Use of a Genomic Classifier in Patients with Interstitial Lung Disease: A Systematic Review and Meta-Analysis

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.