Idiopathic Pulmonary Fibrosis | Current Treatment | Detailed, Expanded Analysis: Treatment Algorithms: Claims Data Analysis (US)

Publish date: January 2019

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Idiopathic pulmonary fibrosis (IPF) is an orphan disease affecting approximately 50,000 people in the United States. It is characterized by an irreversible loss of lung function, leading to high morbidity and mortality; most patients die within three to five years following diagnosis. Pirfenidone (Roche’s Esbriet) and nintedanib (Boehringer Ingelheim’s Ofev) are the only two drugs approved for treatment of adults with mild to moderate IPF. Other than these two disease-modifying therapies (DMTs), physicians also prescribe off-label medications (e.g., corticosteroids, anticoagulants) to treat IPF patients when the risk/benefit profile does not warrant treatment with pirfenidone or nintedanib, or when patients do not receive reimbursement or cannot afford the coinsurance for these two DMTs. Our Treatment Algorithms study provides national patient-level claims data to explore the use of key therapies in both newly diagnosed and recently treated patients, providing insight into the current treatment landscape for IPF.

Questions Answered

  • What patient share do key therapies and brands garner by line of treatment in newly diagnosed IPF patients? What are the quarterly trends in prescribing among recently treated and newly diagnosed IPF patients?
  • What proportion of IPF patients receive drug therapy within one year of diagnosis, and how quickly? What percentage of patients progress to later lines of therapy within one year of diagnosis?
  • What percentage of IPF patients are treated with monotherapy versus combination therapy? What are the most widely used combination therapies?
  • What are the product-level compliance and persistency rates among drug-treated patients with IPF ?

Product Description

Treatment Algorithms: Claims Data Analysis provides detailed analysis of brand usage across different lines of therapy using real-world data patient-level claims data so you can accurately assess your source of business and quantify areas of opportunity for increasing your brand share.

Table of contents

  • Detailed, Expanded Analysis: Treatment Algorithms: Claims Data Analysis (US)
    • Idiopathic Pulmonary Fibrosis | Treatment Algorithms | Claims Data Analysis | US | 2019 Slide Deck

Author(s): Akash Saini, PhD

Akash Saini, is a lead analyst with the Infectious, Niche, and Rare Disease team at Decision Resources Group. Prior to joining Decision Resources Group, Saini was a postdoctoral fellow at the University of Massachusetts Medical School, where he studied mitochondrial dysfunction in amyotrophic lateral sclerosis (ALS). He earned a in Biochemistry and Biotechnology from the International Centre for Genetic Engineering and Biotechnology, New Delhi, where he also studied the underlying disease mechanism in ALS, and an in Biotechnology from Jawaharlal Nehru University, New Delhi, where he studied the biophysical characteristics of amyloid formation.