New research presented at the Sleep 2023 meeting
22 May 2023
The most recent findings from studies involving Mandibular Jaw Movements are presented in the two following abstracts.
Determinants of Apnea-Hypopnea Index Variability during Home Sleep Testing.
The study focused on understanding the factors contributing to short-term AHI variability in 160 adults with suspected OSA. Using multi-night automated home sleep testing, we found that variability in AHI is common at the individual level, which can affect the clinical classification of OSA severity. Sleep time spent in non-REM deep sleep and head supine position emerged as significant predictors of AHI variability.
These findings have important implications for accurate diagnosis and timely treatment of OSA. Grateful for the opportunity to share this research at Sleep conference.
Congrats to Dr. Le Dong, Dr. Tamisier and Dr. Martinot
Respiratory Effort During Sleep And Prevalence of Type 2 Diabetes In Obstructive Sleep Apnea.
Increased respiratory effort (RE) is one of the key features of obstructive sleep apnea (OSA) and contributes to sympathetic overactivity that in turn might participate in glucose homeostasis dysregulation. In this study, the authors showed that respiratory effort is a stronger predictor of OSA-related type 2 diabetes (T2Dia) than common PSG-derived metrics such as the apnea-hypopnea index (AHI). This novel finding was made possible through a new objective measurement of respiratory effort burden (percentage of sleep time spent with increased respiratory effort) derived from mandibular jaw movement (MJM) automated analysis and a machine learning model built to predict T2Dia from clinical data. The analysis included 1128 patients referred for suspicion of OSA randomly assigned to training and validation subsets. The prevalence of T2Dia was approximately 11% in each dataset. The classification model showed good performance for the prediction of T2Dia with an area under the receiver operating characteristic curve (ROC AUC) of 0.92 ±0.01.
These findings highlight the potential of respiratory effort burden, automatically derived from MJM, as a new reliable metric for predicting prevalent T2Dia in patients with OSA and may pave the way for personalized interventions.
Congrats to Dr. Martinot, Dr. Pépin and Dr. Malhotra.
Determinants of Apnea-Hypopnea Index Variability during Home Sleep Testing.
The study focused on understanding the factors contributing to short-term AHI variability in 160 adults with suspected OSA. Using multi-night automated home sleep testing, we found that variability in AHI is common at the individual level, which can affect the clinical classification of OSA severity. Sleep time spent in non-REM deep sleep and head supine position emerged as significant predictors of AHI variability.
These findings have important implications for accurate diagnosis and timely treatment of OSA. Grateful for the opportunity to share this research at Sleep conference.
Congrats to Dr. Le Dong, Dr. Tamisier and Dr. Martinot
Respiratory Effort During Sleep And Prevalence of Type 2 Diabetes In Obstructive Sleep Apnea.
Increased respiratory effort (RE) is one of the key features of obstructive sleep apnea (OSA) and contributes to sympathetic overactivity that in turn might participate in glucose homeostasis dysregulation. In this study, the authors showed that respiratory effort is a stronger predictor of OSA-related type 2 diabetes (T2Dia) than common PSG-derived metrics such as the apnea-hypopnea index (AHI). This novel finding was made possible through a new objective measurement of respiratory effort burden (percentage of sleep time spent with increased respiratory effort) derived from mandibular jaw movement (MJM) automated analysis and a machine learning model built to predict T2Dia from clinical data. The analysis included 1128 patients referred for suspicion of OSA randomly assigned to training and validation subsets. The prevalence of T2Dia was approximately 11% in each dataset. The classification model showed good performance for the prediction of T2Dia with an area under the receiver operating characteristic curve (ROC AUC) of 0.92 ±0.01.
These findings highlight the potential of respiratory effort burden, automatically derived from MJM, as a new reliable metric for predicting prevalent T2Dia in patients with OSA and may pave the way for personalized interventions.
Congrats to Dr. Martinot, Dr. Pépin and Dr. Malhotra.