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. 2021 Jan 1:46:bjaa081.
doi: 10.1093/chemse/bjaa081.

Recent Smell Loss Is the Best Predictor of COVID-19 Among Individuals With Recent Respiratory Symptoms

Richard C Gerkin  1 Kathrin Ohla  2 Maria G Veldhuizen  3 Paule V Joseph  4   5   6 Christine E Kelly  7 Alyssa J Bakke  8 Kimberley E Steele  6   9 Michael C Farruggia  10 Robert Pellegrino  11 Marta Y Pepino  12 Cédric Bouysset  13 Graciela M Soler  14   15 Veronica Pereda-Loth  16 Michele Dibattista  17 Keiland W Cooper  18 Ilja Croijmans  19 Antonella Di Pizio  20 Mehmet Hakan Ozdener  21 Alexander W Fjaeldstad  22 Cailu Lin  21 Mari A Sandell  23 Preet B Singh  24 V Evelyn Brindha  25 Shannon B Olsson  26 Luis R Saraiva  27 Gaurav Ahuja  28 Mohammed K Alwashahi  29 Surabhi Bhutani  30 Anna D'Errico  31 Marco A Fornazieri  32 Jérôme Golebiowski  13 Liang Dar Hwang  33 Lina Öztürk  3 Eugeni Roura  34 Sara Spinelli  35 Katherine L Whitcroft  36 Farhoud Faraji  37 Florian Ph S Fischmeister  38 Thomas Heinbockel  39 Julien W Hsieh  40 Caroline Huart  41 Iordanis Konstantinidis  42 Anna Menini  43 Gabriella Morini  44 Jonas K Olofsson  45 Carl M Philpott  46 Denis Pierron  16 Vonnie D C Shields  47 Vera V Voznessenskaya  48 Javier Albayay  49 Aytug Altundag  50 Moustafa Bensafi  51 María Adelaida Bock  52 Orietta Calcinoni  53 William Fredborg  45 Christophe Laudamiel  54 Juyun Lim  55 Johan N Lundström  56 Alberto Macchi  57   58 Pablo Meyer  59 Shima T Moein  60 Enrique Santamaría  61 Debarka Sengupta  28 Paloma Rohlfs Dominguez  62 Hüseyin Yanik  63 Thomas Hummel  64 John E Hayes  8 Danielle R Reed  21 Masha Y Niv  65 Steven D Munger  66   67 Valentina Parma  68 GCCR Group Author
Collaborators, Affiliations

Recent Smell Loss Is the Best Predictor of COVID-19 Among Individuals With Recent Respiratory Symptoms

Richard C Gerkin et al. Chem Senses. .

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.

Keywords: anosmia; chemosensory; coronavirus; hyposmia; olfactory; prediction.

