Objectives To examine patterns of change and plateau in speech recognition scores in postlingually hearing impaired adult cochlear implant recipients. The study also examines variations in change patterns for different speech materials and testing conditions. Study Design Used systematic review with meta‐analysis. Methods Articles in English reporting speech recognition scores of adults with postlingual hearing loss at pre‐implantation and at least two post‐implantation time points were included. Statistically significant changes were determined by meta‐analysis and the 95% confidence interval. Results A total of 22 articles representing 1954 patients were included. Meta‐analysis of mean difference demonstrated significant improvements in speech recognition score for words in quiet (37.4%; 95% confidence interval [34.7%, 40.7%]), sentences in quiet (49.4%; 95% confidence interval [44.9%, 53.9%]), and sentences in noise (30.8%; 95% confidence interval [25.2%, 36.4%]) from pre‐op to 3 months. Scores continued to increase from 3 to 12 months but did not reach significance. Similarly, significant improvements from pre‐op to 3 months were observed for consonant nucleus consonant (CNC) words in quiet (37.1%; 95% confidence interval [33.8%, 40.4%]), hearing in noise test (HINT) sentences in quiet (46.5%; 95% confidence interval [37.0%, 56.0%]), AzBio sentences in quiet (45.9%; 95% confidence interval [44.2%, 47.5%]), and AzBio sentences in noise (26.4%; 95% confidence interval [18.6%, 34.2%]). HINT sentences in noise demonstrated improvement from pre‐op to 3 months (35.1%; 95% confidence interval [30.0%, 40.3%]) and from 3 to 12 months (15.5%; 95% confidence interval [7.2%, 23.8%]). Conclusions Mean speech recognition scores demonstrate significant improvement within the first 3 months, with no further statistically significant improvement after 3 months. However, large individual variation should be expected and future research is needed to explain the sources of these individual differences. Laryngoscope, 133:1014–1024, 2023

Background: Rhinologists often encounter a broad spectrum of allergic rhinitis (AR) and nonallergic rhinitis (NAR) patients, who can be variably classified based upon timing and severity of disease. Our understanding of the varied quality of life (QOL) impact in different classifications of rhinitis is limited. Thus a more comprehensive understanding of the impact of rhinitis upon our patients, as measured by both patient reported outcome measures (PROMs) and clinical physiologic measures, as well as unique factors associated with disease severity is needed. Methods: A systematic search of databases was performed to identify AR and NAR studies reporting Rhinoconjunctivitis Quality of Life Questionnaire (RQLQ), total nasal symptom score (TNSS), or visual analogue scale (VAS) scores, and physiologic measures including peak nasal inspiratory flow (PNIF) and nasal airflow. Relationships between PROMs, physiologic measures, and associated factors (e.g., allergic status, disease duration) were assessed by weighted correlations and meta-regressions. Results: A total of 171 studies reporting on 33,843 patients were included.Symptoms were more severe in AR than NAR on VAS (p < 0.001). Classification based upon Allergic Rhinitis and its Impact on Asthma (ARIA) guidelines demonstrated differences in PROM severity. There was no significant correlation between PROMs and demographic factors, comorbidities, or physiologic measures. Meta-regression identified a correlation between worse RQLQ scores and shorter disease duration (r = −0.4, p < 0.001). Conclusion: Rhinitic patients have more severe impact upon QOL in the presence of allergy with variable impact upon specific symptom subdomains. PROMs do not correlate with common demographic factors, comorbidities, or physiologic measures of nasal airflow.

Background Rhinologic disease can be responsible for systemic symptoms affecting mood, cognition, and sleep. It is unclear whether sleep disturbance in specific rhinologic disorders (chronic rhinosinusitis [CRS], rhinitis, and nasal septal deviation [NSD]) is an obstructive phenomenon or due to other mechanisms. In this review we examine the impact of CRS, rhinitis, and NSD on objective and subjective sleep outcome metrics and draw comparisons to normal controls and patients with known obstructive sleep apnea (OSA). Methods A systematic review of 4 databases (PubMed, Scopus, Cochrane Library, and Web of Science) was performed. Studies reporting on objective (apnea‐hypopnea index [AHI], respiratory disturbance index [RDI], oxygen nadir) and subjective (Epworth Sleepiness Scale [EpSS], Pittsburgh Sleep Quality Index [PSQI], Fatigue Severity Scale [FSS]) sleep parameters and disease‐specific patient‐reported outcome measures (PROMs; 22‐item Sino‐Nasal Outcome Test [SNOT‐22], Rhinoconjunctivitis Quality of Life Questionnaire [RQLQ], Nasal Obstruction Symptom Evaluation [NOSE]) were included. Results The database search yielded 1414 unique articles, of which 103 were included for analysis. Baseline PROMs were at the high end of normal to abnormal for all 3 conditions: EpSS: CRS (9.8 ± 4.0), rhinitis (9.7 ± 4.3), and NSD (8.9 ± 4.6); and PSQI: CRS (11.0 ± 4.5), rhinitis (6.1 ± 3.7), and NSD (8.6 ± 3.5). Objective measures demonstrated a mild to moderate OSA in the studied diseases: AHI: CRS (10.4 ± 11.5), rhinitis (8.6 ± 8.8), and NSD (13.0 ± 6.9). There were significant differences when compared with reported norms in all measured outcomes (p < 0.001). Conclusion Sleep quality is impacted by rhinologic (CRS, rhinitis, NSD) disease. There is likely a mild obstructive component contributing to poor sleep, but other contributing factors may be involved.

