A diagnostic codes-based algorithm improves accuracy for identification of childhood asthma in archival data sets.

J Asthma

Department of Pediatric and Adolescent Medicine/Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA.

Published: August 2021

Objective: While a single but truncated ICD code (493) had been widely used for identifying asthma in asthma care and research, it significantly under-identifies asthma. We aimed to develop and validate a diagnostic codes-based algorithm for identifying asthmatics using Predetermined Asthma Criteria (PAC) as the reference.

Methods: This is a retrospective cross-sectional study which utilized two different coding systems, the Hospital Adaptation of the International Classification of Diseases, Eighth Revision (H-ICDA) and the International Classification of Diseases, Ninth Revision (ICD-9). The algorithm was developed using two population-based asthma study cohorts, and validated in a validation cohort, a random sample of the 1976-2007 Olmsted County Birth Cohort. Performance of the diagnostic codes-based algorithm for ascertaining asthma status against manual chart review for PAC (gold standard) was assessed by determining both criterion and construct validity.

Results: Among eligible 267 subjects of the validation cohort, 50% were male, 70% white, and the median age at last follow-up was 17 (interquartile range, 8.7-24.4) years. Asthma prevalence by PAC through manual chart review was 34%. Sensitivity and specificity of the codes-based algorithm for identifying asthma were 82% and 98% respectively. Associations of asthma-related risk factors with asthma status ascertained by the code-based algorithm were similar to those by the manual review.

Conclusions: The diagnostic codes-based algorithm for identifying asthmatics improves accuracy of identification of asthma and can be a useful tool for large scale studies in a setting without automated chart review capabilities.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7677164PMC
http://dx.doi.org/10.1080/02770903.2020.1759624DOI Listing

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