A Predictive Model of Progression for Adolescent Idiopathic Scoliosis Based on 3D Spine Parameters at First Visit.

UNLABELLED

MINI: The aim of this prospective cohort study was to improve the prediction of curve progression in AIS. By adding the 3D morphology parameters at first visit, the predictive model explains 65% of the variability. It is one of the greatest advances in the understanding of scoliosis progression in the last 30 years.

STUDY DESIGN

Prospective cohort study.

OBJECTIVE

The objective of the present study was to design a model of AIS progression to predict Cobb angle at full skeletal maturity, based on curve type, skeletal maturation, and 3D spine parameters available at first visit.

SUMMARY OF BACKGROUND DATA

Adolescent idiopathic scoliosis (AIS) is a three-dimensional (3D) spinal deformity that affects 1% of adolescents. Curve severity is assessed using the Cobb angle. Prediction of scoliosis progression remains challenging for the treating physician and is currently based on curve type, severity, and maturity. The objective of this study was to develop a predictive model of final Cobb angle, based on 3D spine parameters at first visit, to optimize treatment.

METHODS

A prospective cohort of AIS patients at first orthopedic visit was enrolled between 2006 and 2010, all with 3D reconstructions. Measurements of five types of descriptors were obtained: angle of plane of maximum curvature, Cobb angles, 3D wedging, rotation, and torsion. A general linear model analysis with backward selection was done with final Cobb angle (either just before surgery or at skeletal maturity) as outcome and 3D spine parameters and clinical parameters as predictors.

RESULTS

Of 195 participants, 172 (88%) were analyzed; average age at presentation was 12.5 ± 1.3 years and mean follow-up to outcome, 3.2 years. The final model includes significant predictors: initial skeletal maturation, curve type, frontal Cobb angle, angle of plane of maximal curvature, and 3D disk wedging (T3-T4, T8-T9) and achieved a determination coefficient (R) = 0.643. Positive and negative predictive values to identify a curve of 35 degrees are 79% and 94%.

CONCLUSION

This study developed a predictive model of spinal curve progression in scoliosis based on first-visit information. The model will help the treating physician to initiate appropriate treatment at first visit.

LEVEL OF EVIDENCE