The objective of this study was to perform translation, cross-cultural adaptation, and validation of the SNOT-22 in the Lithuanian language. This is a prospective case-control study. The study was conducted at the University clinic. The sino-nasal outcome test 22 (SNOT-22) was translated into the Lithuanian language; the pilot study involved 34 patients, the test-retest group consisted of 34 patients with chronic rhinosinusitis (CRS), and the control group of 115 patients with no CRS complaints; 36 patients were evaluated before surgery and 3 months after surgery. The results showed a good internal correlation with Cronbach's alpha-0.89 in the initial test, and 0.93 in the retest; both values suggesting good internal consistency within the SNOT-22. Pearson's correlation coefficient was 0.72 (p < 0.001), revealing good correlation between the initial scores and the retests scores. Our sample of healthy individuals had a median score of 12 points, and the instrument was capable of differentiating between the healthy and the patient group, demonstrating its validity (p < 0.0001). The statistically significant reduction in the post-operative scores, vis-à-vis pre-operative values, demonstrates the responsiveness of the instrument. The minimally important difference was 13 points in the SNOT-22 score. The Lithuanian version of the SNOT-22 is a valid instrument for assessing patients with CRS. It demonstrated good internal consistency, reproducibility, validity, and responsiveness.
The objective of this study is to evaluate the reliability of acoustic voice parameters obtained using smart phone (SP) microphones and investigate the utility of use of SP voice recordings for voice screening. Voice samples of sustained vowel/a/obtained from 118 subjects (34 normal and 84 pathological voices) were recorded simultaneously through two microphones: oral AKG Perception 220 microphone and SP Samsung Galaxy Note3 microphone. Acoustic voice signal data were measured for fundamental frequency, jitter and shimmer, normalized noise energy (NNE), signal to noise ratio and harmonic to noise ratio using Dr. Speech software. Discriminant analysis-based Correct Classification Rate (CCR) and Random Forest Classifier (RFC) based Equal Error Rate (EER) were used to evaluate the feasibility of acoustic voice parameters classifying normal and pathological voice classes. Lithuanian version of Glottal Function Index (LT_GFI) questionnaire was utilized for self-assessment of the severity of voice disorder. The correlations of acoustic voice parameters obtained with two types of microphones were statistically significant and strong (r = 0.73-1.0) for the entire measurements. When classifying into normal/pathological voice classes, the Oral-NNE revealed the CCR of 73.7% and the pair of SP-NNE and SP-shimmer parameters revealed CCR of 79.5%. However, fusion of the results obtained from SP voice recordings and GFI data provided the CCR of 84.60% and RFC revealed the EER of 7.9%, respectively. In conclusion, measurements of acoustic voice parameters using SP microphone were shown to be reliable in clinical settings demonstrating high CCR and low EER when distinguishing normal and pathological voice classes, and validated the suitability of the SP microphone signal for the task of automatic voice analysis and screening.
Background Odontogenic maxillary sinusitis (OMS) and rhinogenic sinusitis (RS) are the main types of chronic rhinosinusitis (CRS) and have a significant impact on health-related quality of life (HRQL), but the difference in HRQL and symptom presentation between them has not been specifically evaluated to date. Obejctive: Our aim was to compare patterns of symptoms and HRQL disease-specific domains in patients affected with these 2 types of CRS. Methods A group of 201 patients with CRS (99 with rhinogenic and 102 with odontogenic origin) completed the Sino-Nasal Outcome Test 22 (SNOT-22) questionnaire before treatment. Data sets were analyzed by using principal component analysis (PCA) to identify a set of symptom components together with the items excluded from PCA, which were then analyzed for differences between patients with OMS and RS. Results PCA of SNOT-22 items identified 5 components: “rhinologic,” “extranasal rhinologic,” “ear/facial,” “sleep and functional disturbance,” and “emotional disturbance.” Sneezing was excluded from PCA and treated as separate outcome variable and was significantly worse in RS patients. Patients with OMS scored significantly higher scores with regard to emotional disturbance, while RS patients scored significantly worse in sleep and functional disturbance. The extra symptom “malodor” was the most different symptom and was significantly worse in OMS patients. The total SNOT-22 score was not significantly different between the groups. Conclusion With controlling of covariates that may influence the severity of the disease, this study showed some significant differences in symptom patterns and HRQL impairment between patients with OMS and RS. Malodor is the most characteristic feature of OMS. Therefore, OMS should always be suspected in patients complaining of bad breath.
The objective of the study was to compare the ability of dental, ENT and radiology specialists to identify the dental cause of maxillary sinusitis with conventional computed tomography, dental and panoramic radiographs. Out of 34 dental records from subjects treated at ENT and Oral and Maxillofacial Surgery Department, LUHS Kaunas Clinics, 22 females and 12 males with the diagnosis of odontogenic maxillary sinusitis, periapical (DPA), panoramic (DPR) and computed tomography (CT) images of posterior maxilla were selected for further studies. In total, 39 sinuses with an odontogenic and 37 sinuses with only rhinogenic cause (control group) were included in the study. Sinuses with mucosal thickening less than 3 mm were excluded from the research. Each image was evaluated by 5 endodontologists, 5 oral surgeons, 6 general dentists, 6 otorhinolaryngologists and an experienced oral radiologist. DPR and DPA views were not evaluated by ENT specialists. The dental cause of maxillary sinusitis was marked according to the given scale. Intraclass correlation coefficient and ROC curve statistical analysis were performed. The best accuracy was observed when CT views were evaluated by experienced oral radiologist and oral surgeons: the AUC was 0.958 and 0.859, respectively. DPR views showed the best accuracy when evaluated by oral surgeons (0.763) and DPA-by endodontologists (0.736). The highest inter-rater agreement was observed between experienced oral radiologist and oral surgeons/otorhinolaryngologists (0.87/0.78) evaluating CT. Sensitivity and specificity of CT were 89.7 and 94.6%, DPR-68.2 and 77.3%, DPA-77.9 and 67%. Identification of dental cause of maxillary sinusitis sometimes is a challenge, which depends on radiological method and, more importantly, on evaluator's experience.
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