Send to

Choose Destination
Ann Am Thorac Soc. 2019 Mar 13. doi: 10.1513/AnnalsATS.201810-672CME. [Epub ahead of print]

Identifying Patients with Pulmonary Arterial Hypertension (PAH) Using Administrative Claims Algorithms.

Author information

Johns Hopkins University School of Medicine, Division of Pulmonary and Critical Medicine, Baltimore, Maryland, United States.
Vanderbilt University Department of Medicine, 171754, Nashville, Tennessee, United States.
University of Pennsylvania Perelman School of Medicine, 14640, Philadelphia, Pennsylvania, United States.
University of Illinois at Chicago College of Pharmacy, Chicago, Illinois, United States.
Xcenda, LLC, Palm Harbor, Florida, United States.
Xcenda LLC, 367842, Palm Harbor, Florida, United States.
United Therapeutics Corp Research and Development, 195490, Research Triangle Park, North Carolina, United States ;
United Therapeutics Corp Research and Development, 195490, Research Triangle Park, North Carolina, United States.
Brown University, Medicine , Providence, Rhode Island, United States.


Retrospective administrative claims database studies provide real-world evidence about treatment patterns, healthcare resource utilization, and costs for patients and are increasingly used to inform policy making, drug formulary, and regulatory decisions. However, there is no standard methodology to identify patients with pulmonary arterial hypertension (PAH) from administrative claims data. Given the number of approved drugs now available for patients with PAH, the cost of PAH treatments, and the significant healthcare resource utilization associated with the care of patients with PAH, there is a considerable need to develop an evidence-based and systematic approach to accurately identify these patients in claims databases. A panel of pulmonary hypertension clinical experts and researchers experienced in retrospective claims database studies convened to review relevant literature and recommend best practices for developing algorithms to identify patients with PAH in administrative claims databases specific to a particular research hypothesis.

Supplemental Content

Full text links

Icon for Atypon
Loading ...
Support Center