Improved quality of life in patients with chronic rhinosinusitis with nasal polyps associated with expanded types of endoscopic sinus surgery: A 2-year retrospective study.

KEY POINTS: Expanded types of functional endoscopic sinus surgery (FESS) significantly improve quality of life and reduce revision surgeries rates, supporting their early application for moderate-to-severe cases. Minimal clinically important difference may play as a crucial role in defining surgical treatment response (i.e., responder and super-responder conditions). Expanded FESS benefits patients with chronic rhinosinusitis with nasal polyps but more data are required to have a clearer understanding of its uses due to varied approaches and reported outcomes in the literature.

Endoscopic-assisted en-bloc pterygomaxillectomy: Identifying an efficient and safe location for the pterygoid osteotomy.

BACKGROUND: For aggressive maxillary sinus and pterygopalatine fossa (PPF) tumors, an en-bloc pterygomaxillectomy may be indicated. METHODS: Five head specimens were used to study the feasibility of an en-bloc pterygomaxillectomy. Eighty-five non-pathological CT scans were used to compare the superior edge of the inferior turbinate (IT) and the middle turbinate tail (MT) as landmarks for the pterygoid osteotomy. RESULTS: Through a combined sublabial-subperiosteal incision and transoral route, a mid-sagittal osteotomy through the hard palate and an axial osteotomy below the infraorbital foramen were performed. For the endoscopic pterygoid osteotomy, an infra-vidian transpterygoid approach was performed, subsequently removing the pterygomaxillectomy en-bloc. As landmarks, the osteotomies at the level of the MT tail and IT resected the pterygoid plates completely, but the IT osteotomy was further away from the vidian canal (7.5 vs. 6 mm). CONCLUSIONS: The endoscopic-assisted en-bloc pterygomaxillectomy is feasible. The IT landmark is safe and ensures complete resection of the pterygoid plates.

Analysis of Prevalence and Predictive Factors of Long-Lasting Olfactory and Gustatory Dysfunction in COVID-19 Patients.

BACKGROUND: Although smell and taste disorders are highly prevalent symptoms of COVID-19 infection, the predictive factors leading to long-lasting chemosensory dysfunction are still poorly understood. METHODS: 102 out of 421 (24.2%) mildly symptomatic COVID-19 patients completed a second questionnaire about the evolution of their symptoms one year after the infection using visual analog scales (VAS). A subgroup of 69 patients also underwent psychophysical evaluation of olfactory function through UPSIT. RESULTS: The prevalence of chemosensory dysfunction decreased from 82.4% to 45.1% after 12 months, with 46.1% of patients reporting a complete recovery. Patients older than 40 years (OR = 0.20; 95% CI: [0.07, 0.56]) and with a duration of loss of smell longer than four weeks saw a lower odds ratio for recovery (OR = 0.27; 95% CI: [0.10, 0.76]). In addition, 28 patients (35.9%) reported suffering from parosmia, which was associated with moderate to severe taste dysfunction at the baseline (OR = 7.80; 95% CI: [1.70, 35.8]). Among the 69 subjects who underwent the UPSIT, 57 (82.6%) presented some degree of smell dysfunction, showing a moderate correlation with self-reported VAS (r = -0.36, p = 0.0027). CONCLUSION: A clinically relevant number of subjects reported persistent chemosensory dysfunction and parosmia one year after COVID-19 infection, with a moderate correlation with psychophysical olfactory tests.

Loss of Smell and Taste Can Accurately Predict COVID-19 Infection: A Machine-Learning Approach.

The COVID-19 outbreak has spread extensively around the world. Loss of smell and taste have emerged as main predictors for COVID-19. The objective of our study is to develop a comprehensive machine learning (ML) modelling framework to assess the predictive value of smell and taste disorders, along with other symptoms, in COVID-19 infection. A multicenter case-control study was performed, in which suspected cases for COVID-19, who were tested by real-time reverse-transcription polymerase chain reaction (RT-PCR), informed about the presence and severity of their symptoms using visual analog scales (VAS). ML algorithms were applied to the collected data to predict a COVID-19 diagnosis using a 50-fold cross-validation scheme by randomly splitting the patients in training (75%) and testing datasets (25%). A total of 777 patients were included. Loss of smell and taste were found to be the symptoms with higher odds ratios of 6.21 and 2.42 for COVID-19 positivity. The ML algorithms applied reached an average accuracy of 80%, a sensitivity of 82%, and a specificity of 78% when using VAS to predict a COVID-19 diagnosis. This study concludes that smell and taste disorders are accurate predictors, with ML algorithms constituting helpful tools for COVID-19 diagnostic prediction.