부산대학교 도서관

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 [greater than or equal to] 18 years old with [greater than or equal to] 2 International Classification of Diseases, Ninth Revision (ICD-9) codes for FM (729.1) [greater than or equal to] 30 days apart during 2012, associated with an integrated delivery network, with [greater than or equal to] 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 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
Background Widespread pain is the hallmark symptom of fibromyalgia (FM), a chronic, complex musculoskeletal condition that remains undiagnosed in a substantial proportion of patients with this disease. (1) While the [...]