Evaluation of 3 Electronic Methods Used to Detect Influenza Diagnoses during 2009 Pandemic

Diversity of parvovirus 4–like viruses in humans, chimpanzees, and monkeys in hunter–prey relationships. genomic characterization of a novel ovine partetravirus and a new genotype of bovine partetravirus. PLoS ONE. To the Editor: Conducting influenza surveillance in hospitals is imperative to detect outbreaks, inform infection control policy, and allocate resources (1). Hospital administrative data could be harnessed for this purpose (2,3) but are not currently used for infection surveillance because of data lag times. Influenza cases could be identified by using International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM), codes within the discharge abstract, pharmacy , and microbiology laboratory information systems. Although these approaches are assumed to accurately identify influenza cases, this assumption has not been widely tested, especially during a pandemic. In this retrospective cohort study, we aimed to identify and evaluate 3 electronic methods of influenza case detection during 1 peak of influenza A(H1N1)pdm09. With ethics board approval, we used the Ottawa Hospital Data Warehouse (OHDW) (Ottawa, ON, Can-ada) to identify 398 adult inpatients at the Ottawa Hospital during Octo-ber–December 2009 who had cardiac , infectious, or respiratory disease diagnoses (ICD-10-CM codes: all J is a relational database containing pharmacy, laboratory, and discharge diagnosis information for inpatients at Ottawa Hospital. We detected influenza in the following ways: influenza diagnosis in the discharge abstract database (DAD) (ICD-10-CM codes J09–J11); prescription for an antiviral drug (oseltamivir, zanami-vir) in the pharmacy system; and a positive laboratory test during the hospital encounter (without specifying test type or specimen) in the laboratory system. We assessed these case definitions against a criterion standard of influenza diagnosis on the hospital chart, determined by a physician reviewer blinded to the electronic values for the case definitions. We constructed 2 × 2 contingency tables for each classification method and calculated sensitivity, specific-ity, positive predictive value (PPV), and likelihood ratios using standard equations. Influenza prevalence in this co-hort was 13.6% (54/398) by our criterion standard. The proportion of male and female patients was equal, with a median age of 69 years (interquartile range 53–81 years). Median length of hospital stay was 6 days (interquar-tile range 1–12 days). A total of 77 (19.3%) patients were admitted to the intensive care unit, and 51 (12.8%) patients died in hospital. Two (0.5%) patients died with a primary diagnosis of influenza.