Details der Publikation - The best COVID-19 predictor is recent smell loss: a cross-sectional study

The best COVID-19 predictor is recent smell loss: a cross-sectional study

Abstract Background COVID-19 has heterogeneous manifestations, though one of the most common symptoms is a sudden loss of smell (anosmia or hyposmia). We investigated whether olfactory loss is a reliable predictor of COVID-19.Methods This preregistered, cross-sectional study used a crowdsourced questionnaire in 23 languages to assess symptoms in individuals self-reporting recent respiratory illness. We quantified changes in chemosensory abilities during the course of the respiratory illness using 0-100 visual analog scales (VAS) for participants reporting a positive (C19+; n=4148) or negative (C19-; n=546) COVID-19 laboratory test outcome. Logistic regression models identified singular and cumulative predictors of COVID-19 status and post-COVID-19 olfactory recovery.Results Both C19+ and C19-groups exhibited smell loss, but it was significantly larger in C19+ participants (mean±SD, C19+: -82.5±27.2 points; C19-: -59.8±37.7). Smell loss during illness was the best predictor of COVID-19 in both single and cumulative feature models (ROC AUC=0.72), with additional features providing negligible model improvement. VAS ratings of smell loss were more predictive than binary chemosensory yes/no-questions or other cardinal symptoms, such as fever or cough. Olfactory recovery within 40 days was reported for ∼50% of participants and was best predicted by time since illness onset.Conclusions As smell loss is the best predictor of COVID-19, we developed the ODoR-19 tool, a 0-10 scale to screen for recent olfactory loss. Numeric ratings ≤2 indicate high odds of symptomatic COVID-19 (4<OR<10), which can be deployed when viral lab tests are impractical or unavailable..

Medienart:	Preprint

Erscheinungsjahr:	2024
Erschienen:	2024

Enthalten in:	bioRxiv.org - (2024) vom: 23. Apr. Zur Gesamtaufnahme - year:2024

Sprache:	Englisch

Beteiligte Personen:	Gerkin, Richard C. [VerfasserIn] Ohla, Kathrin [VerfasserIn] Veldhuizen, Maria G. [VerfasserIn] Joseph, Paule V. [VerfasserIn] Kelly, Christine E. [VerfasserIn] Bakke, Alyssa J. [VerfasserIn] Steele, Kimberley E. [VerfasserIn] Farruggia, Michael C. [VerfasserIn] Pellegrino, Robert [VerfasserIn] Pepino, Marta Y. [VerfasserIn] Bouysset, Cédric [VerfasserIn] Soler, Graciela M. [VerfasserIn] Pereda-Loth, Veronica [VerfasserIn] Dibattista, Michele [VerfasserIn] Cooper, Keiland W. [VerfasserIn] Croijmans, Ilja [VerfasserIn] Di Pizio, Antonella [VerfasserIn] Ozdener, M. Hakan [VerfasserIn] Fjaeldstad, Alexander W. [VerfasserIn] Lin, Cailu [VerfasserIn] Sandell, Mari A. [VerfasserIn] Singh, Preet B. [VerfasserIn] Brindha, V. Evelyn [VerfasserIn] Olsson, Shannon B. [VerfasserIn] Saraiva, Luis R. [VerfasserIn] Ahuja, Gaurav [VerfasserIn] Alwashahi, Mohammed K. [VerfasserIn] Bhutani, Surabhi [VerfasserIn] D’Errico, Anna [VerfasserIn] Fornazieri, Marco A. [VerfasserIn] Golebiowski, Jérôme [VerfasserIn] Hwang, Liang-Dar [VerfasserIn] Öztürk, Lina [VerfasserIn] Roura, Eugeni [VerfasserIn] Spinelli, Sara [VerfasserIn] Whitcroft, Katherine L. [VerfasserIn] Faraji, Farhoud [VerfasserIn] Fischmeister, Florian Ph.S [VerfasserIn] Heinbockel, Thomas [VerfasserIn] Hsieh, Julien W. [VerfasserIn] Huart, Caroline [VerfasserIn] Konstantinidis, Iordanis [VerfasserIn] Menini, Anna [VerfasserIn] Morini, Gabriella [VerfasserIn] Olofsson, Jonas K. [VerfasserIn] Philpott, Carl M. [VerfasserIn] Pierron, Denis [VerfasserIn] Shields, Vonnie D.C. [VerfasserIn] Voznessenskaya, Vera V. [VerfasserIn] Albayay, Javier [VerfasserIn] Altundag, Aytug [VerfasserIn] Bensafi, Moustafa [VerfasserIn] Bock, María Adelaida [VerfasserIn] Calcinoni, Orietta [VerfasserIn] Fredborg, William [VerfasserIn] Laudamiel, Christophe [VerfasserIn] Lim, Juyun [VerfasserIn] Lundström, Johan N. [VerfasserIn] Macchi, Alberto [VerfasserIn] Meyer, Pablo [VerfasserIn] Moein, Shima T. [VerfasserIn] Santamaría, Enrique [VerfasserIn] Sengupta, Debarka [VerfasserIn] Dominguez, Paloma Rohlfs [VerfasserIn] Yanik, Hüseyin [VerfasserIn] Hummel, Thomas [VerfasserIn] Hayes, John E. [VerfasserIn] Reed, Danielle R. [VerfasserIn] Niv, Masha Y. [VerfasserIn] Munger, Steven D. [VerfasserIn] Parma, Valentina [VerfasserIn]

Links:	Volltext [lizenzpflichtig] Volltext [kostenfrei]

Themen:	570 Biology

doi:	10.1101/2020.07.22.20157263

funding:
Förderinstitution / Projekttitel:

PPN (Katalog-ID):	XBI018418570

Internformat


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520			\|a Abstract Background COVID-19 has heterogeneous manifestations, though one of the most common symptoms is a sudden loss of smell (anosmia or hyposmia). We investigated whether olfactory loss is a reliable predictor of COVID-19.Methods This preregistered, cross-sectional study used a crowdsourced questionnaire in 23 languages to assess symptoms in individuals self-reporting recent respiratory illness. We quantified changes in chemosensory abilities during the course of the respiratory illness using 0-100 visual analog scales (VAS) for participants reporting a positive (C19+; n=4148) or negative (C19-; n=546) COVID-19 laboratory test outcome. Logistic regression models identified singular and cumulative predictors of COVID-19 status and post-COVID-19 olfactory recovery.Results Both C19+ and C19-groups exhibited smell loss, but it was significantly larger in C19+ participants (mean±SD, C19+: -82.5±27.2 points; C19-: -59.8±37.7). Smell loss during illness was the best predictor of COVID-19 in both single and cumulative feature models (ROC AUC=0.72), with additional features providing negligible model improvement. VAS ratings of smell loss were more predictive than binary chemosensory yes/no-questions or other cardinal symptoms, such as fever or cough. Olfactory recovery within 40 days was reported for ∼50% of participants and was best predicted by time since illness onset.Conclusions As smell loss is the best predictor of COVID-19, we developed the ODoR-19 tool, a 0-10 scale to screen for recent olfactory loss. Numeric ratings ≤2 indicate high odds of symptomatic COVID-19 (4<OR<10), which can be deployed when viral lab tests are impractical or unavailable.
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The best COVID-19 predictor is recent smell loss: a cross-sectional study

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