1. Biochemistry and Chemical Biology
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Early prediction of in-hospital death of COVID-19 patients: a machine-learning model based on age, blood analyses, and chest x-ray score

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Cite this article as: eLife 2021;10:e70640 doi: 10.7554/eLife.70640

Abstract

An early-warning model to predict in-hospital mortality on admission of COVID-19 patients at an emergency department (ED) was developed and validate using a Machine-Learning model. In total, 2782 patients were enrolled between March 2020 and December 2020, including 2106 patients (first wave) and 676 patients (second wave) in the COVID-19 outbreak in Italy. The first-wave patients were divided into two groups with 1474 patients used to train the model, and 632 to validate it. The 676 patients in the second wave were used to test the model. Age, 17 blood analytes and Brescia chest X-ray score were the variables processed using a Random Forests classification algorithm to build and validate the model. ROC analysis was used to assess the model performances. A web-based death-risk calculator was implemented and integrated within the Laboratory Information System of the hospital. The final score was constructed by age (the most powerful predictor), blood analytes (the strongest predictors were lactate dehydrogenase, D-dimer, Neutrophil/Lymphocyte ratio, C-reactive protein, Lymphocyte %, Ferritin std and Monocyte %), and Brescia chest X-ray score. The areas under the receiver operating characteristic curve obtained for the three groups (training, validating and testing) were 0.98, 0.83 and 0.78, respectively. The model predicts in-hospital mortality on the basis of data that can be obtained in a short time, directly at the ED on admission. It functions as a web-based calculator, providing a risk score which is easy to interpret. It can be used in the triage process to support the decision on patient allocation.

Data availability

We are unable to share the dataset as it contains sensitive personal data collected during the pandemic at Spedali Civili di Brescia. We cannot share the full data since are data from patients. Interested researchers should contact the authors for any query related to data sharing and submit a project proposal Once defined the goal of the study, and the data needed authors will submit the potential project of collaboration to the IRB of Spedali Civili di Brescia to receive approval to access a deidentified dataset. Please note that other informations related to patients can be acquired, always following approoval of IRB of Spedali Civili di Brescia, not only the ones studied in the paper.Anyway, following request to the authors, it will be possible to share processed version of the dataset ( e.g. an Excel sheet with numbers used to plot the graphs and charts of the manuscript).All code used to analyse the data can be found on GitHub at https://github.com/biostatUniBS/BS_EWS

Article and author information

Author details

  1. Emirena Garrafa

    University of Brescia, Brescia, Italy
    For correspondence
    emirena.garrafa@unibs.it
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4761-6892
  2. Marika Vezzoli

    University of Brescia, Brescia, Italy
    Competing interests
    The authors declare that no competing interests exist.
  3. Marco Ravanelli

    University of Brescia, Brescia, Italy
    Competing interests
    The authors declare that no competing interests exist.
  4. Davide Farina

    University of Brescia, Brescia, Italy
    Competing interests
    The authors declare that no competing interests exist.
  5. Andrea Borghesi

    University of Brescia, Brescia, Italy
    Competing interests
    The authors declare that no competing interests exist.
  6. Stefano Calza

    University of Brescia, Brescia, Italy
    Competing interests
    The authors declare that no competing interests exist.
  7. Roberto Maroldi

    University of Brescia, Brescia, Italy
    Competing interests
    The authors declare that no competing interests exist.

Funding

Stefano Calza (PRIN 20178S4EK9)

  • Stefano Calza

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: The Institutional review board aprpoved the study with the entry code NP4000.

Reviewing Editor

  1. Evangelos J Giamarellos-Bourboulis, National and Kapodistrian University of Athens, Medical School, Greece

Publication history

  1. Received: May 24, 2021
  2. Preprint posted: June 13, 2021 (view preprint)
  3. Accepted: October 17, 2021
  4. Accepted Manuscript published: October 18, 2021 (version 1)
  5. Accepted Manuscript updated: October 22, 2021 (version 2)
  6. Version of Record published: October 27, 2021 (version 3)

Copyright

© 2021, Garrafa et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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