Abstract

BackgroundEarly, rapid, and accurate identification of those patients who have severe influenza is important for emergency physicians. Influenza viral load, which has been proposed as a predictor of severe influenza, could be useful in facilitating decision making of resource use. We aimed to derive a clinical prediction rule to indicate probability for inpatient hospitalization for patients with influenza, which includes influenza viral load in addition to other clinical information commonly collected in the emergency department (ED). MethodsWe conducted a 3-year prospective cohort study (2007-2009) of patients with probable influenza infection as suspected by the emergency physician from 3 study sites. Eligible patients were those with excess nasopharyngeal aspirate samples. Influenza viral load was measured using reverse transcription polymerase chain reaction and electrospray ionization mass spectrometry. Clinical information including demographics, underlying illness, vaccination history, hospitalization, and results from clinical laboratory were abstracted from electronic patient records and questionnaires. The prediction rule for hospitalization was derived by the recursive partitioning algorithm (decision tree–type approach) and evaluated by internal 10-fold cross-validation for performance characteristics. ResultsOf 424 ED patients with nasopharyngeal aspirates, 146 infected with influenza were enrolled (median age, 10 years [interquartile range, 4-26]; race, 55% African American; median inpatient length of stay, 3 days [interquartile range, 1-4]; high viral load group [defined as >2.5 million genome copies/mL], 34%). Predictors for hospitalization included underlying illness, age, influenza viral load level, and vaccination history (c statistics, 0.84; sensitivity, 83%; specificity, 76%). ConclusionsThe clinical prediction rule incorporating influenza viral load into the clinical information was indicative of hospitalization and merits further evaluation for determination of ED resource use for patients with influenza.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.