Machine learning-based prognostic model to predict criticality in patients - Evidencio
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
Auteurs: 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
Versie: 1.10
  • Publiek
  • Infectieziekten
  • {{ modelType }}
  • Details
  • Valideer algoritme
  • Bewaar invoer
  • Laad invoer
Weergave
Eenheden

{{ section.title }}

{{ section.description }}

Bereken het resultaat

Vul meer parameters in om de berekening uit te voeren

Predicted probability of mortality: %

{{ resultSubheader }}
{{ $t('download_result_availability') }}
{{ chart.title }}
Resultaat interval {{ additionalResult.min }} tot {{ additionalResult.max }}

Conditionele informatie

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 }}
PRO
Notitie
Notities zijn alleen zichtbaar in de resultaat download en worden niet opgeslagen door Evidencio

Dit algoritme wordt verstrekt voor educatieve, opleidings- en informatieve doeleinden. Het mag niet worden gebruikt ter ondersteuning van medische besluitvorming, of om medische of diagnostische diensten te verlenen. Lees onze volledige disclaimer.

Onderliggende algoritmes Onderdeel van
Opmerkingen
Opmerking
Vul een opmerking in.
Opmerkingen zijn voor iedereen zichtbaar

Algoritme feedback

Nog geen feedback 1 Opmerking {{ model.comments.length }} Opmerkingen
Op {{ comment.created_at }} {{ comment.user.username }} een niet langer geregistreerde auteur schreef:
{{ comment.content }}
logo

Log a.u.b. in om de Evidencio print-functies te gebruiken

Om de Evidencio print-functies te kunnen gebruiken dient u ingelogt te zijn.
Indien u nog geen Evidencio Community Account heeft kunt u eenvoudig een persoonlijk account aanmaken op:

https://www.evidencio.com/registration

Print rapport - Voorbeelden {{ new Date().toLocaleString() }}

Evidencio Community Account voordelen

With an Evidencio Community account you can:

  • Create and publish your own prediction algorithms.
  • Share your prediction algorithms with your colleagues, research group, organization or the world.
  • Review and provide feedback on algorithms that have been shared with you.
  • Validate your algorithms and validate algorithms from other users.
  • Find algorithms based on Title, Keyword, Author, Institute, or MeSH classification.
  • Use and save prediction algorithms and their data.
  • Use patient specific protocols and guidelines based on sequential algorithms and decision trees.
  • Stay up-to-date with new algorithms in your field as they are published.
  • Create your own lists of favorite algorithms 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 algorithms.

Disclaimer: Predictie algoritmes dienen enkel ter ondersteuning en naslag geraadpleegd te worden en zijn geen vervanging voor medische besluitvorming door professionals.
Algemene voorwaarden | Privacy Policy | Disclaimer | Contact | Cookies weigeren |
Evidencio v3.39 © 2015 - 2025 Evidencio. Alle rechten voorbehouden