MARCH 7, 2024
There is evidence that computer analysis of pain maps could help physicians identify those suffering from fibromyalgia, an underdiagnosed condition, according to a recent study.
“Patients who are older or identify as male gender are not getting a diagnosis of fibromyalgia as frequently as we expect. We think this has negative effects on patient outcomes,” study author Benedict J. Alter, MD, PhD, of the University of Pittsburgh, told Pain Medicine News.
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There is evidence that computer analysis of pain maps could help physicians identify those suffering from fibromyalgia, an underdiagnosed condition, according to a recent study.
“Patients who are older or identify as male gender are not getting a diagnosis of fibromyalgia as frequently as we expect. We think this has negative effects on patient outcomes,” study author Benedict J. Alter, MD, PhD, of the University of Pittsburgh, told Pain Medicine News.
The study included an observational cohort of 21,423 patients who presented to pain management clinics between 2016 and 2019 from the University of Pittsburgh’s Patient Outcomes Repository for Treatment registry (J Pain 2024 Feb 12. doi:10.1016/j.jpain.2024.02.003). Researchers hypothesized hierarchical clustering subgroup was associated with fibromyalgia diagnosis, as determined by ICD-10 code. A significant relationship between body map cluster subgroup and fibromyalgia diagnosis was revealed by logistic regression. When controlling for age, gender, anxiety and depression, the cluster subgroup with the most body areas selected was the most likely to receive a diagnosis of fibromyalgia. However, more than two-thirds of the patients in this cluster had not received a clinical fibromyalgia diagnosis.
The researchers found that only 7% of men had a fibromyalgia ICD-10 code diagnosis. Conversely, using the American College of Rheumatology’s Widespread Pain Index (ACR WPI) of Symptom Severity and ACR WPI of Physical Function, the researchers were able to identify fibromyalgia in 26% and 27% of male participants, respectively.
The researchers concluded that underdiagnosis of fibromyalgia is likely due to gender bias. They suggested that coupling patient-reported outcomes, such as a digital pain body map, with machine learning is a promising strategy to reduce bias and improve patient outcomes.
“Bottom line, I would encourage physicians to use a body diagram for patients to report where they experience pain on their body, since this is a quick survey question to do and can signal their physician to do an evaluation for fibromyalgia,” Alter said.
He also suggested that patients talk to their doctors about the history of their pain, so that widespread pain—the hallmark of fibromyalgia—can be identified.
The conclusions from this study are limited by the fact that the researchers relied on diagnosis codes entered in electronic medical records, which do not represent the full scope of patient–physician interactions.
The researchers noted clinicians could be aware of fibromyalgia without entering the code in a chart. The investigators plan to address this limitation by analyzing treatments prescribed at each visit.
Furthermore, Alter indicated that his team plans to validate “informatic approaches to identifying fibromyalgia diagnoses,” which would assist physicians by making identification of patients who may have fibromyalgia easier.
“We think this would improve patient care, since other researchers have shown that identifying fibromyalgia earlier tends to lead to better outcomes for patients,” he concluded.
—Myles Starr
Alter reported no relevant financial disclosures.
