Construction of a predictive model for the progression of adolescent idiopathic scoliosis based on machine learning algorithms

English Keywords:Adolescent idiopathic scoliosis  Scoliosis progress  Machine learning  Prediction model

English Abstract:

  【Abstract】 Objectives: To identify the core predictor variables for adolescent idiopathic scoliosis(AIS) progression using machine learning algorithms and develop a risk prediction model. Methods: A cohort of 361 AIS patients initially diagnosed at the Second Hospital of Shanxi Medical University between January 2018 and June 2023 was enrolled and followed. Demographic data and imaging data of the patients were collected. The patients were stratified into progression(Cobb angle increased ≥6° in the primary curve) and non-progression groups, and were randomly divided into training and test sets at an 8∶2 ratio. Predictor variables were selected using LASSO regression and random survival forest(RSF) algorithms. Three prediction models were developed: a RSF model, a survival support vector machine(SSVM) model, and an extreme gradient boosting(XGBoost) model. An optimal model was selected according to the concordance index(C-index) and integrated Brier score, and the discrimination, accuracy and clinical application value of the optimal model were assessed using receiver operating characteristic(ROC) and area under the curve(AUC), bootstrap-corrected C-index, calibration curves, integrated Brier score, and decision curve analysis(DCA) based on the training set. Results: The scoliosis progression rate of the 361 AIS patients was 41.27%. The predictors consistently identified with LASSO regression and RSF algorithm were initial Cobb angle, Risser sign, apical vertebral rotation, spinal growth rate, brace treatment or not, T1 tilt angle, and apical vertebra translation. The RSF model demonstrated superior performance with a C-index of 0.837(vs. 0.790 for SSVM and 0.743 for XGBoost) and the lowest integrated Brier score(0.084 vs. 0.161 and 0.133). Time-dependent AUC values for the RSF model were 0.903(95%CI: 0.829-0.977) at 6 months, 0.870(95%CI: 0.756-0.985) at 12 months, 0.858(95%CI: 0.742-0.973) at 18 months, and 0.862(95%CI: 0.728-0.997) at 24 months. Bootstrap-corrected C-index remained robust at 0.842(95%CI: 0.749-0.917). Calibration curves showed close alignment between predicted and observed outcomes, with an integrated Brier score of 0.084. DCA revealed clinical utility across threshold probabilities of 5%-20%(6-month), 10%-80%(12-month), 10%-70%(18-month), and 25%-85%(24-month). Conclusions: The RSF model incorporating initial Cobb angle, Risser sign, apical vertebra rotation, spinal growth rate, brace treatment or not, T1 tilt angle, and apical vertebra translation can effectively predict risk probability of scoliosis progression in future for AIS patients after initial diagnosis.