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Recent smell loss is the best predictor of COVID-19: a preregistered, cross-sectional study
Preprint
in En
| PREPRINT-MEDRXIV
| ID: ppmedrxiv-20157263
ABSTRACT
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
cc_by_nc
Full text:
1
Collection:
09-preprints
Database:
PREPRINT-MEDRXIV
Type of study:
Experimental_studies
/
Observational_studies
/
Prognostic_studies
/
Rct
Language:
En
Year:
2020
Document type:
Preprint
Full text:
1
Collection:
09-preprints
Database:
PREPRINT-MEDRXIV
Type of study:
Experimental_studies
/
Observational_studies
/
Prognostic_studies
/
Rct
Language:
En
Year:
2020
Document type:
Preprint