Prediction Model

Logistic regression was chosen because it is interpretable and can be translated into a clinically usable nomogram. A nomogram converts a multivariate statistical model into an individual numeric risk score. Both nomograms showed good discrimination by exceeding the C-index threshold of 0.75. Validated risk prediction tools for suicidal ideation and non-suicidal self-injury in nurses were absent before this study. LASSO-selected variables will be entered into multivariable logistic regression to build the final prediction model. The nomogram assigns point values to variables and maps the summed score to a predicted probability. Separate nomograms were built for suicidal ideation and non-suicidal self-injury using significant multivariate predictors. The study aims to identify CPSP risk factors after hip arthroplasty and develop an individualized prediction model. The final prediction tool will be presented as a nomogram for estimating patient-specific CPSP risk. The article presents the nomograms as practical tools for clinical and occupational health risk assessment.