06 Jul Reliability of Past Medical History in a Single Hospital Participating in Get With The Guidelines‐Stroke Registry
Journal of the American Heart Association, Volume 11, Issue 13, July 5, 2022.
BackgroundThe GWTG (Get With The Guidelines)‐Stroke registry supports clinical research and quality improvement projects that often rely on past medical history elements, the reliability of which remains largely unknown. Here, we evaluated the reliability of specific past medical history elements in a local GWTG–Stroke data set, with particular attention to calculating the CHA2DS2‐VASc score.Methods and ResultsA single‐center cohort was identified by querying the Hospital of the University of Pennsylvania’s GWTG IQVIA Registry Platform for patients admitted with acute ischemic stroke between January 2017 and December 2020, with a previously known history of atrial fibrillation. Demographics and previously known medical history elements were retrieved from the registry to calculate the CHA2DS2‐VASc score. Five neurologists abstracted the same medical history elements from the health records. The κ statistics quantified the reliability of medical history elements and CHA2DS2‐VASc score. Four hundred fifty‐three patients with acute ischemic stroke and previously known atrial fibrillation were included in the cohort. In comparison with manual reabstraction, registry‐based medical history elements were only moderately reliable: congestive heart failure (κ=0.53), hypertension (κ=0.42), diabetes (κ=0.80), prior stroke (κ=0.45), and vascular disease (κ=0.48). However, leveraging these variables to calculate the CHA2DS2‐VASc score was more reliable (κ=0.73).ConclusionsPreviously known medical history elements in the GWTG‐Stroke registry were only modestly reliable in this single‐center study, suggesting caution should be exercised when relying on any individual history elements in registry‐based research. Combining these variables to calculate the CHA2DS2‐VASc score was somewhat more reliable. Multicenter data are needed before assuming generalizability.