Machine learning-based prognostic model to predict criticality in patients with severe Covid-19 infection
Prediction of criticality in patients with severe Covid-19 infection using three clinical features: a machine learning-based prognostic model with clinical data in Wuhan
Research authors: Yan L, Zhang H, Xiao Y, Wang M, Guo Y, Sun C, Tang X, Jing L, Li S, Zhang M, Xiao Y, Cao H, Chen Y, Ren T, Jin J, Wang F, Xiao Y, Huang S, Tan X, Huang N, Jiao B, Zhang Y, Luo A, Cao Z, Xu H, Yuan Y
Details Formula Study characteristics Files & References
★★
Model author
Model ID
2120
Version
1.10
Revision date
2020-04-13
MeSH terms
  • Coronavirus, SARS
  • Clinical Prediction Rule
  • Machine Learning
  • Model type
    Custom model (Conditional)
    Status
    public
    Rating
    Share
    Condition Formula

    Additional information

    Methods: We screened the electronic records of 2,799 patients admitted in Tongji Hospital from January 10th to February 18th, 2020. There were 375 discharged patients including 201 survivors. We built a prognostic prediction model based on XGBoost machine learning algorithm and then tested 29 patients (included 3 patients from other hospital) who were cleared after February 19th.

    Results: The mean age of the 375 patients was 58.83 years old with 58.7% of males. Fever was the most common initial symptom (49.9%), followed by cough (13.9%), fatigue (3.7%), and dyspnea (2.1%). Our model identified three key clinical features, i.e., lactic dehydrogenase (LDH), lymphocyte and High-sensitivity C-reactive protein (hs-CRP), from a pool of more than 300 features. The clinical route is simple to check and can precisely and quickly assess the risk of death. Therefore, it is of great clinical significance

    Source: 
    Yan et al. Prediction of criticality in patients with severe Covid-19 infection using three clinical features: a machine learning-based prognostic model with clinical data in Wuhan. (Preprint). 

    Study Population

    Total population size: 375
    Males: {{ model.numberOfMales }}
    Females: {{ model.numberOfFemales }}

    Continuous characteristics

    Name Mean SD Unit
    Age 58.83 16.46 years
    Name LL Q1 Median Q3 UL Unit
    LDH 196 268.5 593.3 U/L
    hsCRP 1.98 25.8 98.08 mg/L
    Lymphocyte % 4.13 14.35 27.58 %
    Machine learning-based prognostic model to predict criticality in patients with severe Covid-19 infection
    V-1.10-2120.20.04.13
    Refer to Intended Use for instructions before use
    Evidencio B.V., Irenesingel 19, 7481 GJ, Haaksbergen, the Netherlands

    Related files

    Predicted probability of mortality:
    ...
    %

    {{ resultSubheader }}

    {{ model.survival.PITTitle }}

    {{ model.survival.YNETitle }}

    Result
    Note
    Notes are only visible in the result download and will not be saved by Evidencio

    Predicted probability of mortality: %

    {{ resultSubheader }}
    {{ table.title }}
    {{ row }}
    {{ chart.title }}

    Outcome stratification

    Result interval {{ additionalResult.min }} to {{ additionalResult.max }}

    Conditional information

    Result interpretation

    Yan et al. developed a machine learning-based a decision rule using three key features: LDH, hsCRP, and lymphocyte percentage. 

    Context information on risk factors: 
    The increase of LDH reflects tissue/cell destruction and is regarded as a common sign of tissue/cell damage. Serum LDH has been identified as an important biomarker for the activity and severity of Idiopathic Pulmonary Fibrosis (IPF). In patients with severe pulmonary interstitial disease, the increase of LDH is significant and is one of the most important prognostic markers of lung injury. For the critically ill patients with COVID-19, the rise of LDH level indicates an increase of the activity and extent of lung injury.

    The analysis performed by Yan et al, showed that higher serum hs-CRP could be used to predict the risk of death in severe COVID-19 patients. The increase of hs-CRP, an important marker for poor prognosis in ARDS, reflects the persistent state of inflammation.

    Previous results suggest that lymphocytes play vital role in forecasting of progression from mild to critically ill and may serve as a potential therapeutic target. The hypothesis is supported by the results of clinical studies. Moreover, lymphopenia is a common feature in the patients with COVID-19 and might be a critical factor associated with disease severity and mortality.

    Study limitations: 
    First of all, since the proposed machine learning method is purely data driven, its model may vary given a different set of training and validation dataset. Given the limit number of samples in this study, a balance between model complexity and performance was sought. Yet the whole procedure should follow when more data is available.
    Secondly, the performed study was a single-centered, retrospective study, which provides a preliminary assessment of the clinical course and outcome of severe patients.

    Source: 
    Yan et al. Prediction of criticality in patients with severe Covid-19 infection using three clinical features: a machine learning-based prognostic model with clinical data in Wuhan. (Preprint). 

    {{ file.classification }}

    Calculations alone should never dictate patient care, and are no substitute for professional judgement. See our full disclaimer.

    Comments
    Rating
    Comment
    Please enter a comment of rating
    Comments are visible to anyone

    Model feedback

    No feedback yet 1 Comment {{ model.comments.length }} Comments
    Not rated | On {{ comment.created_at }} {{ comment.user.username }} a no longer registered author wrote:
    logo

    Please sign in to enable Evidencio print features

    In order to use the Evidencio print features, you need to be logged in.
    If you don't have an Evidencio Community Account you can create your free personal account at:

    https://www.evidencio.com/registration

    Printed results - Examples {{ new Date().toLocaleString() }}


    Evidencio Community Account Benefits


    With an Evidencio Community account you can:

    • Create and publish your own prediction models.
    • Share your prediction models with your colleagues, research group, organization or the world.
    • Review and provide feedback on models that have been shared with you.
    • Validate your models and validate models from other users.
    • Find models based on Title, Keyword, Author, Institute, or MeSH classification.
    • Use and save prediction models and their data.
    • Use patient specific protocols and guidelines based on sequential models and decision trees.
    • Stay up-to-date with new models in your field as they are published.
    • Create your own lists of favorite models and topics.
    A personal Evidencio account is free, with no strings attached! Join us and help create clarity, transparency, and efficiency in the creation, validation, and use of medical prediction models.

    Disclaimer: Calculations alone should never dictate patient care, and are no substitute for professional judgement.