Symptom phenotyping in people with cystic fibrosis during acute pulmonary exacerbations using machine-learning K-means clustering analysis.

Journal: Journal of cystic fibrosis : official journal of the European Cystic Fibrosis Society
PMID: 38851922

Abstract

INTRODUCTION: People with cystic fibrosis (PwCF) experience frequent symptoms associated with chronic lung disease. A complication of CF is a pulmonary exacerbation (PEx), which is often preceded by an increase in symptoms and a decline in lung function. A symptom cluster is when two or more symptoms co-occur and are related; symptom clusters have contributed meaningful knowledge in other diseases. The purpose of this study is to discover symptom clustering patterns in PwCF during a PEx to illuminate symptom phenotypes and assess differences in recovery from PExs.

Authors

  • Eliana R Gill
    Division of Biobehavioral Nursing and Health Informatics, Department of Nursing, University of Washington, Seattle, WA, United States. Electronic address: ergill@uw.edu.
  • Christopher Dill
    University of Houston, Houston Texas, United States.
  • Christopher H Goss
    Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, Seattle, WA, USA; Division of Pulmonology, Department of Pediatrics, University of Washington, Seattle, WA, USA.
  • Scott D Sagel
    Department of Pediatrics, Children's Hospital Colorado, University of Colorado School of Medicine, Aurora, Colorado, United States.
  • Michelle L Wright
    National Institutes of Health, Bethesda, Maryland, United States.
  • Sharon D Horner
    The University of Texas at Austin School of Nursing, Austin, Texas, United States.
  • Julie A Zuñiga
    The University of Texas at Austin School of Nursing, Austin, Texas, United States.

Keywords

External Resources

Popular Topics
Recent Journals