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Drug-Induced Sleep Endoscopy DISE with Target Controlled Infusion TCI and Bispectral Analysis in Obstructive Sleep Apnea
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Quantitative Data Integration Analysis Method for Cross-Studies: Obstructive Sleep Apnea as an Example.

Rong Zhou1,2,3, Shengrong Zhou4, Qiguang Xia3

  • 1Department of Epidemiology, School of Public Health, Fudan University, Shanghai 200232, China.

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This summary is machine-generated.

This study developed a data integration strategy to identify key indicators for obstructive sleep apnea (OSA). The method successfully pinpointed significant apnea monitor indicators for diagnosing this growing sleep disorder.

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Area of Science:

  • Sleep Medicine
  • Data Science
  • Biostatistics

Background:

  • Obstructive sleep apnea (OSA) prevalence is increasing.
  • Accurate diagnosis requires multiple indicators.
  • Current diagnostic methods need refinement.

Purpose of the Study:

  • To develop a strategy for cross-study screening and integration of quantitative data.
  • To identify significant apnea monitor indicators for OSA diagnosis.
  • To improve the diagnostic accuracy of obstructive sleep apnea.

Main Methods:

  • A systematic literature search was conducted on PubMed.
  • Sleep disorder and OSA datasets were curated from 119 publications (178 studies).
  • Data were analyzed using R package 'meta 4.18-0' calculating p-values and standard mean differences (SMD).

Main Results:

  • 284 sleep-related indicators were filtered, constructing 22 profiles.
  • Apnea hypopnea index significantly decreased across all profiles.
  • Significant changes observed in Total Sleep Time (TST), Rapid Eye Movement (REM) sleep, and oxygen saturation (SaO2).

Conclusions:

  • The proposed data integration strategy effectively identified significant OSA indicators.
  • This approach aids in the multiphenotypic diagnosis of obstructive sleep apnea.
  • Further research can refine these indicators for clinical application.