The Use of Middle Latency Auditory Evoked Potentials (MLAEP) as Methodology for Evaluating Sedation Level in Propofol-Drug Induced Sleep Endoscopy (DISE) Procedure

Arigliani, Michele; Toraldo, Domenico Maurizio; Ciavolino, Enrico; Lattante, Caterina; Conte, Luana; Arima, Susumu; Arigliani, Caterina; Palumbo, Antonio; Benedetto, Michele De

doi:10.3390/ijerph18042070

Cited by 5 publications

(3 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Despite an increase in research and medical attention towards OSA in recent years, it remains a condition that is frequently not diagnosed. This underdiagnosis can be attributed to the lack of biomarkers for identifying the disease [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…Recent advancements in software technologies and Machine Learning (ML) methods have significantly enhanced the development of effective predictive and diagnostic tools, becoming increasingly prevalent in various fields of medical research and applications, including for OSA [12,13,[21][22][23][24][25][26][27][28]. The prediction models described in existing research primarily utilize clinical data, such as demographic information (age and gender), comorbid conditions, anthropometric measures (Body Mass Index (BMI), waist and neck circumferences), symptoms of OSA, and physiological parameters (blood pressure, overnight pulse oximetry, and lung function tests).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning (ML) Models to Enhance the Berlin Questionnaire (BQ) Detection of Obstructive Sleep Apnea (OSA) at-Risk Patients

Conte,

De Nunzio,

Giombi

et al. 2024

Preprint

View full text Add to dashboard Cite

Objective: With just ten questions, the Berlin questionnaire (BQ) stands out as one of the simplest and most widely implemented non-invasive screening tools for detecting subjects at high risk for Obstructive Sleep Apnea (OSA), a still underdiagnosed syndrome characterized by partial or complete obstruction of the upper airways during sleep. The main aim of this study was to enhance the diagnostic accuracy of the BQ through Machine Learning (ML) techniques. Methods: A ML classifier (hereafter, ML-10) was trained using the ten questions of the standard BQ. A simplified variant of the BQ, BQ-2, which comprises only two questions out of the total of ten, was also assessed in a ML context. A 10-fold cross validation scheme was used. Ground truth was provided by the Apnea-Hypopnea Index (AHI) measured by Home Sleep Apnea Testing. Model performance was determined comparing ML-10 and BQ-2 with the standard BQ by the Receiver Operating Characteristic Curve (ROC), Area Under the Curve (AUC), sensitivity, specificity, and accuracy. Results: ML-10 demonstrated superior performance in predicting the risk for OSA compared to the standard BQ and was also capable in classifying OSA with two different AHI thresholds (AHI>=15, AHI>=30), typically used in clinical practice. Remarkably, BQ-2 was also better in sensibility to assess moderate to severe OSA (AHI≥ 15) compared to the ML-10. Conclusions: The study underscores the importance of integrating ML techniques for early OSA detection, suggesting a direction for future research to improve diagnostic processes and patient outcomes in sleep medicine.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning (ML) Models to Enhance the Berlin Questionnaire (BQ) Detection of Obstructive Sleep Apnea (OSA) at-Risk Patients

Conte,

De Nunzio,

Giombi

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…It consists in an upper airway evaluation during a pharmacologically simulated sleep. Yu Lin et al [ 15 ] and other literature evidence [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ] has stated that DISE is superior to the wake-up endoscopy in identifying obstructions sites and types of collapses in the hypopharyngeal and base of the tongue regions. Recently, the use of a middle latency auditory evoked potentials (MLAEP) has been proposed as a good methodology to evaluate the correct level of sedation for patients during DISE procedures [ 16 , 17 ].…”

mentioning

confidence: 99%

Obstructive Sleep Apnea Syndrome: From Symptoms to Treatment

Iannella

Magliulo

Greco

et al. 2022

IJERPH

View full text Add to dashboard Cite

Obstructive sleep apnea (OSA) syndrome is a respiratory sleep disorder characterized by partial or complete recurrent episodes of upper airway collapse that occur during the night. The OSA manifests with a reduction (hypopnea) or complete cessation (apnea) of airflow in the upper airways, associated with breathing effort. OSA is a frequent and often underestimated pathology affecting between 2 and 5% of the middle-aged population. Typical nocturnal symptoms are the persistent snoring and awakenings with dyspnea sensation. On the other hand, diurnal symptoms could be sleepiness, headaches, asthenia, neurological disorders, and impaired personal relationships. Surgery of the velo-pharyngeal region had a huge evolution going from ablative techniques (UP3 and LAUP) to remodeling techniques of the pharyngeal lateral walls.

show abstract

Research progress on the depth of anesthesia monitoring based on the electroencephalogram

He,

Li,

Wang

2024

Ibrain

View full text Add to dashboard Cite

General anesthesia typically involves three key components: amnesia, analgesia, and immobilization. Monitoring the depth of anesthesia (DOA) during surgery is crucial for personalizing anesthesia regimens and ensuring precise drug delivery. Since general anesthetics act primarily on the brain, this organ becomes the target for monitoring DOA. Electroencephalogram (EEG) can record the electrical activity generated by various brain tissues, enabling anesthesiologists to monitor the DOA from real‐time changes in a patient's brain activity during surgery. This monitoring helps to optimize anesthesia medication, prevent intraoperative awareness, and reduce the incidence of cardiovascular and other adverse events, contributing to anesthesia safety. Different anesthetic drugs exert different effects on the EEG characteristics, which have been extensively studied in commonly used anesthetic drugs. However, due to the limited understanding of the biological basis of consciousness and the mechanisms of anesthetic drugs acting on the brain, combined with the effects of various factors on existing EEG monitors, DOA cannot be accurately expressed via EEG. The lack of patient reactivity during general anesthesia does not necessarily indicate unconsciousness, highlighting the importance of distinguishing the mechanisms of consciousness and conscious connectivity when monitoring perioperative anesthesia depth. Although EEG is an important means of monitoring DOA, continuous optimization is necessary to extract characteristic information from EEG to monitor DOA, and EEG monitoring technology based on artificial intelligence analysis is an emerging research direction.

show abstract

The Use of Middle Latency Auditory Evoked Potentials (MLAEP) as Methodology for Evaluating Sedation Level in Propofol-Drug Induced Sleep Endoscopy (DISE) Procedure

Cited by 5 publications

References 47 publications

Machine Learning (ML) Models to Enhance the Berlin Questionnaire (BQ) Detection of Obstructive Sleep Apnea (OSA) at-Risk Patients

Machine Learning (ML) Models to Enhance the Berlin Questionnaire (BQ) Detection of Obstructive Sleep Apnea (OSA) at-Risk Patients

Obstructive Sleep Apnea Syndrome: From Symptoms to Treatment

Research progress on the depth of anesthesia monitoring based on the electroencephalogram

Contact Info

Product

Resources

About