Predicting critical illness on initial diagnosis of COVID-19 based on easily obtained clinical variables: development and validation of the PRIORITY model

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Abstract

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  1. SciScore for 10.1101/2020.11.27.20237966: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: Exclusion criteria were age under 18 years, subsequent admissions of the same patient and refusal or withdrawal of informed consent.
    IRB: 20 The SEMI-COVID-19 Registry was approved by the Provincial Research Ethics Committee of Malaga (Spain) and by Institutional Research Ethics Committees of each participating hospital.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.


    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:
    The strength of our findings should be interpreted in light of some limitations. We carefully selected easily available clinical and demographic variables that could be applied at outpatient setting, the data were collected at the time of hospital admission. In this regard it should be kept in mind that during the first COVID-19 peak many patients were hospitalized despite low symptom severity as part of prudent management since not much was known about clinical disease course. We used registry data collected in a situation of healthcare pressure due to the pandemic peak, so the data quality may be variable across centers. In this regard, it is notable that missing data per predictor variable were relatively low. To reduce the impact of data loss we used imputation. The sensitivity analysis found that our model with imputation was robust compared to the performance of the model with the complete cases. The complete-case dataset was 27% smaller than the imputed dataset, a feature that was favourable compared to a previous model using radiology and laboratory tests 12 where the complete dataset was 35% smaller. So, our rate of patients with missing data is even lower. The impact of other assumptions adopted in the model development were also evaluated. For example, restricting the maximum number of predictors to 8 (as recommended by the expert panel to enhance usability in clinical practice) was found not to limit model performance compared to a 15-predictor model developed wit...

    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.