Subjective and Objective Taste Change After Stapes Surgery Systematic Review and Meta-Analysis.

INTRODUCTION: Iatrogenic injury to the chorda tympani (CT) is a well recognized, although potentially underestimated, consequence of stapes surgery. This study aims to review the currently available literature to determine the incidence and prognosis of taste disturbances in these patients. DATA SOURCES: PubMed, Embase, and Cochrane Library databases. METHODS: Databases were searched according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Search terms included (chorda tympani OR gustatory OR taste OR chemosensory OR dysgeusia OR nervus intermedius) AND (ear surgery OR middle ear OR stapes OR stapedectomy OR stapedotomy). Patients with prospective data collection including preoperative data were further divided by methodology into "objective" and "subjective" assessments of taste dysfunction. A systematic review was performed for all included studies, with meta-analysis using a random-effects model was used for those with comparable methodology and patient populations. RESULTS: Initial search yielded 2,959 articles that were screened according to inclusion and exclusion criteria. Once duplicates were removed, seven studies were identified, representing 173 patients with subjective testing (all seven studies) and 146 with objective testing (five studies). Eighty of 173 patients (46.2%) noted a disturbance in taste at early follow-up, whereas as 26 of 173 (15.0%) noted long-term problems. Objective methodology and result reporting were heterogenous and not amenable to pooled meta-analysis for all studies included. CONCLUSION: Changes in taste occur relatively frequently after stapedectomy. Surgeons should continue to counsel prospective patients as to the risks of both short- and long-term taste disturbances.

The Predictive Value of a Modified Frailty Index on Perioperative Morbidity and Mortality Following Otologic Surgery.

Objectives: Recently, determinants of frailty have become an increasingly recognized perioperative risk stratification tool. This study examines the predictive value of a 5-factor modified frailty index (mFI-5) on perioperative morbidity and mortality in patients undergoing otologic surgery, with a subgroup analysis based on surgery site. Study Design: Cross-sectional analysis. Setting: National surgical quality improvement program dataset 2005-2019. Patients: Current procedural terminology (CPT) codes were used to identify patients undergoing all otologic surgeries. Interventions: Otologic surgeries as indicated by CPT codes, including external ear, middle ear/mastoid, implants, and inner ear/facial nerve subgroups. Main Outcome Measures: Primary outcomes examined in this study included rates of overall complications and life-threatening complications within 30 days after surgery. Overall complications included superficial surgical site infections (SSI), deep incisional SSI, readmission, deep vein thrombosis, life-threatening complications, and mortality. Life-threatening complications included those classified as Clavien-Dindo grade IV: cerebrovascular accident, mechanical ventilation for more than 48 hours, reintubation, pulmonary embolism, acute renal failure, cardiac arrest, and myocardial infarction. Results: A total of 16,859 patients who underwent otologic surgery were identified, resulting in a cohort that was 47.5% male with an average age of 47.6 years (17.1 SD). Multivariable regression analysis of the entire cohort demonstrated a score of 3 or more on the mFI-5 was independently predictive of all postoperative complications (odds ratio (OR): 2.02, P < 0.0001). However, subgroup analysis showed that only "external ear" surgery correlated with mFi-5 (OR 8.03, P = 0.013). Conclusions: Higher frailty scores as measured by the mFI-5 correlate with postoperative morbidity and mortality after otologic surgery, though subgroup analysis reveals an association only with cases performed on the external ear. These findings suggest that for most otologic surgery, the mFI-5 frailty score is not predictive of postoperative complications.

Imputation of Below Detection Limit Missing Data in Chemical Mixture Analysis with Bayesian Group Index Regression.

