Algorithmic Identification of Patients With Aspirin‐Exacerbated Respiratory Disease Using an Electronic Health Record

Abstract

AbstractObjectiveTo determine whether an electronic health record (EHR) system can be used to identify cases of aspirin‐exacerbated respiratory disease (AERD) in an area outside of a regional referral center with low rates of aspirin desensitization therapy.Study DesignRetrospective chart review single academic tertiary care hospital.SettingSingle‐site academic tertiary care hospital.MethodsUsing Epic's SlicerDicer function, an algorithm was created and applied to all patient charts from 2013 to 2021. The algorithm was as follows: “Allergy/Contraindication to NSAIDs OR aspirin” AND “Diagnosis of Nasal polyp AND “Diagnosis of Asthma.” Clinical data including demographics, NSAID reaction, and specialist involvement was collected.ResultsA total of 54 potential cases of AERD were identified. Thirty‐two were determined to have AERD after chart review, yet 12 of these patients (37.5%) had no mention of AERD within the chart. The 54 patients were stratified into 2 cohorts based on reaction to NSAIDs: respiratory (n = 29) or unspecified (n = 25). Of the patients in the respiratory reaction group, 26 were found to have clinical AERD, demonstrating a positive predictive values (PPV) of 89.7%. The overall PPV was 59.3%. Those with a respiratory reaction to NSAIDS listed in the EHR were more likely to have clinical AERD (odds ratio 27.44; confidence interval 6.08‐123.85; p < 0.0001). Only 2 patients (6.3%) underwent aspirin desensitization.ConclusionAERD remains under‐diagnosed in the study population. The informatics algorithm presented here has a high positive predictive value for identifying clinical AERD patients in a geographical area with low rates of aspirin desensitization and may aid in identifying candidates for expanded treatment options.