Development and validation of a nomogram for predicting suicide risk factors in thyroid cancer patients following diagnosis: a population-based retrospective study

TL;DR

This study created a tool to predict suicide risk in thyroid cancer patients using population data, helping doctors identify and support high-risk individuals.

Contribution

The first nomogram for thyroid cancer patients integrating histopathological, therapeutic, and socioeconomic predictors of suicide risk.

Findings

The nomogram achieved C-indexes of 0.760 in training and 0.724 in testing sets, showing good predictive performance.

Calibration curves showed good agreement between predicted and observed outcomes.

Decision curve analysis confirmed the clinical utility of the nomogram.

Abstract

To develop and validate a user-oriented nomogram of suicide risk in thyroid cancer patients to enable clinicians to identify and intervene in a timely manner with high-risk subgroups.

This was a retrospective, population-based cohort study in which patients with thyroid cancer diagnosed from the Surveillance, Epidemiology, and End Results (SEER) database between 2000 and 2020 were include. Optimized features were screened by the least absolute shrinkage and selection operator (LASSO) regression model. Subsequently, we selected variables with nonzero coefficients, entered them into a Cox proportional hazards regression model and constructed a visualized nomogram model predicting suicide. We implemented receiver operating characteristic curves (ROC), calibration curves, decision curve analysis (DCA), and internal validation to assess the discrimination, calibration, clinical applicability, and generalizability of the nomogram.

To our knowledge, this is the first nomogram specifically designed for thyroid cancer patients, integrating histopathological, therapeutic, and socioeconomic predictors. Furthermore, the calibration curves for this nomogram fit well with the diagonal, and the C-indexes for the training and testing sets were 0.760 and 0.724, respectively, and the decision curve analysis indicated clinical benefit.

This study successfully identified risk factors for suicide in patients with thyroid cancer and developed a nomogram that provides patients with an individualized, quantifiable assessment of suicide risk and assists clinicians in identifying and intervening in potential suicides.

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