Fayez Kheir1, Juan Pablo Uribe Becerra2, Brittany Bissell3,4, Marya Ghazipura5, Derrick Herman6, Stephanie M Hon7, Tanzib Hossain8, Yet H Khor9,10, Shandra L Knight11, Michael Kreuter12, Madalina Macrea13, Manoj J Mammen14, Fernando J Martinez15, Venerino Poletti16,17, Lauren Troy18, Ganesh Raghu19, Kevin C Wilson20. 1. Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts. 2. Division of Thoracic Surgery and Interventional Pulmonology, Beth Israel Deaconess Medical Center and Harvard Medical School, Harvard University, Boston, Massachusetts. 3. Acute Care Pharmacy Services, University of Kentucky HealthCare, and. 4. Pharmacy Practice and Science Department, College of Pharmacy, University of Kentucky, Lexington, Kentucky. 5. ZS Associates, Global Health Economics and Outcomes Research, New York, New York. 6. Department of Medicine, The Ohio State University, Columbus, Ohio. 7. Department of Medicine, School of Medicine, Tufts University, Boston, Massachusetts. 8. Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Grossman School of Medicine, New York University Langone Health, New York, New York. 9. Department of Respiratory and Sleep Medicine, Austin Health, Heidelberg, Victoria, Australia. 10. Respiratory Research@Alfred, Central Clinical School, Monash University, Melbourne, Victoria, Australia. 11. Library and Knowledge Services, National Jewish Health, Denver, Colorado. 12. Center for Interstitial and Rare Lung Diseases, Department of Pneumology, University Hospital Heidelberg and German Center for Lung Research, Heidelberg, Germany. 13. Department of Medicine, Veterans Affairs Medical Center, Salem, Virginia. 14. Department of Medicine, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York. 15. Joan and Sanford I. Weill Department of Medicine, New York-Presbyterian Hospital and Weill Cornell Medical Center, Weill Cornell Medical College, Cornell University, New York, New York. 16. Department of Respiratory and Thorax Diseases, GB Morgagni Hospital, Forlì, Italy. 17. Department of Respiratory Diseases and Allergy, Aarhus University Hospital, Aarhus, Denmark. 18. Department of Respiratory and Sleep Medicine, Royal Prince Alfred Hospital, Camperdown, New South Wales, Australia. 19. Department of Medicine, School of Medicine, University of Washington, Seattle, Washington; and. 20. Department of Medicine, School of Medicine, Boston University, Boston, Massachusetts.
Abstract
Background: Usual interstitial pneumonia (UIP) is the histopathologic hallmark of idiopathic pulmonary fibrosis (IPF), the prototypical interstitial lung disease (ILD). Diagnosis of IPF requires that a typical UIP pattern be identified by using high-resolution chest computed tomography or lung sampling. A genomic classifier for UIP has been developed to predict histopathologic UIP by using lung samples obtained through bronchoscopy. Objective: To perform a systematic review to evaluate genomic classifier testing in the detection of histopathologic UIP to inform new American Thoracic Society, European Respiratory Society, Japanese Respiratory Society, and Asociación Latinoamericana del Tórax guidelines. Data Sources: Medline, Embase, and the Cochrane Central Register of Controlled Trials were searched through June 2020. Data Extraction: Data were extracted from studies that enrolled patients with ILD and reported the use of genomic classifier testing. Synthesis: Data were aggregated across studies via meta-analysis. The quality of the evidence was appraised by using the Grading of Recommendations, Assessment, Development, and Evaluation approach. Results: Genomic classifier testing had a sensitivity of 68% (95% confidence interval [CI], 55-73%) and a specificity of 92% (95% CI, 81-95%) in predicting the UIP pattern in ILD. Confidence in an IPF diagnosis increased from 43% to 93% in one cohort and from 59% to 89% in another cohort. Agreement levels in categorical IPF and non-IPF diagnoses measured by using a concordance coefficient were 0.75 and 0.64 in the two cohorts. The quality of evidence was moderate for test characteristics and very low for both confidence and agreement. Conclusions: Genomic classifier testing predicts histopathologic UIP in patients with ILD with a specificity of 92% and improves diagnostic confidence; however, sensitivity is only 68%, and testing is not widely available.
Background: Usual interstitial pneumonia (UIP) is the histopathologic hallmark of idiopathic pulmonary fibrosis (IPF), the prototypical interstitial lung disease (ILD). Diagnosis of IPF requires that a typical UIP pattern be identified by using high-resolution chest computed tomography or lung sampling. A genomic classifier for UIP has been developed to predict histopathologic UIP by using lung samples obtained through bronchoscopy. Objective: To perform a systematic review to evaluate genomic classifier testing in the detection of histopathologic UIP to inform new American Thoracic Society, European Respiratory Society, Japanese Respiratory Society, and Asociación Latinoamericana del Tórax guidelines. Data Sources: Medline, Embase, and the Cochrane Central Register of Controlled Trials were searched through June 2020. Data Extraction: Data were extracted from studies that enrolled patients with ILD and reported the use of genomic classifier testing. Synthesis: Data were aggregated across studies via meta-analysis. The quality of the evidence was appraised by using the Grading of Recommendations, Assessment, Development, and Evaluation approach. Results: Genomic classifier testing had a sensitivity of 68% (95% confidence interval [CI], 55-73%) and a specificity of 92% (95% CI, 81-95%) in predicting the UIP pattern in ILD. Confidence in an IPF diagnosis increased from 43% to 93% in one cohort and from 59% to 89% in another cohort. Agreement levels in categorical IPF and non-IPF diagnoses measured by using a concordance coefficient were 0.75 and 0.64 in the two cohorts. The quality of evidence was moderate for test characteristics and very low for both confidence and agreement. Conclusions: Genomic classifier testing predicts histopathologic UIP in patients with ILD with a specificity of 92% and improves diagnostic confidence; however, sensitivity is only 68%, and testing is not widely available.