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Figures

Figure 1.
Figure 1.
Flow diagram showing participant demographics. Participants included in the prediction of COVID-19 status are framed in blue. Participants included in the smell recovery models are framed in green. Participants included in the replication of a previous study (Parma, Ohla, et al. 2020; Parma, Veldhuizen, et al. 2020) are framed in orange. Gender percentages omit <1% of participants who answered “other” or “preferred not to say.” Participants described in the green boxes are a subset of those described in the blue boxes. n = number of participants; yo = age in years; W = women; M = men; unclear COVID diagnosis = responses “No—I do not have any symptoms,” “Don’t know” or “Other” to survey Question 8 (“Have you been diagnosed with COVID-19?”).
Figure 2.
Figure 2.
Chemosensory ability and nasal obstruction in C19+ and C19− participants. Self-reported smell (A, B), taste (C, D), chemesthesis (E, F), and nasal obstruction (G, H: formulated as “How blocked was your nose?”) before and during respiratory illness in C19+ (darker shades) and C19− (lighter shades) participants. Ratings were given on 0–100 visual analog scales. Left panels (A, C, E, G) show mean values. Right panels (B, D, F, H) show distributions of the change scores (during minus before). Thicker sections indicate relatively more subjects (higher density of responses). The thick black horizontal bar indicates the median, the shaded area within each violin indicates the interquartile range. Each dot represents the rating of a single participant. * indicates P < 10–4, ** indicates P < 10–23.
Figure 3.
Figure 3.
Smell loss is the strongest predictor of COVID-19 status. (A) A normalized measure of association (Cramer’s V) between binary or categorical responses on COVID-19 status. V = 0 reflects no association between the response and COVID-19 status; V = 1 reflects a perfect association; V > 0.1 is considered a meaningful association. Variables in red are positively associated with C19+ (odds ratio > 1); variables in blue are negatively associated with C19+ (odds ratio < 1). (B) Logistic regression is used to predict COVID-19 status from individual variables. Top-10 single variables are ranked by performance (cross-validated area under the ROC curve, AUC). Chemosensory-related variables (bold) show greater predictive accuracy than non-chemosensory variables (non-bold). Responses provided on the numeric scale (italic) were more informative than binary responses (non-italic). Red arrows indicate differences in prediction quality (in AUC) between variables. (C) Adding variables to “Smell During Illness” results in little improvement to the model; only Days Since Onset of Respiratory Symptoms (DOS) relative to survey completion date yields meaningful improvement. (D) ROC curves for several models. A model using “Smell during illness” (Smell Only, abbreviated “Smell” in the figure) is compared against models containing this feature along with DOS, as well as models including the three cardinal CDC variables (fever, dry cough, difficulty breathing). “Full” indicates a regularized model fit using 70 survey variables, which achieves prediction accuracy similar to the parsimonious model “Smell Only + DOS.”
Figure 4.
Figure 4.
Smell loss, recovery, and time course. (A, B) Joint distribution of smell loss (during minus before illness ratings) and smell recovery (after minus during illness ratings) for C19+ (A) and C19− (B) participants. Darker color indicates a higher probability density; the color map is shared between (A) and (B); dashed lines are placed at a third of the way across the rating scale to aid visualization of the clusters. Severe smell loss that is either persistent (lower left) or recovered (upper left) was more common in C19+ than C19−. n indicates the number of participants in each panel. % indicates the percentage of participants of the given COVID status in each quadrant. (C) In C19+ participants who lost their sense of smell (Recovered Smell + Persistent Smell Loss), the degree of smell recovery (right y axis) increased over ~30 days since onset of respiratory symptoms before plateauing; the degree of reported smell change (left y axis) did not vary in that window of observation. Solid lines indicate the mean of the measure, the shaded region indicates the 95% confidence interval.
Figure 5.
Figure 5.
The odds of a COVID-19 diagnosis as a function of olfactory ability in individuals with respiratory symptoms. (A) The solid line indicates the probability of a COVID-19 diagnosis as a function of “Smell during illness” ratings in our sample. The shaded region indicates the 95% confidence interval. (B) The solid line expresses the probability of a COVID-19+ diagnosis as a function of “Smell during illness” in odds (p/[1−p]); it is shown on a logarithmic scale. The shaded region indicates the 95% confidence interval. (C) Stylized depiction of change in the odds of a COVID-19 diagnosis and of the odds ratio. (D) The ODoR-19 tool. After healthcare providers or contact tracers have excluded previous smell and/or taste disorders (such as those resulting from head trauma, chronic rhinosinusitis, or previous viral illness) in patients with respiratory symptoms, the patient can be asked to rate their current ability to smell on a scale from 0 to 10, with 0 being no sense of smell and 10 being excellent sense of smell. If the patient reports a value below or equal to 3, there is a high (red) or moderate (orange) probability that the patient has COVID-19. Values in yellow (ratings above 3) cannot rule out COVID-19.

Update of

  • The best COVID-19 predictor is recent smell loss: a cross-sectional study.
    Gerkin RC, Ohla K, Veldhuizen MG, Joseph PV, Kelly CE, Bakke AJ, Steele KE, Farruggia MC, Pellegrino R, Pepino MY, Bouysset C, Soler GM, Pereda-Loth V, Dibattista M, Cooper KW, Croijmans I, Di Pizio A, Ozdener MH, Fjaeldstad AW, Lin C, Sandell MA, Singh PB, Brindha VE, Olsson SB, Saraiva LR, Ahuja G, Alwashahi MK, Bhutani S, D'Errico A, Fornazieri MA, Golebiowski J, Hwang LD, Öztürk L, Roura E, Spinelli S, Whitcroft KL, Faraji F, Fischmeister FPS, Heinbockel T, Hsieh JW, Huart C, Konstantinidis I, Menini A, Morini G, Olofsson JK, Philpott CM, Pierron D, Shields VDC, Voznessenskaya VV, Albayay J, Altundag A, Bensafi M, Bock MA, Calcinoni O, Fredborg W, Laudamiel C, Lim J, Lundström JN, Macchi A, Meyer P, Moein ST, Santamaría E, Sengupta D, Domínguez PP, Yanık H, Boesveldt S, de Groot JHB, Dinnella C, Freiherr J, Laktionova T, Mariño S, Monteleone E, Nunez-Parra A, Abdulrahman O, Ritchie M, Thomas-Danguin T, Walsh-Messinger J, Al Abri R, Alizadeh R, Bignon E, Cantone E, Cecchini MP, Chen J, Guàrdia MD, Hoover KC, Karni N, Navarro M, Nolden AA, Mazal PP, Rowan NR, Sarabi-Jamab A, Archer NS, Chen B, Di Valerio EA, Feeney EL, Frasnelli J, Hannum M, Hopkins C, Klein H, Mignot C, Mucigna… See abstract for full author list ➔ Gerkin RC, et al. medRxiv [Preprint]. 2020 Jul 28:2020.07.22.20157263. doi: 10.1101/2020.07.22.20157263. medRxiv. 2020. Update in: Chem Senses. 2021 Jan 1;46:bjaa081. doi: 10.1093/chemse/bjaa081. PMID: 32743605 Free PMC article. Updated. Preprint.

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