Patulous Eustachian Tube Dysfunction Symptoms Following Balloon Dilation.

OBJECTIVE: Clinicians increasingly perform balloon dilation of the Eustachian tube (BDET) to treat obstructive Eustachian tube dysfunction (OETD) refractory to medical management. Reported complications have been limited and include patulous Eustachian tube dysfunction (PETD). This multicenter study investigates the incidence of PETD and associated factors. METHODS: Consecutive patients at three academic centers undergoing BDET (January 2014-November 2019) for OETD refractory to medical therapy were included. PETD was diagnosed by patient-reported symptoms of autophony of voice and/or breathing. Associated factors studied include age, sex, comorbidities, balloon size, duration of inflation, repeat BDET, and adjunctive procedures. RESULTS: BDET procedures (n = 295 Eustachian tubes) were performed on 182 patients. Mean age was 38.4 years (SD 21.0; range 7-78) and 41.2% were female. Twenty cases of PETD (6.8% of procedures; 9.3% of patients) occurred following BDET. Risk of PETD did not vary by institution, comorbidities, or adjunctive procedure. Age ≤18 years (adjusted risk ratio [RR] = 3.26; 95% confidence interval [CI]: 1.24, 8.54; p = 0.02), repeat BDET (RR = 3.26; 95% CI: 2.15, 4.96; p < 0.001), and severe preoperative Eustachian tube inflammation (RR = 2.83; 95% CI: 1.10, 7.28; p = 0.03) were associated with increased risk of developing PETD in the multivariable model. Most symptoms were reported as mild or intermittent. CONCLUSION: BDET caused PETD symptoms in approximately 7% of dilated Eustachian tubes in this study with increased risk for younger patients and those with severe inflammation or undergoing repeat dilations. Although most cases were self-limited, symptoms can persist. Awareness of risk factors may aid clinicians in limiting this complication. LEVEL OF EVIDENCE: 4 Laryngoscope, 133:3152-3157, 2023.

Management of children with co-occurring sleep disordered breathing and hearing loss.

OBJECTIVES: To examine hearing loss incidence in a pediatric otolaryngology patients with sleep disordered breathing, and secondarily, identify possible disparities in management. METHODS: A retrospective case-control study of pediatric patients with sleep-disordered breathing or obstructive sleep apnea from 2012 to 2019 was conducted at a private, not-for-profit, academic, tertiary care center. Study parameters were extracted from the electronic medical record and compared in study groups of patients with sleep-disordered breathing generated based on presence of hearing loss (cases) versus absence (controls). Study parameters were also compared in study groups based on timing of hearing loss onset. RESULTS: 14.8% of patients with sleep disordered breathing had hearing loss. Hearing loss was associated with an increased risk of undergoing adenotonsillectomy (OR 1.632 [1.294-2.058], p < 0.001, adjusted for age). In patients who underwent polysomnographic testing, 9.8% patients had pre-existing hearing loss and 12% patients developed hearing loss over the study period. Patients with pre-existing hearing loss and those who developed hearing loss had significantly more visits with otolaryngology compared to controls (p < 0.001). Hearing loss did not significantly delay adenotonsillectomy. More patients who developed hearing loss had adenotonsillectomies (OR 2.475 [1.672-3.663], p < 0.001, adjusted for age) versus controls. This difference was not identified in patients with pre-existing hearing loss. CONCLUSION: Patients with evidence of hearing loss in addition to sleep disordered breathing had more adenotonsillectomies performed and more clinic visits. Further work must be done to understand the associations and implications of hearing loss in this population.

Severe Versus Very Severe Pediatric Obstructive Sleep Apnea Outcomes After Adenotonsillectomy.

OBJECTIVES/HYPOTHESIS: Adenotonsillectomy (AT) is generally considered the first line treatment for pediatric patients with obstructive sleep apnea (OSA). Pediatric patients with severe OSA have worse outcomes after AT than patients with milder OSA. It is currently unclear if this group of higher morbidity patients should be subdivided further. This study investigates patients with severe pediatric OSA to determine if there are differences in postsurgical outcomes based on initial severity of sleep disordered breathing, medical comorbidities, or demographic factors. STUDY DESIGN: Retrospective cohort study at a single tertiary referral center. METHODS: Patients aged 2-18 who underwent polysomnogram (PSG) from October 2012 to January 2019, had an apnea-hypopnea index (AHI) >10, and subsequently underwent AT were identified using a filter through electronic medical record. A total of 112 patients underwent both pre- and postoperative PSG. Bivariate analysis was conducted via Pearson chi-square test. Univariate and multivariate analyses via binary logistic and multinomial linear regressions were performed using SPSS. RESULTS: Of the 112 patients included in this study, 68 patients were identified as having severe OSA (AHI = 10-20) and 44 as having very severe OSA (AHI > 20). Very severe OSA patients were significantly less likely to be cured of sleep disordered breathing or have their OSA reduced to mild OSA. Obese patients were found to have less reduction in AHI after AT. CONCLUSIONS: The postsurgical outcomes of patients with severe and very severe OSA are significantly different indicating that patients traditionally categorized as having severe OSA may need to be further subcategorized. LEVEL OF EVIDENCE: 4 Laryngoscope, 132:1855-1860, 2022.

Utility of deep learning for the diagnosis of otosclerosis on temporal bone CT.

OBJECTIVE: Diagnosis of otosclerosis on temporal bone CT images is often difficult because the imaging findings are frequently subtle. Our aim was to assess the utility of deep learning analysis in diagnosing otosclerosis on temporal bone CT images. METHODS: A total of 198 temporal bone CT images were divided into the training set (n = 140) and the test set (n = 58). The final diagnosis (otosclerosis-positive or otosclerosis-negative) was determined by an experienced senior radiologist who carefully reviewed all 198 temporal bone CT images while correlating with clinical and intraoperative findings. In deep learning analysis, a rectangular target region that includes the area of the fissula ante fenestram was extracted and fed into the deep learning training sessions to create a diagnostic model. Transfer learning was used with the deep learning model architectures of AlexNet, VGGNet, GoogLeNet, and ResNet. The test data set was subsequently analyzed using these models and by another radiologist with 3 years of experience in neuroradiology following completion of a neuroradiology fellowship. The performance of the radiologist and the deep learning models was determined using the senior radiologist's diagnosis as the gold standard. RESULTS: The diagnostic accuracies were 0.89, 0.72, 0.81, 0.86, and 0.86 for the subspecialty trained radiologist, AlexNet, VGGNet, GoogLeNet, and ResNet, respectively. The performances of VGGNet, GoogLeNet, and ResNet were not significantly different compared to the radiologist. In addition, GoogLeNet and ResNet demonstrated non-inferiority compared to the radiologist. CONCLUSIONS: Deep learning technique may be a useful supportive tool in diagnosing otosclerosis on temporal bone CT. KEY POINTS: • Deep learning can be a helpful tool for the diagnosis of otosclerosis on temporal bone CT. • Deep learning analyses with GoogLeNet and ResNet demonstrate non-inferiority when compared to the subspecialty trained radiologist. • Deep learning may be particularly useful in medical institutions without experienced radiologists.