Early prediction of hospital admission for emergency department patients - Evidencio
Early prediction of hospital admission for emergency department patients
Patients presenting at the emergency department (ED) are at risk for hospital admission, functional decline and mortality, with older patients having even higher risks. The current model was developed to assess potential differences in independent predictors between age groups. 

The model is developed as part of the APOP study (acronym for: Acutely Presenting Older Patient).

The model contains two separate equations, one for patients aged <70 years, and the other for patients ≥70 years. The latter model did not use age, gender, type of specialist and heart rate as individual predictors. 

The model predicts a patients' individual probability of hospital admission. 

Please note that the model is lacking proper external validation and might not provide accurate predictions. Therefore, outcomes should be interpreted with care before implementing the model in clinical practice. 
Research authors: Jacinta A. Lucke, Jelle de Gelder, Fleur Clarijs, Christian Heringhaus, Anton J.M. de Craen, Anne J. Fogteloo, Gerard J. Blauw, Bas de Groot, Simon P. Mooijaart
Version: 1.25
  • Public
  • Emergency medicine
  • {{ modelType }}
  • Details
  • Validate algorithm
  • Save input
  • Load input
Display
Units

{{section.title}}

{{section.description}}

Calculate the result

Set more parameters to perform the calculation

The predicted probability of hospital admission is: %

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

Conditional information

The models created in this study indicate that predictors of hospital admission from the ED are similar for younger and older patients, but differ in their prognostic capabilities. The overall prognostic  ability of the models was greater for the patients under 70, but the model for older patients is better at identifying the group of patients very likely to be admitted.

These results constitute preparatory work towards creating a screening instrument that could adequately predict hospital admission, particularly for older adults.

Note: It is uncertain whether the outcomes of this model are clinically useful in other hospitals since the model has not been externally validated. The admission rate in the patients used to develop the model may be different in other hospitals. 

 

{{ file.classification }}
PRO
Note
Notes are only visible in the result download and will not be saved by Evidencio

This algorithm is provided for educational, training and information purposes. It must not be used to support medical decision making, or to provide medical or diagnostic services. Read our full disclaimer.

Underlying algorithms Part of
Comments
Comment
Please enter a comment
Comments are visible to anyone

Algorithm feedback

No feedback yet 1 Comment {{ model.comments.length }} Comments
On {{ comment.created_at }} {{ comment.user.username }} a no longer registered author wrote:
{{ comment.content }}
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 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: Calculations alone should never dictate patient care, and are no substitute for professional judgement.
Terms & Conditions | Privacy Policy | Disclaimer | Contact | Refuse cookies |
Evidencio v3.37 © 2015 - 2025 Evidencio. All Rights Reserved