TY  - JOUR
PY  - 2024//
TI  - Development of a prediction model for suicidal ideation in patients with advanced cancer: a multicenter, real-world, pan-cancer study in China
JO  - Cancer medicine
A1  - He, Yi
A1  - Pang, Ying
A1  - Yang, Wenlei
A1  - Su, Zhongge
A1  - Wang, Yu
A1  - Lu, Yongkui
A1  - Jiang, Yu
A1  - Zhou, Yuhe
A1  - Han, Xinkun
A1  - Song, Lihua
A1  - Wang, Liping
A1  - Li, Zimeng
A1  - Lv, Xiaojun
A1  - Wang, Yan
A1  - Yao, Juntao
A1  - Liu, Xiaohong
A1  - Zhou, Xiaoyi
A1  - He, Shuangzhi
A1  - Zhang, Yening
A1  - Song, Lili
A1  - Li, Jinjiang
A1  - Wang, Bingmei
A1  - Ke, Yang
A1  - He, Zhonghu
A1  - Tang, Lili
SP  - e7439
EP  - e7439
VL  - 13
IS  - 12
N2  - BACKGROUND: Patients diagnosed with advanced stage cancer face an elevated risk of suicide. We aimed to develop a suicidal ideation (SI) risk prediction model in patients with advanced cancer for early warning of their SI and facilitate suicide prevention in this population. <br><br>PATIENTS AND METHODS: We consecutively enrolled patients with multiple types of advanced cancers from 10 cancer institutes in China from August 2019 to December 2020. Demographic characteristics, clinicopathological data, and clinical treatment history were extracted from medical records. Symptom burden, psychological status, and SI were assessed using the MD Anderson Symptom Inventory (MDASI), Hospital Anxiety and Depression Scale (HADS), and Patient Health Questionnaire-9 (PHQ-9), respectively. A multivariable logistic regression model was employed to establish the model structure. <br><br>RESULTS: In total, 2814 participants were included in the final analysis. Nine predictors including age, sex, number of household members, history of previous chemotherapy, history of previous surgery, MDASI score, HADS-A score, HADS-D score, and life satisfaction were retained in the final SI prediction model. The model achieved an area under the curve (AUC) of 0.85 (95% confidential interval: 0.82-0.87), with AUCs ranging from 0.75 to 0.95 across 10 hospitals and higher than 0.83 for all cancer types. <br><br>CONCLUSION: This study built an easy-to-use, good-performance predictive model for SI. Implementation of this model could facilitate the incorporation of psychosocial support for suicide prevention into the standard care of patients with advanced cancer.<p />  <p>Language: en</p>
LA  - en
SN  - 2045-7634
UR  - http://dx.doi.org/10.1002/cam4.7439
ID  - ref1
ER  -