Patient-centred understanding of quality of life, symptoms, health equity in cystic fibrosis (PULSE-CF): study protocol for an observational study.

Authors

Alexander Evan Soltoff, Jane Lowers, Cade Hovater, Kasey Seymour, Elisabeth P Dellon, Brandi Middour-Oxler, Chris Driggers, Reva Crandall, Joshua D Chandler, Nicholas A Giordano, Min Huang, Anne Fitzpatrick, Arlene Stecenko, Ryan A Harris, Rishikesan Kamaleswaran, Scott Gillespie, Dio Kavalieratos

Published in

BMJ open respiratory research. Volume 13. Issue 1. Sep 25, 2026. Epub Sep 25, 2026.

Abstract

Cystic fibrosis (CF) is a progressive, life-limiting condition affecting more than 160 000 people worldwide. Despite major therapeutic advances that have extended life expectancy, people living with CF continue to experience substantial physical, emotional and social symptom burdens that impair quality of life (QOL). Symptoms in CF often co-occur, indicating the presence of specific symptom clustering. Different clusters of co-occurring symptoms may reflect distinct underlying biological mechanisms influenced by social determinants of health. This study aims to identify clusters of co-occurring symptoms among adults with CF, characterise the biological pathways that may underlie these clusters and examine how social and structural factors influence symptom experiences and QOL.
This is a two-site, prospective mixed-methods cohort study enrolling approximately 140 adults with CF from two academic medical centres in the Southern United States. Participants will be followed for 12 months using validated patient-reported outcome measures to assess symptoms and QOL at baseline and at 3, 6, 9 and 12 months. Clinical data will be collected through chart review, and biospecimens (plasma and exhaled breath condensate) will be obtained at baseline, 6 months and 12 months for metabolomic analysis. Qualitative interviews and healthcare experience surveys will be conducted to explore how social, structural and clinical factors influence symptom burden and care experiences. Statistical analyses will use unsupervised machine learning methods to identify symptom clusters and metabolic endotypes as well as longitudinal regression models to examine associations between symptom patterns and QOL outcomes.
This study involves human participants and was approved by the Emory University Institutional Review Board (2025P011492). All participants will provide informed consent prior to enrolment. We will publish the results of this study in a peer-reviewed journal.

PMID:
42791029
Bibliographic data and abstract were imported from PubMed on 26 Sep 2026.

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