Document Detail


Predicting effective continuous positive airway pressure in sleep apnea using an artificial neural network.
MedLine Citation:
PMID:  17512788     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
BACKGROUND: Mathematical formulas have been less than adequate in assessing the optimal continuous positive airway pressure (CPAP) level in patients with obstructive sleep apnea (OSA). The objectives of the study were (1) to develop an artificial neural network (ANN) using demographic and anthropometric information to predict optimal CPAP level based on an overnight titration study and (2) to compare the predicted pressures derived from the ANN to the pressures computed from a previously described regression equation. METHODS: A general regression neural network was used to develop the predictive model. The derivation cohort included 311 consecutive patients who underwent CPAP titration at a University-affiliated Sleep Center. The model was validated subsequently on 98 participants from a private sleep laboratory. RESULTS: The correlation coefficients between the optimal pressure determined by the titration study and the predicted pressure by the ANN were 0.86 (95% confidence interval [CI] 0.83-0.88; p<0.001) for the derivation cohort and 0.85 (95% CI 0.78-0.9; p<0.001) for the validation cohort, respectively. Whereas there was no significant difference between the optimal pressure obtained during overnight polysomnography and the predicted pressure estimated by the ANN (p=0.4), the estimated pressure derived from the regression equation underestimated the optimal pressure in both the derivation and the validation group, respectively. CONCLUSION: The optimal CPAP level predicted by the ANN provides a more accurate assessment of the pressure derived from the historic regression equation.
Authors:
Ali A El Solh; Zaher Aldik; Moutaz Alnabhan; Brydon Grant
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Publication Detail:
Type:  Journal Article     Date:  2007-05-18
Journal Detail:
Title:  Sleep medicine     Volume:  8     ISSN:  1389-9457     ISO Abbreviation:  Sleep Med.     Publication Date:  2007 Aug 
Date Detail:
Created Date:  2007-07-02     Completed Date:  2007-11-13     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  100898759     Medline TA:  Sleep Med     Country:  Netherlands    
Other Details:
Languages:  eng     Pagination:  471-7     Citation Subset:  IM    
Affiliation:
Western New York Respiratory Research Center, Department of Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, University at Buffalo, School of Medicine and Biomedical Sciences, 462 Grider Street, Buffalo, NY, USA. solh@buffalo.edu
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MeSH Terms
Descriptor/Qualifier:
Adult
Cohort Studies
Continuous Positive Airway Pressure / methods*
Female
Humans
Male
Middle Aged
Models, Biological*
Neural Networks (Computer)*
Polysomnography
Predictive Value of Tests
Reproducibility of Results
Sleep Apnea, Obstructive / therapy*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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