The Envisia Genomic Classifier is the first commercially available genomic test to improve physicians’ ability to differentiate IPF from other interstitial lung diseases (ILD) without the need for invasive, risky and costly surgery. The test harnesses the power of RNA sequencing and machine learning to provide a genomic pattern of Usual Interstitial Pneumonia (UIP), which is critical to the diagnosis of idiopathic pulmnonary fibrosis (IPF) and is associated with a poor prognosis across ILD sub-types.

Each year across the United States and Europe, approximately 200,000 patients are suspected of having an ILD. IPF is among the most common and most deadly of these lung-scarring diseases. It is notoriously difficult to diagnose, often leading to treatment delays, prolonged misdiagnosis, patient distress and added healthcare expense. Obtaining a timely IPF diagnosis is important, particularly given the availability of anti-fibrotics that can slow the progression of the disease. Further, data show that immunosuppressive agents – which are frequently used to treat many ILDs – are actually harmful to patients with IPF.

Limitations in current technologies make IPF challenging to differentiate from other ILDs. Physicians routinely use high-resolution computed tomography (HRCT) to help identify UIP, but this approach often provides inconclusive results, which can lead to invasive diagnostic procedures.

In two independent, prospective clinical validation studies, the Envisia classifier identified UIP with a combined specificity of 91 percent and sensitivity of 63 percent. These findings mean that the test would be expected to identify two-thirds of UIP cases with a high degree of accuracy, while minimizing the number of false positive results, providing physicians with greater confidence in their diagnosis and prognosis.