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ObjectivesTo address concerns related to the safety profile of both Food and Drug Administration (FDA)–approved and non–FDA‐approved intranasal corticosteroid (INCS) use in the adult population.Data SourceSystematic review of MEDLINE, PubMed, and EMBASE databases using a comprehensive search strategy including all INCS formulations and adverse events. The study design was developed using Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines. Additional sources were identified from study references of relevant articles.Review MethodsA structured literature search was conducted. Each study was graded for level of evidence using the Oxford Centre for Evidence‐Based Medicine. Extracted data included population size, study design, drug (dosage, route, and frequency), presence of hypothalamus pituitary axis suppression, ocular symptoms, and treatment‐related adverse events.ResultsA total of 60 studies met inclusion criteria. The studies included use of INCS as metered nasal sprays, drops, injections, aerosols, and irrigations. There were no persistent abnormalities in cortisol level or intraocular pressure change. Meta‐analysis of epistaxis showed a significantly increased risk in the FDA‐approved treatment group in comparison with control (risk ratio 1.56; 95% confidence interval, 1.13‐2.14; P =. 007).ConclusionsOverall, it appears that the use of both FDA and published non‐FDA application of INCS are safe in the adult population. Meta‐analysis demonstrated an increased risk of epistaxis in patients using INCS compared with placebo. Otherwise, there was no significant difference between in adults in the treatment group and placebo group. As an important caveat, the interpretation of safety of nonstandard INCS is restricted to delivery methods and dosages published in the literature.

As rhinologists return to practice amid SARS‐CoV‐2, special considerations are warranted given the unique features of their subspecialty. Rhinologist manipulation of nasal tissue, proximity, and frequent aerosol‐generating procedures (AGPs) create high risk for infection transmission. There are 4 areas of special consideration to mitigate risk: (1) previsit planning for risk stratification/mitigation, (2) appropriate personal protective equipment, (3) preprocedural testing, and (4) environmental controls. During previsit planning, risk factors of the patient and procedures are considered. High‐risk AGPs are identified by duration, proximity, manipulation of high–viral load tissue, and use of powered instrumentation. Appropriate personal protective equipment includes selection of respiratory and eye protection. COVID‐19 testing can screen for asymptomatic carriers prior to high‐risk procedures; however, alternative testing methods are required in rhinologic patients not eligible for nasopharyngeal testing due to nasal obstruction or skull base defects. Last, AGPs in rhinologic practices require considerations of room air handling and environmental controls.

BackgroundSubtyping chronic rhinosinusitis (CRS) by tissue eosinophilia has prognostic and therapeutic implications, and is difficult to predict using peripheral eosinophil counts or polyp status alone. The objective of this study was to test machine learning for prediction of eosinophilic CRS (eCRS).MethodsInput variables were defined as peripheral eosinophil count, urinary leukotriene E4 (uLTE4) level, and polyp status. The output was diagnosis of eCRS, defined as tissue eosinophil count >10 per high‐power field. Patients undergoing surgery for CRS were retrospectively reviewed for complete datasets. Univariate analysis was performed for each input as a predictor of eCRS. Logistic regression and artificial neural network (ANN) machine learning models were developed using random and surgeon‐specific training/test datasets.ResultsA total of 80 patients met inclusion criteria. In univariate analysis, area under the receiver operator characteristic curve (AUC) for peripheral eosinophil count and uLTE4 were 0.738 (95% confidence interval [CI], 0.616 to 0.840) and 0.728 (95% CI, 0.605 to 0.822), respectively. Presence of polyps was 94.1% sensitive, but 51.7% specific. Logistic regression models using random and surgeon specific datasets resulted in AUC of 0.882 (95% CI, 0.665 to 0.970) and 0.945 (95% CI, 0.755 to 0.995), respectively. ANN models resulted in AUC of 0.918 (95% CI, 0.756 to 0.975) and 0.956 (95% CI, 0.828 to 0.999) using random and surgeon‐specific datasets, respectively. Model comparison of logistic regression and ANN was not statistically different. All machine learning models had AUC greater than univariate analyses (all p < 0.003).ConclusionMachine learning of 3 clinical inputs has the potential to predict eCRS with high sensitivity and specificity in this patient population. Prospective investigation using larger and more diverse populations is warranted.

Background Past research in population health trends has shown that injuries form a substantial burden of population health loss. Regular updates to injury burden assessments are critical. We report Global Burden of Disease (GBD) 2017 Study estimates on morbidity and mortality for all injuries. Methods We reviewed results for injuries from the GBD 2017 study. GBD 2017 measured injury-specific mortality and years of life lost (YLLs) using the Cause of Death Ensemble model. To measure non-fatal injuries, GBD 2017 modelled injury-specific incidence and converted this to prevalence and years lived with disability (YLDs). YLLs and YLDs were summed to calculate disability-adjusted life years (DALYs). Findings In 1990, there were 4 260 493 (4 085 700 to 4 396 138) injury deaths, which increased to 4 484 722 (4 332 010 to 4 585 554) deaths in 2017, while age-standardised mortality decreased from 1079 (1073 to 1086) to 738 (730 to 745) per 100 000. In 1990, there were 354 064 302 (95% uncertainty interval: 338 174 876 to 371 610 802) new cases of injury globally, which increased to 520 710 288 (493 430 247 to 547 988 635) new cases in 2017. During this time, age-standardised incidence decreased non-significantly from 6824 (6534 to 7147) to 6763 (6412 to 7118) per 100 000. Between 1990 and 2017, age-standardised DALYs decreased from 4947 (4655 to 5233) per 100 000 to 3267 (3058 to 3505). Interpretation Injuries are an important cause of health loss globally, though mortality has declined between 1990 and 2017. Future research in injury burden should focus on prevention in high-burden populations, improving data collection and ensuring access to medical care.