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Disease Phenotypes in Refractory Musculoskeletal Pain Syndromes Identified by Unsupervised Machine Learning.

Thomas Hügle¹, Tiffany Prétat¹, Marc Suter¹

¹University Hospital Lausanne and University of Lausanne, Lausanne, Switzerland.

ACR Open Rheumatology

|August 30, 2024

Summary

This study identified five distinct patient clusters within chronic musculoskeletal pain syndromes, revealing key factors influencing treatment response. Personalized therapeutic strategies are crucial for managing these complex conditions effectively.

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Area of Science:

Pain Medicine
Clinical Psychology
Biomarker Research

Background:

Chronic musculoskeletal pain syndromes, including fibromyalgia, are heterogeneous and often treatment-resistant.
These conditions impose significant socioeconomic burdens, necessitating individualized treatment approaches.
Current understanding lacks detailed patient stratification for optimized care.

Purpose of the Study:

To identify and visualize distinct patient clusters within refractory musculoskeletal pain syndromes.
To analyze a comprehensive set of clinical variables, including immunologic, psychosomatic, wearable, and sleep biomarkers.
To develop a predictive model for treatment response in chronic pain patients.

Main Methods:

Hierarchical agglomerative clustering was used to identify five patient phenotypes from data of 202 individuals in a multimodal pain program.
Clinical variables encompassed demographics, comorbidities, medication, psychological assessments, and wearable/sleep data.
A predictive model for Brief Pain Inventory (BPI) response was generated, and digital personas were created using DALL-E.

Main Results:

Key distinguishing factors among clusters included living situation, BMI, joint pain, alexithymia, psychiatric comorbidity, and medication use.
Three clusters showed pain reduction, one showed functional improvement, and one responded to virtual reality intervention.
Strong opioids, trazodone, neuroleptic treatment, and living alone were negative predictors for pain reduction.

Conclusions:

The study successfully identified and visualized clinically relevant chronic musculoskeletal pain subtypes.
A predictive model for multimodal treatment response was developed, aiding personalized therapy design.
Digital personas and avatars show potential for future personalized treatment modalities and clinical trials.