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Predicting optimal CPAP by neural network reduces titration failure: a randomized study |
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| Authors: | Ali El Solh Morohunfolu Akinnusi Anil Patel Abid Bhat Rachel TenBrock |
| Affiliation: | (1) VA Western New York Health Care System, Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Western New York Respiratory Research Center, Buffalo, NY, USA;(2) Department of Social and Preventive Medicine, State University of New York at Buffalo School of Medicine and Biomedical Sciences, Buffalo, NY, USA;(3) Division of Pulmonary and Critical Care Medicine, Department of Medicine, Truman Medical Center, Hospital Hill, University of Missouri at Kansas City, Kansas City, MO, USA;(4) Medical Research, VA Western New York Health Care System, Bldg. 20 (151) VISN02 3495 Bailey Avenue, Buffalo, NY 14215-1199, USA |
| Abstract: | PurposeContinuous positive airway pressure (CPAP) is considered the standard therapy for obstructive sleep apnea syndrome. In the absence of standard protocol, CPAP titration may be unsuccessful. The purpose of this study was to test the hypothesis that application of an artificial neural network (ANN) to CPAP titration would achieve an optimal CPAP pressure within a shorter time interval and would lead to a decrease in CPAP titration failure. |
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