Objectives: To investigate whether migraine is independently associated with tinnitus and subjective hearing loss (HL) in a large national database. Methods: The de-identified 1999 to 2004 National Health and Nutrition Examination Survey database was retrospectively queried for subjects aged 18 to 65. HL and tinnitus were subjectively reported by subjects. Results: A total of 12,962 subjects (52.9% female) with a mean age of 38.1 AE 14.6 years were included. This consisted of 2,657 (20.5%), 2,344 (18.1%), and 2,582 (19.9%) subjects who had migraine, subjective-HL, and tinnitus, respectively. In patients with tinnitus or subjective-HL, migraine was reported in 35.6% and 24.5%, respectively. Migraineurs were more likely to have subjective-HL (25.0% vs. 16.6%, p < 0.001) and tinnitus (34.6% vs. 16.9%, p < 0.001) compared to the nonmigraineurs. This corresponded to migraine having an odds ratio of 1.5 (95% confidence interval [CI] 1.3-1.7, p < 0.001) and 2.2 (95% CI 2.0-2.4, p < 0.001) for subjective-HL and tinnitus, respectively. After adjusting for confounders, subjective-HL (odds ratio [OR] ¼ 1.2, 95% CI 1.1-1.4, p ¼ 0.003), tinnitus (OR ¼ 2.1, 95% CI 1.9-2.3, p < 0.001), and neck pain (OR ¼ 4.0, 95% CI 3.6-4.5, p < 0.001) were more common in migraineurs. Among migraineurs, a higher proportion of those with tinnitus also had subjective-HL compared to those without tinnitus (40.0% vs. 15.3%, p < 0.001), and a higher proportion of those with subjective-HL also had tinnitus compared to those without HL (58.1% vs. 27.3%, p < 0.001). Conclusions: This study suggests an independent association between migraine with subjective-HL and tinnitus. Otologic migraine, which is the effects of migraine on the ear, may be partly responsible for the link between HL, tinnitus, neck pain, and migraine.

Objectives: To evaluate the presence of migraine features in patients with persistent postural-perceptual dizziness (PPPD). Methods: In a retrospective survey study, consecutive patients presenting to a tertiary care neurotology clinic during an 18-month period were given questionnaires about headache and dizziness symptoms. The survey responses plus history and examination of the patient were used to diagnose patients with PPPD. The prevalence of migraine headache, vestibular migraine (VM), and migraine characteristics was evaluated. Results: In total, 36 subjects with PPPD were included in the study. The mean age of the subjects was 56 ± 16 years with a female (72%) predominance. A total of 19 (53%) patients met the International Classification of Headache Disorders criteria for migraine headache, and 6 of those (17%) met the criteria for definite VM. Of the patients who did not meet full migraine headache criteria, 6 (17%) patients met 4 of 5 criteria, and 5 (14%) patients met 3 of 5 criteria. There was no significant difference between PPPD patients who fulfilled full migraine headache criteria and those who did not in sensitivity to light, sound, smells, weather changes, feelings of mental fog/confusion, and sinus pain/facial pressure. Conclusions: This study demonstrates that a majority of patients with PPPD fulfill the criteria for migraine headache. A large proportion of PPPD patients who do not meet the full criteria for migraine headache still meet a majority of the migraine headache criteria. This suggests an association between the 2 conditions. PPPD may be a part of the spectrum of otologic migraine, where migraine manifests as otologic symptoms.

