201. Giving a Voice to Patients With Smell Disorders Associated With COVID-19: Cross-Sectional Longitudinal Analysis Using Natural Language Processing of Self-Reports
- Author
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Nick S Menger, Arnaud Tognetti, Michael C Farruggia, Carla Mucignat, Surabhi Bhutani, Keiland W Cooper, Paloma Rohlfs Domínguez, Thomas Heinbockel, Vonnie D C Shields, Anna D'Errico, Veronica Pereda-Loth, Denis Pierron, Sachiko Koyama, and Ilja Croijmans
- Subjects
Public aspects of medicine ,RA1-1270 - Abstract
BackgroundSmell disorders are commonly reported with COVID-19 infection. The smell-related issues associated with COVID-19 may be prolonged, even after the respiratory symptoms are resolved. These smell dysfunctions can range from anosmia (complete loss of smell) or hyposmia (reduced sense of smell) to parosmia (smells perceived differently) or phantosmia (smells perceived without an odor source being present). Similar to the difficulty that people experience when talking about their smell experiences, patients find it difficult to express or label the symptoms they experience, thereby complicating diagnosis. The complexity of these symptoms can be an additional burden for patients and health care providers and thus needs further investigation. ObjectiveThis study aims to explore the smell disorder concerns of patients and to provide an overview for each specific smell disorder by using the longitudinal survey conducted in 2020 by the Global Consortium for Chemosensory Research, an international research group that has been created ad hoc for studying chemosensory dysfunctions. We aimed to extend the existing knowledge on smell disorders related to COVID-19 by analyzing a large data set of self-reported descriptive comments by using methods from natural language processing. MethodsWe included self-reported data on the description of changes in smell provided by 1560 participants at 2 timepoints (second survey completed between 23 and 291 days). Text data from participants who still had smell disorders at the second timepoint (long-haulers) were compared with the text data of those who did not (non–long-haulers). Specifically, 3 aims were pursued in this study. The first aim was to classify smell disorders based on the participants’ self-reports. The second aim was to classify the sentiment of each self-report by using a machine learning approach, and the third aim was to find particular food and nonfood keywords that were more salient among long-haulers than those among non–long-haulers. ResultsWe found that parosmia (odds ratio [OR] 1.78, 95% CI 1.35-2.37; P
- Published
- 2024
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