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Author Spotlight: Traditional Chinese Medicine for Sleep Disorders in Acute COPD — A Safe, Cost-Effective Approach
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Identification of sleep phenotypes in COPD using machine learning-based cluster analysis.

Javad Razjouyan1, Nicola A Hanania2, Sara Nowakowski1

  • 1VA's Health Services Research and Development Service (HSR&D), Center for Innovations in Quality, Effectiveness, and Safety, Michael E. DeBakey VA Medical Center, Houston, TX, 77030, USA; Big Data Scientist Training Enhancement Program, VA Office of Research and Development, Washington, DC, 20420, USA; VA Quality Scholars Coordinating Center, IQuESt, Michael E. DeBakey VA Medical Center, Houston, TX, 77030, USA; Section of Pulmonary and Critical Care Medicine, Department of Medicine, Baylor College of Medicine, Houston, TX, 77030, USA.

Respiratory Medicine
|May 6, 2024
PubMed
Summary
This summary is machine-generated.

Five patient clusters with chronic obstructive pulmonary disease (COPD) were identified using sleep data. Total sleep time and sleep efficiency significantly impact mortality risk in COPD patients.

Keywords:
COPDComorbiditiesPhenotypesSleep disorders

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Area of Science:

  • Pulmonary Medicine
  • Sleep Medicine
  • Data Science

Background:

  • Disturbed sleep significantly impacts quality of life and predicts adverse outcomes in patients with chronic obstructive pulmonary disease (COPD).
  • Objective sleep parameters are crucial for understanding disease progression and patient stratification in COPD.

Purpose of the Study:

  • To identify distinct phenotypic clusters in COPD patients using objective sleep parameters.
  • To evaluate the association between these clusters and all-cause mortality for improved risk stratification.

Main Methods:

  • Longitudinal observational cohort study utilizing nationwide Veterans Health Administration data.
  • Unsupervised machine learning (K-means clustering) applied to polysomnography data from 9992 COPD patients.
  • Cox regression analysis and Kaplan-Meier estimates used to assess mortality associations.

Main Results:

  • Five distinct patient clusters were identified based on age, comorbidity burden, and sleep parameters.
  • Mortality rates varied significantly across clusters, from 9.4% to 42% overall.
  • Total sleep time and sleep efficiency demonstrated significant associations with mortality in specific clusters.

Conclusions:

  • Objective sleep parameters are vital for phenotypic characterization and mortality risk assessment in COPD.
  • The identified clusters offer a novel approach to stratify COPD patients based on sleep characteristics.
  • Further research into sleep-disordered breathing interventions may improve outcomes for high-risk COPD patients.