Study Objectives: Peripheral arterial tonometry (PAT)–based technology represents a validated portable monitoring modality for the diagnosis of OSA. We assessed the diagnostic accuracy of PAT-based technology in a large point-of-care cohort of patients studied with concurrent polysomnography (PSG). Methods: During study enrollment, all participants suspected to have OSA and tested by in-laboratory PSG underwent concurrent PAT device recordings. Results: Five hundred concomitant PSG and WatchPat tests were analyzed. Median (interquartile range) PSG AHI was 18 (8–37) events/h and PAT AHI3% was 25 (12–46) events/h. Average bias was + 4 events/h. Diagnostic concordance was found in 42%, 41%, and 83% of mild, moderate, and severe OSA, respectively (accuracy = 53%). Among patients with PAT diagnoses of moderate or severe OSA, 5% did not have OSA and 19% had mild OSA; in those with mild OSA, PSG showed moderate or severe disease in 20% and no OSA in 30% of patients (accuracy = 69%). On average, using a 3% desaturation threshold, WatchPat overestimated disease prevalence and severity (mean + 4 events/h) and the 4% threshold underestimated disease prevalence and severity by −6 events/h. Conclusions: Although there was an overall tendency to overestimate the severity of OSA, a significant percentage of patients had clinically relevant misclassifications. As such, we recommend that patients without OSA or with mild disease assessed by PAT undergo repeat in-laboratory PSG. Optimized clinical pathways are urgently needed to minimize therapeutic decisions instituted in the presence of diagnostic uncertainty.

Outside 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.