This large-scale cohort study enrolled all full-time students (N=2,160) aged 16-18 years from a vocational high school in Wuhan during September 2022. The inclusion criteria required: Complete responses on core psychosocial measures (NSSI, depressive symptoms, etc.); Guardian-approved informed consent. Exclusion criteria eliminated: Questionnaires with >20% missing items in key domains (n=51); Invalid responses showing response bias (e.g., identical scores across 90% items, n=28); Participants outside the 16-18 age range (n=0). The final analytical sample comprised 2,081 students (916 males, 1,165 females), representing 96.34% of initial participants. The study was approved by the Ethics Committee of Wuhan Mental Health Center (Ethics No. KY2021.11.01), and informed consent was obtained from the participating students and their guardians.

Demographic characteristics, school bullying, critical incidents, emotional problems (depression and anxiety), problem behaviors (smoking, drinking, gambling, substance abuse behaviors), and borderline personality disorder (BPD) were investigated.

Demographics: the subjects’ gender, grade level, whether they were an only child, parent’s education level and occupation, and family economic status were mainly included.

Depression: The PHQ-9 scale was used, which consists of nine questions divided into four levels ranging from “not at all” to “almost every day” on a 3-point scale. After completing the retest of the PHQ-9, Cronbach’s alpha coefficient of the total score was 0.85, and the PHQ-9 had good reliability in the assessment of depression in adolescents (26).

Anxiety: The GAD-7 was mainly used. The Generalized Anxiety Scale (GAS) was developed by Spitzer et al. and consists of 7 items based on a total score. The sensitivity of the Chinese version of the GAD-7 was 86.2% with a specificity of 95.5% and a Kappa value of 0.825, suggesting that the GAD-7 has a good validity scale validity (27).

BPD: The borderline subscale of PDQ-4+ was mainly used, and “yes” and “no” were used for scoring, “yes” was scored as 1, and “no” was scored as 0. The PDQ-4+ was mainly used for scoring. “The PDQ-4+ retest reliability coefficients ranged from 0.50 to 0.80 (P < 0.01) with split-half reliability indices ranging from 0.50 to 0.93 and alpha coefficients ranging from 0.56 to 0.78; this is suitable for use in screening scales (28).

Problem behaviors: alcohol, tobacco, gambling, and the presence of addictive or psychoactive substance use were included, with presence scored as “1” and absence scored as “0.”

Critical incident: the Primary Care Post-Traumatic Stress Disorder (PC-PTSD-5) scale was used: it assesses the five symptoms of PTSD: re-experiencing, numbness, avoidance, heightened alertness, and negative change, with good diagnostic accuracy and good reliability.

School bullying: The main investigation was whether the subject had suffered from school bullying in the past 12 months. The score for having experienced bullying in school is “1”, and the score for not having experienced bullying in school is “0”.

NSSI Behavior: The main investigation is whether the subject has experienced NSSI in the past 2 weeks, with NSSI behavior being scored as “1” and no NSSI behavior being scored as “0”.

Descriptive analyses were performed using SPSS 26.0 software and the process of differentiating between training and validation sets, lasso regression, logistic regression, construction of column line plots, and their internal validation were performed using R software (version 4.0.0). The data were randomly divided into training and validation sets with a ratio of 7:3. The training set was used for variable selection and model construction, while the validation set was used to evaluate the effectiveness of the model. Categorical variables were described by their frequencies and percentages. Continuous variables were represented by interquartile spacing because they did not fit the normal distribution (see Appendix A). The training set was analyzed using univariate logistic analysis to compare the relationship of different variables with the NSSI. All tests were two-tailed and p<0.05 was statistically significant.

Significant predictors of NSSI were screened using the Lasso regression model. Lasso regression reduces the correlation between variables and ensures that subsequently generated models are not overfitted (29). 16 Logistic regression models were then constructed to further remove confounders. Combining the above methods to screen for characteristic variables, the four optimal variables were selected to construct a column-line plot. The accuracy of the model is determined by calculating the area under the curve (AUC). Calibration curves provide a visual demonstration of the consistency of the model’s predicted probabilities with actual observations. Finally, the decision curve (DCA) is used to evaluate the value of the predictive model in clinical decision-making (30).

Collinearity diagnostics revealed no substantial multicollinearity (all VIFs < 2.0; see Appendix A). Of the 2,081 adolescents surveyed, the age group was concentrated in the 15-18 age group, with 916 males and 1,165 females; 957 were in the first year of high school, 868 in the second year, and 256 in the third year of high school. The total number of those who had experienced NSSI was 187, with an overall detection rate of 9.0% (187/2081); the NSSI detection rate for males (4.3%) was lower than that for females (12.7%), which was statistically different. Lower grades, poorer family finances, unclear marital status of parents, presence of problematic behaviors, experiencing a major event, having depression and anxiety, and BPD were all risk factors associated with self-injury. See Table 1 for details.

The dataset was randomly divided into training sets and validation sets in the ratio of 7:3, as detailed in Table 2 , and there was no significant difference between the two groups (P>0.05). The training set totaled 1457 and the number of NSSIs was 136; the validation set was 624 and the number of NSSIs was 51.

Data analysis was performed on the training set, where categorical variables were analyzed using univariate analysis of variance and continuous variables were tested using non-parametric tests, and the final factors screened out as having a significant effect were gender, age, grade, family economic status, problem behavior, critical incidents, anxiety and depressive moods, and borderline tendencies (all p<0.05), as detailed in Table 3 .

To avoid overfitting, the lasso regression analysis was continued, and the results of lasso regression are shown in Figure 1 and Table 4 . Figure 1A shows the cross-validation graph, Figure 1B shows the coefficient graph, and Table 4 shows the specific values of lasso regression, the value is not 0 that is, the independent variables that have a significant effect on the dependent variable. The final factors screened out as having a significant effect were gender, problem behavior, critical incident, depression and anxiety, and BPD.

To further exclude the influence of confounding factors on the results, multifactorial logistic regression was adopted, as detailed in Table 5 . Eventually, through the above two screening methods, four NSSI-related risk factors were screened out, which were gender, problem behavior, depression score, and BPD.

Table 6 shows the logistic regression model after removing all overfitting factors and confounders, showing better results (all P < 0.05).

Four high-risk factors for NSSI were selected, and a nomogramical model was developed to predict the propensity for NSSI. Each factor had a corresponding score, and these 4 scores were added up to obtain a total score, which then corresponded to the following risk scales to obtain the probability of NSSI. The higher the total score, the higher the probability of NSSI. (See Figure 2 ).

The predictive model was evaluated using its area under the curve (AUC). The larger the value of AUC, the higher the accuracy of the model. The AUCs of the column-line plots for the training and validation sets are 0.820 and 0.750, respectively, as shown in Figure 3 , indicating that the model is moderately accurate. The predictive ability of the model is fair.

The closer the Bias-corrected line or Apparent line is to the Ideal line, the higher the consistency between the predicted and actual values, using the Calibration curves to judge the model fit. As shown in Figure 4 , both groups of Bias-corrected lines are closer to the Ideal line, which indicates a better model fit.

The clinical benefits of the model were assessed using decision curve analysis (DCA). As detailed in Figure 5 , when the model curve is above the “ALL” and “NONE” curves, the model is well-identified, calibrated, clinically applicable, and generic in this range.