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Predicting the outcome of ankylosing spondylitis therapy.

Author: BraunJürgen, CollantesEduardo, DeodharAtul, DijkmansBen, GeusensPiet, HsuBenjamin, InmanRobert D, RahmanMahboob U, SieperJoachim, Vander CruyssenBert, VastesaegerNathan, WangYanxin, van der HeijdeDésirée

Journal ArticleAn article published in an academic or professional journal that reports original research or scholarly analysis.
Randomized Controlled TrialA clinical trial that randomly assigns participants to treatment and control groups to evaluate the effects of an intervention.
Research Support, Non-U.S. Gov'tResearch funding provided by government agencies outside the United States.

Overview

Age, Bath ankylosing spondylitis functional index (BASFI) score, enthesitis, therapy, C-reactive protein (CRP) and HLA-B27 genotype were associated with outcomes in ankylosing spondylitis (AS). Their combined use enables adequate prediction of outcome resulting from anti-TNF and conventional therapy in various AS subpopulations.
Paper Details　
Original Abstract of the Article :
OBJECTIVES: To create a model that provides a potential basis for candidate selection for anti-tumour necrosis factor (TNF) treatment by predicting future outcomes relative to the current disease profile of individual patients with ankylosing spondylitis (AS). METHODS: ASSERT and GO-RAISE trial dat...See full text at original site
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引用元：
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3086037/

データ提供：米国国立医学図書館（NLM）

Predicting the Outcome of Ankylosing Spondylitis Therapy: A Guide for Clinicians

Ankylosing spondylitis (AS) is a chronic inflammatory disease that affects the spine. This research investigates the factors that can be used to predict the outcome of anti-tumor necrosis factor (TNF) therapy for AS. Think of this research as a desert guide, helping to navigate the complex terrain of AS treatment.

Predictive Model for AS Therapy: Identifying Key Factors

The study identified a set of factors, including age, disease activity, and genetic markers, that can be used to predict the outcome of AS therapy. This is like creating a detailed map of the desert, highlighting the key landmarks that can be used to guide decision-making.

Implications for Personalized Treatment Strategies

The findings could lead to more personalized treatment strategies for AS patients, enabling clinicians to select the most effective therapies for individual patients. This is like customizing a desert expedition, ensuring that the right tools and resources are available for a successful journey.

Dr.Camel's Conclusion

This research offers valuable insights into the factors that influence the outcome of AS therapy. By developing a predictive model, clinicians can make more informed treatment decisions, leading to improved outcomes for patients. This research is like a desert explorer discovering a new oasis, offering a more effective and personalized approach to managing this challenging condition.

Date :
  1. Date Completed 2011-07-13
  2. Date Revised 2022-04-09
Further Info :

Pubmed ID

21402563

DOI: Digital Object Identifier

PMC3086037

SNS
PICO Info
in preparation
Languages

English

Positive IndicatorAn AI analysis index that serves as a benchmark for how positive the results of the study are. Note that it is a benchmark and requires careful interpretation and consideration of different perspectives.

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