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Related Concept Videos

Sleep Apnea01:21

Sleep Apnea

Sleep apnea is a condition where breathing stops intermittently during sleep, often leading to significant health issues. Each episode can last from 10 to 20 seconds or more and is frequently accompanied by a brief arousal from sleep. This disturbance, largely unnoticed by the individual, can lead to severe daytime fatigue. Commonly, individuals seek help after being informed by their partners about loud snoring and noticeable breathing pauses during sleep.
The condition is more prevalent among...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Sleep-Wake Cycles01:24

Sleep-Wake Cycles

Sleep is an essential physiological process vital to maintaining overall well-being. The reticular activating system (RAS), a network of neurons in the brainstem, regulates wakefulness and sleep. While it may seem passive, sleep consists of distinct cycles, each with its unique characteristics and functions. Two key sleep phases are non-rapid eye movement (NREM) and  rapid eye movement (REM).
NREM Sleep
NREM sleep comprises four progressive stages that seamlessly merge:
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...

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Updated: May 28, 2026

Drug-Induced Sleep Endoscopy (DISE) with Target Controlled Infusion (TCI) and Bispectral Analysis in Obstructive Sleep Apnea
07:54

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Published on: December 6, 2016

Recognition of Obstructive Sleep Apnea: An Exploratory Bayesian Modeling Analysis.

Maria Perifanou-Sotiri1, Evaggelia Anyfanti2, Eleftherios Meletis2

  • 1Department of Respiratory Medicine, Faculty of Medicine, School of Health Sciences, University of Thessaly, 41110 Larissa, Greece.

Journal of Personalized Medicine
|May 26, 2026
PubMed
Summary
This summary is machine-generated.

This study explored how patient data predicts sleep apnea severity. Routinely collected clinical variables can help estimate disease severity, aiding future diagnostic models for sleep apnea testing.

Keywords:
AHIBayesian analysisODIcomorbiditiespolysomnography at homeretrospective

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Area of Science:

  • Sleep Medicine
  • Medical Informatics

Background:

  • Two main diagnostic methods for sleep studies exist: in-laboratory polysomnography (PSG) and home sleep apnea testing (HSAT).
  • While HSAT is convenient and cost-effective, criteria for its use are complex and not solely based on apnea-hypopnea index (AHI) or oxygen desaturation index (ODI).

Purpose of the Study:

  • To investigate associations between common demographic, clinical, and symptom data and objective sleep apnea severity indices (AHI and ODI).
  • To generate hypotheses for future patient-level model development for diagnostic approach selection.

Main Methods:

  • Retrospective analysis of 1100 individuals who underwent PSG.
  • Included demographic, clinical, and symptom variables analyzed against AHI and ODI.
  • Used variable selection, backward elimination, and Bayesian multivariable linear regression with Hamiltonian Monte Carlo methods.

Main Results:

  • Male gender, BMI, Epworth Sleepiness Scale (ESS) score, reported sleep breathing interruptions, and COPD predicted AHI.
  • Male gender, BMI, ESS score, sleep breathing interruptions, daytime sleepiness, obesity, and COPD predicted ODI.
  • COPD showed an inverse association with both AHI and ODI.

Conclusions:

  • Routinely available clinical data can be integrated into a Bayesian framework to estimate pre-test probability of sleep apnea severity.
  • This exploratory study provides a hypothesis-generating foundation for future validated tools to guide HSAT versus PSG selection.