Objective: The risk of suicide can be decreased by accurately identifying high-risk suicide groups and implementing the right interventions. The aim of this study was to develop a predictive nomogram for suicide attempts (SA) in patients with first-episode drug-naïve (FEDN) major depressive disorder (MDD).
Methods: A cross-sectional investigation was conducted, enrolling 1,718 patients diagnosed with FEDN MDD who provided comprehensive clinical data between September 2016 and December 2018. Anthropometric and sociodemographic data were collected from the participants. The severity of depression and anxiety in all subjects was assessed using the 17-item Hamilton Depression Scale (HAMD-17) and the Hamilton Anxiety Scale (HAMA), respectively. Additionally, thyroid hormone levels, lipid profile parameters, and fasting blood glucose (FBG) were measured. The confirmation of SA history relied on an amalgamation of medical records, patient interviews, and family interviews. Random allocation assigned participants to either the training group (70%, n = 1,204) or the validation group (30%, n = 514). In the training group, LASSO analysis and multivariate regression were employed to identify the relevant variables associated with SA. Subsequently, a nomogram was developed based on the selected risk factors to predict the probability of SA within the training group. To assess the accuracy of the prediction, the area under the receiver operating characteristic curve (AUC) was utilized, and calibration plots were employed to evaluate calibration. Additionally, decision curve analysis (DCA) was performed to assess the precision of the prediction model. Finally, internal validation was carried out using the validation group.
Results: We have successfully developed a readily applicable nomogram that utilizes HAMD, HAMA, thyroid stimulating hormone (TSH), thyroid peroxidase antibody (TPOAb), and systolic blood pressure (SBP) parameters to forecast the likelihood of SA in Chinese patients with FEDN MDD. In both our training and validation groups, the pooled area under the ROC for SA risk was determined to be 0.802 (95% CI: 0.771 to 0.832) and 0.821 (95% CI: 0.774 to 0.868), respectively. Calibration analysis demonstrated a favorable alignment between the predicted probabilities from the nomogram and the actual probabilities. Decision curve analysis confirmed the clinical utility of the nomogram. To facilitate the utilization of the nomogram by clinicians and researchers, an online version is available at https://doctorjunjunliu.shinyapps.io/dynnomapp/.
Conclusions: We constructed and validated a nomogram capable of early identification of FEDN MDD patients with a high risk of SA, thereby contributing to the implementation of effective suicide prevention programs.