Can Hospital Discharge Databases Be Used to Follow Ischemic Stroke Incidence?

Abstract

Background and Purpose—: Because acute ischemic strokes (ISs) are mainly hospitalized, hospital discharge data could be used to routinely follow their incidence management. We aimed to assess sensitivity and positive predictive value of the French hospital discharge database (HDD) to identify patients with acute IS using a prospective and exhaustive cohort (AVC69) of acute IS cases. Methods—: A selection algorithm based on IS diagnosis coded with the International Classification of Diseases ( ICD-10 ) and cerebral imaging codes was used to identify all hospital stays with the primary diagnosis of IS in the HDD of the university hospitals of the Rhône area. Cases identified through HDD search were compared with IS cases identified through an exhaustive cohort study conducted in the Rhône district and confirmed on medical records review. Results—: There were 465 confirmed cases of IS hospitalized in 1 of the 4 university hospitals during the study period. The HDD search identified 313 among those (true-positive cases) but missed 152 cases (false-negative cases). The sensitivity of the HDD search was 67.3% (95% confidence interval, 63.1–71.5), and the positive predictive value was 95.1% (95% confidence interval, 92.8–97.4). Additionally, HDD search retrieved 16 cases, which were not eventually IS (false positives). Sensitivity was better when patients were hospitalized in neurological departments. Conclusions—: The lack of sensitivity to identify acute IS patients through HDD search does not seem to be accurate enough to validate the use of these data for incidence estimates. Efforts have to be made to improve the coding quality.