Suppr超能文献

Measuring disease activity to predict therapeutic outcome in Graves' ophthalmopathy.

作者信息

Terwee C B, Prummel M F, Gerding M N, Kahaly G J, Dekker F W, Wiersinga W M

机构信息

Department of Clinical Epidemiology and Biostatistics, Academic Medical Centre, University of Amsterdam, The Netherlands.

出版信息

Clin Endocrinol (Oxf). 2005 Feb;62(2):145-55. doi: 10.1111/j.1365-2265.2005.02186.x.

Abstract

OBJECTIVE

The concept of disease activity in Graves' ophthalmopathy (GO) might explain why as many as one-third of patients do not respond to immunosuppressive treatment, because only patients in the active stage of disease are expected to respond. The hypothesis was adopted that a parameter used to measure disease activity should be able to predict a response to immunosuppressive treatment. The aim of this study was to develop a multivariate prediction model in which all previous tested activity parameters are integrated.

DESIGN AND PATIENTS

We included 66 consecutive patients with untreated moderately severe GO who had been euthyroid for at least 2 months. All patients were treated with radiotherapy. Measurements Treatment efficacy after 6 months follow-up was used as the primary outcome measure. Disease severity and 15 different disease activity parameters were assessed before treatment. Univariate and multivariate logistic regression models were used to predict response (model 1) or no change (model 2).

RESULTS

In multivariate analyses, we found that duration of GO, soft tissue involvement, elevation, soluble interleukin-2 receptor (sIL-2R), soluble CD30 (sCD30), eye muscle reflectivity and octreotide uptake ratio were significant predictors of a response to radiotherapy. Gender, duration of GO, soft tissue involvement, eye muscle reflectivity, IL-6 and urinary glycosaminoglycan (GAG) excretion were significant predictors of no change upon radiotherapy. Prognostic score charts were developed for use in clinical practice to calculate the probability of response (model 1) and the probability of no change (model 2) for each new patient. Finally we used a combination of both models to define a recommended treatment modality for each individual patient, based on both the predicted probabilities of response and no change. We were able to identify the correct treatment (based on a comparison with the observed response) in 89% of the patients.

CONCLUSIONS

Although we strongly recommend that our results should be confirmed in other studies, our findings are the first evidence for the idea that disease (in)activity should determine which kind of treatment should be used.

摘要

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验