Background: Higher suicide rates were observed in patients diagnosed with lymphoma. In this study, we accurately identified patients with high-risk lymphoma for suicide by constructing a nomogram with a view to effective interventions and reducing the risk of suicide.
Methods: 235,806 patients diagnosed with lymphoma between 2000 and 2020 were picked from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into training (N = 165,064) and validation set (N = 70,742). A combination of the Least absolute shrinkage and selection operator (LASSO) and Cox proportional hazards regression identified the predictors that constructed the nomogram. To assess the discrimination, calibration, clinical applicability, and generalization of this nomogram, we implemented receiver operating characteristic curves (ROC), calibration curves, decision curve analysis (DCA), and internal validation. The robustness of the results was assessed by the competing risks regression model.
Results: Age at diagnosis, gender, ethnicity, marital status, stage, surgery, radiotherapy, and annual household income were key predictors of suicide in lymphoma patients. A nomogram was created to visualize the risk of suicide after a lymphoma diagnosis. The c-index for the training set was 0.773, and the validation set was 0.777. The calibration curve for the nomogram fitted well with the diagonal and the clinical decision curve indicated its clinical benefit.
Limitation: The effects of unmeasured and unnoticed biases and confounders were difficult to eliminate due to retrospective studies.
Conclusion: A convenient and reliable model has been constructed that will help to individualize and accurately quantify the risk of suicide in patients diagnosed with lymphoma.
Keywords: Lymphoma; Machine learning; Nomogram; Suicide.
Copyright © 2024 Elsevier B.V. All rights reserved.