Back Pain in Adolescents and Young Adults with Idiopathic Scoliosis—Identifying Factors Associated with Significant Pain—A Multivariate Logistic Regression Analysis

Abstract

(1) Background: Previous data show that patients with idiopathic scoliosis (IS) can be classified into two groups according to pain intensity. This paper aims to determine which factors can independently predict the likelihood of belonging to a high-level pain group. (2) Methods: The study used a prospective, multicenter, cross-sectional design. Two-hundred and seventy-two patients with IS (mean age 18.1 years) (females 83.5%) were included. The sample was divided into two groups. The PAIN group comprised 101 patients (37.1%) with an average NRS of 5.3. The NO-PAIN group consisted of 171 patients (62.9%) with an average NRS of 1.1. Data on various factors such as comorbidities, family history, curve magnitude, type of treatment, absenteeism, anxiety, depression, kinesiophobia, family environment, and social relationships were collected. Statistical analysis consisted of multivariate logistic regression analysis to identify independent predictors of high-level pain. (3) Results: In the final model, including modifiable and non-modifiable predictors, age (OR 1.07 (1.02–1.11)); Absenteeism (OR 3.87 (1.52–9.87)), HAD anxiety (OR 1.18 (1.09–1.29)) and an indication for surgery (OR 2.87 (1.28–6.43)) were associated with an increased risk of pain. The overall model is significant at p = 0.0001 level and correctly predicts 72.6% of the responses. (4) Conclusions: Age, an indication for surgery, anxiety, and work/school absenteeism are the variables that independently determine the risk of belonging to the high-level pain group (NRS > 3).