• Octavian C Ioachimescu, Swapan A Dholakia, Saiprakash B Venkateshiah, Barry Fields, Arash Samarghandi, Neesha Anand, Rina Eisenstein, Mary-Margaret Ciavatta, J Shirine Allam, and Nancy A Collop.
• Department of Medicine, Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Emory University School of Medicine, Atlanta, Georgia, USA oioachi@emory.edu.
• J. Investig. Med. 2020 Dec 1; 68 (8): 1370-1378.

AbstractOutside sleep laboratory settings, peripheral arterial tonometry (PAT, eg, WatchPat) represents a validated modality for diagnosing obstructive sleep apnea (OSA). We have shown before that the accuracy of home sleep apnea testing by WatchPat 200 devices in diagnosing OSA is suboptimal (50%-70%). In order to improve its diagnostic performance, we built several models that predict the main functional parameter of polysomnography (PSG), Apnea Hypopnea Index (AHI). Participants were recruited in our Sleep Center and underwent concurrent in-laboratory PSG and PAT recordings. Statistical models were then developed to predict AHI by using robust functional parameters from PAT-based testing, in concert with available demographic and anthropometric data, and their performance was confirmed in a random validation subgroup of the cohort. Five hundred synchronous PSG and WatchPat sets were analyzed. Mean diagnostic accuracy of PAT was improved to 67%, 81% and 85% in mild, moderate-severe or no OSA, respectively, by several models that included participants' age, gender, neck circumference, body mass index and the number of 4% desaturations/hour. WatchPat had an overall accuracy of 85.7% and a positive predictive value of 87.3% in diagnosing OSA (by predicted AHI above 5). In this large cohort of patients with high pretest probability of OSA, we built several models based on 4% oxygen desaturations, neck circumference, body mass index and several other variables. These simple models can be used at the point-of-care, in order to improve the diagnostic accuracy of the PAT-based testing, thus ameliorating the high rates of misclassification for OSA presence or disease severity.© American Federation for Medical Research 2020. Re-use permitted under CC BY-NC. No commercial re-use. Published by BMJ.

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