%0 Journal Article
%A Gerkin, Richard C
%A Ohla, Kathrin
%A Veldhuizen, Maria G
%A Joseph, Paule V
%A Kelly, Christine E
%A Bakke, Alyssa J
%A Steele, Kimberley E
%A Farruggia, Michael C
%A Pellegrino, Robert
%A Pepino, Marta Y
%A Bouysset, Cédric
%A Soler, Graciela M
%A Pereda-Loth, Veronica
%A Dibattista, Michele
%A Cooper, Keiland W
%A Croijmans, Ilja
%A Di Pizio, Antonella
%A Ozdener, Mehmet Hakan
%A Fjaeldstad, Alexander W
%A Lin, Cailu
%A Sandell, Mari A
%A Singh, Preet B
%A Brindha, V Evelyn
%A Olsson, Shannon B
%A Saraiva, Luis R
%A Ahuja, Gaurav
%A Alwashahi, Mohammed K
%A Bhutani, Surabhi
%A D’Errico, Anna
%A Fornazieri, Marco A
%A Golebiowski, Jérôme
%A Dar Hwang, Liang
%A Öztürk, Lina
%A Roura, Eugeni
%A Spinelli, Sara
%A Whitcroft, Katherine L
%A Faraji, Farhoud
%A Fischmeister, Florian Ph S
%A Heinbockel, Thomas
%A Hsieh, Julien W
%A Huart, Caroline
%A Konstantinidis, Iordanis
%A Menini, Anna
%A Morini, Gabriella
%A Olofsson, Jonas K
%A Philpott, Carl M
%A Pierron, Denis
%A Shields, Vonnie D C
%A Voznessenskaya, Vera V
%A Albayay, Javier
%A Altundag, Aytug
%A Bensafi, Moustafa
%A Bock, María Adelaida
%A Calcinoni, Orietta
%A Fredborg, William
%A Laudamiel, Christophe
%A Lim, Juyun
%A Lundström, Johan N
%A Macchi, Alberto
%A Meyer, Pablo
%A Moein, Shima T
%A Santamaría, Enrique
%A Sengupta, Debarka
%A Rohlfs Dominguez, Paloma
%A Yanik, Hüseyin
%A Hummel, Thomas
%A Hayes, John E
%A Reed, Danielle R
%A Niv, Masha Y
%A Munger, Steven D
%A Parma, Valentina
%A Boesveldt, Sanne
%A de Groot, Jasper H B
%A Dinnella, Caterina
%A Freiherr, Jessica
%A Laktionova, Tatiana
%A Marino, Sajidxa
%A Monteleone, Erminio
%A Nunez-Parra, Alexia
%A Abdulrahman, Olagunju
%A Ritchie, Marina
%A Thomas-Danguin, Thierry
%A Walsh-Messinger, Julie
%A Al Abri, Rashid
%A Alizadeh, Rafieh
%A Bignon, Emmanuelle
%A Cantone, Elena
%A Paola Cecchini, Maria
%A Chen, Jingguo
%A Dolors Guàrdia, Maria
%A Hoover, Kara C
%A Karni, Noam
%A Navarro, Marta
%A Nolden, Alissa A
%A Portillo Mazal, Patricia
%A Rowan, Nicholas R
%A Sarabi-Jamab, Atiye
%A Archer, Nicholas S
%A Chen, Ben
%A Di Valerio, Elizabeth A
%A Feeney, Emma L
%A Frasnelli, Johannes
%A Hannum, Mackenzie E
%A Hopkins, Claire
%A Klein, Hadar
%A Mignot, Coralie
%A Mucignat, Carla
%A Ning, Yuping
%A Ozturk, Elif E
%A Peng, Mei
%A Saatci, Ozlem
%A Sell, Elizabeth A
%A Yan, Carol H
%A Alfaro, Raul
%A Cecchetto, Cinzia
%A Coureaud, Gérard
%A Herriman, Riley D
%A Justice, Jeb M
%A Kaushik, Pavan Kumar
%A Koyama, Sachiko
%A Overdevest, Jonathan B
%A Pirastu, Nicola
%A Ramirez, Vicente A
%A Roberts, S Craig
%A Smith, Barry C
%A Cao, Hongyuan
%A Wang, Hong
%A Balungwe Birindwa, Patrick
%A Baguma, Marius
%T Recent Smell Loss Is the Best Predictor of COVID-19 Among Individuals With Recent Respiratory Symptoms
%J Chemical senses
%V 46
%@ 1464-3553
%C Oxford
%I Oxford Univ. Press
%M FZJ-2021-03996
%P bjaa081
%D 2021
%X In a preregistered, cross-sectional study, we investigated whether olfactory loss is a reliable predictor of COVID-19 using 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 univariate and multivariate predictors of COVID-19 status and post-COVID-19 olfactory recovery. 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 univariate and multivariate models (ROC AUC = 0.72). Additional variables provide negligible model improvement. VAS ratings of smell loss were more predictive than binary chemosensory yes/no-questions or other cardinal symptoms (e.g., fever). Olfactory recovery within 40 days of respiratory symptom onset was reported for ~50% of participants and was best predicted by time since respiratory symptom onset. We find that quantified smell loss is the best predictor of COVID-19 amongst those with symptoms of respiratory illness. To aid clinicians and contact tracers in identifying individuals with a high likelihood of having COVID-19, we propose a novel 0–10 scale to screen for recent olfactory loss, the ODoR-19. We find that numeric ratings ≤2 indicate high odds of symptomatic COVID-19 (4 < OR < 10). Once independently validated, this tool could be deployed when viral lab tests are impractical or unavailable.
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:33367502
%U <Go to ISI:>//WOS:000645030100001
%R 10.1093/chemse/bjaa081
%U https://juser.fz-juelich.de/record/902048