title: A novel method for detecting inpatient pediatric asthma encounters using administrative data.
abstract: Multiple methods for detecting asthma encounters are used today in public surveillance, quality reporting, and clinical research. Failure to detect asthma encounters can make it difficult to measure the scope and effectiveness of hospital or community-based interventions important in comparative effectiveness research and accountable care. Given the pairing of asthma with certain respiratory conditions, the objective of this study was to develop and test an asthma detection algorithm with specificity and sensitivity using 2 criteria: (1) principal discharge diagnosis and (2) asthma diagnosis code position. A medical record review was conducted (n=191) as the gold standard for identifying asthma encounters given objective criteria. The study team observed that for certain principal respiratory diagnoses (n=110), the observed odds ratio that encounters were for asthma when asthma was coded in the second or third code position was not significantly different than when asthma was coded as the principal diagnosis, 0.36 (P=0.42) and 0.18 (P=0.14), respectively. In contrast, the observed odds ratio was significantly different when asthma was coded in the fourth or fifth positions (P<.001). This difference remained after adjusting for covariates. Including encounters with asthma in 1 of the 3 first positions increased the detection sensitivity to 0.84 [95% confidence interval (CI): 0.76-0.92] while increasing the false positive rate to 0.19 [95% CI: 0.07-0.31]. Use of the proposed algorithm significantly improved the reporting accuracy [0.83 95%CI:0.76-0.90] over use of (1) the principal diagnosis alone [0.55 95% CI:0.46-0.64] or (2) all encounters with asthma 0.66 [95% CI:0.57-0.75]. Bed days resulting from asthma encounters increased 64% over use of the principal diagnosis alone. Given these findings, an algorithm using certain respiratory principal diagnoses and asthma diagnosis code position can reliably improve asthma encounter detection for population-based health impact measurement.
authors.affiliation: 1 Research and Sponsored Programs, Children's Hospitals and Clinics of Minnesota , Minneapolis, Minnesota.
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Knighton
authors.forename: Andrew
authors.initials: A
authors.lastname: Flood
authors.forename: Brian
authors.initials: B
authors.lastname: Harmon
authors.forename: Patti
authors.initials: P
authors.lastname: Smith
authors.forename: Carrie
authors.initials: C
authors.lastname: Crosby
authors.forename: Nathaniel R
authors.initials: NR
authors.lastname: Payne
mesh_terms: D000465:Algorithms; D001249:Asthma / Q000145:classification / Q000175:diagnosis*; D002648:Child; D002675:Child, Preschool; D059019:Clinical Coding; D005260:Female; D006760:Hospitalization; D006801:Humans; D038801:International Classification of Diseases; D008297:Male; D008485:Medical Audit; D016017:Odds Ratio; D012372:ROC Curve