Case Series of Antrovestibular Polyp: An Unusual Growth of Antral Polyp Toward the Nasal Vestibule Through the Anterior Fontanelle

Background and Objectives@#This case series is aimed to introduce a new term, antrovestibular polyp (AVP), for an antral polyp herniating anteriorly toward the nasal vestibule and to describe an antral polyp direction of growth through the anterior and posterior fontanelles.Materials and Method: This is a retrospective study involving review of patients who underwent surgery due to maxillary sinus polyp herniating anteriorly toward the nasal vestibular area or posteriorly toward the choana at a tertiary training hospital from January 2007 through July 2016. Their demographic data, computed tomography scan findings, and endoscopic evaluations were analyzed. @*Results@#This study included 49 subjects; 8 (16.33%, 6 males) with AVP and 41 (83.67%, 24 males) with antrochoanal polyps (ACP). The mean ages of AVP and ACP patients were 9 and 14.4 years, respectively (p=0.006). The subjects were identified as AVP when computed tomography scan showed an antral polyp directed anteriorly toward the nasal vestibular area, while polyps growing toward the choana were identified as ACP. Endoscopic review showed that AVP grew out through an accessory ostium located anterior to the uncinate process at the area of the anterior fontanelle, while ACP started from an accessory ostium of the posterior fontanelle or a widened maxillary natural ostium.

Prediction of Obstructive Sleep Apnea Based on Respiratory Sounds Recorded Between Sleep Onset and Sleep Offset

OBJECTIVES: To develop a simple algorithm for prescreening of obstructive sleep apnea (OSA) on the basis of respiratorysounds recorded during polysomnography during all sleep stages between sleep onset and offset. METHODS: Patients who underwent attended, in-laboratory, full-night polysomnography were included. For all patients, audiorecordings were performed with an air-conduction microphone during polysomnography. Analyses included allsleep stages (i.e., N1, N2, N3, rapid eye movement, and waking). After noise reduction preprocessing, data were segmentedinto 5-s windows and sound features were extracted. Prediction models were established and validated with10-fold cross-validation by using simple logistic regression. Binary classifications were separately conducted for threedifferent threshold criteria at apnea hypopnea index (AHI) of 5, 15, or 30. Prediction model characteristics, includingaccuracy, sensitivity, specificity, positive predictive value (precision), negative predictive value, and area under thecurve (AUC) of the receiver operating characteristic were computed. RESULTS: A total of 116 subjects were included; their mean age, body mass index, and AHI were 50.4 years, 25.5 kg/m2, and23.0/hr, respectively. A total of 508 sound features were extracted from respiratory sounds recorded throughoutsleep. Accuracies of binary classifiers at AHIs of 5, 15, and 30 were 82.7%, 84.4%, and 85.3%, respectively. Predictionperformances for the classifiers at AHIs of 5, 15, and 30 were AUC, 0.83, 0.901, and 0.91; sensitivity, 87.5%,81.6%, and 60%; and specificity, 67.8%, 87.5%, and 94.1%. Respective precision values of the classifiers were89.5%, 87.5%, and 78.2% for AHIs of 5, 15, and 30. CONCLUSION: This study showed that our binary classifier predicted patients with AHI of ≥15 with sensitivity and specificityof >80% by using respiratory sounds during sleep. Since our prediction model included all sleep stage data, algorithmsbased on respiratory sounds may have a high value for prescreening OSA with mobile devices.

Analysis of Sleep Questionnaires of Commercial Vehicle Operators in Korea / 대한이비인후과학회지

BACKGROUND AND OBJECTIVES: Obstructive sleep apnea (OSA) is highly prevalent in commercial vehicle operators (CMVOs). This study aimed to evaluate the poor sleep quality, daytime sleepiness, and the prevalence of self-reported OSA in CMVOs. SUBJECTS AND METHOD: We performed a retrospective review of the medical records of patients who visited a single institution with sleep problems from 2011 January to 2016 December. Among the patients, a total of 38 CMVOs was analyzed. Clinical information, questionnaires about sleep quality (Pittsburg sleep questionnaire, PSQI), excessive daytime sleepiness (Epworth sleepiness scale, ESS) and risk factors for OSA (STOP-Bang) were analyzed. The frequency of motor vehicle accidents and near accidents was assessed, and polysomnography (PSG) was used for OSA diagnosis purposes. RESULTS: The mean age of the study population was 45.3±11.8 years. The average score of PSQI, ESS, and STOP-Bang were 6.75±4.22, 10.79±7.12, and 4.62±3.34, respectively. A significant association between near accidents and high-risk group of OSA was observed [odds ratio (OR)=2.73, 95% confidence interval (CI)=1.08–4.48]. Subjects with poor sleep quality showed significantly increased risk of near accidents (OR=2.34, 95% CI=1.01–3.56). Receiver operating characteristic curves of STOP-Bang questionnaire using apnea-hypopnea index (cut-off value=5) indicates that suspected OSA group predicted by STOP-Bang score was significantly correlated with OSA severity (area under curve=0.72, sensitivity 77.1%, specificity 59.4%). CONCLUSION: Administration of STOP-Bang questionnaire before a PSG can identify high-risk subjects, supporting its further use in OSA screening of CMVOs.

