Predicting severity of community-acquired pneumonia with the Pneumonia Severity Index (PSI) score
The PSI prediction rule accurately identifies the patients with community-acquired pneumonia who are at low risk for death and other adverse outcomes. This prediction rule may help physicians make more rational decisions about hospitalization for patients with pneumonia.
Research authors: Fine MJ, Auble TE, Yealy DM, Hanusa BH, Weissfeld LA, Singer DE, Coley CM, Marrie TJ, and Kapoor WN.
Version: 1.27
  • Details
  • Validate model
  • Save input
  • Load input

Calculate the result

Set more parameters to perform the calculation

Total pneumonia severity index (PSI) score: points

{{ resultSubheader }}
{{ chart.title }}
Result interval {{ additionalResult.min }} to {{ additionalResult.max }}

Conditional information

How this model should be used:
The PSI prediction rule identifies three distinct risk classes (I, II, and III) of patients who are at sufficiently low risk for death and other adverse medical outcomes that physicians can consider outpatient treatment or an abbreviated course of inpatient care for them.1 The risk stratification provided by de PSI model could also help target low-risk patients at the time of admission for whom rapid conversion from intravenous to oral antimicrobial therapy and early discharge might be appropriate.

Model performance: 
No significant differences in mortality in each of the five PSI risk classes were found among three large study cohorts:

  • MedisGroups derivation cohort (N=14,199 patients)
  • MedisGroups validation cohort (N=38,039 patients)
  • PORT validation cohort (N=2287 patients)
Although this study provides preliminary evidence that the PSI prediction rule could help physicians determine when hospital care is appropriate for patients with community-acquired pneumonia, firm recommendations for its clinical use will depend on future prospective trials to confirm its effectiveness and safety. Furthermore, although the PSI exhibits a high discriminatory power for assigning appropriate risk class, it is complicated to calculate, limiting its clinical application.

Source:
  1. Fine MJ, Auble TE, Yealy DM et al. A prediction rule to identify low-risk patients with community-acquired pneumonia. N Engl J Med 1997;336:243-50.

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

This model 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 models Part of
Comments
Comment
Please enter a comment
Comments are visible to anyone

Model 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 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.
Evidencio v3.25 © 2015 - 2024 Evidencio. All Rights Reserved