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. 2017 Mar;139(3):819-825.e6.
doi: 10.1016/j.jaci.2016.05.048. Epub 2016 Jul 25.

Automated identification of an aspirin-exacerbated respiratory disease cohort

Katherine N Cahill  1 , Christina B Johns  2 , Jing Cui  3 , Paige Wickner  3 , David W Bates  4 , Tanya M Laidlaw  3 , Patrick E Beeler  5
Affiliations

Automated identification of an aspirin-exacerbated respiratory disease cohort

Katherine N Cahill et al. J Allergy Clin Immunol. 2017 Mar.

Abstract

Background: Aspirin-exacerbated respiratory disease (AERD) is characterized by 3 clinical features: asthma, nasal polyposis, and respiratory reactions to cyclooxygenase-1 inhibitors (nonsteroidal anti-inflammatory drugs). Electronic health records (EHRs) contain information on each feature of this triad.

Objective: We sought to determine whether an informatics algorithm applied to the EHR could electronically identify patients with AERD.

Methods: We developed an informatics algorithm to search the EHRs of patients aged 18 years and older from the Partners Healthcare system over a 10-year period (2004-2014). Charts with search terms for asthma, nasal polyps, and record of respiratory (cohort A) or unspecified (cohort B) reactions to nonsteroidal anti-inflammatory drugs were identified as "possible AERD." Two clinical experts reviewed all charts to confirm a diagnosis of "clinical AERD" and classify cases as "diagnosed AERD" or "undiagnosed AERD" on the basis of physician-documented AERD-specific terms in patient notes.

Results: Our algorithm identified 731 "possible AERD" cases, of which 638 were not in our AERD patient registry. Chart review of cohorts A (n = 511) and B (n = 127) demonstrated a positive predictive value of 78.4% for "clinical AERD," which rose to 88.7% when unspecified reactions were excluded. Of those with clinical AERD, 12.4% had no mention of AERD by any treating caregiver and were classified as "undiagnosed AERD." "Undiagnosed AERD" cases were less likely than "diagnosed AERD" cases to have been seen by an allergist/immunologist (38.7% vs 93.2%; P < .0001).

Conclusions: An informatics algorithm can successfully identify both known and previously undiagnosed cases of AERD with a high positive predictive value. Involvement of an allergist/immunologist significantly increases the likelihood of an AERD diagnosis.

Keywords: Aspirin-exacerbated respiratory disease; asthma; chronic rhinosinusitis; clinical decision support; electronic health record; nasal polyps; nonsteroidal anti-inflammatory drugs; structured query language.

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Figures

Figure 1
Figure 1. Venn diagram of the clinical characteristics of cases identified by an AERD bioinformatics algorithm
From 2,647,842 patients seen within the Partners Healthcare system between 12/2004 and 11/2014 aged 18 and older, we identified cases with a diagnosis of asthma, nasal polyps, and/or NSAID allergy. NSAID allergy was restricted to only those with a specified respiratory reaction to NSAIDs or an unspecified (“unknown”) reaction. The cohort of “possible AERD” cases, in yellow, lies at the intersection of all three clinical characteristics. n – sample size.
Figure 2
Figure 2. Flow chart for the assessment of the possible AERD cohort
PPV for identifying AERD in subjects with asthma, nasal polyposis and a recorded respiratory reaction to an NSAID (Cohorts A) = 88.7%. PPV for identifying AERD subjects having a recorded respiratory or unspecified reaction to an NSAID (Cohort A+B) not previously enrolled in the AERD registry = 78.4%. PPV for algorithm identifying all patients with AERD (Cohort A+B+AERD registry) = 81.1%. n – sample size. * - 732 charts were initially identified by the algorithm and one test chart was excluded.
Figure 3
Figure 3
Venn diagram of the possible AERD cases identified by the AERD algorithm (SQL#1–3), AERD specific search terms (SQL#4), and the BWH AERD Registry.
See this image and copyright information in PMC

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