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Related Experiment Video

Updated: Sep 4, 2025

Drug-Induced Sleep Endoscopy DISE with Target Controlled Infusion TCI and Bispectral Analysis in Obstructive Sleep Apnea
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Identifying phenotypes of obstructive sleep apnea using cluster analysis.

Kavitha Venkatnarayan1, Uma Maheswari Krishnaswamy2, Nithin Kumar Reddy Rajamuri3

  • 1Department of Pulmonary Medicine, St. John's National Academy of Health Sciences, Bengaluru, 560034, India.

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|July 14, 2022
PubMed
Summary
This summary is machine-generated.

This study identified three distinct obstructive sleep apnea (OSA) phenotypes based on patient data. These OSA clusters show different characteristics, paving the way for personalized treatment approaches.

Keywords:
Cluster analysisClustersObstructive sleep apneaPhenotypes

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Area of Science:

  • Sleep Medicine
  • Pulmonology
  • Internal Medicine

Background:

  • Obstructive sleep apnea (OSA) management is increasingly individualized due to advances in understanding its pathophysiology and clinical presentation.
  • Identifying distinct OSA phenotypes is a crucial first step towards personalized treatment strategies.

Purpose of the Study:

  • To identify distinct phenotypes of obstructive sleep apnea (OSA).
  • To lay the groundwork for individualized OSA management by characterizing patient clusters.

Main Methods:

  • Included 100 patients diagnosed with OSA via level 1 polysomnography (PSG).
  • Collected clinical data, comorbidities, anthropometry, and sleepiness scores.
  • Performed cluster analysis on PSG data (apnea-hypopnea index, positional indices, sleep stages, T90) to identify phenotypes.

Main Results:

  • Three distinct OSA clusters emerged from the analysis.
  • Cluster 1: Older, obese individuals with severe OSA, more comorbidities, and higher T90.
  • Cluster 2: Younger, less obese males with snoring, witnessed apnea, moderate, and supine-predominant OSA.
  • Cluster 3: Middle-aged, obese males with mild OSA, fewer comorbidities, and lower T90.

Conclusions:

  • The study successfully identified three OSA clusters with unique demographic, anthropometric, and PSG features.
  • Further research with larger sample sizes and additional parameters is needed to refine phenotype characterization and personalize OSA management.