key: cord-0720747-urung0nv authors: Gerkin, R. C.; Ohla, K.; Veldhuizen, M. G.; Joseph, P. V.; Kelly, C. E.; Bakke, A. J.; Steele, K. E.; Farruggia, M. C.; Pellegrino, R.; Pepino, M. Y.; Bouysset, C.; Soler, G. M.; Pereda-Loth, V.; Dibattista, M.; Cooper, K. W.; Croijmans, I.; Di Pizio, A.; Ozdener, M. H.; Fjaeldstad, A. W.; Lin, C.; Sandell, M. A.; Singh, P. B.; Brindha, V. E.; Olsson, S. B.; Saraiva, L. R.; Ahuja, G.; Alwashahi, M. K.; Bhutani, S.; D'Errico, A.; Fornazieri, M. A.; Golebiowski, J.; Hwang, L.-D.; Öztürk, L.; Roura, E.; Spinelli, S.; Whitcroft, K. L.; Faraji, F.; Fischmeister, F. P. S.; Heinbockel, T.; Hsieh, J title: Recent smell loss is the best predictor of COVID-19: a preregistered, cross-sectional study date: 2020-07-26 journal: medRxiv : the preprint server for health sciences DOI: 10.1101/2020.07.22.20157263 sha: 151ffe8e2bef3cd4fc3c6e314afa4df35d2c2ba1 doc_id: 720747 cord_uid: urung0nv 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 no significant 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 (10 The novel coronavirus SARS-CoV-2 responsible for the global COVID-19 pandemic has left a staggering level of morbidity, mortality, and societal and economic disruption in its wake. 1 Early publications [2] [3] [4] [5] [6] [7] [8] indicate that sudden smell and taste loss are cardinal, early and potentially specific symptoms of COVID-19, 9 including in otherwise asymptomatic individuals. [10] [11] [12] [13] While fever and cough are common symptoms of diverse viral infections, the potential specificity of early chemosensory loss to COVID-19 could make it valuable in screening and diagnosis. Anosmia and other chemosensory disorders have serious health and quality-of-life consequences for patients. However, the general lack of awareness of anosmia and other chemosensory disorders by clinicians and the public, including their association with upper respiratory infections, 14 contributed to an underappreciated role of chemosensory symptoms in the diagnosis of COVID-19. Additionally, the impact of smell loss as a clinical consequence of COVID-19 has not been adequately addressed. Thus, there is an urgent need to better define the chemosensory dysfunctions associated with COVID-19 and to determine their relevance as predictors of this disease. It is critical to develop rapid clinical tools to efficiently and effectively integrate chemosensory assessments into COVID-19 screening and treatment protocols. Information on the duration and reversibility of post-COVID-19 chemosensory impairment is also lacking. We used binary, categorical and continuous self-report measures to determine the chemosensory phenotype, along with other symptoms and characteristics, of COVID-19-positive (C19+) and COVID-19-negative (C19-) individuals who had reported recent symptoms of respiratory illness. Using those results in logistic regression models, we identified predictors of COVID-19 and recovery from smell loss. Finally, we propose the Olfactory Determination Rating scale for COVID-19 (ODoR-19), a quick, simple-to-use, telemedicine-friendly tool to improve the utility of current COVID-19 screening protocols, particularly when access to rapid testing for SARS-CoV-2 is limited. This preregistered, 15 cross-sectional online study was approved by the Office of Research Protections of The Pennsylvania State University (STUDY00014904); it is in accordance with the revised Declaration of Helsinki, and compliant with privacy laws in the U.S.A. and European Union. Data reported here were collected between April 7 and July 3, 2020 from the Global Consortium for Chemosensory Research (GCCR) core questionnaire (Appendix 1 and https://gcchemosensr.org), 8 an online crowdsourced survey deployed in 32 languages that used binary response and categorical questions (e.g. Appendix 1, Questions 6, 9) and visual analog scales (e.g., Appendix 1, Question 13) to measure self-reported chemosensory ability and other symptoms in adults with current or recent respiratory illness. Data reported here include responses in Arabic, Bengali, Chinese (Simplified and Traditional), Danish, Dutch, English, Farsi, Finnish, French, German, Greek, Hebrew, Hindi, Italian, Japanese, Korean, Norwegian, Portuguese, Russian, Spanish, Swedish, Turkish, and Urdu. . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 26, 2020. . https://doi.org/10.1101/2020.07. 