Comparison of manual versus automatic continuous positive airway pressure titration and the development of a predictive equation for therapeutic continuous positive airway pressure in Chinese patients with obstructive sleep apnoea

Respirology ◽  
2013 ◽  
Vol 18 (3) ◽  
pp. 528-533 ◽  
Author(s):  
Jiaying Luo ◽  
Sichang Xiao ◽  
Zhihui Qiu ◽  
Ning Song ◽  
Yuanming Luo
Keyword(s):  
Continuous Positive Airway Pressure ◽  
Obstructive Sleep Apnoea ◽  
Airway Pressure ◽  
Positive Airway Pressure ◽  
Sleep Apnoea ◽  
Chinese Patients ◽  
Predictive Equation ◽  
Obstructive Sleep

Predictive equation for optimal Continuous Positive Airway Pressure (CPAP) in children with Obstructive Sleep Apnoea (OSA)

2019 ◽  
Author(s):  
Hern Zi Joelle Chong ◽  
Arun Pugalenthi ◽  
Ram Bajpai ◽  
Oon Hoe Teoh ◽  
Zai Ru Cheng ◽  
...  
Keyword(s):  
Continuous Positive Airway Pressure ◽  
Obstructive Sleep Apnoea ◽  
Airway Pressure ◽  
Positive Airway Pressure ◽  
Sleep Apnoea ◽  
Predictive Equation ◽  
Obstructive Sleep

scholarly journals Predictive equation for optimal continuous positive airway pressure in children with obstructive sleep apnoea

2020 ◽  
Vol 6 (2) ◽  
pp. 00312-2019
Author(s):  
Joelle Chong ◽  
Ram Bajpai ◽  
Oon Hoe Teoh ◽  
Arun Pugalenthi ◽  
John C. Allen ◽  
...  
Keyword(s):  
Down Syndrome ◽  
Continuous Positive Airway Pressure ◽  
Obstructive Sleep Apnoea ◽  
Airway Pressure ◽  
Positive Airway Pressure ◽  
Sleep Apnoea ◽  
Arterial Oxygen ◽  
Predictive Equation ◽  
Z Score ◽  
Obstructive Sleep

AimA subgroup of children with obstructive sleep apnoea (OSA) requires treatment with continuous positive airway pressure (CPAP). This study's aims were: 1) to determine if the optimal CPAP for the treatment of OSA in children correlates with body mass index (BMI); 2) to determine the correlation between polysomnographic variables and optimal CPAP in children with OSA; and 3) to develop a CPAP predictive equation for children with OSA.MethodsThis was a retrospective study of children with OSA who underwent CPAP titration studies. Patients with craniofacial abnormalities (except Down syndrome) and neuromuscular diseases were excluded. Polysomnograms were done using Sandman Elite. Correlations between optimal CPAP, clinical and polysomnographic variables were analysed. A multivariable linear regression model for optimal CPAP was developed.Results198 children (mean±sd age 13.1±3.6 years) were studied. Optimal CPAP had a significant positive correlation with age (rho=0.216, p=0.002), obstructive apnoea-hypopnoea index (rho=0.421, p<0.001), 3% oxygen desaturation index (rho=0.417, p<0.001), rapid eye movement respiratory disturbance index (rho=0.378, p<0.001) and BMI z-score (rho=0.160, p=0.024); and a significant negative correlation with arterial oxygen saturation measured by pulse oximetry nadir (rho= −0.333, p<0.001). The predictive equation derived was:Optimal CPAP (cmH2O)=6.486+0.273·age (years)−0.664·adenotonsillectomy(no=1, yes=0)+2.120·Down syndrome (yes=1, no=0)+0.280·BMI z-score.ConclusionThe equation developed may help to predict optimal CPAP in children with OSA. Further studies are required to validate this equation and to determine its applicability in different populations.

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