Identifying patients with pulmonary arterial hypertension using administrative claims algorithms

Stephen C. Mathai, Anna Ryan Hemnes, Scott Manaker, Rebekah H. Anguiano, Bonnie B. Dean, Vishal Saundankar, Peter Classi, Andrew C. Nelsen, Kathryn Gordon, Corey E. Ventetuolo

Research output: Contribution to journalArticle

Abstract

Retrospective administrative claims database studies provide real-world evidence about treatment patterns, healthcare resource use, 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 use 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.

Original languageEnglish (US)
Pages (from-to)797-806
Number of pages10
JournalAnnals of the American Thoracic Society
Volume16
Issue number7
DOIs
StatePublished - 2019

Keywords

  • Administrative claims
  • Pulmonary arterial hypertension
  • Pulmonary hypertension

ASJC Scopus subject areas

  • Pulmonary and Respiratory Medicine

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    Mathai, S. C., Hemnes, A. R., Manaker, S., Anguiano, R. H., Dean, B. B., Saundankar, V., Classi, P., Nelsen, A. C., Gordon, K., & Ventetuolo, C. E. (2019). Identifying patients with pulmonary arterial hypertension using administrative claims algorithms. Annals of the American Thoracic Society, 16(7), 797-806. https://doi.org/10.1513/AnnalsATS.201810-672CME