Covid19Risk.ai: An Open Source Repository and Online Calculator of Prediction Models for Early Diagnosis and Prognosis of Covid-19

  1. Iva Halilaj
  2. Avishek Chatterjee
  3. Yvonka van Wijk
  4. Guangyao Wu
  5. Brice van Eeckhout
  6. Cary Oberije
  7. Philippe Lambin

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Abstract

Background: The current pandemic has led to a proliferation of predictive models being developed to address various aspects of COVID-19 patient care. We aimed to develop an online platform that would serve as an open source repository for a curated subset of such models, and provide a simple interface for included models to allow for online calculation. This platform would support doctors during decision-making regarding diagnoses, prognoses, and follow-up of COVID-19 patients, expediting the models’ transition from research to clinical practice. Methods: In this pilot study, we performed a literature search in the PubMed and WHO databases to find suitable models for implementation on our platform. All selected models were publicly available (peer reviewed publications or open source repository) and had been validated (TRIPOD type 3 or 2b). We created a method for obtaining the regression coefficients if only the nomogram was available in the original publication. All predictive models were transcribed on a practical graphical user interface using PHP 8.0.0, and were published online together with supporting documentation and links to the associated articles. Results: The open source website currently incorporates nine models from six different research groups, evaluated on datasets from different countries. The website will continue to be populated with other models related to COVID-19 prediction as these become available. This dynamic platform allows COVID-19 researchers to contact us to have their model curated and included on our website, thereby increasing the reach and real-world impact of their work. Conclusion: We have successfully demonstrated in this pilot study that our website provides an inclusive platform for predictive models related to COVID-19. It enables doctors to supplement their judgment with patient-specific predictions from externally validated models in a user-friendly format. Additionally, this platform supports researchers in showcasing their work, which will increase the visibility and use of their models.

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  1. Version published to 10.3390/biomed1010003
  2. ScreenIT

    SciScore for 10.1101/2021.01.05.425384: (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
    We reviewed the PubMed database of the National Center for Biotechnology Information (NCBI) and the World Health Organization (WHO) database for COVID-19 publications from December 2019 to June 2020.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)

    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 method we used for retrieving coefficients of a regression model from a nomogram has certain limitations. For one, the accuracy is highly dependent on the resolution of the published model. Another limitation is that though the coefficients of the model are retrieved, the standard error for the coefficients of the parameters cannot be obtained from a nomogram alone. However, the method can be applied to any nomogram, making it a tool that can be broadly used, not restricted to COVID-19.

    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:
    • No conflict of interest statement was detected. If there are no conflicts, we encourage authors to explicit state so.
    • 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.

  3. Version published to 10.1101/2021.01.05.425384 on bioRxiv