There is growing scientific interest in identifying the multitude of chemical exposures related to human diseases through mixture analysis. In this paper, we address the issue of below detection limit (BDL) missing data in mixture analysis using Bayesian group index regression by treating both regression effects and missing BDL observations as parameters in a model estimated through a Markov chain Monte Carlo algorithm that we refer to as pseudo-Gibbs imputation. We compare this with other Bayesian imputation methods found in the literature (Multiple Imputation by Chained Equations and Sequential Full Bayes imputation) as well as with a non-Bayesian single-imputation method. To evaluate our proposed method, we conduct simulation studies with varying percentages of BDL missingness and strengths of association. We apply our method to the California Childhood Leukemia Study (CCLS) to estimate concentrations of chemicals in house dust in a mixture analysis of potential environmental risk factors for childhood leukemia. Our results indicate that pseudo-Gibbs imputation has superior power for exposure effects and sensitivity for identifying individual chemicals at high percentages of BDL missing data. In the CCLS, we found a significant positive association between concentrations of polycyclic aromatic hydrocarbons (PAHs) in homes and childhood leukemia as well as significant positive associations for polychlorinated biphenyls (PCBs) and herbicides among children from the highest quartile of household income. In conclusion, pseudo-Gibbs imputation addresses a commonly encountered problem in environmental epidemiology, providing practitioners the ability to jointly estimate the effects of multiple chemical exposures with high levels of BDL missingness.

Childhood waist growth curves and adult diabetes.

Our primary objectives are to empirically identify distinct childhood groups based on trajectories of waist circumference (WC) and waist circumference index measurements, and then to estimate associations between these groups and adult diabetes incidence, as well as other outcomes, including blood pressure, body size, body composition, and hemoglobin levels. Childhood WC and height measurements as well as various adult measurements are taken from participants in the Fels Longitudinal Study. Childhood groups are identified using group-based trajectory modeling. Associations between the resulting group probabilities and adult outcomes are examined using mixed models. Our results show that distinct childhood groups are identifiable for both waist size measurements, with growth curves exhibited by these groups becoming distinguishably separate at around 4 years of age. Higher probabilities for groups exhibiting the larger waist size for either measurement were estimated to have higher risks of developing diabetes in adulthood. Associations were also observed between group probabilities and systolic blood pressure, diastolic blood pressure, and various anthropomorphic measurements, with most associations consistently occurring in early adulthood. These findings expand upon the existing literature, showing that childhood trends in waist size, distinguishable at ages as early as 4 years, are associated with adult Type-2 diabetes.

Social Media and Academic Impact: Do Early Tweets Correlate With Future Citations?

OBJECTIVE: Determine whether social media platforms can influence article impact as measured by citations. METHODS: Cross-sectional study that analyzed articles published in the top 10 otolaryngology journals by Eigenfactor score in January 2015. Total accumulated Twitter mentions and citations were recorded in 2021. The main outcomes examined the difference in citations, tweets, article types, and author counts accumulated over a 5-year period for all articles that were either tweeted or nontweeted. RESULTS: A total of 3094 articles were included for analysis. The average article was cited 11.2 ± 13.2 times and tweeted 2.10 ± 4.0 times. Sixty-four percent of the articles had at least one tweet. Over the study period, there was a statistically significant difference in mean number of citations between tweeted articles (12.1 ± 15.0) versus nontweeted articles (9.6 ± 10.5) citations, representing a 26% difference (P < .001). Review articles had the highest mean citations (19.4 ± 23.4) while editorials had the lowest mean citations (2.8 ± 6.9). Tweets peaked in the year of publication, but citations continued to rise in the subsequent years. Tweeted articles' peak citation rate change was +1.27 mean citations per year, compared to +0.99 mean citations per year in nontweeted articles. The mean author count in tweeted articles (5.40 ± 3.1) was not significantly different than the mean author count in nontweeted articles (5.19 ± 2.65, P = .0794). CONCLUSION: These data suggest a moderate correlation between tweets and article citations, but a clear difference in the number of citations in articles tweeted versus those with no tweets. Thus, dissemination of knowledge may be impacted by social medial platforms such as Twitter.

Bayesian Group Index Regression for Modeling Chemical Mixtures and Cancer Risk.

