How Variable are Patient Comorbidity Profiles Among Practicing Otolaryngologists?

OBJECTIVE: To determine whether certain groups of otolaryngologists (ORLs) are treating cohorts of patients with more comorbidities. STUDY DESIGN: Cross-sectional population-based analysis. SETTING: 2019 Medicare Provider Utilization and Payment Dataset. METHODS: Each ORL's average Medicare hierarchical condition category (HCC) risk score, a comorbidity index calculated from a patient's comorbidities, was collected. These were stratified and compared by various physician characteristics, including practice region and rurality, years in practice, gender, subspecialty, and setting (academic vs community). RESULTS: Among 8959 ORLs, the mean HCC risk score for Medicare patients was 1.35 ± 0.35. On univariate analysis, ORLs practicing in urban (compared to rural), ORLs in academic settings (compared to community), and early career ORLs all had a patient population with a higher HCC risk score (P < .001 for all). On multivariate analysis controlling for gender, rurality, graduation year, and region, rural setting was associated with decreased odds of having a high-risk patient population (odds ratio: 0.58 [95% confidence interval, CI: 0.48-0.71]; P < .001), while those more recently graduated has an increased risk (2000-2009: 1.41 [1.01-1.96], P = .046; 2010-2015: 2.30 [1.63-3.25], P < .001). In a separate subgroup analysis, subspecialty differences were seen and community setting was associated with decreased odds of having a high-risk patient population (0.36 [0.23-0.55]; P < .001). CONCLUSION: There is variability in patient comorbidity profiles among ORLs, with those in urban settings, those more recently graduated, and those in academic settings treating a group with more comorbidities. As the comorbidity burden may increase the cost of practice and complications, these findings may have important implications for health inequity.

Comparison of patient-reported outcomes and clinical characteristics among patients with pituitary macroadenomas and giant adenomas.

KEY POINTS: Patients with giant adenomas are more likely to have tumor extension into the paranasal sinuses. Compared to macroadenomas, giant adenomas are not associated with worse preoperative SNOT-22 scores.

Impact of facility volume on survival in primary endoscopic surgery for sinonasal squamous cell carcinoma.

OBJECTIVES: To evaluate the impact of facility volume on outcomes following primary endoscopic surgical management of sinonasal squamous cell carcinoma (SNSCC). METHODS: The 2010-2016 National Cancer DataBase (NCDB) was queried for patients diagnosed with T1-T4a SNSCC surgically treated endoscopically as the primary treatment modality. Factors associated with overall survival (OS) were evaluated, including facility volume. RESULTS: A total of 330 patients who underwent endoscopic surgical management of SNSCC were treated at 356 unique facilities designated as either low-volume (LVC; treating 1-2 cases; 0-75th percentile), intermediate-volume centers (IVC; 3-4 cases total; 75th-90th percentile), or 144 high-volume (HVC; treating 5+ cases total; >90th percentile) centers. HVC treated patients with higher T staging (42.1 % vs. 29.8 %) and tumors in the maxillary sinus (26.9 % vs. 13.2 %) and ethmoid sinus (10.3 % vs. ≤8.3 %), while LVCs treated lower T stage tumors (70.2 % vs. 57.9 %) and tumors that were located in the nasal cavity (70.2-78.5 % vs. 62.8 %). On multivariable analysis, factors associated with decreased OS included higher T stage (T3/T4a vs. T1/T2; OR 1.92, 95 % CI 1.06-3.47) and older age (>65 vs. <65; OR 2.69, 95 % CI 1.62-4.49). Cases treated at high-volume centers were not associated with a higher likelihood of OS when compared to low-volume centers (OR 0.70, 95 % CI 0.36-1.35). CONCLUSIONS: HVC are treating more primary tumors of the maxillary and ethmoid sinuses and tumors with higher T stages with endoscopic approaches, although this does not appear to be associated with increased OS. SHORT SUMMARY: Sinonasal squamous cell carcinoma (SNSCC) presents late in disease process with poor prognosis. We investigated the impact of facility volume on outcomes following endoscopic treatment of SNSCC. High-volume centers treat more advanced and complex disease with comparable OS.

Deep Learning Approach for Differentiating Etiologies of Pediatric Retinal Hemorrhages: A Multicenter Study.

