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.
Tyrimų autoriai: 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.
Versija: 1.31
  • Viešoji svetainė
  • Reumatologija
  • {{ modelType }}
  • Išsami informacija
  • Patvirtinti modelį
  • Išsaugoti įvestį
  • Įkrovos įvestis
  • lang lang

Apskaičiuokite rezultatą

Nustatykite daugiau parametrų skaičiavimams atlikti

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

{{ resultSubheader }}
{{ chart.title }}
Rezultatų intervalas {{ additionalResult.min }} į {{ additionalResult.max }}

Sąlyginė informacija

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
Pastaba
Pastabos matomos tik rezultatų atsisiuntimo metu ir \"Evidencio\" jų neišsaugo.

Šis modelis pateikiamas švietimo, mokymo ir informavimo tikslais. Jis neturi būti naudojamas medicininiams sprendimams priimti arba medicininėms ar diagnostinėms paslaugoms teikti. Perskaitykite visą mūsų disclaimer.

Pagrindiniai modeliai Dalis
Komentarai
Komentaras
Įveskite komentarą
Komentarai matomi visiems

Atsiliepimai apie modelį

Atsiliepimų dar nėra 1 komentaras {{ model.comments.length }} Komentarai
Svetainėje {{ comment.created_at }} {{ comment.user.username }} nebeužregistruotas autorius rašė:
{{ comment.content }}
logo

Prisijunkite, kad įjungtumėte \"Evidencio\" spausdinimo funkcijas

Kad galėtumėte naudotis \"Evidencio\" spausdinimo funkcijomis, turite būti prisijungę.
Jei neturite \"Evidencio\" bendruomenės paskyros, galite susikurti nemokamą asmeninę paskyrą adresu:

https://www.evidencio.com/registration.

Atspausdinti rezultatai - Pavyzdžiai {{ new Date().toLocaleString() }}

\"Evidencio\" bendruomenės paskyros privalumai

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.

Atsakomybės apribojimas: vien tik skaičiavimai niekada neturėtų nulemti pacientų priežiūros ir negali pakeisti profesionalaus vertinimo.
Terms & Conditions | Privacy Policy | Disclaimer | Contact | Refuse cookies |
Evidencio v3.25 © 2015 - 2024 Evidencio. All Rights Reserved