Paul L Aronson1, Derek J Williams2, Cary Thurm3, Joel S Tieder4, Elizabeth R Alpern5, Lise E Nigrovic6, Amanda C Schondelmeyer7, Fran Balamuth8, Angela L Myers9, Russell J McCulloh9, Evaline A Alessandrini10, Samir S Shah7,11, Whitney L Browning2, Katie L Hayes8, Elana A Feldman4, Mark I Neuman6. 1. Department of Pediatrics, Section of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut. 2. Division of Hospital Medicine, Department of Pediatrics, The Monroe Carell Jr Children's Hospital at Vanderbilt, Vanderbilt University School of Medicine, Nashville, Tennessee. 3. Children's Hospital Association, Overland Park, Kansas. 4. Division of Hospital Medicine, Department of Pediatrics, Seattle Children's Hospital, University of Washington School of Medicine, Seattle, Washington. 5. Division of Emergency Medicine, Ann and Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, Illinois. 6. Division of Emergency Medicine, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts. 7. Division of Hospital Medicine, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio. 8. The Center for Pediatric Clinical Effectiveness and Division of Emergency Medicine, Department of Pediatrics, The Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania. 9. Division of Infectious Diseases, Department of Pediatrics, Children's Mercy Hospital, University of Missouri-Kansas City School of Medicine, Kansas City, Missouri. 10. James M. Anderson Center for Health Systems Excellence and Division of Emergency Medicine, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio. 11. Division of Infectious Diseases, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio.
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.
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.
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Authors: Colleen K Gutman; K Casey Lion; Paul Aronson; Carla Fisher; Carma Bylund; Antionette McFarlane; Xiangyang Lou; Mary D Patterson; Ahmed Lababidi; Rosemarie Fernandez Journal: BMJ Open Date: 2022-09-20 Impact factor: 3.006