Predicting Nonresponse to Multicomponent Treatment in Fibromyalgia: Development and Validation of a Machine Learning Model.

BACKGROUND

Multicomponent programs combining therapeutic exercise, cognitive-behavioral therapy, and pain neuroscience education demonstrate overall efficacy for fibromyalgia (FM). However, a substantial proportion of patients do not achieve clinically meaningful improvement. This study aimed to identify predictors of nonresponse and to develop a prognostic classifier model.

METHODS

Participants (n=788) from multiple randomized controlled trials received a standardized 12-week multicomponent intervention. This secondary analysis defined nonresponse as a <20% reduction in Fibromyalgia Impact Questionnaire Revised (FIQR) scores. A machine learning approach called least absolute shrinkage and selection operator (LASSO) regularization was used to train a classifier. Model performance was assessed by external validation in a holdout sample, with the area under the curve (AUC) and mean squared error as evaluation indices.

RESULTS

Higher baseline anxiety (B=0.010), depression (B=0.007), kinesiophobia (B=0.005), and FM severity (B=-0.004), along with lower physical function (B=-0.014), younger age (B=-0.005), and lower body mass index (B=-0.010), were associated with nonresponse. The model demonstrated adequate classification accuracy out-of-sample (AUC=0.657; 95% CI: 0.586-0.728). A prototype calculator incorporating these predictors was developed.

CONCLUSION

Psychological, functional, and demographic factors were linked to nonresponse to multicomponent treatment in FM. Although predictive accuracy was limited, these findings support further validation of stratification approaches to inform treatment planning.