ArticlemedRxiv : the preprint server for health sciences2024
Use of electronic health record data to identify hospital-associated
Article in medRxiv : the preprint server for health sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Clinical research focused on the burden and impact of Methods: We performed a validation study using a random sample of adult inpatients at a large academic hospital setting in Portland, Oregon from January 2018 to March 2020. We excluded patients with CDI on admission and those with short lengths of stay (< 4 days). We tested a multi-component algorithm to identify HA-CDI; case patients were required to have received an inpatient course of metronidazole, oral vancomycin, or fidaxomicin and have at least one of the following: a positive Results: Our case definition algorithm identified HA-CDI cases with 94% accuracy (95% Confidence Interval (CI): 88% to 97%). We achieved 100% sensitivity (94% to 100%), 89% specificity (81% to 95%), 88% positive predictive value (78% to 94%), and 100% negative predictive value (95% to 100%). Requiring a positive Conclusions: Our algorithm accurately detected true HA-CDI cases from EHR data in our patient population. A multi-component algorithm performs better than any isolated component. Requiring a positive laboratory test for
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