Hunt and Hess classification of subarachnoid hemorrhage - Evidencio
Hunt and Hess classification of subarachnoid hemorrhage
The Hunt and Hess classification enables classification of the severity of a subarachnoid hemorrhage and predicts its mortality.
Research authors: Hunt WE, Hess RM
Version: 1.16
  • Public
  • Traumatology
  • {{ modelType }}
  • Details
  • Validate algorithm
  • Save input
  • Load input
Display
Units

{{section.title}}

Calculate the result

Set more parameters to perform the calculation

Hunt & Hess risk score: points

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

Conditional information

 

Model performance:
  • In a study bij Hunt & Huss (1968) including 275 cases, the mortality was 20% for patients admitted to the hospital at grade I or II, whereas in patients who reached the operating room for any procedure whatever at grade I or II, it was 14%. The difference in mortality was due to a number of instances of early fatal rebleeding. 
  • Worldwide, different scales are used to assess the clinical condition on admission after aneurysmal subarachnoid hemorrhage. In addition to the prognostic value, the inter-rater variability should be taken into account when deciding which scale preferably should be used.
  • In a validation study by Degen et al (2011) including 50 subarachnoid hemorrhage patients, the Hunt and Hess scale showed moderate interobserver agreement (weighted kappa value: 0.48; 95% CI, 0.36–0.59). The World Federation of Neurological Surgeons and the Prognosis on Admission of Aneurysmal Subarachnoid Hemorrhage scales both showed good interobserver agreement (0.64 and 0.60 respectively) with overlapping CI. 

{{ 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 v3.36 © 2015 - 2025 Evidencio. All Rights Reserved