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.

Cirugía virtual para pacientes con obstrucción nasal: empleo de un software basado en dinámica de fluidos (MeComLand(R), Digbody(R) & Noseland(R)) para documentar parámetros objetivos de flujo y optimizar resultados quirúrgicos / Virtual surgery for patients with nasal obstruction: Use of computational fluid dynamics (MeComLand(R), Digbody(R) & Noseland(R)) to document objective flow parameters and optimise surgical results

INTRODUCCIÓN: La dinámica de fluidos computacional (CFD) es una herramienta matemática que permite analizar el flujo aéreo. Presentamos un software innovador basado en CFD para mejorar los resultados de la cirugía nasal. MÉTODOS: Mediante colaboración de ingenieros especialistas en mecánica de fluidos y otorrinolaringólogos se ha desarrollado un software de fácil uso denominado MeComLand(R), que utilizando los cortes de tomografía computarizada de un paciente permite obtener gran cantidad de información como flujo, presiones, temperatura, velocidad o fricción sobre la pared de las fosas nasales. El programa DigBody(R) permite modificar en 3 D la anatomía del modelo y realizar cirugías virtuales para simular resultados antes de la cirugía real. Por último, NoseLand(R) permite viajar virtualmente por el interior de la fosa nasal, mostrando todo tipo de magnitudes termo-fluido mecánicas. OBJETIVO: Presentar un programa innovador para mejorar los resultados de la cirugía nasal. Emplear este software sobre cortes tomográficos de un paciente con desviación septal para planificar distintas opciones quirúrgicas (septoplastia, turbinectomía, spreader-grafts, colgajo en J y combinaciones) a fin de conseguir la mejor alternativa con la menor morbilidad. RESULTADOS: La combinación de todos los procedimientos considerados no produce los mejores resultados en cuanto a flujo nasal. Estos se consiguen asociando septoplastia y turbinectomía. La turbinectomía aislada obtuvo resultados muy similares a la septoplastia. CONCLUSIONES: La técnica computacional CFD proporciona una información complementaria valiosa en el diagnóstico del paciente con obstrucción nasal y sobre los resultados de distintas alternativas quirúrgicas respecto al flujo nasal, contribuyendo a un mejor manejo del enfermo. El software MeComLand(R) con sus respectivos módulos DigBody(R) y NoseLand(R) suponen una alternativa barata y no invasiva al estudio funcional del paciente con obstrucción nasal

INTRODUCTION: Computational fluid dynamics (CFD) is a mathematical tool to analyse airflow. We present a novel CFD software package to improve results following nasal surgery for obstruction. METHODS: A group of engineers in collaboration with otolaryngologists have developed a very intuitive CFD software package called MeComLand(R), which uses the patient's cross-sectional (tomographic) images, thus showing in detail results originated by CFD such as airflow distributions, velocity profiles, pressure, or wall shear stress. NOSELAND(R) helps medical evaluation with dynamic reports by using a 3D endoscopic view. Using this CFD-based software a patient underwent virtual surgery (septoplasty, turbinoplasty, spreader grafts, lateral crural J-flap and combinations) to choose the best improvement in nasal flow. OBJECTIVE: To present a novel software package to improve nasal surgery results. To apply the software on CT slices from a patient affected by septal deviation. To evaluate several surgical procedures (septoplasty, turbinectomy, spreader-grafts, J-flap and combination among them) to find the best alternative with less morbidity. RESULTS: The combination of all the procedures does not provide the best nasal flow improvement. Septoplasty plus turbinoplasty obtained the best results. Turbinoplasty alone rendered almost similar results to septoplasty in our simulation. CONCLUSIONS: CFD provides useful complementary information to cover diagnosis, prognosis, and follow-up of nasal pathologies based on quantitative magnitudes linked to fluid flow. MeComLand(R), DigBody(R) and NoseLand(R) represent a non-invasive, low-cost alternative for the functional study of patients with nasal obstruction

Virtual surgery for patients with nasal obstruction: Use of computational fluid dynamics (MeComLand®, Digbody® & Noseland®) to document objective flow parameters and optimise surgical results. / Cirugía virtual para pacientes con obstrucción nasal: empleo de un software basado en dinámica de fluidos (MeComLand®, Digbody® & Noseland®) para documentar parámetros objetivos de flujo y optimizar resultados quirúrgicos.

INTRODUCTION: Computational fluid dynamics (CFD) is a mathematical tool to analyse airflow. We present a novel CFD software package to improve results following nasal surgery for obstruction. METHODS: A group of engineers in collaboration with otolaryngologists have developed a very intuitive CFD software package called MeComLand®, which uses the patient's cross-sectional (tomographic) images, thus showing in detail results originated by CFD such as airflow distributions, velocity profiles, pressure, or wall shear stress. NOSELAND® helps medical evaluation with dynamic reports by using a 3D endoscopic view. Using this CFD-based software a patient underwent virtual surgery (septoplasty, turbinoplasty, spreader grafts, lateral crural J-flap and combinations) to choose the best improvement in nasal flow. OBJECTIVE: To present a novel software package to improve nasal surgery results. To apply the software on CT slices from a patient affected by septal deviation. To evaluate several surgical procedures (septoplasty, turbinectomy, spreader-grafts, J-flap and combination among them) to find the best alternative with less morbidity. RESULTS: The combination of all the procedures does not provide the best nasal flow improvement. Septoplasty plus turbinoplasty obtained the best results. Turbinoplasty alone rendered almost similar results to septoplasty in our simulation. CONCLUSIONS: CFD provides useful complementary information to cover diagnosis, prognosis, and follow-up of nasal pathologies based on quantitative magnitudes linked to fluid flow. MeComLand®, DigBody® and NoseLand® represent a non-invasive, low-cost alternative for the functional study of patients with nasal obstruction.