Place des biomarqueurs dans le dépistage du SAHOS. Une revue narrative de la littérature

Hauquiert, B.; Drion, E. F.; Deflandre, Eric

doi:10.1016/j.rmr.2021.04.005

Cited by 2 publications

(1 citation statement)

References 80 publications

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“… 26 The application of these biomarkers may contribute to making the screening less subjective and enabling mass screening. 27 …”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Prediction Model of Adult Obstructive Sleep Apnea Based on Systematically Evaluated Common Clinical Biochemical Indicators

Huang,

Zhuang,

Zheng

et al. 2024

NSS

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Objective Obstructive sleep apnea (OSA) is a common and potentially fatal sleep disorder. The purpose of this study was to construct an objective and easy-to-promote model based on common clinical biochemical indicators and demographic data for OSA screening. Methods The study collected the clinical data of patients who were referred to the Sleep Medicine Center of the Second Affiliated Hospital of Fujian Medical University from December 1, 2020, to July 31, 2023, including data for demographics, polysomnography (PSG), and 30 biochemical indicators. Univariate and multivariate analyses were performed to compare the differences between groups, and the Boruta method was used to analyze the importance of the predictors. We selected and compared 10 predictors using 4 machine learning algorithms which were “Gaussian Naive Bayes (GNB)”, “Support Vector Machine (SVM)”, “K Neighbors Classifier (KNN)”, and “Logistic Regression (LR)”. Finally, the optimal algorithm was selected to construct the final prediction model. Results Among all the predictors of OSA, body mass index (BMI) showed the best predictive efficacy with an area under the receiver operating characteristic curve (AUC) = 0.699; among the predictors of biochemical indicators, triglyceride-glucose (TyG) index represented the best predictive performance (AUC = 0.656). The LR algorithm outperformed the 4 established machine learning (ML) algorithms, with an AUC (F1 score) of 0.794 (0.841), 0.777 (0.827), and 0.732 (0.788) in the training, validation, and testing cohorts, respectively. Conclusion We have constructed an efficient OSA screening tool. The introduction of biochemical indicators in ML-based prediction models can provide a reference for clinicians in determining whether patients with suspected OSA need PSG.

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“… 26 The application of these biomarkers may contribute to making the screening less subjective and enabling mass screening. 27 …”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Prediction Model of Adult Obstructive Sleep Apnea Based on Systematically Evaluated Common Clinical Biochemical Indicators

Huang,

Zhuang,

Zheng

et al. 2024

NSS

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Update on the perioperative management of obstructive sleep apnea adult patients: a narrative review

Raven,

Azongmo,

Bonhomme

2024

Acta Anaest Belg

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Obstructive Sleep apnea (OSA) is the most frequently encountered sleep disorder. It is characterized by repetitive and intermittent closure of the upper airway and is associated with significant predictable peri-operative cardio- respiratory co-morbidities. This pathology is under-diagnosed, even though it can be encountered in a third of the surgical population. Its complexity and heterogeneity make the perioperative management of those patients a challenge and requires adaptation. This article describes the pathophysiology of the condition, and its perioperative management. It revisits management algorithms and care pathways, to provide the reader with up-to-date information for the improvement of patient care.

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Place des biomarqueurs dans le dépistage du SAHOS. Une revue narrative de la littérature

Cited by 2 publications

References 80 publications

A Machine Learning Prediction Model of Adult Obstructive Sleep Apnea Based on Systematically Evaluated Common Clinical Biochemical Indicators

A Machine Learning Prediction Model of Adult Obstructive Sleep Apnea Based on Systematically Evaluated Common Clinical Biochemical Indicators

Update on the perioperative management of obstructive sleep apnea adult patients: a narrative review

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