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A Computable Electronic Health Record ARDS Classifier and the Association Between the MUC5B Promoter Polymorphism and
V Eric Kerchberger1, J Brennan McNeil1, Neil Zheng1
1Department of Medicine (V. E. K., J. B. M., J. A. B., Q. F., L. B. W.), the Department of Biomedical Informatics (V. E. K., N. Z., W.-Q. W.), Vanderbilt University Medical Center; the Department of Cell and Developmental Biology (J. A. B.), the Department of Pathology, Microbiology and Immunology (J. A. B., L. B. W.), Vanderbilt University, Nashville; Quillen College of Medicine (J. B. M.), East Tennessee State University, Johnson City, TN; Brigham and Women's Hospital (N. Z.), Boston, MA; Genentech, Inc. (D. C., C. M. R.), South San Francisco; and the Department of Medicine (A. J. R.), Stanford University, Palo Alto, CA.
Background:
Large population-based DNA biobanks linked to electronic health records (EHRs) may provide novel opportunities to identify genetic drivers of ARDS.
Research Question:
Can a computerized algorithm identify ARDS in a large EHR biobank database, and can this be used to identify ARDS genetic risk factors?
Study Design And Methods:
We developed a classifier algorithm to identify a diagnosis of ARDS as identified from the electronic health record (EHR-ARDS) using diagnostic billing codes, laboratory test results, and chest radiography report text. The classifier model performance was evaluated against investigator-adjudicated ARDS using standard classification metrics including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the Cohen κ value. After confirming acceptable classifier performance, we evaluated the association between EHR-ARDS and the MUC5B promoter polymorphism rs35705950 in 2 parallel genotyped cohorts: a prospective biomarker cohort of critically ill adults (Validating Acute Lung Injury Biomarkers for Diagnosis [VALID]) and a retrospective cohort from our institution's de-identified EHR biobank, BioVU.
Results:
We included 2,795 patients from VALID and 9,025 hospitalized participants from BioVU. EHR-ARDS showed moderate agreement with investigator-adjudicated ARDS (VALID: sensitivity, 0.86; specificity, 0.70; PPV, 0.49; NPV, 0.93; and k, 0.45; BioVU: sensitivity, 0.94; specificity, 0.81; PPV, 0.66; NPV, 0.97; and k, 0.67). We observed a significant age-gene interaction effect for EHR-ARDS in VALID: among older patients, rs35705950 was associated with increased EHR-ARDS risk (OR, 1.37; 95% CI, 1.05-1.78; P = .019), whereas among younger patients, this association was absent (OR, 0.92; 95% CI, 0.70-1.21; P = .55). In BioVU, rs35705950 was associated with EHR-ARDS among all participants (OR, 1.20; 95% CI, 1.01-1.43; P = .043); however, this effect did not vary by age.
Interpretation:
The MUC5B promoter polymorphism was associated with EHR-ARDS in 2 parallel cohorts of at-risk adults. An age-gene effect modification was observed in VALID, whereas BioVU identified a consistent association between MUC5B and EHR-ARDS regardless of age. Our study highlights the potential for EHR biobanks to enable precision medicine ARDS studies.
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