NA. Laryngoscope, 128:363-368, 2018.

Objective Cleft lip and/or cleft palate (CLP) is the most common major congenital malformation of the head and neck. Previous studies suggested an association between fetal opioid exposure and CLP. This study seeks to evaluate the associations between CLP and neonatal abstinence syndrome (NAS) in the United States. Study Design Population-based inpatient registry analysis. Setting Academic medical center. Subjects and Methods Kids’ Inpatient Database (2016) was used to identify weighted in-hospital births with diagnoses of CLP or NAS. Demographic information was obtained. Results Among 3.8 million weighted in-hospital births, prevalence rates of CLP in the NAS and non-NAS populations were 3.13 and 1.35 per 1000, respectively. The odds ratios for patients with NAS developing CLP, isolated cleft palate, isolated cleft lip, and cleft lip and palate when compared with the reference population were 2.33 (95% CI, 1.87-2.91; P < .001), 4.97 (95% CI, 3.84-6.43; P < .001), 1.01 ( P = .98), and 0.80 ( P = .46). Independent predictors of CLP within the NAS population included median household income for patients’ zip code, race, hospital region, payment method, and maternal use of tobacco or other drugs of addiction. The binary logistic regression model accounting for possible confounding variables produced an odds ratio of 1.74 (95% CI, 1.36-2.23; P < .001) for the association between NAS and CLP. Conclusion Our study found an association between NAS and CLP, specifically isolated cleft palate, suggesting that prenatal exposure to opioids may be an environmental risk factor in the development of CLP.

Objective Otolaryngology is considered high risk for Coronavirus Disease 2019 (COVID-19) exposure and spread. This has led to a transition to telemedicine and directly impacts patient volume, evaluation and management practices. The objective of this study is to determine the impact of COVID-19 on patient characteristics in relation to outpatient attendance, ancillary testing, medical therapy, and surgical decision making. Methods A retrospective case series at an academic medical center was performed. Outpatient appointments from October 2019 (pre-COVID) and March 16–April 10, 2020 (COVID) were analyzed. Prevalence rates and odds ratios were used to compare demographics, visit characteristics, ancillary tests, medication prescribing, and surgical decisions between telemedicine and in-person visits, before and during COVID. Results There was a decrease in scheduled visits during the COVID timeframe, for both in-person and telemedicine visits, with a comparable proportion of no-shows. There was a higher overall percentage of Hispanic/Latino patients who received care during the COVID timeframe (OR = 1.43; 95% CI = 1.07–1.90) in both groups, although primary language was not significantly associated with attendance. There were fewer ancillary tests ordered (OR = 0.54) and more medications prescribed (OR = 1.59) during COVID telemedicine visits compared with pre-COVID in-person visits. Conclusion COVID-19 has rapidly changed the use of telemedicine. Telemedicine can be used as a tool to reach patients with severe disease burden. Continued healthcare reform, expanded access to affordable care, and efficient use of resources is essential both during the current COVID-19 pandemic and beyond. Level of evidence IV.

Objectives/Hypothesis: Interaction with voice recognition systems, such as Siri™ and Alexa™, is an increasingly important part of everyday life. Patients with voice disorders may have difficulty with this technology, leading to frustration and reduction in quality of life. This study evaluates the ability of common voice recognition systems to transcribe dysphonic voices.Study Design: Retrospective evaluation of "Rainbow Passage" voice samples from patients with and without voice disorders.Methods: Participants with (n = 30) and without (n = 23) voice disorders were recorded reading the "Rainbow Passage". Recordings were played at standardized intensity and distance-to-dictation programs on Apple iPhone 6S™, Apple iPhone 11 Pro™, and Google Voice™. Word recognition scores were calculated as the proportion of correctly transcribed words. Word recognition scores were compared to auditory-perceptual and acoustic measures.Results: Mean word recognition scores for participants with and without voice disorders were, respectively, 68.6% and 91.9% for Apple iPhone 6S™ (P < .001), 71.2% and 93.7% for Apple iPhone 11 Pro™ (P < .001), and 68.7% and 93.8% for Google Voice™ (P < .001). There were strong, approximately linear associations between CAPE-V ratings of overall severity of dysphonia and word recognition score, with correlation coefficients (R 2 ) of 0.609 (iPhone 6S™), 0.670 (iPhone 11 Pro™), and 0.619 (Google Voice™). These relationships persisted when controlling for diagnosis, age, gender, fundamental frequency, and speech rate (P < .001 for all systems).Conclusion: Common voice recognition systems function well with nondysphonic voices but are poor at accurately transcribing dysphonic voices. There was a strong negative correlation with word recognition scores and perceptual voice evaluation. As our society increasingly interfaces with automated voice recognition technology, the needs of patients with voice disorders should be considered.