Objective/Hypothesis: The ADHERE Registry is a multicenter prospective observational study following outcomes of upper airway stimulation (UAS) therapy in patients who have failed continuous positive airway pressure therapy for obstructive sleep apnea (OSA). The aim of this registry and purpose of this article were to examine the outcomes of patients receiving UAS for treatment of OSA.Study Design: Cohort Study.Methods: Demographic and sleep study data collection occurred at baseline, implantation visit, post‐titration (6 months), and final visit (12 months). Patient and physician reported outcomes were also collected. Post hoc univariate and multivariate analysis was used to identify predictors of therapy response, defined as ≥50% decrease in Apnea‐Hypopnea Index (AHI) and AHI ≤20 at the 12‐month visit.Results: The registry has enrolled 1,017 patients from October 2016 through February 2019. Thus far, 640 patients have completed their 6‐month follow‐up and 382 have completed the 12‐month follow‐up. After 12 months, median AHI was reduced from 32.8 (interquartile range [IQR], 23.6–45.0) to 9.5 (IQR, 4.0–18.5); mean, 35.8 ± 15.4 to 14.2 ± 15.0, P < .0001. Epworth Sleepiness Scale was similarly improved from 11.0 (IQR, 7–16) to 7.0 (IQR, 4–11); mean, 11.4 ± 5.6 to 7.2 ± 4.8, P < .0001. Therapy usage was 5.6 ± 2.1 hours per night after 12 months. In a multivariate model, only female sex and lower baseline body mass index remained as significant predictors of therapy response.Conclusions: Across a multi‐institutional study, UAS therapy continues to show significant improvement in subjective and objective OSA outcomes. This analysis shows that the therapy effect is durable and adherence is high.Level of Evidence: 2Laryngoscope, 130:1333–1338, 2020

Electronic nose technology has been developed over the past 15 years in the field of chemistry as an electronic equivalent of the biologic mechanism of smell. Since its inception, it has been well recognized that there is great potential in applying this technology to the field of medicine. This review discusses those areas of medicine in which electronic nose technology has been applied. For each area, this review addresses the scope of the medical problem that has been studied, how the electronic nose technology may help address the medical problem, and the results of such studies to date. Next generation electronic noses will be refined to better analyze specific disease states. This will require further evaluation of the specific volatiles to be tested. This information may then be brought to bear on refinement of the chemistry of the electronic nose sensors, making them more sensitive and specific for the particular disease of interest. The ultimate goal of work in this arena is to make an electronic nose that is portable, fast, inexpensive and, therefore, suitable for use in the examination room or at the bedside, making it facile as a diagnostic tool.

Background: Having previously established that an electronic nose (enose) can distinguish among bacteria samples, between cerebrospinal fluid leak and serum, and can identify patients with ventilator-associated pneumonia, we hypothesized that bacterial sinusitis could be diagnosed by sampling exhaled gas with an enose. Methods: Using a nasal continuous positive airway pressure mask, we sampled gas exhaled through the nose of patients with sinusitis and compared them with controls. Data were first projected onto the principal components and then classified by support vector machine (SVM), a machine learning algorithm for pattern recognition. Results: SVM analysis showed good discrimination using three approaches. First, 11 samples were used to create a training set that was used to predict whether individual samples from each set were a member of the control or infected sets. The enose was correct 98.4% of the time. Second, one-half of the samples from each of the same 11 control and infected groups were used to construct a training set, which was used to predict infection in the remaining samples. The enose was correct 82% of the time. Finally, 68 samples (34 positive and 34 controls) were analyzed using a leave-one-out scheme for creating training sets and testing sets. This method, designed to reflect the generalization property of the SVM classifier, scored a classification rate of 72%. Conclusion: Using the enose to sample nasal exhalation from patients with suspected sinusitis, we were able to predict correctly the diagnosis of sinusitis in at least 72% of the samples. The next step will be to do forward prediction using this model.