There has been a growing interest in the literature on multiple environmental risk factors for diseases and an increasing emphasis on assessing multiple environmental exposures simultaneously in epidemiologic studies of cancer. One method used to analyze exposure to multiple chemical exposures is weighted quantile sum (WQS) regression. While WQS regression has been demonstrated to have good sensitivity and specificity when identifying important exposures, it has limitations including a two-step model fitting process that decreases power and model stability and a requirement that all exposures in the weighted index have associations in the same direction with the outcome, which is not realistic when chemicals in different classes have different directions and magnitude of association with a health outcome. Grouped WQS (GWQS) was proposed to allow for multiple groups of chemicals in the model where different magnitude and direction of associations are possible for each group. However, GWQS shares the limitation of WQS of a two-step estimation process and splitting of data into training and validation sets. In this paper, we propose a Bayesian group index model to avoid the estimation limitation of GWQS while having multiple exposure indices in the model. To evaluate the performance of the Bayesian group index model, we conducted a simulation study with several different exposure scenarios. We also applied the Bayesian group index method to analyze childhood leukemia risk in the California Childhood Leukemia Study (CCLS). The results showed that the Bayesian group index model had slightly better power for exposure effects and specificity and sensitivity in identifying important chemical exposure components compared with the existing frequentist method, particularly for small sample sizes. In the application to the CCLS, we found a significant negative association for insecticides, with the most important chemical being carbaryl. In addition, for children who were born and raised in the home where dust samples were taken, there was a significant positive association for herbicides with dacthal being the most important exposure. In conclusion, our approach of the Bayesian group index model appears able to make a substantial contribution to the field of environmental epidemiology.

Relationship between pain-related comorbidities and quality of life in patients with symptoms of chronic rhinosinusitis.

INTRODUCTION: Facial pain is a common manifestation of sinonasal disease but may be due to a variety of other conditions. Misattribution of pain to chronic rhinosinusitis may result in worse quality of life in populations both with and without objective evidence of sinonasal disease. The purpose of this study was to determine if there is an association between pain-related comorbidities and worse chronic rhinosinusitis specific quality of life in patients with and without objective evidence of sinonasal inflammation. METHODS: Retrospective cohort study of 299 patients meeting diagnostic criteria for sinusitis evaluated at a tertiary academic medical center from 2017 to 2018. Objective evidence was measured using the Lund-Kennedy and Lund-MacKay scoring systems; for the purposes of this study a score >3 on either scale was considered indicative of disease. Quality of life was determined by the rhinosinusitis disability index. RESULTS: A total of 191 patients were included in the study, with an average age of 52.7. (SD=15.3). The average Lund-Kennedy and Lund-MacKay scores were 4.7 and 8.3, respectively. The average rhinosinusitis disability index was 32.1. When stratified by the presence of pain-related comorbidities, there was no significant difference in Lund-Kennedy (p = 0.203), Lund-MacKay (p = 0.101), or rhinosinusitis disability index (p = 0.421). CONCLUSION: Although prior studies have suggested a correlation between the presence of pain-related comorbidities and worse chronic rhinosinusitis specific quality of life, this relationship was not evident within the current cohort of patients. The relationship between pain and sinusitis specific quality of life is likely complex and requires further research to fully elucidate.

Assessment of Grouped Weighted Quantile Sum Regression for Modeling Chemical Mixtures and Cancer Risk.

Individuals are exposed to a large number of diverse environmental chemicals simultaneously and the evaluation of multiple chemical exposures is important for identifying cancer risk factors. The measurement of a large number of chemicals (the exposome) in epidemiologic studies is allowing for a more comprehensive assessment of cancer risk factors than was done in earlier studies that focused on only a few chemicals. Empirical evidence from epidemiologic studies shows that chemicals from different chemical classes have different magnitudes and directions of association with cancers. Given increasing data availability, there is a need for the development and assessment of statistical methods to model environmental cancer risk that considers a large number of diverse chemicals with different effects for different chemical classes. The method of grouped weighted quantile sum (GWQS) regression allows for multiple groups of chemicals to be considered in the model such that different magnitudes and directions of associations are possible for each group of chemicals. In this paper, we assessed the ability of GWQS regression to estimate exposure effects for multiple chemical groups and correctly identify important chemicals in each group using a simulation study. We compared the performance of GWQS regression with WQS regression, the least absolute shrinkage and selection operator (lasso), and the group lasso in estimating exposure effects and identifying important chemicals. The simulation study results demonstrate that GWQS is an effective method for modeling exposure to multiple groups of chemicals and compares favorably with other methods used in mixture analysis. As an application, we used GWQS regression in the California Childhood Leukemia Study (CCLS), a population-based case-control study of childhood leukemia in California to estimate exposure effects for many chemical classes while also adjusting for demographic factors. The CCLS analysis found evidence of a positive association between exposure to the herbicide dacthal and an increased risk of childhood leukemia.