Browse ATS 2021 Abstracts

HomeProgram ▶ Browse ATS 2021 Abstracts

ATS 2021 will feature presentations of original research from accepted abstracts. Mini Symposia and Thematic Poster Sessions are abstract based sessions.

Please use the form below to browse scientific abstracts and case reports accepted for ATS 2021. Abstracts presented at the ATS 2021 will be published in the Online Abstract Issue of the American Journal of Respiratory and Critical Care Medicine, Volume 203, May 3, 2021.

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Envisia Genomic Classifier Helps Improve Multidisciplinary Diagnoses of Complex Interstitial Lung Diseases

Session Title
A1877 - Envisia Genomic Classifier Helps Improve Multidisciplinary Diagnoses of Complex Interstitial Lung Diseases
Author Block: L. H. Lancaster1, D. A. Lynch2, T. V. Colby3, L. Lofaro4, D. Pankratz5, S. Walsh5, J. Huang5, J. Burbanks-Ivey4, S. Bhorade6, G. Kennedy7, BRAVE study group; 1Pulmonary and Critical Care Med, Vanderbilt Univ Medical Ctr, Nashville, TN, United States, 2Radiology, Natl Jewish Health, Denver, CO, United States, 3Mayo Clinic Scottsdale, Scottsdale, AZ, United States, 4Clinical Operations, Veracyte, Inc, South San Francisco, CA, United States, 5R & D, Veracyte, Inc, South San Francisco, CA, United States, 6Medical Affairs, Veracyte, Inc, South San Francisco, CA, United States, 7CSO/CMO, Veracyte, Inc, South San Francisco, CA, United States.
RATIONALE:Interstitial lung disease (ILD) is a heterogeneous group for which determining an accurate diagnosis remains a challenge. Recent advances in diagnosing ILD include multidisciplinary discussion (MDD) and utilizing less invasive techniques to identify a usual interstitial pattern (UIP), especially in patients who are poor candidates for surgical lung biopsy. The Envisia Genomic Classifier (EGC) is a clinically validated molecular test for UIP in transbronchial biopsies. We describe the impact of EGC and histopathology in informing diagnoses in undiagnosed ILD patients. METHODS:Sixty-one patients with suspected ILD from an independent prospective validation cohort for EGC with available HRCT images and a confirmed histopathological diagnosis were evaluated by MDD (expert pulmonologist, radiologist and pathologist). HRCT scans were interpreted by the expert radiologist according to Fleischner Society Guidelines. Patient cases were initially evaluated with clinical information and HRCT scan interpretation by the pulmonologist who provided an ILD diagnosis with confidence level. EGC and histopathology were then sequentially added and ILD diagnosis with confidence level was determined at each step by the expert MDD. RESULTS: The mean age was 63.2+ 12.0 years and 57% were male. Fifty patients (82%) had an HRCT ILD pattern that was “Most Consistent with non UIP”, 4 (6.6%) were “Indeterminate for UIP”, 7 (11%) were either “Probable UIP” or “Typical UIP”. Based on clinical factors and HRCT alone, 31 (51%) patients had a diagnosis of “Unclassifiable ILD.” After EGC results and histopathology were added, the number of Unclassifiable ILD patients decreased to 12 (20%) (p-value = 0.0004). In addition to the 7 (11%) patients with HRCT Probable or Typical UIP pattern, EGC identified 23 (38%) additional UIP+ patients. Twelve (20%) additional patients were UIP+ on histopathology leading to a total of 42 (69%) UIP+ patients by either EGC or histopathology and 19 (31%) UIP- patients by both EGC and histopathology. UIP+ patients were older (67.5 + 9.4 versus 54.0 + 12.3) and more likely to be male (69% versus 32%) compared to the non- UIP patients. In addition, UIP+ patients were less likely to have an unclassifiable ILD diagnosis (12% versus 37%) compared to non- UIP patients. CONCLUSION:Among newly presenting ILD patients, making an early accurate diagnosis remains challenging. The addition of the Envisia Classifier and histopathology, especially those with a UIP+ pattern, significantly decreased the number of unclassifiable ILDs. The Envisia Classifier assists in making a diagnosis in patients with diverse underlying ILD diagnoses.