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Construction and evaluation of leukemia suicide risk predictive model based on SEER database
Shan Zheng1,2,3, Yuxin Tong1,2,3, Jiayi Chen1,2,3
1Department of Hematology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China.
Frontiers in Psychiatry
|March 10, 2025
Summary
Suicide risk in leukemia patients can now be predicted using a new model. This tool helps identify high-risk individuals for timely interventions, potentially reducing preventable deaths.
Area of Science:
- Oncology
- Psychiatry
- Biostatistics
Background:
- Leukemia patients exhibit an elevated suicide rate.
- Accurate suicide risk assessment is crucial for intervention.
Purpose of the Study:
- Develop and validate a predictive model for suicide risk in leukemia patients.
- Facilitate early identification of high-risk individuals in clinical settings.
Main Methods:
- Utilized a large cohort (194,584 patients) from the SEER database (2000-2020).
- Employed Cox proportional hazards model and nomogram construction.
- Validated model performance using concordance index (C-index) and receiver operating characteristic (ROC) curves.
Main Results:
- Key predictors include age, gender, race, residence, marital status, and histologic type.
- Achieved high discrimination with C-indexes of 0.798 (training) and 0.776 (validation).
- Calibration plots confirmed strong agreement between predicted and actual outcomes; Kaplan-Meier curves showed significant risk stratification.
Conclusions:
- An intuitive and robust predictive model for leukemia patient suicide risk has been developed.
- This tool can aid in reducing preventable deaths through targeted interventions.
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