Predicting Adolescent Idiopathic Scoliosis among Chinese Children and Adolescents.

objectiveAdolescent idiopathic scoliosis (AIS) affects 1%-4% of adolescents in the early stages of puberty, but there is still no effective prediction method. This study aimed to establish a prediction model and validated the accuracy and efficacy of this model in predicting the occurrence of AIS.

methodsData was collected from a population-based school scoliosis screening program for AIS in China. A sample of 884 children and adolescents with the radiological lateral Cobb angle ≥ 10° was classified as an AIS case, and 895 non-AIS subjects with a Cobb angle < 10° were randomly selected from the screening system. All selected subjects were screened by visual inspection of clinical signs, the Adam's forward-bending test (FBT), and the measurement of angle of trunk rotation (ATR). LR and receiver operating characteristic (ROC) curves were used to preliminarily screen the influential factors, and LR models with different adjusted weights were established to predict the occurrence of AIS.

resultsMultivariate LR and ROC curves indicated that angle of thoracic rotation (adjusted odds ratios (AOR) = 5.18 - 10.06), angle of thoracolumbar rotation (AOR = 4.67 - 7.22), angle of lumbar rotation (AOR = 6.97 - 8.09), scapular tilt (area under the curve (AUC) = 0.77, 95% CI: 0.75-0.80), shoulder-height difference, lumbar concave, and pelvic tilt were the risk predictors for AIS. LR models with different adjusted weights (by AOR, AUC, and AOR+AUC) performed similarly in predicting the occurrence of AIS compared with multivariate LR. The sensitivity (82.55%-83.27%), specificity (82.59%-83.33%), Youden's index (0.65-0.67), positive predictive value (82.85%-83.58%), negative predictive value (82.29%-83.03%), and total accuracy (82.57%-83.30%) manifested that LR could accurately identify patients with AIS.

conclusionsLR model is a relatively high accurate and feasible method for predicting AIS. Increased performance of LR models using clinically relevant variables offers the potential to early identify high-risk groups of AIS.