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.
Autorzy badań: 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.
Wersja: 1.31
  • Publiczny
  • Reumatologia
  • {{ modelType }}
  • Szczegóły
  • Walidacja modelu
  • Zapisywanie danych wejściowych
  • Wejście obciążenia
  • lang lang

Obliczanie wyniku

Ustaw więcej parametrów, aby wykonać obliczenia

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

{{ resultSubheader }}
{{ chart.title }}
Interwał wyników {{ additionalResult.min }} do {{ additionalResult.max }}

Informacje warunkowe

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
Uwaga
Notatki są widoczne tylko w pobranym wyniku i nie zostaną zapisane przez Evidencio

Niniejszy model służy celom edukacyjnym, szkoleniowym i informacyjnym. Nie może być wykorzystywany do wspierania podejmowania decyzji medycznych ani do świadczenia usług medycznych lub diagnostycznych. Przeczytaj naszą pełną wersję disclaimer.

Modele leżące u podstaw Część
Komentarze
Komentarz
Wprowadź komentarz
Komentarze są widoczne dla każdego

Modelowa informacja zwrotna

Brak opinii 1 Komentarz {{ model.comments.length }} Komentarze
Na {{ comment.created_at }} {{ comment.user.username }} niezarejestrowany już autor napisał:
{{ comment.content }}
logo

Zaloguj się, aby włączyć funkcje drukowania Evidencio

Aby korzystać z funkcji drukowania Evidencio, musisz być zalogowany.
Jeśli nie masz konta społecznościowego Evidencio, możesz utworzyć bezpłatne konto osobiste pod adresem:

https://www.evidencio.com/registration

Wydrukowane wyniki - przykłady {{ new Date().toLocaleString() }}

Korzyści z konta społecznościowego 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.

Zastrzeżenie: Same obliczenia nigdy nie powinny dyktować opieki nad pacjentem i nie zastępują profesjonalnego osądu.
Terms & Conditions | Privacy Policy | Disclaimer | Contact | Refuse cookies |
Evidencio v3.25 © 2015 - 2024 Evidencio. All Rights Reserved