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| Fältnamn | Indikatorer | Metadata |
|---|---|---|
| 000 | 06911naa a2201789 4500 | |
| 001 | oai:prod.swepub.kib.ki.se:232743605 | |
| 003 | SwePub | |
| 008 | 240701s2020 | |||||||||||000 ||eng| | |
| 024 | 7 | a http://kipublications.ki.se/Default.aspx?queryparsed=id:2327436052 URI |
| 024 | 7 | a https://doi.org/10.1101/2020.07.22.201572632 DOI |
| 040 | a (SwePub)ki | |
| 041 | a engb eng | |
| 042 | 9 SwePub | |
| 072 | 7 | a vet2 swepub-contenttype |
| 072 | 7 | a art2 swepub-publicationtype |
| 100 | 1 | a Gerkin, RC4 aut |
| 245 | 1 0 | a The best COVID-19 predictor is recent smell loss: a cross-sectional study |
| 264 | 1 | b Cold Spring Harbor Laboratory,c 2020 |
| 520 | a BackgroundCOVID-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.MethodsThis 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.ResultsBoth 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.ConclusionsAs 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. | |
| 700 | 1 | a Ohla, K4 aut |
| 700 | 1 | a Veldhuizen, MG4 aut |
| 700 | 1 | a Joseph, PV4 aut |
| 700 | 1 | a Kelly, CE4 aut |
| 700 | 1 | a Bakke, AJ4 aut |
| 700 | 1 | a Steele, KE4 aut |
| 700 | 1 | a Farruggia, MC4 aut |
| 700 | 1 | a Pellegrino, R4 aut |
| 700 | 1 | a Pepino, MY4 aut |
| 700 | 1 | a Bouysset, C4 aut |
| 700 | 1 | a Soler, GM4 aut |
| 700 | 1 | a Pereda-Loth, V4 aut |
| 700 | 1 | a Dibattista, M4 aut |
| 700 | 1 | a Cooper, KW4 aut |
| 700 | 1 | a Croijmans, I4 aut |
| 700 | 1 | a Di Pizio, A4 aut |
| 700 | 1 | a Ozdener, MH4 aut |
| 700 | 1 | a Fjaeldstad, AW4 aut |
| 700 | 1 | a Lin, C4 aut |
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| 700 | 1 | a Öztürk, L4 aut |
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| 700 | 1 | a Hsieh, JW4 aut |
| 700 | 1 | a Huart, C4 aut |
| 700 | 1 | a Konstantinidis, I4 aut |
| 700 | 1 | a Menini, A4 aut |
| 700 | 1 | a Morini, G4 aut |
| 700 | 1 | a Olofsson, JK4 aut |
| 700 | 1 | a Philpott, CM4 aut |
| 700 | 1 | a Pierron, D4 aut |
| 700 | 1 | a Shields, VDC4 aut |
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| 700 | 1 | a Fredborg, W4 aut |
| 700 | 1 | a Laudamiel, C4 aut |
| 700 | 1 | a Lim, J4 aut |
| 700 | 1 | a Lundström, JNu Karolinska Institutet4 aut |
| 700 | 1 | a Macchi, A4 aut |
| 700 | 1 | a Meyer, P4 aut |
| 700 | 1 | a Moein, ST4 aut |
| 700 | 1 | a Santamaría, E4 aut |
| 700 | 1 | a Sengupta, D4 aut |
| 700 | 1 | a Domínguez, PP4 aut |
| 700 | 1 | a Yanık, H4 aut |
| 700 | 1 | a Boesveldt, S4 aut |
| 700 | 1 | a de Groot, JHB4 aut |
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| 700 | 1 | a Freiherr, J4 aut |
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| 700 | 1 | a Monteleone, E4 aut |
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| 700 | 1 | a Abdulrahman, O4 aut |
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| 700 | 1 | a Mazal, PP4 aut |
| 700 | 1 | a Rowan, NR4 aut |
| 700 | 1 | a Sarabi-Jamab, A4 aut |
| 700 | 1 | a Archer, NS4 aut |
| 700 | 1 | a Chen, B4 aut |
| 700 | 1 | a Di Valerio, EA4 aut |
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| 700 | 1 | a Frasnelli, J4 aut |
| 700 | 1 | a Hannum, M4 aut |
| 700 | 1 | a Hopkins, C4 aut |
| 700 | 1 | a Klein, H4 aut |
| 700 | 1 | a Mignot, C4 aut |
| 700 | 1 | a Mucignat, C4 aut |
| 700 | 1 | a Ning, Y4 aut |
| 700 | 1 | a Ozturk, EE4 aut |
| 700 | 1 | a Peng, M4 aut |
| 700 | 1 | a Saatci, O4 aut |
| 700 | 1 | a Sell, EA4 aut |
| 700 | 1 | a Yan, CH4 aut |
| 700 | 1 | a Alfaro, R4 aut |
| 700 | 1 | a Cecchetto, C4 aut |
| 700 | 1 | a Coureaud, G4 aut |
| 700 | 1 | a Herriman, RD4 aut |
| 700 | 1 | a Justice, JM4 aut |
| 700 | 1 | a Kaushik, PK4 aut |
| 700 | 1 | a Koyama, S4 aut |
| 700 | 1 | a Overdevest, JB4 aut |
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| 700 | 1 | a Ramirez, VA4 aut |
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| 700 | 1 | a Smith, BC4 aut |
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| 700 | 1 | a Baguma, M4 aut |
| 700 | 1 | a Hummel, T4 aut |
| 700 | 1 | a Hayes, JE4 aut |
| 700 | 1 | a Reed, DR4 aut |
| 700 | 1 | a Niv, MY4 aut |
| 700 | 1 | a Munger, SD4 aut |
| 700 | 1 | a Parma, V4 aut |
| 710 | 2 | a Karolinska Institutet4 org |
| 773 | 0 | t medRxiv : the preprint server for health sciencesd : Cold Spring Harbor Laboratory |
| 856 | 4 | u https://www.medrxiv.org/content/medrxiv/early/2020/07/28/2020.07.22.20157263.full.pdf |
| 856 | 4 8 | u http://kipublications.ki.se/Default.aspx?queryparsed=id:232743605 |
| 856 | 4 8 | u https://doi.org/10.1101/2020.07.22.20157263 |
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