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Birol Emir,1 Elizabeth T Masters,1 Jack Mardekian,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), a chronic musculoskeletal condition characterized by widespread pain and a constellation of symptoms, remains challenging and is often delayed. Methods: Random forest modeling of electronic medical records was used to identify variables that may facilitate earlier FM identification and diagnosis. Subjects aged ≥18 years with two or more listings of the International Classification of Diseases, Ninth Revision, (ICD-9) code for FM (ICD-9 729.1) ≥30 days apart during the 2012 calendar year were defined as cases among subjects associated with an integrated delivery network and who had one or more health care provider encounter in the Humedica database in calendar years 2011 and 2012. Controls were without the FM ICD-9 codes. Seventy-two demographic, clinical, and health care resource utilization variables were entered into a random forest model with downsampling to account for cohort imbalances ( |
