Subarachnoid Hemorrhage: The Ottawa Hospital Experience

Title: Subarachnoid Hemorrhage: The Ottawa Hospital Experience
Authors: English, Shane
Date: 2014
Embargo: 2018-12-18T16:21:06Z
Abstract: 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 The sparse Canadian epidemiologic literature on 1° SAH is outdated and relies on diagnostic coding for case ascertainment which misses true cases and incorrectly labels non-cases.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.
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