Prognostic model development for risk of curve progression in adolescent idiopathic scoliosis: a prospective cohort study of 127 patients

Acta Orthop. 2024 Sep 13:95:536-544. doi: 10.2340/17453674.2024.41911.

Abstract

Background and purpose: The study's purpose was to develop and internally validate a prognostic survival model exploring baseline variables for adolescent idiopathic scoliosis curve progression.

Methods: A longitudinal prognostic cohort analysis was performed on trial data (n = 135) including girls and boys, Cobb angle 25-40°, aged 9-17 years, remaining growth > 1 year, and previously untreated. Prognostic outcome was defined as curve progression of Cobb angle of > 6° prior to skeletal maturity. 34 candidate prognostic variables were tested. Time-to-event was measured with 6-month intervals. Cox proportional hazards regression survival model (CoxPH) was used for model development and validation in comparison with machine learning models (66.6/33.3 train/test data set). The models were adjusted for treatment exposure.

Results: The final primary prognostic model included 127 patients, predicting progress with acceptable discriminative ability (concordance = 0.79, 95% confidence interval [CI] 0.72-0.86). Significant prognostic risk factors were Risser stage of 0 (HR 4.6, CI 2.1-10.1, P < 0.001), larger major curve Cobb angle (HRstandardized 1.5, CI 1.1-2.0, P = 0.005), and higher score on patient-reported pictorial Spinal Appearance Questionnaire (pSAQ) (HRstandardized 1.4, CI 1.0-1.9, P = 0.04). Treatment exposure, entered as a covariate adjustment, contributed significantly to the final model (HR 3.1, CI 1.5-6.0, P = 0.001). Sensitivity analysis displayed that CoxPH maintained acceptable discriminative ability (AUC 0.79, CI 0.65-0.93) in comparison with machine learning algorithms.

Conclusion: The prognostic model (Risser stage, Cobb angle, pSAQ, and menarche) predicted curve progression of > 6° Cobb angle with acceptable discriminative ability. Adding patient report of the pSAQ may be of clinical importance for the prognosis of curve progression.

MeSH terms

  • Adolescent
  • Child
  • Cohort Studies
  • Disease Progression*
  • Female
  • Humans
  • Longitudinal Studies
  • Machine Learning
  • Male
  • Prognosis
  • Proportional Hazards Models
  • Prospective Studies
  • Risk Factors
  • Scoliosis* / diagnostic imaging

Grants and funding

AA and PG were financially supported by the Swedish Research Council (Government funded competitive grant: Dnr 521-2012-1771); the regional agreement on medical training and clinical research (ALF) between Stockholm health care region & Karolinska Institutet (Government-funded competitive grants: FoUI-948087; FoUI-951336; FoUI-952892). PG: Swedish Society of Spinal Surgeons (non-profit NGO-funded competitive grant). AA: the Joanna Cocozza Foundation for Children’s Medical Research, Linköping University. The funders had no role in the design and conduct of the current study or approval and publication of the manuscript.