An Inter- and Intra-Rater Agreement Assessment of Two Novel Classification Systems for Coronal Imbalance in Adult Scoliosis and Spine Deformity.

OBJECTIVE

Coronal malalignment is a common feature of adult spinal deformity, and accurate classification is essential for diagnosis and treatment planning. However, variations in interpretation among clinicians can impact classification consistency. By assessing the reliability and applicability of these systems across different medical experts, this study seeks to establish a standardized approach to enhance clinical outcomes. This study aimed to evaluate the inter- and intra-observer agreement of two classification systems for coronal malalignment in adult spinal deformity patients, as proposed by Qiu et al. and Obeid et al.

METHODS

We analyzed 70 cases of adult spinal deformity collected between January 1, 2010, and April 20, 2023, using the classification systems proposed by Qiu et al. and Obeid et al. To assess inter- and intra-rater agreement, the same group of researchers re-evaluated all cases in a random order after a 4-week interval. We used the kappa statistic (κ) for inter- and intra-rater agreement assessment.

RESULTS

Qiu's classification system: Inter-rater agreement: Substantial agreement (κ = 0.76; 95% CI: 0.72-0.80) for Type A, Type B, and Type C. Intra-rater agreement: Nearly perfect agreement (κ = 0.83; 95% CI: 0.78-0.89) within raters for Type A, Type B, and Type C. Obeid's classification system: Inter-rater agreement: Almost perfect agreement (κ = 0.85; 95% CI: 0.83-0.87) for Type 0, Type 1, and Type 2. Complete system: Substantial agreement (κ = 0.68; 95% CI: 0.65-0.71) for all types and subtypes. Intra-rater agreement: Almost perfect at the type level (κ = 0.88; 95% CI: 0.83-0.93) and substantial at the subtype level (κ = 0.75; 95% CI: 0.65-0.85).

CONCLUSIONS

The research findings indicate a high level of agreement between the classification system described by Qiu et al. and the classification system proposed by Obeid et al. This agreement supports the widespread adoption and utilization of these classification systems in future clinical studies.