Objectives: To describe a cohort of patients presenting with long-term sudden sensorineural hearing loss (SSNHL) treated with prophylactic migraine and intratympanic steroid therapy. Methods: Patients presenting to a neurotology clinic at least 6 weeks from SSNHL onset were included. All patients received migraine prophylactic medication (nortriptyline, topiramate, and/or verapamil) and lifestyle changes for at least 6 weeks, as well as intratympanic steroid injections, if appropriate.Results: Twenty-one patients (43% female) with a mean age of 64 AE 11 years who presented 9 AE 8 months (median ¼ 5) from symptom onset were included. Posttreatment hearing thresholds were significantly improved compared with pretreatment thresholds at 500 Hz (49 AE 19 dB versus 55 AE 20 dB, p ¼ 0.01), 1000 Hz (52 AE 19 dB versus 57 AE 21 dB, p ¼ 0.03), low-frequency pure-tone average (53 AE 15 dB versus 57 AE 17 dB, p ¼ 0.01), and speech-frequency pure-tone average (57 AE 13 dB versus 60 AE 15 dB, p ¼ 0.02). Posttreatment word-recognition-score (WRS) and speech-recognition-threshold (SRT) were also significantly improved (45 AE 28% versus 70 AE 28% and 57 AE 18 dB versus 50 AE 16 dB, respectively, both p < 0.01). Notably, !15% improvement in WRS and !10 dB improvement in SRT was observed in 13 (68%) and 8 (40%) patients, respectively. Of the 11 patients who presented with initial < 50% WRS, 8 (73%) had improved posttreatment >50% WRS with an average improvement of 39 AE 9%. Conclusions: Migraine medications in addition to intratympanic steroid injections significantly improved SRT and hearing frequencies in 40% and 29% of SSNHL patients, respectively, while significant WRS recovery was observed in most (68%) patients. This suggests SSNHL may be an otologic migraine phenomenon, which may be at least partially reversible even after the traditional 30-day postonset window.

Objectives: To develop a multiclass-classifier deep learning model and website for distinguishing tympanic membrane (TM) pathologies based on otoscopic images. Methods: An otoscopic image database developed by utilizing publicly available online images and open databases was assessed by convolutional neural network (CNN) models including ResNet-50, Inception-V3, Inception-Resnet-V2, and MobileNetV2. Training and testing were conducted with a 75:25 breakdown. Area under the curve of receiver operating characteristics (AUC-ROC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were used to compare different CNN models' performances in classifying TM images. Results: Our database included 400 images, organized into normal (n ¼ 196) and abnormal classes (n ¼ 204), including acute otitis media (n ¼ 116), otitis externa (n ¼ 44), chronic suppurative otitis media (n ¼ 23), and cerumen impaction (n ¼ 21). For binary classification between normal versus abnormal TM, the best performing model had average AUC-ROC of 0.902 (MobileNetV2), followed by 0.745 (Inception-Resnet-V2), 0.731 (ResNet-50), and 0.636 (Inception-V3).Accuracy ranged between 0.73-0.77, sensitivity 0.72-0.88, specificity 0.58-0.84, PPV 0.68-0.81, and NPV 0.73-0.83. Macro-AUC-ROC for MobileNetV2 based multiclass-classifier was 0.91, with accuracy of 66%. Binary and multiclassclassifier models based on MobileNetV2 were loaded onto a publicly accessible and user-friendly website (https://headneckml.com/tympanic). This allows the readership to upload TM images for real-time predictions using the developed algorithms. Conclusions: Novel CNN algorithms were developed with high AUC-ROCs for differentiating between various TM pathologies. This was further deployed as a proof-ofconcept publicly accessible website for real-time predictions.

Objective/Hypothesis: To assess and characterize online ratings and comments on pediatric otolaryngologists and determine factors that correlate with higher ratings.Study Design: Online database analysis. Methods: All American Society of Pediatric Otolaryngology (ASPO) members were queried on Healthgrades, Vitals, RateMDs, and Yelp for their online ratings and comments as of June 2020. Ratings were normalized for comparison on a fivepoint Likert scale. All comments were categorized based on context and positive or negative quality.Results: Of the 561 ASPO members, 489 (87%) were rated on at least one online platform. Of those rated, 410 (84%) were on Healthgrades, 429 (88%) on Vitals, 236 (48%) on RateMDs, and 72 (15%) on Yelp. Across all platforms, the average overall rating was 4.13 AE 0.03 (range, 1.00-5.00). We found significant positive correlations between overall ratings and specific ratings (P < .001) on all individual topics. In addition, the majority of all narrative comments were related to perceived physician bedside manner and clinical outcome, with negative comments correlating negatively with overall score (P < .05). Time spent with the physician was the only category in which both positive and negative comments showed significant correlation with the overall physician rating (P = .016 and P = .017, respectively). Attending a top-ranked medical school or residency program did not correlate with higher or lower ratings.Conclusions: Online ratings and comments for pediatric otolaryngologists are largely influenced by patient and parent perceptions of physician competence, comforting bedside manner, and office and time management.