Automated identification of an aspirin-exacerbated respiratory disease cohort - 19/04/17
, Christina B. Johns, BA b, Jing Cui, MD, PhD a, b, Paige Wickner, MD, MPH a, b, David W. Bates, MD, MSc a, c, Tanya M. Laidlaw, MD a, b, Patrick E. Beeler, MD a, c, dAbstract |
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.
Le texte complet de cet article est disponible en PDF.Key words : Aspirin-exacerbated respiratory disease, electronic health record, asthma, nasal polyps, nonsteroidal anti-inflammatory drugs, chronic rhinosinusitis, structured query language, clinical decision support
Abbreviations used : AERD, BWH, COX-1, EHR, ICD-9, ICD-10, IQR, NSAID, PPV, RPDR, SQL
Plan
| This work was supported by the National Institutes of Health (NIH) (grant nos. NIH K23 HL111113 and NIH K23 AI118804-01), the Swiss National Science Foundation (grant no. P2BSP3_148619 to P.E.B.), departmental funds, and generous contributions from the Vinik and Kaye Families. |
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| Disclosure of potential conflict of interest: K. N. Cahill receives grant support from the National Institutes of Health. P. Wickner serves as a consultant for Amag Pharmaceuticals. D. W. Bates is a coinventor on Patent No. 6029138 held by Brigham and Women's Hospital. T. M. Laidlaw receives grant support from the National Institutes of Health. P. E. Beeler received grant support from the Swiss National Science Foundation and is an employee of the University Hospital in Zurich. The rest of the authors declare that they have no relevant conflicts of interest. |
Vol 139 - N° 3
P. 819 - mars 2017 Retour au numéro
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