title: An algorithm to diagnose influenza infection: evaluating the clinical importance and impact on hospital costs of screening with rapid antigen detection tests.
abstract: Rapid antigen detection tests (RADTs) are immunoassays that produce results in 15 min or less, have low sensitivity (50 %), but high specificity (95 %). We studied the clinical impact and laboratory savings of a diagnostic algorithm for influenza infection using RADTs as a first-step technique during the influenza season. From January 15th to March 31st 2014, we performed a diagnostic algorithm for influenza infection consisting of an RADT for all respiratory samples received in the laboratory. We studied all the patients with positive results for influenza infection, dividing them into two groups: Group A with a negative RADT but positive reference tests [reverse transcription polymerase chain reaction (RT-PCR) and/or culture] and Group B with an initial positive RADT. During the study period, we had a total of 1,156 patients with suspicion of influenza infection. Of them, 217 (19 %) had a positive result for influenza: 132 (11 %) had an initial negative RADT (Group A) and 85 (7 %) had a positive RADT (Group B). When comparing patients in Group A and Group B, we found significant differences, as follows: prescribed oseltamivir (67 % vs. 82 %; p = 0.02), initiation of oseltamivir before 24 h (89 % vs. 97 %; p = 0.03), antibiotics prescribed (89 % vs. 67 %; p = <0.01), intensive care unit (ICU) admissions after diagnosis (23 % vs. 14 %; p = 0.05), and need for supplementary oxygen (61 % vs. 47 %; p = 0.01). An influenza algorithm including RADTs as the first step improves the time of administration of proper antiviral therapy, reduces the use of antibiotics and ICU admissions, and decreases hospital costs.
authors.affiliation: Clinical Microbiology and Infectious Diseases, Hospital General Universitario Gregorio Marañón, Madrid, Spain, marcelag.delv@gmail.com.
authors.forename: M
authors.initials: M
authors.lastname: González-Del Vecchio
authors.forename: P
authors.initials: P
authors.lastname: Catalán
authors.forename: V
authors.initials: V
authors.lastname: de Egea
authors.forename: A
authors.initials: A
authors.lastname: Rodríguez-Borlado
authors.forename: C
authors.initials: C
authors.lastname: Martos
authors.forename: B
authors.initials: B
authors.lastname: Padilla
authors.forename: B
authors.initials: B
authors.lastname: Rodríguez-Sanchez
authors.forename: E
authors.initials: E
authors.lastname: Bouza
mesh_terms: D000328:Adult; D000465:Algorithms; D000998:Antiviral Agents / Q000627:therapeutic use; D002648:Child; D002675:Child, Preschool; D015331:Cohort Studies; D003955:Diagnostic Tests, Routine / Q000191:economics / Q000379:methods*; D005260:Female; D017721:Hospital Costs; D006801:Humans; D007251:Influenza, Human / Q000175:diagnosis* / Q000188:drug therapy; D008297:Male; D008403:Mass Screening / Q000191:economics / Q000379:methods*; D008875:Middle Aged
chemical_list: D000998:Antiviral Agents