-Lin
• Is Part Of: Archives of suicide research. Volume 27:Issue 2(2023)
• Page Start: 644
• Page End: 659
• Publication Date: 2023-04-03
• Subjects: Cancer -- calibration -- discrimination -- nomogram model -- suicide ideation; Suicide -- Periodicals; 179.7
• DOI: 10.1080/13811118.2022.2035289
• Languages: English
• ISSNs: 1381-1118

Abstract

Abstract: Objective: The objective of this study was to establish a nomogram model to predict SI in patients with cancer and further evaluate its performance. Method: This study was performed among 390 patients in oncology departments of Affiliated Hospital of Nantong University from April 2020 to January 2021. Of these, eligible patients who were diagnosed with cancer were split into training and validation cohorts according the ratio of 2:1 randomly. In the training cohort, multivariate regression was performed to determine the independent variables related to SI. A nomogram was built incorporating these variables. The model performance was evaluated by an independent validation cohort. Results: The prevalence of SI in patients with cancer was 22.31% and 19.23% in training and validation cohorts, respectively. The nomogram model suggested independent variables for SI, including depression, emotional function, time after diagnosis, family function and educational status. The area under the curve (AUC) was 0.93 (95%CI, 0.90–0.97) and 0.82 (95%CI, 0.74–0.90) in training and validation cohorts respectively, which indicated good discrimination of the nomogram in predicting SI in cancer patients. The p-value of the goodness of fit (GOF) test was 0.197 and 0.974 in training and validation cohorts respectively, suggesting our nomogram model has acceptable calibration power, and the calibration curves further indicated good calibration power. Conclusion: In conclusion, the nomogram model for predicting individualized probability of SI could help clinical caregivers estimate the risk of SI in patients with cancer and provide appropriate management.