PS1-19: Using Electronic Data Extraction to Identify Subjects with Metabolic Syndrome: A Validation Using Manual Chart Review

Background/AimsMetabolic syndrome (MetS) increases risk for developing serious health conditions, but MetS is rarely recorded as a diagnosis in clinical practice. The purpose of this study was to compare an electronic data extraction process to a manual chart review in identifying patients with MetS.MethodsElectronic health records (EHR) of 48 randomly selected Geisinger Rural Aging Study (GRAS) participants (24 males, 24 females; age =65) were selected for review. A trained auditor collected information for each participant on biochemical measurements (including triglycerides, HDL-cholesterol, glucose); blood pressure and measured height and weight. MetS was defined as having three of the five criteria based on ATP III guidelines. However, since waist circumference was not available for all participants, BMI >30 kg/m2 was used. Records were reviewed for a 48-month period, starting at date of GRAS study entry. Demographics, diagnosis codes, laboratory data, past medical history, medications, progress notes, and physician comments were included in the audit. Independently, an electronic data extraction of the EHR was used to identify subjects with MetS. Rates of agreement between the manual chart review and data extraction process were calculated. Discrepancies were examined for sources of disagreement and statistical agreement was assessed with Cohen?s Kappa.ResultsAs expected, very few subjects with an ICD-9 diagnosis code for MetS were identified (n=2).The manual chart review identified 27 subjects (56%) that met criteria based on ATP III guidelines. The independent electronic process identified 25 of these 27 subjects as having MetS and found an additional 2 subjects (inadvertently missed during manual chart review). The two cases that were missed using the electronic process had abnormal glucose values that were not available from the electronic data extraction. Agreement between the two methods was almost perfect (kappa=0.92).ConclusionsThis validation study demonstrates that an electronic data extraction process for identifying MetS has substantial agreement with the gold standard, a manual review of health records for clinical data. This process has the advantage of quickly querying large amounts of data that may be missed by manual chart review and can aid in collection of data for health outcomes research.