Since China's initial anecdotal reports of coronavirus disease 2019 (COVID-19), there have been a growing number of studies describing smell and/or taste dysfunction (STD). Objective: The aim was to investigate the frequency and severity of STD in COVID-19 patients and to evaluate the association with demographic characteristics, hospital admission, symptoms, comorbidities, and blood biomarkers. A multicenter cross-sectional study on SARS-CoV-2 positive patients (n=846) and controls (n=143) from 15 Spanish Hospitals. Data of STD with an in-person survey was collected prospectively. STD severity was categorized by visual analogue scale. STD onset time, recovery rate, time to recovery, hospital admission, pneumonia diagnosis, comorbidities, smoking and symptoms were analyzed. STD were at least 2-fold more common in COVID-19-positive compared to controls. In COVID-19-positive, hospitalized patients were older, with lower frequency of STD and recovered earlier than out-patients. Stratified analysis by severity of STD showed that more than a half of COVID-19 subjects presented severe loss of smell (53.7%) or taste (52.2%), in >90% this impairment was of both senses. In the multivariate analysis, an older age (>60yo), being hospitalized and an increased level of C-reactive protein were factors associated with a better sense of smell and/or taste. COVID-19-positive patients reported improvement of smell (45.6%) and taste (46.1%) at the time of the survey, 90.6% in less than two weeks' post-infection. STD is a common symptom in COVID-19, and mainly present in young and non-hospitalized patients. More studies are needed to evaluate the follow-up of the chemosensory impairment.

Nasal obstruction (NO) is defined as the subjective perception of discomfort or difficulty in the passage of air through the nostrils. It is a common reason for consultation in primary and specialized care and may affect up to 30%-40% of the population. It affects quality of life (especially sleep) and lowers work efficiency. The aim of this document is to agree on how to treat NO, establish a methodology for evaluating and diagnosing it, and define an individualized approach to its treatment. NO can be unilateral or bilateral, intermittent or persistent and may be caused by local or systemic factors, which may be anatomical, inflammatory, neurological, hormonal, functional, environmental, or pharmacological in origin. Directed study of the medical history and physical examination are key for diagnosing the specific cause. NO may be evaluated using subjective assessment tools (visual analog scale, symptom score, standardized questionnaires) or by objective estimation (active anterior rhinomanometry, acoustic rhinometry, peak nasal inspiratory flow). Although there is little correlation between the results, they may be considered complementary and not exclusive. Assessing the impact on quality of life through questionnaires standardized according to the underlying disease is also advisable. NO is treated according to its cause. Treatment is fundamentally pharmacological (topical and/or systemic) when the etiology is inflammatory or functional. Surgery may be necessary when medical treatment fails to complement or improve medical treatment or when other therapeutic approaches are not possible. Combinations of surgical techniques and medical treatment may be necessary.

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.

Allergic rhinitis is a global healthcare problem due to its high prevalence, impact on individuals and socioeconomic burden for the nations. Allergic rhinitis severity evaluation is the key to a correct treatment, prevention of comorbidities and improving the quality of life of patients. This evaluation should be made with a simple, easy, fast but accurate and reliable methodology, both in a primary care and specialist setting. The visual analogue scale (VAS) meets all requirements to be the ideal tool to assess allergic rhinitis severity and has already been validated by using a single cut-off point, but this classification in two degrees of severity suffer from not allocating the patients uniformly and from giving a blind interval to classify the patients when the score is between 5 to 6 cm. The main objective of our study is to describe the optimal cut-off points by using a VAS to discriminate between three degrees of allergic rhinitis severity (mild, moderate, and severe) following the ARIA modified severity criteria that has been previously validated. Sensitivity, specificity, positive and negative predictive values just like receiver operating characteristic curves were used to select the best cut-off values. In a cross-sectional multicentre study with 3,572 patients included we have found that VAS has a significant correlation with nasal symptom score and quality of life and that the best cut-off points to differentiate between mild, moderate an severe allergic rhinitis are a VAS score of 4 and 7, respectively. Allergic rhinitis severity could be assessed in three degrees by using VAS in a simple, easy, and accurate method.