Accuracy of diagnosis codes to identify febrile young infants using administrative data

for the Febrile Young Infant Research Collaborative

Research output: Contribution to journalArticle

5 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)787-793
Number of pages7
JournalJournal of hospital medicine
Volume10
Issue number12
DOIs
StatePublished - Dec 1 2015

Fingerprint

Fever
International Classification of Diseases
Confidence Intervals
Medical Records
Health Information Systems
Infection
Practice Guidelines
Hospital Emergency Service
Cross-Sectional Studies
Pediatrics
Sensitivity and Specificity

ASJC Scopus subject areas

  • Leadership and Management
  • Internal Medicine
  • Fundamentals and skills
  • Health Policy
  • Care Planning
  • Assessment and Diagnosis

Cite this

Accuracy of diagnosis codes to identify febrile young infants using administrative data. / for the Febrile Young Infant Research Collaborative.

In: Journal of hospital medicine, Vol. 10, No. 12, 01.12.2015, p. 787-793.

Research output: Contribution to journalArticle

for the Febrile Young Infant Research Collaborative. / Accuracy of diagnosis codes to identify febrile young infants using administrative data. In: Journal of hospital medicine. 2015 ; Vol. 10, No. 12. pp. 787-793.
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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.",
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AU - Tieder, Joel S.

AU - Alpern, Elizabeth R.

AU - Nigrovic, Lise E.

AU - Schondelmeyer, Amanda C.

AU - Balamuth, Fran

AU - Myers, Angela L.

AU - Mcculloh, Russell J.

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AU - Shah, Samir S.

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N2 - 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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