Studies on two stage sludge digestion, 1928-29

At head of title: State of Illinois. Department of registration and education. Division of the state Water survey."Bulletins of the state water survey": 1 leaf at end."Literature cited": p. 92.Enumeration continues from preceding title

Measuring disease activity to predict therapeutic outcome in Graves' ophthalmopathy

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 use