22.20157263 doi: medRxiv preprint Participants (272) A convenience sample of 52,334 volunteers accessed the GCCR questionnaire; 25,620 met e criteria (≥19 years old, respiratory illness or suspicion thereof within the past two weeks). After applyi registered exclusion criteria, 15,747 participants were included in reported analyses (Figure 1 ). Participants included in the prediction of COVID-19 status are highlighted in blue. Participants included in the smel recovery models are highlighted in green. Participants included in the replication of our prior work are highlighted in orange. N = number of participants; yo = age in years; W = women; M = men. Gender percentages do not include participants who answered "other" or "preferred not to say". Participants described in the green boxes are a subse those described in the boxes listed above them. Based on the self-reported outcome of a COVID-19 lab test, participants were labeled as eithe (positive result) or C19-(negative result). The specific collider bias characterizing this sample (high fra C19+ participants and high prevalence of chemosensory disorders in both groups) underestimates the p correlation between smell loss and COVID-19 ( Figure S1) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted July 26, 2020. . sample underestimates the positive association between smell loss and C19+ ( Figure S1 , Table S1 ). The country-wise fraction of C19+ participants is correlated (r~0.45) when responses from the same calendar week are aligned ( Figure S2 ). These findings are in line with other comparisons between crowdsourced versus representative health data, 16 confirming that trends identified in crowdsourced data reasonably approximate population data. Because the GCCR cohort is not demographically balanced, it should not be used to estimate prevalence. However, the representative YouGov cohort indicates globally ~33% of C19+ individuals report smell loss (Table S1 ). Statistical analyses were performed in Python 3.7.6 using the pandas, 17 scikit-learn, 18 and statsmodels 19 packages. The data and annotated code is included as supplemental material and will be publicly available on GitHub (http://github.com/GCCR/GCCR002) upon publication. Missing values for covariates used in prediction models of COVID-19 status and smell recovery were imputed as follows: binary features = 0.5, numeric features = median, categorical variables = "Missing". Prediction targets themselves were never imputed. Responses incompatible with model generalization (e.g., open ended questions) were excluded. A one-hot encoding was applied to all categorical variables to produce binary indicators of category membership. L1regularized logistic regression (penalty α =1) consistently produced sparse models with comparable crossvalidation accuracy and were therefore the prediction test of choice. Model quality was measured using receiver operating characteristic (ROC) area under the curve (AUC). Cross-validation was performed in 100 random splits of 80% training set and 20% test set, and ROC curves are concatenated over each test set. ROC curves are computed on predicted probabilities from each model, circumventing the high-cardinality bias of AUC. For single feature models, AUC is independent of most modeling details, including all rank-invariant decisions. To correctly compute p-values for model coefficients, the normalized data were standardized (mean 0, variance 1) and then coefficients back-transformed to normalized form after fitting. A preregistered replication of our prior study 8 confirmed that reported smell, taste, and chemesthesis abilities drop significantly in both lab-tested C19+ participants and those diagnosed by clinical assessment (Figure S3 , Table S2 ). Next, we compared lab-tested C19+ and C19-participants. C19+ participants reported a greater loss of smell (C19+: -82.5±27.2 points; C19-: -59.8±37.7 points; p=2.2e-46, extreme evidence of difference: BF 10 =8.97e+61; Figure 2A Table S3 ). Self-reported changes in smell, taste, and chemesthesis were highly correlated within both groups (C19+: 0.710.1 is considered a meaningful association. Features in positively associated with C19+ (odds ratio > 1); features in blue are negatively associated with C19+ (odds ratio < Logistic regression is used to predict COVID-19 status from individual features. Top-10 single features are ra performance (cross-validated area under the ROC curve, AUC). Chemosensory-related features (bold) show predictive accuracy than non-chemosensory features (non-bold). Responses provided on the numeric scale (ital more informative than binary responses (non-italic). Red arrows indicate differences in prediction quality (i between features. (C) Adding features to "Smell During Illness" results in little improvement to the model; only Day Onset of Respiratory Symptoms (DOS) yields meaningful improvement. (D) ROC curves for several models. A using "Smell during illness" (Smell Only, abbreviated "Smell" in figure) is compared against models containing this along with DOS, as well as models including the three cardinal CDC features (fever, dry cough, difficulty breathing indicates a regularized model fit using 70 dozen survey features, which achieves prediction accuracy simila parsimonious model "Smell Only+DOS". ramer's V) ponse and in red are io < 1). (B) ranked by w greater talic) were (in AUC) ays Since . A model his feature ing). "Full" ilar to the . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 26, 2020. . Recovery from smell loss was modest (approximately half the initial average loss) in C19+ participants with full or partial resolution of respiratory symptoms. Overall, self-reported, post-illness olfactory ability was still lower for C19+ (39.9±34.7) than C19-(52.2±35.2, p=2.8e-11, Figure S6A ). However, the mean recovery of smell (after illness relative to during illness) was greater for C19+ (30.5±35.7) than C19-(24.6±31.9, p=0.0002, Figure S6B ). A similar but smaller effect of COVID-19 status on recovery was observed for taste ( Figure S6C , D), while little to no association with COVID-19 was observed for recovery of chemesthesis ( Figure S6E ,F) or nasal obstruction (Figure S6G,H) . When illness-induced change in olfactory function (during minus before illness) and recovery of olfactory function (after minus during illness) were evaluated, we identified three respondent clusters: those self-reporting no loss of smell (Intact Smell), those reporting recovery from smell loss (Recovered Smell), and those reporting smell loss without recovery by up to 40 days (Persistent Smell Loss, Figure 4 , Table S3 ). Intact smell was reported by only 8.5% of the participants in the C19+ group but by 27.5% in the C19-group (p=3.8e-31). A greater proportion of C19+ participants were included in both the Recovered Smell group (C19+: 40.9%, C19-: 33.3%; p=4.9e-10) and the Persistent Smell Loss group (C19+: 50.7%, C19-: 39.2%; p=5e-5; Figure 4A , B). C19+ participants in both the Recovered Smell and Persistent Smell Loss clusters reported a similar extent of olfactory loss, irrespective of time since respiratory symptom onset. By contrast, the rate of self-reported smell recovery increased over time, with a plateau at 30 days ( Figure 4C ). Finally, DOS was the best predictor (AUC=0.62) between the Persistent Smell Loss and the Recovered Smell groups ( Figure S6A , Table S3 ). Our results indicate that a continuous rating of current olfactory function is the single best predictor of COVID-19 and improves the discrimination between C19+ and C19-over a binary question on smell loss. For example, the Smell Only model can reach a specificity of 0.83 at the low end of the VAS (sensitivity=0.36, cutoff=0). We propose here a numeric variation of the rating scale (0-10), the ODoR-19, that can be administered in person or via telemedicine to improve early COVID-19 screening for individuals without preexisting smell and/or taste disorders. Responses to the ODoR-19 scale ≤ 2 indicate high odds of COVID-19 positivity (4 3 as moderate evidence, BF > 10 as strong evidence, BF > 30 as very strong evidence and BF > 100 as extreme evidence for H 0 or H 1 . Change means the rating "before" illness minus the rating "during" illness on the 0-100 visual-analog scale. Δ indicates the mean difference in change between lab-test and clinically-assessed COVID-19-positive subjects, while σ indicates the standard deviation. D indicates effect size (Cohen's D). p indicates p-value from a Mann-Whitney U-test. In contrast to the prediction of the pre-registration, we found statistically significant differences between groups. However, the effect sizes are small and thus unlikely to be of practical importance. We asked how accurately COVID-19 status could be predicted from the survey