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

Rodrigo López-García^1,2, Mayte Serrat³, Juan P Sanabria-Mazo^4,5

¹Escoles Universitaries Gimbernat, Autonomous University of Barcelona, Sant Cugat del Vallès.

The Clinical Journal of Pain

|March 2, 2026

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This summary is machine-generated.

Predictors of treatment non-response in fibromyalgia (FM) were identified. Psychological factors like anxiety and depression, along with lower physical function, were linked to poor outcomes in multicomponent FM programs.

Area of Science:

Rheumatology
Pain Medicine
Psychiatry

Background:

Multicomponent programs (exercise, CBT, pain education) are effective for fibromyalgia (FM).
However, many patients do not achieve significant symptom improvement.
Identifying non-responders is crucial for personalized treatment.

Purpose of the Study:

To identify predictors of non-response to multicomponent FM treatment.
To develop a prognostic classifier model for treatment outcomes.

Main Methods:

Secondary analysis of 788 participants from randomized controlled trials.
Non-response defined as <20% reduction in Fibromyalgia Impact Questionnaire Revised (FIQR) scores.
Least Absolute Shrinkage and Selection Operator (LASSO) regularization used for classifier training and external validation.

Keywords:

LASSO regression fibromyalgia machine-learning multicomponent nonpharmacological approach nonresponse

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Main Results:

Higher baseline anxiety, depression, kinesiophobia, and FM severity predicted non-response.
Lower physical function, younger age, and lower BMI were also associated with non-response.
The model showed adequate classification accuracy (AUC=0.657).

Conclusions:

Psychological, functional, and demographic factors predict non-response to FM treatment.
Findings support further validation of stratification approaches for treatment planning.
A prototype calculator was developed to incorporate identified predictors.