Recommendations for the clinical management of patients receiving macitentan for pulmonary arterial hypertension (PAH): A Delphi consensus document

Franck F. Rahaghi, Hassan M. Alnuaimat, Rana L.A. Awdish, Vijay P. Balasubramanian, Robert C. Bourge, Charles D. Burger, John Butler, C. Gregory Cauthen, Murali M. Chakinala, Bennett P. deBoisblanc, Michael S. Eggert, Peter Engel, Jeremy Feldman, J. Wesley McConnell, Myung Park, Jeffrey S. Sager, Namita Sood, Harold I. Palevsky

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

In patients treated with macitentan (Opsumit_, Actelion Pharmaceuticals Ltd., Basel, Switzerland) for pulmonary arterial hypertension (PAH), prevention and/or effective management of treatment-related adverse events may improve adherence. However, management of these adverse events can be challenging and the base of evidence and clinical experience for macitentan is limited. In the absence of evidence, consensus recommendations from physicians experienced in using macitentan to treat PAH may benefit patients and physicians who are using macitentan. Consensus recommendations were developed by a panel of physicians experienced with macitentan and PAH using a modified Delphi process. Over three iterations, panelists developed and refined a series of statements on the use of macitentan in PAH and rated their agreement with each statement on a Likert scale. The panel of 18 physicians participated and developed a total of 118 statements on special populations, add-on therapy, drug-drug interactions, warnings and precautions, hospitalization and functional class, and adverse event management. The resulting consensus recommendations are intended to provide practical guidance on real-world issues in using macitentan to treat patients with PAH.

Original languageEnglish
Pages (from-to)702-711
Number of pages10
JournalPulmonary Circulation
Volume7
Issue number3
DOIs
StatePublished - Sep 2017

Keywords

  • Adverse events
  • Endothelin receptor antagonist
  • Therapy adherence

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