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.

COVID-19 Pandemic and its Impact on the Management of Head and Neck Cancer in the Spanish Healthcare System.

Introduction Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has represented a major challenge for healthcare systems worldwide, changing the habits of physicians. A reorganization of healthcare activity has been necessary, limiting surgical activity to essential cases (emergencies and oncology), and improving the distribution of health resources. Objective To analyze the impact of the COVID-19 pandemic on head and neck cancer surgery management in Spain. Methods A cross-sectional study, through an anonymous and voluntary online survey distributed to 76 Spanish otorhinolaryngology departments. Results A total of 44 centers completed the survey, 65.9% of which were high-volume. A total of 45.5% of them had to stop high-priority surgery and 54.5% of head and neck surgeons were relocated outside their scope of practice. Surgeons reported not feeling safe during their usual practice, with a decrease to a 25% of airway procedures. A total of 29.5% were "forced" to deviate from the "standard of care" due to the epidemiological situation. Conclusions Approximately half of the departments decreased their activity, not treating their patients on a regular basis, and surgeons were reassigned to other tasks. It seems necessary that the head and neck surgeons balance infection risk with patient care. The consequences of the reported delays and changes in daily practice should be evaluated in the future in order to understand the real impact of the pandemic on the survival of head and neck cancer patients.

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.