Envisia evidence library

The Envisia classifier positive result may serve as a biomarker for FVC by identifying the genomic signature of UIP in patients without definite UIP on CT.  Results for the study showed that patients with an Envisia positive result show a greater decline in FVC over time compared to Envisia negative patients​. The Envisia classifier could have utility in identifying patients with IPF and non-IPF progressive pulmonary fibrosis and underlying UIP earlier in their disease, allowing for aggressive therapy before significant, irreversible FVC loss.

Researchers retrospectively analyzed data from 135 patients enrolled in the BRAVE trial who underwent evaluation for an undiagnosed ILD, had an Envisia Genomic Classifier result, and had multiple forced vital capacity (FVC) measurements to assess lung function over time. These findings show that an Envisia positive result may help identify patients in whom two drug immunosuppressive therapy is harmful. These results further support the utility of Envisia, highlighting the potential of an Envisia positive result to inform both diagnosis and treatment decisions, identifying patients in whom antifibrotic therapy may be preferred.

This systematic review summarizes four published studies in which the Envisia classifier identified UIP with a combined 92% specificity and 68% sensitivity compared to histopathologic UIP to confidently inform UIP and provide important diagnostic information. Confidence in diagnosing IPF was significantly improved in two separate studies when the Envisia classifier result was added to clinical factors and HRCT. 

In 96 patients with suspected ILD the Envisia Genomic Classifier identified UIP regardless of HRCT pattern. These results suggest that recognition of a UIP pattern by Envisia combined with HRCT and clinical factors in a multidisciplinary discussion may assist clinicians in making an interstitial lung disease (especially IPF) diagnosis without the need for a surgical lung biopsy.

Envisia identified usual interstitial pneumonia in transbronchial lung biopsy samples from 49 patients with 88% specificity and 70% sensitivity.

The clinical utility data confirms the use of the Envisia Classifier to increase physician confidence in diagnosis of IPF, and recommendation for antifibrotic therapy, while also reducing invasive and potentially risky surgical lung biopsies (SLBs).

The Envisia Genomic Classifier Demonstrates Consistent Performance Across Gender, Age Group, and Smoking Status

Envisia Genomic Classifier enables physicians to more confidently diagnose idiopathic pulmonary fibrosis (IPF), when results from high-resolution CT (HRCT) imaging are not definitive.

Authors demonstrated proof of principle that genomic analysis and machine learning with Envisia improves the utility of TBB for the diagnosis of UIP, with greater sensitivity and specificity than pathology in TBB alone.

Results show that the development of a genomic signature that predicts usual interstitial pneumonia is feasible. These findings are an important first step towards the development of a molecular test that could be applied to bronchoscopy samples, thus avoiding surgery in the diagnosis of idiopathic pulmonary fibrosis.

The authors demonstrated that the suggested methodologies appropriately addressed challenges of the sample size, disease heterogeneity and technical batch effects and developed a highly accurate and robust classifier leveraging RNA sequencing for the classification of UIP.