Electronic medical record data to identify variables associated with a fibromyalgia diagnosis: importance of health care resource utilization
Authors Masters ET, Mardekian J, Emir B, Clair A, Kuhn M, Silverman S
Received 25 September 2014
Accepted for publication 3 November 2014
Published 5 March 2015 Volume 2015:8 Pages 131—138
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Dr Michael E Schatman
Elizabeth T Masters,1 Jack Mardekian,1 Birol Emir,1 Andrew Clair,1 Max Kuhn,2 Stuart L Silverman,3
1Pfizer, Inc., New York, NY, 2Pfizer, Inc., Groton, CT, 3Cedars-Sinai Medical Center, Los Angeles, CA, USA
Background: Diagnosis of fibromyalgia (FM) is often challenging. Identifying factors associated with an FM diagnosis may guide health care providers in implementing appropriate diagnostic and management strategies.
Methods: This retrospective study used the de-identified Humedica electronic medical record (EMR) database to identify variables associated with an FM diagnosis. Cases (n=4,296) were subjects ≥18 years old with ≥2 International Classification of Diseases, Ninth Revision (ICD-9) codes for FM (729.1) ≥30 days apart during 2012, associated with an integrated delivery network, with ≥1 encounter with a health care provider in 2011 and 2012. Controls without FM (no-FM; n=583,665) did not have the ICD-9 codes for FM. Demographic, clinical, and health care resource utilization variables were extracted from structured EMR data. Univariate analysis identified variables showing significant differences between the cohorts based on odds ratios (ORs).
Results: Consistent with FM epidemiology, FM subjects were predominantly female (78.7% vs 64.5%; P<0.0001) and slightly older (mean age 53.3 vs 52.7 years; P=0.0318). Relative to the no-FM cohort, the FM cohort was characterized by a higher prevalence of nearly all evaluated comorbidities; the ORs suggested a higher likelihood of an FM diagnosis (P<0.0001), especially for musculoskeletal and neuropathic pain conditions (OR 3.1 for each condition). Variables potentially associated with an FM diagnosis included higher levels of use of specific health care resources including emergency-room visits, outpatient visits, hospitalizations, and medications. Units used per subject for emergency-room visits, outpatient visits, hospitalizations, and medications were also significantly higher in the FM cohort (P<0.0001), confirming resource utilization as an important variable associated with an FM diagnosis.
Conclusion: Significant differences between the FM and no-FM cohorts were observed for nearly all the demographic, clinical, and health care resource variables, suggesting an association with FM diagnosis. These results also support use of EMR data for identifying variables associated with FM, which may help in the diagnosis and management of this condition.
Keywords: retrospective database analysis, predictors, musculoskeletal pain, observational study, real world data
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