Giving a Voice to Patients With Smell Disorders Associated With COVID-19: Cross-Sectional Longitudinal Analysis Using Natural Language Processing of Self-Reports (Preprint)

Author:

Menger Nick SORCID,Tognetti ArnaudORCID,Farruggia Michael CORCID,Mucignat CarlaORCID,Bhutani SurabhiORCID,Cooper Keiland WORCID,Rohlfs Domínguez PalomaORCID,Heinbockel ThomasORCID,Shields Vonnie D CORCID,D'Errico AnnaORCID,Pereda-Loth VeronicaORCID,Pierron DenisORCID,Koyama SachikoORCID,Croijmans IljaORCID

Abstract

BACKGROUND

Smell 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.

OBJECTIVE

This 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.

METHODS

We 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.

RESULTS

We found that parosmia (odds ratio [OR] 1.78, 95% CI 1.35-2.37; <i>P</i>&lt;.001) as well as hyposmia (OR 1.74, 95% CI 1.34-2.26; <i>P</i>&lt;.001) were more frequently reported in long-haulers than in non–long-haulers. Furthermore, a significant relationship was found between long-hauler status and sentiment of self-report (<i>P</i>&lt;.001). Finally, we found specific keywords that were more typical for long-haulers than those for non–long-haulers, for example, fire, gas, wine, and vinegar.

CONCLUSIONS

Our work shows consistent findings with those of previous studies, which indicate that self-reports, which can easily be extracted online, may offer valuable information to health care and understanding of smell disorders. At the same time, our study on self-reports provides new insights for future studies investigating smell disorders.

Publisher

JMIR Publications Inc.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3