Establishment and validation of a nomogram for suicidality in Chinese secondary school students

Accurately identifying high-risk of suicide groups and conducting appropriate interventions are important to reduce the risk of suicide. In this study, a nomogram technique was used to develop a predictive model for the suicidality of secondary school students based on four aspects: individual characteristics; health risk behaviors; family factors; and school factors. A total of 9338 secondary school students were surveyed using the stratified cluster sampling method, and subjects were randomly divided into a training set (n = 6366) and a validation set (n = 2728). In the former, the results of the lasso regression and random forest were combined, from which 7 optimal predictors of suicidality were determined. These were used to construct a nomogram. This nomogram's discrimination, calibration, clinical applicability, and generalization were assessed using receiver operating characteristic curves (ROC), calibration curves, decision curve analysis (DCA), and internal validation. Gender, depression symptoms, self-injury, running away from home, parents' relationship, relationship with father, and academic stress were found to be significant predictors of suicidality. The area under the curve (AUC) of the training set was 0.806, while that of the validation data was 0.792. The calibration curve of the nomogram was close to the diagonal, and the DCA showed the nomogram was clinically beneficial across a range of thresholds of 9–89 %. Causal inference is limited due to the cross-sectional design. An effective tool was constructed for predicting suicidality among secondary school students, which should help school healthcare personnel assess this information about students and also identify high-risk groups.