Background: Primary subarachnoid hemorrhage (1°SAH) is an important disease that causes significant morbidity and mortality. The sparse Canadian epidemiologic literature on 1° SAH is outdated and relies on diagnostic coding for case ascertainment which misses true cases and incorrectly labels non-cases. Objectives: Primary objective was to identify all patients with 1° SAH presenting to the Ottawa Hospital (TOH) between July 1, 2002 and June 30, 2011 by deriving and validating a search algorithm using an enriched administrative database. Secondary objectives included: 1) determine incidence and case-fatality rates (CFR) of 1° SAH at TOH; and 3) derive and validate a method to identify 1° SAH using routinely collected administrative data. Methods: A cohort of 1° SAH patients were identified with a case-defining algorithm that was derived and validated using a combination of cerebrospinal fluid analysis results and text-search algorithms of both cranial imaging and post-mortem reports. The incidence of 1° SAH was calculated using the total number of hospital encounters over the same time period. CFR was calculated by linking to vital statistic data of hospitalized patients at discharge. An optimal1° SAH prediction model was derived and validated using binomial recursive partitioning built with independent variables obtained from routinely collected administrative data. Results: Using the case-defining algorithm, 831 patients were identified with a 1° SAH over the study period. Hospital incidence of 1° SAH was 17.2 events per 10,000 inpatient encounters (or 0.17% of encounters) with a case-fatality rate of 18.1%. A validated SAH prediction model based on administrative data using a recursive partitioning model had a sensitivity of 96.5% (95% CI 93.9-98.0), a specificity of 99.8% (95%CI 99.6-99.9), and a +LR of 483 (95% CI 254-879). This results in a post-test probability of disease of 45%. Conclusion: We identified almost all cases of 1° SAH at our hospital using an enriched administrative data. Accurately identifying such patients with routinely collected health administrative data is possible, providing important opportunities to examine and study this patient population. Further studies, involving multiple centres are needed to reproduce these results.

Objective: 1) Systematic review on the accuracy of Chronic Rhinosinusitis (CRS) identification in administrative databases; 2) Develop an administrative data algorithm to identify CRS patients who have endoscopic sinus surgery (ESS). Methods: A chart review was performed for all ESS surgical encounters at The Ottawa Hospital from 2011-12. Cases were defined as encounters in which ESS for performed for Otolaryngologist-diagnosed CRS. An algorithm to identify patients who underwent ESS for CRS was developed using diagnostic and procedural codes within health administrative data. This algorithm was internally validated. Results: Only three studies meeting inclusion criteria were identified in the systematic review and showed inaccurate CRS identification. The final algorithm using administrative and chart review data found that encounters having at least one CRS diagnostic code and one ESS procedural code had excellent accuracy for identifying ESS: sensitivity 96.0% sensitivity, specificity 100%, and positive predictive value 95.4%. Internal validation showed similar accuracy. Conclusion: Most published AD studies examining CRS do not consider the accuracy of case identification. We identified a simple algorithm based on administrative database codes accurately identified ESS-CRS encounters.