Retinal hemorrhages in pediatric patients can be a diagnostic challenge for ophthalmologists. These hemorrhages can occur due to various underlying etiologies, including abusive head trauma, accidental trauma, and medical conditions. Accurate identification of the etiology is crucial for appropriate management and legal considerations. In recent years, deep learning techniques have shown promise in assisting healthcare professionals in making more accurate and timely diagnosis of a variety of disorders. We explore the potential of deep learning approaches for differentiating etiologies of pediatric retinal hemorrhages. Our study, which spanned multiple centers, analyzed 898 images, resulting in a final dataset of 597 retinal hemorrhage fundus photos categorized into medical (49.9%) and trauma (50.1%) etiologies. Deep learning models, specifically those based on ResNet and transformer architectures, were applied; FastViT-SA12, a hybrid transformer model, achieved the highest accuracy (90.55%) and area under the receiver operating characteristic curve (AUC) of 90.55%, while ResNet18 secured the highest sensitivity value (96.77%) on an independent test dataset. The study highlighted areas for optimization in artificial intelligence (AI) models specifically for pediatric retinal hemorrhages. While AI proves valuable in diagnosing these hemorrhages, the expertise of medical professionals remains irreplaceable. Collaborative efforts between AI specialists and pediatric ophthalmologists are crucial to fully harness AI's potential in diagnosing etiologies of pediatric retinal hemorrhages.

Artificial neural network prediction of post-thyroidectomy outcome.

OBJECTIVES: The goal of this study was to develop a deep neural network (DNN) for predicting surgical/medical complications and unplanned reoperations following thyroidectomy. DESIGN, SETTING, AND PARTICIPANTS: The 2005-2017 American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database was queried to extract patients who underwent thyroidectomy. A DNN consisting of 10 layers was developed with an 80:20 breakdown for training and testing. MAIN OUTCOME MEASURES: Three primary outcomes of interest, including occurrence of surgical complications, medical complications, and unplanned reoperation were predicted. RESULTS: Of the 21 550 patients who underwent thyroidectomy, medical complications, surgical complications and reoperation occurred in 1723 (8.0%), 943 (4.38%) and 2448 (11.36%) patients, respectively. The DNN performed with an area under the curve of receiver operating characteristics of .783 (medical complications), .709 (surgical complications) and .703 (reoperations). Accuracy, specificity and negative predictive values of the model for all outcome variables ranged 78.2%-97.2%, while sensitivity and positive predictive values ranged 11.6%-62.5%. Variables with high permutation importance included sex, inpatient versus outpatient and American Society of Anesthesiologists class. CONCLUSIONS: We predicted surgical/medical complications and unplanned reoperation following thyroidectomy via development of a well-performing ML algorithm. We have also developed a web-based application available on mobile devices to demonstrate the predictive capacity of our models in real time.

Deep Learning Estimation of 10-2 Visual Field Map Based on Circumpapillary Retinal Nerve Fiber Layer Thickness Measurements.

PURPOSE: To estimate central 10-degree visual field (VF) map from spectral-domain optical coherence tomography (SD-OCT) retinal nerve fiber layer thickness (RNFL) measurements in glaucoma with artificial intelligence. DESIGN: Artificial intelligence (convolutional neural networks) study. METHODS: This study included 5352 SD-OCT scans and 10-2 VF pairs from 1365 eyes of 724 healthy patients, patients with suspected glaucoma, and patients with glaucoma. Convolutional neural networks (CNNs) were developed to estimate the 68 individual sensitivity thresholds of 10-2 VF map using all-sectors (CNNA) and temporal-sectors (CNNT) RNFL thickness information of the SD-OCT circle scan (768 thickness points). 10-2 indices including pointwise total deviation (TD) values, mean deviation (MD), and pattern standard deviation (PSD) were generated using the CNN-estimated sensitivity thresholds at individual test locations. Linear regression (LR) models with the same input were used for comparison. RESULTS: The CNNA model achieved an average pointwise mean absolute error of 4.04 dB (95% confidence interval [CI] 3.76-4.35) and correlation coefficient (r) of 0.59 (95% CI 0.52-0.64) over 10-2 map and the mean absolute error and r of 2.88 dB (95% CI 2.63-3.15) and 0.74 (95% CI 0.67-0.80) for MD, and 2.31 dB (95% CI 2.03-2.61) and 0.59 (95% CI 0.51-0.65) for PSD estimations, respectively, significantly outperforming the LRA model. CONCLUSIONS: The proposed CNNA model improved the estimation of 10-2 VF map based on circumpapillary SD-OCT RNFL thickness measurements. These artificial intelligence methods using SD-OCT structural data show promise to individualize the frequency of central VF assessment in patients with glaucoma and would enable the reallocation of resources from patients at lowest risk to those at highest risk of central VF damage.

Combining Optical Coherence Tomography and Optical Coherence Tomography Angiography Longitudinal Data for the Detection of Visual Field Progression in Glaucoma.

