Predicting effective continuous positive airway pressure in Taiwanese patients with obstructive sleep apnea syndrome

I. Feng Lin, Ming Lung Chuang*, Yu Fang Liao, Ning Hung Chen, Hsueh Yu Li

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

26 Scopus citations

Abstract

Background and Purpose: Race/ethnicity plays an important role in determining body size and the severity of obstructive sleep apnea syndrome (OSAS), perhaps affecting the lowest effective continuous positive airway pressure (CPAP) [Peff] required to abolish sleep apneas. This study aimed to determine Peff using an optimal regression model to facilitate the titration of CPAP in Taiwanese people and to compare the findings of a regression model to the previously reported models developed in Caucasian populations. Methods: 121 patients with moderate to severe OSAS were studied. All were titrated for nasal CPAP at a university hospital. The patients underwent separate polysomnographic studies to diagnose OSAS and to determine the Peff for home use. Seven polysomnographic variables were included in the analysis: apnea/hypopnea index (AHI), desaturation index (DI), desaturation duration, mean oxyhemoglobin saturation (SPO2), mean desaturation, nadir SPO2, and snore index. Multiple linear regression was used to model the effects of anthropometric and polysomnographic variables on Peff. A data-splitting cross-validation study and model fit criteria were used to select the predictors and assess the model performance. Results: Most of the subjects were obese men who suffered severe OSAS with significant oxyhemoglobin desaturation and daytime sleepiness, and who required 8 cm H2O of CPAP. Age, body mass index (BMI), neck circumference (NC), AHI, DI, mean SPO2, and daytime somnolence score were significantly associated with Peff. The final prediction model (n = 121) was estimated as Peff = 0.52 + 0.174 × BMI + 0.042 × AHI, while the previously reported equation uses BMI, NC, and AHI. The prediction equation was more accurate in predicting Peff in this Taiwanese population as compared to the previously reported equations designed from a Caucasian sample. Conclusions: Obesity and severity of sleep apnea are the 2 most important predictors of CPAP setting effectively abolishing the apneas. After inclusion of BMI and AHI in the model, other variables (such as NC do not significantly improve the prediction of Peff, suggesting that ethic differences may play a role in predicting Peff.

Original languageEnglish
Pages (from-to)215-221
Number of pages7
JournalJournal of the Formosan Medical Association
Volume102
Issue number4
StatePublished - 01 04 2003
Externally publishedYes

Keywords

  • Algorithm
  • Obesity
  • Polysomnography
  • Positive-pressure respiration
  • Sleep apnea, obstructive

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