Our pilot study suggests that telemedicine-based management of OSA patients is feasible in terms of patient functional outcomes and overall satisfaction with care. Future studies should include larger populations to further elucidate these findings while assessing provider- and patient-related cost effectiveness.

We attempted to validate a two-stage strategy to screen for severe obstructive sleep apnea syndrome (s-OSAS) among hypertensive outpatients, with polysomnography (PSG) as the gold standard. Using a prospective design, we recruited outpatients with hypertension from medical outpatient clinics. Interventions included: 1) assessment of clinical data; 2) home sleep testing (HST); and 3) 12-channnel, in-laboratory PSG. We developed models using clinical or HST data alone (single-stage models) or clinical data in tandem with HST (two-stage models) to predict s-OSAS. For each model, we computed area-under-receiver-operating-characteristic curves (AUC), sensitivity, specificity, negative likelihood ratio, and negative post-test probability (NPTP). Models were then rank-ordered based upon AUC values and NPTP. HST used alone had limited accuracy (AUC=0.727, ,NPTP = 2.9%). However, models that used clinical data in tandem with HST were more accurate in identifying s-OSAS, with lower NPTP: 1) facial morphometrics (AUC=0.816, NPTP=0.6%); 2) neck circumference (AUC=0.803, NPTP=1.7%); and Multivariable Apnea Prediction Score (AUC = 0.799, NPTP =1.5%) where sensitivity, specificity and NPTP were evaluated at optimal thresholds. Therefore, HST combined with clinical data can be useful in identifying s-OSAS in hypertensive outpatients, without incurring greater cost and patient burden associated with in-laboratory PSG. These models were less useful in identifying OSAS of any severity.

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 AHI 3% 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.