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Seeking out SARI: an automated search of electronic health records

  • John C. O'Horo (a1) (a2), Mikhail Dziadzko (a3), Amra Sakusic (a4), Rashid Ali (a4), M. Rizwan Sohail (a1) (a2) (a5), Daryl J. Kor (a2) (a3) and Ognjen Gajic (a2) (a4)...

Abstract

The definition of severe acute respiratory infection (SARI) – a respiratory illness with fever and cough, occurring within the past 10 days and requiring hospital admission – has not been evaluated for critically ill patients. Using integrated electronic health records data, we developed an automated search algorithm to identify SARI cases in a large cohort of critical care patients and evaluate patient outcomes. We conducted a retrospective cohort study of all admissions to a medical intensive care unit from August 2009 through March 2016. Subsets were randomly selected for deriving and validating a search algorithm, which was compared with temporal trends in laboratory-confirmed influenza to ensure that SARI was correlated with influenza. The algorithm was applied to the cohort to identify clinical differences for patients with and without SARI. For identifying SARI, the algorithm (sensitivity, 86.9%; specificity, 95.6%) outperformed billing-based searching (sensitivity, 73.8%; specificity, 78.8%). Automated searching correlated with peaks in laboratory-confirmed influenza. Adjusted for severity of illness, SARI was associated with more hospital, intensive care unit and ventilator days but not with death or dismissal to home. The search algorithm accurately identified SARI for epidemiologic study and surveillance.

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Copyright

Corresponding author

Author for correspondence: John C. O'Horo, E-mail: OHoro.John@mayo.edu

References

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