Determination of candidate predictors for chiropractic treatment outcome of spinal pain using a machine learning framework for small datasets.

OBJECTIVES

To develop a machine learning (ML) approach to explore self-reported factors predictive for recovery in a small set of spinal pain patients.

METHODS

In this prospective cohort study, patients (N = 96; mean age = 44.5 ± 16.5 years; 53 female) completed an extensive questionnaire at baseline and after 1 and 3 months. Prediction targets were defined as binary outcomes (recovery/non-recovery) based on improvement at 3 months for pain intensity, disability, quality of life, and Patient Global Impression of Change. The ML-approach included three steps: selection of candidate baseline features (SHapley Additive exPlanations, SHAP); predictor validation (Leave-One-Out Cross-Validation, LOOCV) and permutation testing; testing for the ability to generalize.

RESULTS

Area under the curve (AUC) values were 0.93-0.99 in LOOCV and 0.62-0.90 in ensemble cross-validation (CV). SHAP analyses revealed higher recovery odds with positive treatment expectations and higher self-efficacy, younger age, lower body mass index, and fewer comorbidities. Psychological dysfunction generally hindered recovery.

CONCLUSIONS

This explorative study suggests that the current ML framework may identify candidate predictors of chiropractic treatment outcome in spinal pain from a small but phenotypically rich dataset. Given the performance drop between LOOCV and ensemble CV, current findings are hypothesis-generating. Prospective replication in adequately powered cohorts is necessary.