Hierarchical Clustering Applied to Chronic Pain Drawings Identifies Undiagnosed Fibromyalgia: Implications for Busy Clinical Practice

Abstract

Currently-used assessments for fibromyalgia require clinicians to suspect a fibromyalgia diagnosis, a process susceptible to unintentional bias. Automated assessments of standard patient-reported outcomes (PROs) could be used to prompt formal assessments, potentially reducing bias. We sought to determine whether hierarchical clustering of patient-reported pain distribution on digital body map drawings predicted fibromyalgia diagnosis. Using an observational cohort from the University of Pittsburgh's Patient Outcomes Repository for Treatment registry, which contains PROs and electronic medical record data from 21,423 patients (March 17, 2016-June 25, 2019) presenting to pain management clinics, we tested the hypothesis that hierarchical clustering subgroup was associated with fibromyalgia diagnosis, as determined by ICD-10 code. Logistic regression revealed a significant relationship between the body map cluster subgroup and fibromyalgia diagnosis. The cluster subgroup with the most body areas selected was the most likely to receive a diagnosis of fibromyalgia when controlling for age, gender, anxiety, and depression. Despite this, more than two-thirds of patients in this cluster lacked a clinical fibromyalgia diagnosis. In an exploratory analysis to better understand this apparent underdiagnosis, we developed and applied proxies of fibromyalgia diagnostic criteria. We found that proxy diagnoses were more common than ICD-10 diagnoses, which may be due to less frequent clinical fibromyalgia diagnosis in men. Overall, we find evidence of fibromyalgia underdiagnosis, likely due to gender bias. Coupling PROs that take seconds to complete, such as a digital pain body map, with machine learning is a promising strategy to reduce bias in fibromyalgia diagnosis and improve patient outcomes. PERSPECTIVE: This investigation applies hierarchical clustering to patient-reported, digital pain body maps, finding an association between body map responses and clinical fibromyalgia diagnosis. Rapid, computer-assisted interpretation of pain body maps would be clinically useful in prompting more detailed assessments for fibromyalgia, potentially reducing gender bias.

Keywords: Chronic pain; Cluster analysis; Fibromyalgia; Machine learning; Pain measurement.