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Accuracy of diagnosis codes to identify febrile young infants using administrative data
Paul L Aronson1, Derek J Williams2, Cary Thurm3
1Department of Pediatrics, Section of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut.
Insights
Identifying febrile infants using administrative data is crucial. An algorithm including admission or discharge diagnosis of fever offers improved accuracy for research and clinical guidelines.
Area of Science:
- Pediatric Health Informatics
- Clinical Epidemiology
Background:
- Administrative data are valuable for optimizing febrile infant management.
- Utilizing this data aids in developing clinical practice guidelines.
Purpose of the Study:
- To identify the most accurate International Classification of Diseases, Ninth Revision (ICD-9) diagnosis coding strategies.
- To improve the identification of febrile infants in administrative datasets.
Main Methods:
- Retrospective cross-sectional study involving 1790 infants across eight emergency departments.
- Compared four ICD-9 diagnosis code groups against a medical record reference standard for fever (≥100.4°F).
- Evaluated algorithm predictive accuracy using sensitivity, specificity, and positive/negative predictive values.
Main Results:
- Of 1790 infants, 766 (42.8%) had fever.
- A strategy using admission or discharge diagnosis of fever showed higher sensitivity (71.1%) and specificity (97.7%).
- This combined approach yielded the highest positive predictive value (86.9%).
Conclusions:
- An identification strategy incorporating admission or discharge diagnosis of fever is recommended for studies using administrative data.
- This approach enhances the accuracy of identifying febrile infants.
- Potential underclassification of patients remains a limitation to consider.
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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