CKD-EPI creatinine equation (2009)
The CKD-EPI creatinine equation is based on the same four variables as the MDRD Study equation, but uses a 2-slope spline to model the relationship between estimated GFR and serum creatinine, and a different relationship for age, sex and race. The equation was reported to perform better and with less bias than the MDRD Study equation, especially in patients with higher GFR. This results in reduced misclassification of CKD.
Les auteurs de la recherche: Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF, Feldman HI, Kusek JW, Eggers P, Van Lente F, Greene T, and Coresh J.
Version: 1.16
  • Public
  • Néphrologie
  • {{ modelType }}
  • Détails
  • Valider le modèle
  • Sauvegarder l'entrée
  • Entrée de la charge
  • lang lang

Calculer le résultat

Définir d'autres paramètres pour effectuer le calcul

Estimated GFR: ml/min/1.73m2

{{ resultSubheader }}
{{ chart.title }}
Intervalle de résultats {{ additionalResult.min }} à {{ additionalResult.max }}

Informations conditionnelles

The CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) equation was developed in an effort to create a more precise formula to estimate glomerular filtrate rate (GFR) from serum creatinine and other readily available clinical parameters, especially at when actual GFR is >60 mL/min per 1.73m2.

Researchers pooled data from multiple studies to develop and validate this new equation. They randomly divided 10 studies which included 8254 participants, into separate data sets for development and internal validation. 16 additional studies, which included 3896 participants, were used for external validation.

The CKD-EPI equation performed better than the MDRD (Modification of Diet in Renal Disease Study) equation, especially at higher GFR, with less bias and greater accuracy. When looking at NHANES (National Health and Nutrition Examination Survey) data, the median estimated GFR was 94.5 mL/min per 1.73 m2 vs. 85.0 mL/min per 1.73 m2, and the prevalence of chronic kidney disease was 11.5% versus 13.1%.

{{ file.classification }}
PRO
Note
Les notes ne sont visibles que dans le téléchargement des résultats et ne sont pas sauvegardées par Evidencio.

Ce modèle est fourni à des fins d'éducation, de formation et d'information. Il ne doit pas être utilisé pour aider à la prise de décision médicale ou pour fournir des services médicaux ou de diagnostic. Lire l'intégralité de notre disclaimer.

Modèles sous-jacents Une partie de
Commentaires
Commentaire
Veuillez saisir un commentaire
Les commentaires sont visibles par tous

Retour d'information sur le modèle

Pas encore de retour d'information 1 Commentaire {{ model.comments.length }} Commentaires
Sur {{ comment.created_at }} {{ comment.user.username }} un auteur qui n'est plus enregistré a écrit :
{{ comment.content }}
logo

Veuillez vous connecter pour activer les fonctions d'impression d'Evidencio

Pour utiliser les fonctions d'impression d'Evidencio, vous devez être connecté.
Si vous n'avez pas de compte communautaire Evidencio, vous pouvez créer un compte personnel gratuit à:

https://www.evidencio.com/registration

Résultats imprimés - Exemples {{ new Date().toLocaleString() }}

Avantages du compte communautaire Evidencio

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.

Clause de non-responsabilité : les calculs ne doivent jamais dicter les soins aux patients et ne remplacent pas le jugement d'un professionnel.
Terms & Conditions | Privacy Policy | Disclaimer | Contact | Refuse cookies |
Evidencio v3.25 © 2015 - 2024 Evidencio. All Rights Reserved