Decision trees for COVID-19 prognosis learned from patient data: Desaturating the ER with Artificial Intelligence

  1. Nikolas Bernaola
  2. Guillermo de Lima
  3. Miguel Riaño
  4. Lucia Llanos
  5. Sarah Heili-Frades
  6. Olga Sanchez
  7. Antonio Lara
  8. Guillermo Plaza
  9. Cesar Carballo
  10. Paloma Gallego
  11. Pedro Larrañaga
  12. Concha Bielza

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Abstract

Objectives

To present a model that enhances the accuracy of clinicians when presented with a possibly critical Covid-19 patient.

Methods

A retrospective study was performed with information of 5,745 SARS-CoV2 infected patients admitted to the Emergency room of 4 public Hospitals in Madrid belonging to Quirón Salud Health Group (QS) from March 2020 to February 2021. Demographics, clinical variables on admission, laboratory markers and therapeutic interventions were extracted from Electronic Clinical Records. Traits related to mortality were found through difference in means testing and through feature selection by learning multiple classification trees with random initialization and selecting the ones that were used the most. We validated the model through cross-validation and tested generalization with an external dataset from 4 hospitals belonging to Sanitas Hospitals Health Group. The usefulness of two different models in real cases was tested by measuring the effect of exposure to the model decision on the accuracy of medical professionals.

Results

Of the 5,745 admitted patients, 1,173 died. Of the 110 variables in the dataset, 34 were found to be related with our definition of criticality (death in <72 hours) or all-cause mortality. The models had an accuracy of 85% and a sensitivity of 50% averaged through 5-fold cross validation. Similar results were found when validating with data from the 4 hospitals from Sanitas. The models were found to have 11% better accuracy than doctors at classifying critical cases and improved accuracy of doctors by 12% for non-critical patients, reducing the cost of mistakes made by 17%.

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    SciScore for 10.1101/2022.05.09.22274832: (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
    The ones that were not and were over the 50% completion threshold were imputed using an iterative imputation procedure from scikit-learn in Python (Buck, 1960; Pedregosa et al., 2011).
    scikit-learn
    suggested: (scikit-learn, RRID:SCR_002577)
    Python
    suggested: (IPython, RRID:SCR_001658)

    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.

    Results from scite Reference Check: We found no unreliable references.

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  2. Version published to 10.1101/2022.05.09.22274832v1 on medRxiv