New research: Respiratory effort during sleep and prevalent hypertension in obstructive sleep apnoea.
03 April 2023
Exciting research findings have just been published by coauthors Dr. Jean-Benoit Martinot, Nhat-Nam Le-Dong, Dr. Atul Malhotra, and Dr. Jean-Louis Pépin in the study titled "Respiratory effort during sleep and prevalent hypertension in obstructive sleep apnoea." The study aimed to explore the relationship between respiratory effort and hypertension in patients with obstructive sleep apnoea (OSA).
The key takeaway from this study is remarkable: the proportion of sleep time spent with increased respiratory effort, automatically derived from mandibular jaw movements (MJM), emerged as a superior predictor of prevalent hypertension in patients with OSA compared to traditional polysomnography (PSG) metrics such as the apnoea-hypopnoea index (AHI).
Using a machine learning model, the researchers developed a predictive model for prevalent hypertension, incorporating clinical data, conventional PSG indices, and MJM-derived parameters, including the percentage of sleep time spent with increased respiratory effort (RE_MJM, %TST). The model was evaluated on a training subset and a validation subset comprising a total of 1,127 patients.
The findings were remarkable: RE_MJM demonstrated excellent accuracy in predicting prevalent hypertension, even surpassing the standard PSG metrics. This innovative metric proved to be the best predictor after considering clinical risk factors, such as age, sex, body mass index, neck circumference, and time with oxygen desaturation.
The identification of the proportion of sleep time spent with increased respiratory effort derived from MJM as a potential reliable metric for predicting hypertension in OSA patients is groundbreaking. It sheds new light on the mechanisms connecting respiratory effort and hypertension in OSA, leading to improved risk stratification and potential advancements in the management of OSA-related hypertension.
The key takeaway from this study is remarkable: the proportion of sleep time spent with increased respiratory effort, automatically derived from mandibular jaw movements (MJM), emerged as a superior predictor of prevalent hypertension in patients with OSA compared to traditional polysomnography (PSG) metrics such as the apnoea-hypopnoea index (AHI).
Using a machine learning model, the researchers developed a predictive model for prevalent hypertension, incorporating clinical data, conventional PSG indices, and MJM-derived parameters, including the percentage of sleep time spent with increased respiratory effort (RE_MJM, %TST). The model was evaluated on a training subset and a validation subset comprising a total of 1,127 patients.
The findings were remarkable: RE_MJM demonstrated excellent accuracy in predicting prevalent hypertension, even surpassing the standard PSG metrics. This innovative metric proved to be the best predictor after considering clinical risk factors, such as age, sex, body mass index, neck circumference, and time with oxygen desaturation.
The identification of the proportion of sleep time spent with increased respiratory effort derived from MJM as a potential reliable metric for predicting hypertension in OSA patients is groundbreaking. It sheds new light on the mechanisms connecting respiratory effort and hypertension in OSA, leading to improved risk stratification and potential advancements in the management of OSA-related hypertension.