Chart validation of inpatient ICD-9-CM administrative diagnosis codes for ischemic stroke among IGIV users in the Sentinel Distributed Database. - Ammann, Eric M.; Leira, Enrique C.; Winiecki, Scott K.; Nagaraja, Nandakumar; Dandapat, Sudeepta; Carnahan, Ryan M.; Schweizer, Marin L.; Torner, James C.; Fuller, Candace C.; Leonard, Charles E.; Garcia, Crystal; Pimentel, Madelyn; et al. (Medicine, 2017)
The Sentinel Distributed Database (SDD) is a large database of patient-level medical and prescription records, primarily derived from insurance claims and electronic health records, and is sponsored by the U.S. Food and Drug Administration for drug safety assessments. In this chart validation study, we report on the positive predictive value (PPV) of inpatient ICD-9-CM acute ischemic stroke (AIS) administrative diagnosis codes (433.x1, 434.xx, and 436) in the SDD.As part of an assessment of the risk of thromboembolic adverse events following treatment with intravenous immune globulin (IGIV), charts were obtained for 131 potential post-IGIV AIS cases. Charts were abstracted by trained nurses and then adjudicated by stroke experts using pre-specified diagnostic criteria.Case status could be determined for 128 potential AIS cases, of which 34 were confirmed. The PPVs for the inpatient AIS diagnoses recorded in the SDD were 27% overall [95% confidence interval (95% CI): 19-35], 60% (95% CI: 32-84) for principal-position diagnoses, 42% (95% CI: 28-57) for secondary diagnoses, and 6% (95% CI: 2-15) for position-unspecified diagnoses (which in the SDD generally originate from separate physician claims associated with an inpatient stay).Position-unspecified diagnoses were unlikely to represent true AIS cases. PPVs for principal and secondary inpatient diagnosis codes were higher, but still meaningfully lower than estimates from prior chart validation studies. The low PPVs may be specific to the IGIV user study population. Additional research is needed to assess the validity of AIS administrative diagnosis codes in other study populations within the SDD.