Accuracy of diagnosis codes to identify febrile young infants using administrative data. Issue 12 (6th August 2015)

• Record Type: Journal Article
• Authors: Aronson, Paul L.; Williams, Derek J.; Thurm, Cary; Tieder, Joel S.; Alpern, Elizabeth R.; Nigrovic, Lise E.; Schondelmeyer, Amanda C.; Balamuth, Fran; Myers, Angela L.; McCulloh, Russell J.; Alessandrini, Evaline A.; Shah, Samir S.; Browning, Whitney L.; Hayes, Katie L.; Feldman, Elana A.; Neuman, Mark I.
• Is Part Of: Journal of hospital medicine. Volume 10:Issue 12(2015)
• Page Start: 787
• Page End: 793
• Publication Date: 2015-08-06
• Subjects: Hospital care -- Periodicals; Clinical medicine -- Periodicals; 610
• DOI: 10.1002/jhm.2441
• Languages: English
• ISSNs: 1553-5592

Abstract

Abstract : BACKGROUND: Administrative data can be used to determine optimal management of febrile infants and aid clinical practice guideline development. OBJECTIVE: Determine the most accurate International Classification of Diseases, Ninth Revision (ICD‐9) diagnosis coding strategies for identification of febrile infants. DESIGN: Retrospective cross‐sectional study. SETTING: Eight emergency departments in the Pediatric Health Information System. PATIENTS: Infants aged <90 days evaluated between July 1, 2012 and June 30, 2013 were randomly selected for medical record review from 1 of 4 ICD‐9 diagnosis code groups: (1) discharge diagnosis of fever, (2) admission diagnosis of fever without discharge diagnosis of fever, (3) discharge diagnosis of serious infection without diagnosis of fever, and (4) no diagnosis of fever or serious infection. EXPOSURE: The ICD‐9 diagnosis code groups were compared in 4 case‐identification algorithms to a reference standard of fever ≥100.4° F documented in the medical record. MEASUREMENTS: Algorithm predictive accuracy was measured using sensitivity, specificity, and negative and positive predictive values. RESULTS: Among 1790 medical records reviewed, 766 (42.8%) infants had fever. Discharge diagnosis of fever demonstrated high specificity (98.2%, 95% confidence interval [CI]: 97.8‐98.6) but low sensitivity (53.2%, 95% CI: 50.0‐56.4). A case‐identification algorithm of admission or discharge diagnosis of fever exhibited higher sensitivity (71.1%, 95% CI: 68.2‐74.0), similar specificity (97.7%, 95% CI: 97.3‐98.1), and the highest positive predictive value (86.9%, 95% CI: 84.5‐89.3). CONCLUSIONS: A case‐identification strategy that includes admission or discharge diagnosis of fever should be considered for febrile infant studies using administrative data, though underclassification of patients is a potential limitation. Journal of Hospital Medicine 2015;10:787–793. © 2015 Society of Hospital Medicine