Prediction of 3 months MTX non-response (DAS28>3.2) in early rheumatoid arthritis
Validation of prediction model to identify DMARD-naïve rheumatoid arthritis patients with high risk of insufficient response to MTX using clinical variables.
Les auteurs de la recherche: Gosselt HR, Verhoeven MMA, de Rotte MCFJ, Pluijm SMF, Muller IB, Jansen G, Tekstra J, Bulatović-Ćalasan M, Heil SG, Lafeber FPJG, Hazes JMW, and de Jonge R.
Version: 1.31
  • Détails
  • Valider le modèle
  • Sauvegarder l'entrée
  • Entrée de la charge

Calculer le résultat

Définir d'autres paramètres pour effectuer le calcul

Probability of MTX non-response after 3 months of treatment: %

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

Informations conditionnelles

How this model should be used: 
This prediction model could assist in identification of insufficient responders at diagnosis. For patients with high probability of insufficient response to MTX, additional biologics or JAK-inhibitors could be prescribed. For those with low probabilities of insufficient response, these expensive treatments could be spared. This distinction at diagnosis could save precious time for insufficient responders, allowing earlier control of disease activity resulting in better long-term outcomes.

Model performance: 
Discriminative power of the model was assessed through evaluating the area under the receiver operating characteristic curve (AUC). The AUC of the model was 0.75 (95% CI: 0.69 – 0.81), indicating that the model correctly classified patients in 75% of the cases.

Goodness-of-fit between the predicted probabilities and observed values was tested using the Hosmer-Lemeshow test. The associated P-value was 0.634, indicating good model fit. 

Decisions on appropriate risk cut-offs:
Taking into consideration the “window of opportunity” for optimal treatment we consider it crucial to adequately treat insufficient MTX responders with additional bDMARDs/tsDMARDs. Therefore, our goal for this prediction model was to identify as many insufficient responders as possible, while at the same time attempting to restrict the use of bDMARDs/tsDMARDs to those patients who really need them, hence to avoid misclassification of sufficient responders. Considering this, a cut-off probability of 70% (of insufficient response) could be chosen.

At this cut-off, 75% of patients classified as insufficient responder match actual insufficient responders (PPV) and could be treated with additional bDMARDs/tsDMARDs. Additionally, at this cut-off 86% of all sufficient responders would be correctly classified as such (specificity) and could be spared additional treatment.

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