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Obstructive sleep apnea: A categorical cluster analysis and visualization.

D Ferreira-Santos1, P Pereira Rodrigues1

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Summary
This summary is machine-generated.

This study identified three distinct patient groups for obstructive sleep apnea (OSA) using cluster analysis. These findings highlight the heterogeneity of OSA and suggest a need to revise patient profiling.

Keywords:
Clinical presentationsCluster visualizationData miningObstructive sleep apnea

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Area of Science:

  • Sleep Medicine
  • Data Science in Healthcare
  • Clinical Phenotyping

Background:

  • Obstructive sleep apnea (OSA) is a common but heterogeneous sleep disorder.
  • Current characterization often overlooks patient variability, potentially delaying diagnosis.
  • Cluster analysis offers a method to explore this heterogeneity in sleep disorders.

Purpose of the Study:

  • To investigate the heterogeneity of obstructive sleep apnea (OSA).
  • To identify distinct patient subgroups within OSA using cluster analysis.
  • To visualize and communicate OSA patient clusters effectively.

Main Methods:

  • Applied k-modes cluster analysis to categorical variables for OSA patient grouping.
  • Utilized demographic, clinical, and comorbidity data from derivation and validation cohorts.
  • Employed k-nearest neighbors for missing value imputation and chi-square for feature selection.

Main Results:

  • Identified three distinct OSA patient clusters based on 13 variables.
  • Cluster 1: Middle-aged males with witnessed apneas and alcohol consumption.
  • Cluster 2: Middle-aged women with increased neck circumference, non-restorative sleep, and headaches.
  • Cluster 3: Obese elderly males with increased neck circumference, witnessed apneas, and alcohol consumption.
  • Validation cohort demonstrated similar cluster proportions, confirming cluster reliability.

Conclusions:

  • Distinct patient subgroups within OSA exist, challenging the stereotypical view.
  • These findings necessitate a re-evaluation of baseline characteristics for OSA patients.
  • The identified clusters can inform more personalized diagnostic and treatment approaches.