Development of a Predictive Score for COVID-19 Diagnosis based on Demographics and Symptoms in Patients Attended at a Dedicated Screening Unit

  1. Alexandre P Zavascki
  2. Marcelo B Gazzana
  3. Joao Pedro Bidart
  4. Patricia S Fernandes
  5. Aline Galiotto
  6. Cristiane T Kawski
  7. Luiz A Nasi
  8. Diego R Falci

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

Background: The diagnosis of COVID-19 based on clinical evaluation is difficult because symptoms often overlap with other respiratory diseases. A clinical score predictive of COVID-19 based on readily assessed variables may be useful in settings with restricted or no access to molecular diagnostic tests. Methods: A score based on demographics and symptoms was developed in a cross-sectional study including patients attended in a dedicated COVID-19 screening unit. A backward stepwise logistic regression model was constructed and values for each variable were assigned according to their β coefficient values in the final model. Receiver operating characteristic (ROC) curve was constructed and its area under the curve (AUC) was calculated. Results: A total of 464 patients were included: 98 (21.1%) COVID-19 and 366 (78.9%) non-COVID-19 patients. The score included variables independently associated with COVID-19 in the final model: age equal or above 60 years (2 points), fever (2), dyspnea (1), fatigue (1 point) and coryza (-1). Score values were significantly higher in COVID-19 than non-COVID-19 patients: median (Interquartile Range), 3 (2-4), and 1 (0-2), respectively; P<0.001. The score had an AUC of 0.80 (95% Confidence Interval [CI], 0.76-0.86). The specificity of scores equal or greater than 4 and 5 points were 90.4 (95%CI, 87.0-93.3) and 96.2 (95%CI, 93.7-97.9), respectively. Conclusions: This preliminary score based on patients symptoms is a feasible tool that may be useful in setting with restricted or no access to molecular tests in a pandemic period, owing to the high specificity. Further studies are required to validate the score in other populations.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.05.14.20101931: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementIACUC: The study was approved by the institutional ethical committee (protocol number 4.018.709) Molecular diagnosis of SARS-CoV-2: Confirmation of COVID-19 diagnosis was done with one positive reverse-transcriptase polymerase chain reaction (RT-PCR) performed as previously reported [11].
    RandomizationA preliminary validation was performed in a randomly selected sample with 25% of patients of the score development population.
    BlindingResearchers registering the symptoms in the database were blinded to the outcome (COVID-19 status) since data collection from medical records were done in the same day or the day after the consultation before RT-PCR results were available.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    v.18.0 (SPSS Inc., Chicago, IL)
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Our study has several limitations that must be underlined. First, it was based on retrospective data from medical records and although clinical interview has been made by dedicated physicians, there was no standard questionnaire for it. So, it is possible that a less perceived symptoms that has been not specifically questioned by the physician might be missed. On the other hand, it is less likely that the main complains have been forgotten by the patients and not spontaneously referred. Second, symptoms not registered in medical records were assumed as not existent, when in fact there might be just not recorded by the physician. Third, we excluded COVID-19 based on only one negative test in most patients, which turns possible that in non-COVID-19 group there may be missed patients who would become positive with a second test. Nonetheless, this potential bias would decrease the specificity of the score. We have also not evaluated routinely and standardly other infectious etiology for patients’ diseases thus we were limited to classified patients as non-COVID-19. However, it does not affect our results considering the study purpose. Fifth, some symptoms recently added to the COVID-19 list have not been investigated such as chills, hyposmia and dysgeusia. We believe that especially the latter two symptoms have the potential to increase the score specificity and must be further evaluated [14]. Finally, performance of the score was satisfactory in the preliminary validation carrie...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    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

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.05.14.20101931v1 on medRxiv