PURPOSE: To use longitudinal optical coherence tomography (OCT) and OCT angiography (OCTA) data to detect glaucomatous visual field (VF) progression with a supervised machine learning approach. DESIGN: Prospective cohort study. METHODS: One hundred ten eyes of patients with suspected glaucoma (33.6%) and patients with glaucoma (66.4%) with a minimum of 5 24-2 VF tests and 3 optic nerve head and macula images over an average follow-up duration of 4.1 years were included. VF progression was defined using a composite measure including either a "likely progression event" on Guided Progression Analysis, a statistically significant negative slope of VF mean deviation or VF index, or a positive pointwise linear regression event. Feature-based gradient boosting classifiers were developed using different subsets of baseline and longitudinal OCT and OCTA summary parameters. The area under the receiver operating characteristic curve (AUROC) was used to compare the classification performance of different models. RESULTS: VF progression was detected in 28 eyes (25.5%). The model with combined baseline and longitudinal OCT and OCTA parameters at the global and hemifield levels had the best classification accuracy to detect VF progression (AUROC = 0.89). Models including combined OCT and OCTA parameters had higher classification accuracy compared with those with individual subsets of OCT or OCTA features alone. Including hemifield measurements significantly improved the models' classification accuracy compared with using global measurements alone. Including longitudinal rates of change of OCT and OCTA parameters (AUROCs = 0.80-0.89) considerably increased the classification accuracy of the models with baseline measurements alone (AUROCs = 0.60-0.63). CONCLUSIONS: Longitudinal OCTA measurements complement OCT-derived structural metrics for the evaluation of functional VF loss in patients with glaucoma.

A Web-Based Deep Learning Model for Automated Diagnosis of Otoscopic Images.

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 = 196) and abnormal classes (n = 204), including acute otitis media (n = 116), otitis externa (n = 44), chronic suppurative otitis media (n = 23), and cerumen impaction (n = 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 multiclass-classifier 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-of-concept publicly accessible website for real-time predictions.

Adapting Personal Therapies Using a Mobile Application for Tinnitus Rehabilitation: A Preliminary Study.

OBJECTIVES: To develop a smartphone application providing sound therapy and cognitive behavioral therapy (CBT) for treating tinnitus and performing a proof-of-concept pilot study evaluating its potential efficacy. METHODS: An interactive smartphone application available on iOS and Android platforms was developed, which provided an 8-week tinnitus-specific CBT and personalized and frequency-matched sound therapy. Included patients presented to our tertiary clinic between 2017 and 2018, while those waitlisted were regarded as controls. Three surveys were administrated: Tinnitus Handicap Inventory (THI), Generalized Anxiety Disorder 7-item (GAD-7), and Perceived Stress Scale (PSS). RESULTS: A total of 30 patients enrolled in this study consisting of 20 treatment and 10 control patients and mean age was 55.4 ± 11.6 years. Treatment and control patients had similar age, sex, and pre-enrolment GAD and PSS (all P > .05). Baseline THI scores were also similar between treatment and control cohorts (50.1 ± 21.9 vs 62.0 ± 20.7; P = .15). After 8 weeks, though changes in GAD and PSS scores were similar (P > .05), the treatment group reported a significantly greater improvement in THI scores (17.7 ± 15.8 vs 5.3 ± 10.5, P = .04). CONCLUSIONS: This pilot study demonstrated potentially promising efficacy of a smartphone-based CBT and sound therapy platform for treating tinnitus and encourages future randomized controlled trials on this treatment modality.

Prediction of vestibular schwannoma recurrence using artificial neural network.

OBJECTIVES: To compare two statistical models, namely logistic regression and artificial neural network (ANN), in prediction of vestibular schwannoma (VS) recurrence. METHODS: Seven hundred eighty-nine patients with VS diagnosis completed an online survey. Potential predictors for recurrence were derived from univariate analysis by reaching the cut off P value of .05. Those nine potential predictors were years since treatment, surgeon's specialty, resection amount, and having incomplete eye closure, dry eye, double vision, facial pain, seizure, and voice/swallowing problem as a complication following treatment. Multivariate binary logistic regression model was compared with a four-layer 9-5-10-1 feedforward backpropagation ANN for prediction of recurrence. RESULTS: The overall recurrence rate was 14.5%. Significant predictors of recurrence in the regression model were years since treatment and resection amount (both P < .001). The regression model did not show an acceptable performance (area under the curve [AUC] = 0.64; P = .27). The regression model's sensitivity and specificity were 44% and 69%, respectively and correctly classified 56% of cases. The ANN showed a superior performance compared to the regression model (AUC = 0.79; P = .001) with higher sensitivity (61%) and specificity (81%), and correctly classified 70% of cases. CONCLUSION: The constructed ANN model was superior to logistic regression in predicting patient-answered VS recurrence in an anonymous survey with higher sensitivity and specificity. Since artificial intelligence tools such as neural networks can have higher predictive abilities compared to logistic regression models, continuous investigation into their utility as complementary clinical tools in predicting certain surgical outcomes is warranted.

