Recent Smell Loss Is the Best Predictor of COVID-19 Among Individuals With Recent Respiratory Symptoms
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Abstract
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.
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SciScore for 10.1101/2020.07.22.20157263: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources Statistical analyses: Statistical analyses were performed in Python 3.7.6 using the pandas,17 scikit-learn,18 and statsmodels19 packages. Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:This self-selection bias could be viewed as a limitation since the C19-group also showed chemosensory loss. However, finding …
SciScore for 10.1101/2020.07.22.20157263: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources Statistical analyses: Statistical analyses were performed in Python 3.7.6 using the pandas,17 scikit-learn,18 and statsmodels19 packages. Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:This self-selection bias could be viewed as a limitation since the C19-group also showed chemosensory loss. However, finding difference between groups in a sample with a higher barrier for discriminating between C19+ and C19-supports the robustness of this tool when used in a typical clinical population; our collider bias analysis also suggests that our findings are likely conservative estimates (Figure S1, Table S1). Our results suggest that chemosensory impairment has strong COVID-19 predictive value and is useful when access to viral testing is limited or absent. As with any self-report measure, veracity of self-reports cannot be guaranteed. However, the ability to screen individuals in real-time should outweigh this potential confound.21 While objective smell tests are the gold standard for assessing olfactory function,22,23 they are costly, time-consuming to administer, and can require in-person interactions with potentially infectious patients.23,24 By contrast, the ODoR-19 is free, quick, and can be administered in person or remotely. We cannot exclude that our C19-sample contains COVID-19 false negatives.25 However, self-reported smell during illness distinguishes between C19+ and C19-, but not between randomly shuffled cases, suggesting that the difference between C19+ and C19-, even in a sample with over-represented chemosensory dysfunction, is substantial and can be captured via self-report. Approximately half of the participants in the C19+ group recovered their s...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- No conflict of interest statement was detected. If there are no conflicts, we encourage authors to explicit state so.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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SciScore for 10.1101/2020.07.22.20157263: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Watson Research Center, 60School of Biological Sciences, Institute for Research in Fundamental Sciences, 61Clinical Neuroproteomics Unit, Navarrabiomed-IdiSNA, 62Psychology and Anthropology, University of Extremadura, 63Department of Electrical and Electronics Engineering, Mersin University, 90Smell & Taste Clinic, Department of Otorhinolaryngology, TU Dresden, 108The Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, 109 Department of Pharmacology and Therapeutics, University of Florida, Department of Psychology, 1110Temple University … SciScore for 10.1101/2020.07.22.20157263: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Watson Research Center, 60School of Biological Sciences, Institute for Research in Fundamental Sciences, 61Clinical Neuroproteomics Unit, Navarrabiomed-IdiSNA, 62Psychology and Anthropology, University of Extremadura, 63Department of Electrical and Electronics Engineering, Mersin University, 90Smell & Taste Clinic, Department of Otorhinolaryngology, TU Dresden, 108The Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, 109 Department of Pharmacology and Therapeutics, University of Florida, Department of Psychology, 1110Temple University Non-byline authors (to be listed as collaborators in PubMed under the GCCR Group Author): Sanne Boesveldt,64 Jasper H.B. de Groot,65 Caterina Dinnella,35 Jessica Freiherr,66 Tatiana Laktionova,48 Sajidxa Mariño,67 Erminio Monteleone,35 Alexia Nunez-Parra,68 Olagunju Abdulrahman,69 Marina Ritchie,18 Thierry Thomas-Danguin,70 Julie Walsh-Messinger,71 Rashid Al Abri,29 Rafieh Alizadeh,72 Emmanuelle Bignon,13 Elena Cantone,73 Maria Paola Cecchini,74 Jingguo Chen,75 Maria Dolors Guàrdia,76 Kara C. PubMedsuggested: (PubMed, SCR_004846)Statistical analyses Statistical analyses were performed in Python 3.7.6 using the pandas,17 scikit-learn,18 and statsmodels19 packages. Pythonsuggested: (IPython, SCR_001658)Data from additional tools added to each annotation on a weekly basis.
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