Self-Rated Smell Ability Enables Highly Specific Predictors of COVID-19 Status: A Case–Control Study in Israel

  1. Noam Karni
  2. Hadar Klein
  3. Kim Asseo
  4. Yuval Benjamini
  5. Sarah Israel
  6. Musa Nammary
  7. Keren Olshtain-Pops
  8. Ran Nir-Paz
  9. Alon Hershko
  10. Mordechai Muszkat
  11. Masha Y Niv

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Abstract

Background

Clinical diagnosis of coronavirus disease 2019 (COVID-19) is essential to the detection and prevention of COVID-19. Sudden onset of loss of taste and smell is a hallmark of COVID-19, and optimal ways for including these symptoms in the screening of patients and distinguishing COVID-19 from other acute viral diseases should be established.

Methods

We performed a case–control study of patients who were polymerase chain reaction–tested for COVID-19 (112 positive and 112 negative participants), recruited during the first wave (March 2020–May 2020) of the COVID-19 pandemic in Israel. Patients reported their symptoms and medical history by phone and rated their olfactory and gustatory abilities before and during their illness on a 1–10 scale.

Results

 Changes in smell and taste occurred in 68% (95% CI, 60%–76%) and 72% (95% CI, 64%–80%) of positive patients, with odds ratios of 24 (range, 11–53) and 12 (range, 6–23), respectively. The ability to smell was decreased by 0.5 ± 1.5 in negatives and by 4.5 ± 3.6 in positives. A penalized logistic regression classifier based on 5 symptoms had 66% sensitivity, 97% specificity, and an area under the receiver operating characteristics curve (AUC) of 0.83 on a holdout set. A classifier based on degree of smell change was almost as good, with 66% sensitivity, 97% specificity, and 0.81 AUC. The predictive positive value of this classifier was 0.68, and the negative predictive value was 0.97.

Conclusions

Self-reported quantitative olfactory changes, either alone or combined with other symptoms, provide a specific tool for clinical diagnosis of COVID-19. A simple calculator for prioritizing COVID-19 laboratory testing is presented here.

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  1. ScreenIT

    SciScore for 10.1101/2020.07.30.20164327: (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
    SentencesResources
    Confidence intervals and p-values for the log-odds were estimated from the glm function using the logistic link implemented in the statistical software R (https://www.r-project.org/).
    https://www.r-project.org/
    suggested: (R Project for Statistical Computing, RRID:SCR_001905)

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Study limitations: The method of patient recruitment is one of the limitations of this study: social media-based recruitment may limit participants’ representation as it targets mostly younger patients, with internet access and social media accounts. Word of mouth recruitment was used as well and contributes as well to creating a sample that is not necessarily representative of the general population. Male and female patients were not fully matched across positives (64% males) and negatives (48% males), in accord with higher % of males (56%) among COVID-19 patients in Israel. Importantly, symptoms-based classifiers cannot capture asymptomatic COVID-19 patients. Therefore, low probability established with our classifiers should not be considered as a predictor of negative COVID-19 status. In other words, our classifiers are not SNOUT (‘Sensitive test when Negative rules OUT the disease’) but can definitely be referred to as ‘Specific test when Positive rules IN the disease’ (SPIN). While chemosensory loss is a dominant feature of symptomatic COVID-19 patients, about 30% of them do not report such loss. Our sample was not large enough to include many positive patients in this subgroup, and further studies are needed to capture distinctive characteristics of COVID-19 patients with intact smell and taste. Additionally, our sample was composed of light to moderately ill patients, thus the classifiers reported are not necessarily applicable to patients with severe forms of COVID-19...

    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:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • 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.

    About SciScore

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  2. Version published to 10.1093/ofid/ofaa589
  3. Version published to 10.1101/2020.07.30.20164327 on medRxiv