Non-small cell lung cancer (NSCLC) accounts for the majority of lung cancer cases, which has been widely concerned. Suicide is a significant non-cancer cause of death among cancer patients, with NSCLC patient, especially older males, exhibiting elevated risk. Despite this, targeted research on suicide in NSCLC remains limited. This study assessed the standardized mortality ratio (SMR) of suicide among middle-aged and elderly NSCLC patients and developed a predictive nomogram for suicide risk. A retrospective analysis was conducted utilizing data from the Surveillance, Epidemiology, and End Results database for NSCLC patients (2004–2015). Baseline demographics and clinical features were collected, and the SMR and proportional mortality ratio for suicide were calculated. Significant risk factors were identified through univariate and multivariate logistic regression analyses. These predictors formed the basis for developing a nomogram, which underwent validation via receiver operating characteristic analysis, calibration plots, and decision curve analysis. The overall suicide SMR in NSCLC patients was 7.9 (95% confidence intervals CI: 7.0–8.8) times that of the general population. Proportional mortality ratio analysis revealed that male patients had higher suicide rates compared to female groups during 2004 to 2015. Notably, 56% of suicides occurred within 1 year of diagnosis, increasing to 70% within 2 years. Logistic regression identified gender ( P < .001), race ( P = .039), and radiotherapy ( P = .019) as key predictors. These variables were subsequently incorporated into a predictive nomogram for clinical risk stratification. Using receiver operating characteristic curves evaluated discriminative capability of the model, the area under the curve was 0.717 (95% CI: 0.676–0.757) for the training cohort and 0.671 (95% CI: 0.619–0.724) for the validation group. Gender, race, and radiotherapy significantly affect suicide risk in NSCLC patients. The developed nomogram offers a practical instrument for identifying high-risk individuals, enabling timely and targeted interventions.
Xu et al. (Fri,) studied this question.