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Automated identification of an aspirin-exacerbated respiratory disease cohort


Cahill, Katherine N; Johns, Christina B; Cui, Jing; Wickner, Paige; Bates, David W; Laidlaw, Tanya M; Beeler, Patrick E (2017). Automated identification of an aspirin-exacerbated respiratory disease cohort. Journal of Allergy and Clinical Immunology, 139(3):819-825.e6.

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.

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.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Clinic for Geriatric Medicine
04 Faculty of Medicine > Institute for Regenerative Medicine (IREM)
Dewey Decimal Classification:570 Life sciences; biology
610 Medicine & health
Date:2017
Deposited On:25 Oct 2016 13:08
Last Modified:08 Dec 2017 20:38
Publisher:Elsevier
ISSN:0091-6749
Free access at:PubMed ID. An embargo period may apply.
Publisher DOI:https://doi.org/10.1016/j.jaci.2016.05.048
PubMed ID:27567328

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