Revisiting the Clinical Scoring System for the Prognosis of Chronic Rhinosinusitis with Nasal Polyps

PURPOSE: To evaluate the Japanese Epidemiological Survey of Refractory Eosinophilic Chronic Rhinosinusitis (JESREC) classification, a clinical scoring system, for predicting disease control status in chronic rhinosinusitis with nasal polyps (CRSwNP) and to investigate prognostic factors. MATERIALS AND METHODS: In total, 134 CRSwNP patients who underwent functional endoscopic sinus surgery after maximal medical treatment were enrolled. These patients were categorized into four groups according to JESREC classification: 1) non-eosinophilic CRSwNP (non-ECRSwNP), 2) mild eosinophilic CRSwNP (ECRSwNP), 3) moderate ECRSwNP, and 4) severe ECRSwNP. Disease control status among the patients was evaluated at 1 year after surgery, and the patients were divided into two groups (disease-controlled and disease-uncontrolled groups) for the investigation of prognostic factors. RESULTS: There was no significant difference in disease control status between non-ECRSwNP and ECRSwNP groups (p=0.970). Age, Lund-Mackay CT scores, global osteitis scores, tissue neutrophil count, and tissue eosinophil count were associated with disease control status. In subgroup analysis of the non-ECRSwNP group, only high tissue neutrophil count was related with disease control status, whereas for the ECRSwNP group, young age, high Lund-Mackay CT scores, high global osteitis scores, and high tissue and blood eosinophil counts were associated with disease control status. CONCLUSION: No difference in disease control status was identified between non-ECRSwNP and ECRSwNP cases. Tissue neutrophilia, however, appeared to be associated with disease control status in non-ECRSwNP cases, whereas tissue and blood eosinophilia was associated with ECRSwNP cases.

Analysis of Sleep Questionnaires of Commercial Vehicle Operators in Korea / 대한이비인후과학회지

BACKGROUND AND OBJECTIVES@#Obstructive sleep apnea (OSA) is highly prevalent in commercial vehicle operators (CMVOs). This study aimed to evaluate the poor sleep quality, daytime sleepiness, and the prevalence of self-reported OSA in CMVOs.SUBJECTS AND METHOD: We performed a retrospective review of the medical records of patients who visited a single institution with sleep problems from 2011 January to 2016 December. Among the patients, a total of 38 CMVOs was analyzed. Clinical information, questionnaires about sleep quality (Pittsburg sleep questionnaire, PSQI), excessive daytime sleepiness (Epworth sleepiness scale, ESS) and risk factors for OSA (STOP-Bang) were analyzed. The frequency of motor vehicle accidents and near accidents was assessed, and polysomnography (PSG) was used for OSA diagnosis purposes.@*RESULTS@#The mean age of the study population was 45.3±11.8 years. The average score of PSQI, ESS, and STOP-Bang were 6.75±4.22, 10.79±7.12, and 4.62±3.34, respectively. A significant association between near accidents and high-risk group of OSA was observed [odds ratio (OR)=2.73, 95% confidence interval (CI)=1.08–4.48]. Subjects with poor sleep quality showed significantly increased risk of near accidents (OR=2.34, 95% CI=1.01–3.56). Receiver operating characteristic curves of STOP-Bang questionnaire using apnea-hypopnea index (cut-off value=5) indicates that suspected OSA group predicted by STOP-Bang score was significantly correlated with OSA severity (area under curve=0.72, sensitivity 77.1%, specificity 59.4%).@*CONCLUSION@#Administration of STOP-Bang questionnaire before a PSG can identify high-risk subjects, supporting its further use in OSA screening of CMVOs.