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BACKGROUND: Viral respiratory infections are a leading cause of worldwide mortality and exert the potential to cause global socioeconomic crises. However, inexpensive, efficacious, and rapidly deployable strategies to reduce viral transmission are increasingly important in the setting of an ongoing pandemic, though not entirely understood. This article provides a comprehensive review of commonly employed nonpharmacological interventions to interrupt viral spread and provides evidence-based recommendations for their use. METHODOLOGY: A systematic review of three databases was performed. Studies with defined endpoints of subjects receiving one of five interventions (nasal washing, gargling, personal protective equipment (PPE), social distancing, and hand hygiene) were included. An evidence-based review of the highest level of evidence, with recommendations, was created in accordance with a previously described, rigorous, iterative process. RESULTS: Fifty-four primary studies were included. The most commonly studied intervention was hand hygiene, followed by PPE, gargling, saline nasal washing, and social distancing. CONCLUSIONS: Mask use and hand hygiene are strong recommendations for prevention of viral transmission. Donning gloves, gowns, and eye protection are a recommendation in healthcare settings. Saline nasal washing and gargling are options in selected populations. Although an aggregate level of evidence is not provided, the authors recommend social distancing.

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Objective To determine the impact of treatment for patients with nasal obstruction secondary to allergic rhinitis (AR) and nasal septal deviation (NSD) on sleep quality. Data Sources Primary studies were identified though PubMed, Scopus, Cochrane Library, and Web of Science. Review Methods A systematic review was performed by querying databases for articles published through August 2020. Studies were included that reported on objective sleep parameters (apnea-hypopnea index) and sinonasal and sleep-specific patient-reported outcome measures: Rhinoconjunctivitis Quality of Life Questionnaire, Nasal Obstruction Symptom Evaluation, Epworth Sleepiness Scale (EpSS), and Pittsburgh Sleep Quality Index (PSQI). Results The database search yielded 1414 unique articles, of which 28 AR and 7 NSD studies were utilized for meta-analysis. A total of 9037 patients (8515 with AR, 522 with NSD) were identified with a mean age of 35.0 years (35.3 for AR, 34.0 for NSD). Treatment for AR and NSD significantly improved subjective sleep quality. For AR, the EpSS mean difference was −1.5 (95% CI, –2.4 to –0.5; P = .002) and for the PSQI, –1.7 (95% CI, –2.1 to –1.2; P < .00001). For NSD, the EpSS mean difference was −3.2 (95% CI, –4.2 to –2.2; P < .00001) and for the PSQI, –3.4 (95% CI, –6.1 to –0.6; P = .02). Conclusion Subjective sleep quality significantly improved following treatment for AR and NSD. There were insufficient data to demonstrate that objective metrics of sleep quality similarly improved.

Background Chronic rhinosinusitis (CRS) presents with broad and systemic manifestations, including impaired sleep; however, the impact of CRS treatments upon sleep is unknown. Objective To establish the effect of medical or surgical CRS treatment on subjective and objective sleep metrics for patients not previously diagnosed with sleep apnea. Methods Review of PubMed, Scopus, Web of Science, and the Cochrane Library was performed from the databases’ date of inception through August 13, 2020, for studies evaluating the effect of CRS treatment on sleep quality. All studies reporting on subjective and objective sleep parameters for patients with CRS, with completed pre- and posttreatment data were included. Studies composed of patients with diagnosed sleep apnea were excluded. Results Sixteen unique studies reporting data on a total of 1770 patients (mean age, 50.6 ± 15.6 (n = 1675) years) following treatment for CRS were included. Patient-reported outcome measures, Epworth Sleepiness Scale, Pittsburgh Sleep Quality Index, and Fatigue Severity Scale, demonstrated mean posttreatment differences of −2.8 (95% CI: −4.9 to −0.7), −2.4 (95% CI: −3.7 to −1.2), and −1.2 (95% CI: −1.6 to −0.7), respectively. The SNOT-22 and its sleep domain demonstrated a mean posttreatment difference of −23.5 (95% CI: −31.7 to −15.3) and −5.4 (95% CI: −6.8 to −4.0), respectively. EpSS, FSS and SNOT-22 exceeded their respective reported MCID values. Objective findings did not significantly change with treatment; mean difference: AHI: 0.7 (95% CI: −1.5 to 2.9), oxygen nadir: 0.3 (95% CI: −0.4 to 0.9). Conclusions Treatment of CRS may lead to clinically meaningful reduction in disease burden and improvements in both overall sleep quality and patient-reported fatigue. Despite clinically meaningful quality of life improvements, objective sleep parameters did not demonstrate corresponding posttreatment improvements.