Medical Malpractice of Vestibular Schwannoma: A 40-Year Review of the United States Legal Databases.

OBJECTIVES: To analyze medical malpractice lawsuit trends pertaining to cases of vestibular schwannomas (VS). METHODS: Two major computerized legal databases (LexisNexis and WestLaw) were queried and reviewed for evaluation of all the US state and federal court records from civil trials alleging malpractice between 1976 and 2016. RESULTS: A total of 32 VS cases were identified. Allegations were divided into four categories: misdiagnosis/delayed diagnosis (47%), postoperative complications (44%), failure of informed consent or information sharing (16%), and other (3%). Postoperative complications included facial nerve paralysis, myocardial infarction, meningitis, and intracranial hemorrhage. Judgment amounts ranged from $300,000 to $2,000,000. The specialist type was specified for 24 of the 32 cases (75%): neurosurgeons (n = 9; 37%), neurotologists (n = 6; 25%), general otolaryngologists (n = 5; 21%), primary care physicians (n = 4; 17%), neurologists (n = 3; 12%), radiologists (n = 3; 12%), anesthesiologists (n = 2; 8%), radiation oncologists (n = 1; 4%), and general surgeon (n = 1; 4%). Of these 24 cases, (n = 9; 37%) two or more physicians were named as defendants in the lawsuit. CONCLUSIONS: Enhanced physician-patient communication, ensuring proper and adequate patient consent procedures, and proper documentation are good practices that may decrease the likelihood of lawsuits.

Fetal Bovine Serum-Derived Extracellular Vesicles Persist within Vesicle-Depleted Culture Media.

It is known that culture media (CM) promotes cellular growth, adhesion, and protects explanted primary brain cells from in vitro stresses. The fetal bovine serum (FBS) supplement used in most CM, however, contains significant quantities of extracellular vesicles (EVs) that confound quantitative and qualitative analyses from the EVs produced by the cultured cells. We quantitatively tested the ability of common FBS EV-depletion protocols to remove exogenous EVs from FBS-supplemented CM and evaluated the influence such methods have on primary astrocyte culture growth and viability. We assessed two methodologies utilized for FBS EV removal prior to adding to CM: (1) an 18-h ultracentrifugation (UC); and (2) a commercial EV-depleted FBS (Exo-FBS™). Our analysis demonstrated that Exo-FBS™ CM provided the largest depletion (75%) of total FBS EVs, while still providing 6.92 × 108 ± 1.39 × 108 EVs/mL. In addition, both UC and Exo-FBS™ CM resulted in poor primary astrocyte cell growth and viability in culture. The two common FBS EV-depletion methods investigated, therefore, not only contaminate in vitro primary cell-derived EV analyses, but also provide a suboptimal environment for primary astrocyte cell growth and viability. It appears likely that future CM optimization, using a serum-free alternative, might be required to advance analyses of cell-specific EVs isolated in vitro.

Different aspects of kidney function in well-controlled congenital hypothyroidism.

OBJECTIVE: Congenital hypothyroidism (CH) increases the prevalence of kidney and urogenital malformations. There are limited studies considering different aspects of kidney function in well-controlled CH patients. We evaluated some features of kidney function in euthyroid children with CH who have been receiving thyroxine hormone since early life. METHODS: This cross-sectional study was conducted in Isfahan, Iran, on 74 children aged 2-15 years old (36 CH patients and 38 healthy children). Inclusion criteria for CH patients were euthyroidism at the time of the survey and initiation of replacement therapy during the early neonatal period. Kidney ultrasound evaluation was performed in all participants. Serum biochemistry included urea, creatinine, sodium (Na), potassium (K), magnesium, calcium, and cystatin C levels. Urine electrolytes, fraction excretion (FE) of electrolytes and microalbumin, and glomerular filtration rate (GFR) were also determined. RESULTS: The male/female ratio was 0.8/1 and 1.5/1 in the patient and control groups, respectively. Mean age and height did not differ significantly between the two groups. Ultrasound evaluation of the kidney revealed that the anteroposterior diameter of the right kidney was significantly higher in CH patients as compared to healthy subjects. No significant difference was observed between GFRs in patients with CH and healthy children. The mean values for FENa and FEK were significantly higher in the patient group. CONCLUSIONS: Increased FENa and FEK may be a manifestation of impaired tubular maturation in CH. More longitudinal studies are needed to evaluate kidney function in CH patients.