responses. The data matrix had strictly non-negative values and was normalized (column-wise min=0, max=1) to apply regularization in an equitable fashion across features and give regression coefficients the same interpretation for each feature. Compared with the main text, models with similar AUC values (but with non-zero coefficients for additional, likely spurious features) were obtained for smaller values of α , and inferior results for larger ones (which contained fewer or no non-zero coefficients). Quantitatively similar AUC values were obtained for other models predicting COVID-19 status using multiple features including ridge regression and random forest, but L1regularized logistic regression consistently produced sparser models with comparable cross-validation accuracy. Each logistic regression model included an intercept term and one or more normalized features. Each model attempted to predict, using the value of the response to a single question (and an additive constant), whether a subject reported a C19+ or C19-status. Coefficients in a logistic regression model can be interpreted as changes in odds, or as odds ratios when two values are compared. Each ROC curve -constructed using predictions on holdout test sets and concatenated over these test sets --summarizes the tradeoff between sensitivity (fraction of C19+ cases correctly identified) and specificity (fraction of C19-cases correctly identified) as the threshold value for the predictor is varied. Value of using a scale rather than a binary response to detect C19+ is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 26, 2020. . on either a continuous 100-point scale or a downsampled 10-point numeric version of the scale i informative about symptoms themselves and about COVID-19 status (given the symptoms) than s binary responses (Figure S5 ). Figure S5 . (A) Relative information available from the distribution of responses to the two primary "Smell" survey questions. Binary refers to the yes/no question about symptomatic smell loss. A relative information of 1 would cor to a question whose response is perfectly informative about COVID-19 status. By contrast, a similar question aske numeric scale (0-100, the original scale; or a hypothetical 10-point scale obtained by rounding responses) contains substantially more information due to the resolution of the scale. A 10-point scale may be familiar from clinical self of pain. (B) The relative mutual information about COVID status contained in the survey response is also higher fo numeric scale or the hypothetical 10-point scale than for the binary question. We applied the same predictive modeling framework used in Figure 4 to try to predict smell recovery i participants. In other words, we asked which survey responses predicted that a subject would fall i Recovered Smell rather than the Persistent Smell Loss cluster, given both smell loss during the disea C19+ status. The only predictive feature of any practical significance was "Days Since Onset" of resp symptoms (AUC=0.62), indicating that those who experienced their first respiratory symptoms less rece more likely to have Recovered Smell (Figure S6A) . Adding additional features to the model provided improvement (AUC=0.65 for the optimal model), but overall it was difficult to predict whether a C19+ par would exhibit Recovered Smell or Persistent Smell Loss based on the data available ( Figure S6B) . Ta includes the means and SD by recovery group for C19+ and C19-participants. is more soliciting orrespond ked on a ins lf-reports for the full y in C19+ ll into the ease and espiratory cently are d modest articipant Table S3 . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted July 26, 2020. . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted July 26, 2020. . https://doi.org/10.1101/2020.07.22.20157263 doi: medRxiv preprint . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted July 26, 2020. . https://doi.org/10.1101/2020.07.22.20157263 doi: medRxiv preprint APPENDIX 1 GCCR core questionnaire The core questionnaire of the Global Consortium for Chemosensory Research (GCCR) has been deployed in Compusense Cloud in 32 languages. The questionnaire was published previously 8 and also appears in the NIH Office of Behavioral and Social Sciences Research (OBSSR) research tools for Responses to the GCCR core questionnaire in 23 languages were collected between April 7 and July 2, 2020 and included in the final dataset, on which we conducted the analyses reported in this paper. . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted July 26, 2020. Smell and taste alterations in Covid-19: a cross-sectional analysis of different cohorts Self-reported Olfactory and Taste Disorders in Patients With Severe Acute Respiratory Coronavirus 2 Infection: A Cross-sectional Study Association of chemosensory dysfunction and COVID-19 in patients presenting with influenza-like symptoms Olfactory and gustatory dysfunctions as a clinical presentation of mild-to-moderate forms of the coronavirus disease (COVID-19): a multicenter European study Smell dysfunction: a biomarker for COVID-19 Anosmia in COVID-19 patients More than smell -COVID-19 is associated with severe impairment of smell, taste, and chemesthesis Real-time tracking of self-reported symptoms to predict potential COVID-19 Quantifying additional COVID-19 symptoms will save lives Clinical characteristics of asymptomatic and symptomatic patients with mild COVID-19 Standardized Testing Demonstrates Altered Odor Detection Sensitivity and Hedonics in Asymptomatic College Students as SARS-CoV-2 Emerged Locally Presentation of new onset anosmia during the COVID-19 pandemic A primer on viral-associated olfactory loss in the era of COVID-19 Is olfactory loss a sensitive symptomatic predictor of COVID-19? A preregistered, crowdsourced study Crowdsourced data collection for public health: A comparison with nationally representative, population tobacco use data pandas-dev/pandas: Pandas 1.0.5. Zenodo; 2020 Scikit-learn: Machine Learning in Python Econometric and statistical modeling with python Self-reported olfactory loss associates with outpatient clinical course in COVID-19 The Science of Real-Time Data Capture: Self-Reports in Health Research Sniffin' Sticks': Olfactory Performance Assessed by the Combined Testing of Odor Identification, Odor Discrimination and Olfactory Threshold University of pennsylvania smell identification test: A rapid quantitative olfactory function test for the clinic Updated Sniffin' Sticks normative data based on an extended sample of 9139 subjects Variation in False-Negative Rate of Reverse Transcriptase Polymerase Chain Reaction-Based SARS-CoV-2 Tests by Time Since Exposure Patterns of smell recovery in 751 patients affected by the COVID-19 outbreak Prevalence and Risk Factors of Self-Reported Smell and Taste Alterations: Results from the 2011-2012 US National Health and Nutrition Examination Survey (NHANES) Sense of smell disorder and health-related quality of life Olfactory Disorders and Quality of Life--An Updated Review Nutrition and taste and smell dysfunction Associations of olfactory dysfunction with anthropometric and cardiometabolic measures: Findings from the 2013-2014 national health and nutrition examination survey (NHANES) Olfaction as a marker for depression in humans Smell and taste disorders in primary care Smell and taste disorders The authors wish to thank all study participants, patients, and patient advocates that have contributed to this project, including members of the AbScent Facebook group. The authors wish to thank Micaela Hayes, MD for her input on the clinical relevance of this project, Shannon Alshouse and Olivia Christman for their help in implementing the survey, Sara Lipson for her support, and the international online survey research firm YouGov for providing data gathered with the Imperial College London YouGov Covid 19 Behaviour Tracker. Richard C. Gerkin is an advisor for Climax Foods, Equity Compensation (RCG); John E. Hayes has consulted for for-profit food/consumer product corporations in the last 3 years on projects wholly unrelated to this study; also, he is Director of the Sensory Evaluation Center at Penn State, which routinely conducts product tests for industrial clients to facilitate experiential learning for students. Since 2018 Thomas Hummel collaborates with and received funding from Sony, Stuttgart, Germany; Smell and Taste Lab, Geneva, Switzerland; Takasago, Paris, France: aspuraclip, Berlin, Germany. Christine E. Kelly is the founder of AbScent, a charity registered in England and Wales, No. 1183468. Christophe Laudamiel has received fundings from scent related institutions and corporations, however for work totally unrelated to the field of the present study.