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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.
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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.
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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.
