Upper airway stimulation device failure: A 7-year single center experience.

OBJECTIVE: To identify and report a single center experience with upper airway stimulator device-related failures. STUDY DESIGN: Retrospective case series. SETTING: Single tertiary academic center. METHODS: Retrospective data on 352 patients who underwent UAS surgery with an Inspire device from 2016 to 2023 was collected, including demographics, comorbidities, and nature of device failure requiring revision surgery. RESULTS: Out of the 348 patients included in our analysis, 16 (4.6 %) required revision due to device failure, with an average interval of 772 days (∼2 years) between initial implant and revision. Most failures were attributed to respiratory sensing lead damage (n = 11, 68.8 %), resulting in high system impedance and subsequent device malfunction. Lead fracture causes varied, including idiopathic occurrences and potential trauma. Lead migration was noted in one case (6.3 %), where the hypoglossal electrode detached from the nerve. Two patients (12.3 %) required implantable pulse generator (IPG) replacement, one after experiencing trauma and the other due to unclear source of malfunction. One patient (6.3 %) required complete system replacement following high lead impedance and absent tongue motion. The last patient required replacement of both the IPG and respiratory lead after experiencing high lead impedance (6.3 %). CONCLUSION: Respiratory sensing lead fracture emerged as the leading cause of device failure in this cohort, underscoring the need to address this under-reported issue, potentially linked to the time lapse after device implantation.

Right Versus Left Cuff Position for Upper Airway Stimulation.

OBJECTIVE: Upper airway stimulation (UAS) is a treatment option for obstructive sleep apnea in which electrical stimulation is applied to the hypoglossal nerve. Nerve branches that control tongue protrusion are located inferiorly. Due to positioning, left-sided implants are typically placed with an inferiorly oriented electrode cuff (L-down) as opposed to superiorly on the right (R-up). In this study, we assess the impact of left- versus right-sided UAS on patient outcomes. STUDY DESIGN: Retrospective cohort study. SETTING: Tertiary Academic Medical Center. METHODS: Patients who underwent UAS implantation between 2016 and 2021 with an L-down or R-up oriented cuff as confirmed by X-ray were included. Data were collected retrospectively. Most recent sleep study variables were used for analysis. RESULTS: A total of 190 patients met the inclusion criteria. The average age was 61.0 ± 11.0 years, with 55 (28.9%) females. L-down orientation was present in 21 (11.1%) patients vs 169 (88.9%) R-up. Indications for L-down included hunting/shooting (n = 15), prior radiation/surgery (n = 4), central port (n = 1), and brachial plexus injury (n = 1). Adherence was higher among L-down patients (47.1 vs 41.0 hours use/week, P = .037) in univariate analysis, with a similar time to adherence data collection (4.4 vs 4.2 months, P = .612), though this finding was not maintained in the multivariate regression analysis. Decrease in apnea-hypopnea index (21.3 vs 22.8, P = .734), treatment success (76.5% vs 84.0%, P = .665), functional threshold (1.5 vs 1.6, P = .550), therapeutic amplitude (2.3 vs 2.4, P = .882), and decrease in Epworth Sleepiness Scale (4.9 vs 2.6, P = .060) were not significantly different between cohorts. CONCLUSION: This study is the first to examine the orientation of the UAS electrode cuff concerning the electrodes' natural position and the potential effect on postoperative outcomes. Our study found no significantly different treatment outcomes between the L-down versus R-up cohort, with the exception of device adherence, which was significantly higher in the L-down group on univariate analysis though not on multivariate analysis. Future studies with larger patient cohorts are needed to further investigate this potential relationship between treatment outcomes and electrode cuff orientation.

Predicting Therapeutic Response to Hypoglossal Nerve Stimulation Using Deep Learning.

OBJECTIVES: To develop and validate machine learning (ML) and deep learning (DL) models using drug-induced sleep endoscopy (DISE) images to predict the therapeutic efficacy of hypoglossal nerve stimulator (HGNS) implantation. METHODS: Patients who underwent DISE and subsequent HGNS implantation at a tertiary care referral center were included. Six DL models and five ML algorithms were trained on images from the base of tongue (BOT) and velopharynx (VP) from patients classified as responders or non-responders as defined by Sher's criteria (50% reduction in apnea-hypopnea index (AHI) and AHI < 15 events/h). Precision, recall, F1 score, and overall accuracy were evaluated as measures of performance. RESULTS: In total, 25,040 images from 127 patients were included, of which 16,515 (69.3%) were from responders and 8,262 (30.7%) from non-responders. Models trained on the VP dataset had greater overall accuracy when compared to BOT alone and combined VP and BOT image sets, suggesting that VP images contain discriminative features for identifying therapeutic efficacy. The VCG-16 DL model had the best overall performance on the VP image set with high training accuracy (0.833), F1 score (0.78), and recall (0.883). Among ML models, the logistic regression model had the greatest accuracy (0.685) and F1 score (0.813). CONCLUSION: Deep neural networks have potential to predict HGNS therapeutic efficacy using images from DISE, facilitating better patient selection for implantation. Development of multi-institutional data and image sets will allow for development of generalizable predictive models. LEVEL OF EVIDENCE: N/A Laryngoscope, 2024.