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Juvenile Idiopathic Arthritis is a heterogeneous disease with significant variability in long-term outcome and treatment response; this occurs both between and within JIA subtypes, as defined by the current International League Against Rheumatism (ILAR) classification. There are currently no robust clinical or biological predictors of outcome or treatment response in JIA. Given that JIA is a complex genetic disorder, genetic studies provide an opportunity to address this issue, and whilst previous studies have often been limited by statistical power to identify the likely modest effect sizes, the recent establishment of a number of international consortia for JIA genetics has allowed this issue to be resolved. This presentation will summarize the current understanding of the genetic basis of JIA susceptibility, prognosis and treatment response and will describe how these genetic associations may further our understanding of the molecular mechanisms and immunological pathways involved in this disease. In addition it will provide insights into how we might utilise this data to progress towards the ultimate goals of predicting long-term disease outcomes at onset, predicting drug response, and move towards more targeted treatment options for children with JIA.

Purpose Careful monitoring for infection is warranted in children with SLE, especially those receiving immunosuppressive drugs. Infection is not only the leading cause of death in pediatric SLE, but it is also associated with morbidity. It has been observed that recurrent major infections can predict poorer disease outcome. Prevention of infection and consequent disease damage is therefore a major challenge in managing SLE. The primary goal of our study is to describe the pattern of major infections in a cohort of patients with pediatric SLE. Our secondary aim is to identify whether there is an association between major infections, immunosuppressive treatment, and disease damage.

Purpose The concept of ”syndrome” is not only used for diagnosis but also as an outcome to assess successful treatment in Traditional Chinese medicine (TCM). Integrated medicine assessment mode is based on the combination of disease outcome and syndrome. Seattle Angina Questionnaire (SAQ) is a kind of disease outcome, but there is no syndrome questionnaire available. The current syndrome outcome is based on the “Guidelines for clinical research on Chinese new herbal medicines”, which comes from the textbooks, and its psychometric properties have not been evaluated. This study is to develop a questionnaire to assess the phlegm syndrome for angina.

Introduction Remission is an important goal of therapy in rheumatoid arthritis (RA), but data on duration of remission are lacking. Our objective was to describe the duration of remission in RA, assessed by different criteria. Methods We evaluated patients from the Brigham and Women's Rheumatoid Arthritis Sequential Study (BRASS) not in remission at baseline with at least 2 years of follow-up. Remission was assessed according to the Disease Activity Score 28-C-reactive protein (DAS28-CRP4), Simplified Disease Activity Index (SDAI), and Clinical Disease Activity Index (CDAI) scores, and the recently proposed American College of Rheumatology (ACR)/European League against Rheumatism (EULAR) criteria for remission. Analyses were performed by using Kaplan-Meier survival curves. Results We identified 871 subjects with ≥2 years of follow-up. Of these subjects, 394 were in remission at one or more time-points and not in remission at baseline, according to at least one of the following criteria: DAS28-CRP < 2.6 (n = 309), DAS28-CRP < 2.3 (n = 275), SDAI (n = 168), CDAI (n = 170), and 2010 ACR/EULAR (n = 158). The median age for the 394 subjects at entrance to BRASS was 56 years; median disease duration was 8 years; 81% were female patients; and 72% were seropositive. Survival analysis performed separately for each remission criterion demonstrated that < 50% of subjects remained in remission 1 year later. Median remission survival time was 1 year. Kaplan-Meier curves of the various remission criteria did not significantly differ (P = 0.29 according to the log-rank test). Conclusions This study shows that in clinical practice, a minority of RA patients are in sustained remission.

Methods The study was performed in 25 JIA patients who donated paired serum samples prior and one year after continous etanercept therapy. Basic clinical data (six core set variables defined in ACR PEDI outcome score) were recorded along with alkalyne DNase I serum levels using the method where acid soluble nucleotides are determined spectrophotometrically at 260 nm. Treatment schedule of etanercept was 0,4mg/kg body weight subcutaneously twice weekly.

Background A previous study evaluated the intra-tumoral heterogeneity observed in the uptake of F-18 fluorodeoxyglucose (FDG) in pre-treatment positron emission tomography (PET) scans of cancers of the uterine cervix as an indicator of disease outcome. This was done via a novel statistic which ostensibly measured the spatial variations in intra-tumoral metabolic activity. In this work, we argue that statistic is intrinsically non-spatial, and that the apparent delineation between unsuccessfully- and successfully-treated patient groups via that statistic is spurious. Methods We first offer a straightforward mathematical demonstration of our argument. Next, we recapitulate an assiduous re-analysis of the originally published data which was derived from FDG-PET imagery. Finally, we present the results of a principal component analysis of FDG-PET images similar to those previously analyzed. Results We find that the previously published measure of intra-tumoral heterogeneity is intrinsically non-spatial, and actually is only a surrogate for tumor volume. We also find that an optimized linear combination of more canonical heterogeneity quantifiers does not predict disease outcome. Conclusions Current measures of intra-tumoral metabolic activity are not predictive of disease outcome as has been claimed previously. The implications of this finding are: clinical categorization of patients based upon these statistics is invalid; more sophisticated, and perhaps innately-geometric, quantifications of metabolic activity are required for predicting disease outcome.

Elevated serum levels of cardiac-specific troponin I (cTnI) are claimed to be associated with the degree of sepsis-related cardiac dysfunction and with the disease outcome in critically ill. We tested this association in a specific group of ICU patients with post-traumatic sepsis.

Background The associations of pesticide exposure with disease outcomes are estimated without the benefit of a randomized design. For this reason and others, these studies are susceptible to systematic errors. I analyzed studies of the associations between alachlor and glyphosate exposure and cancer incidence, both derived from the Agricultural Health Study cohort, to quantify the bias and uncertainty potentially attributable to systematic error. Methods For each study, I identified the prominent result and important sources of systematic error that might affect it. I assigned probability distributions to the bias parameters that allow quantification of the bias, drew a value at random from each assigned distribution, and calculated the estimate of effect adjusted for the biases. By repeating the draw and adjustment process over multiple iterations, I generated a frequency distribution of adjusted results, from which I obtained a point estimate and simulation interval. These methods were applied without access to the primary record-level dataset. Results The conventional estimates of effect associating alachlor and glyphosate exposure with cancer incidence were likely biased away from the null and understated the uncertainty by quantifying only random error. For example, the conventional p-value for a test of trend in the alachlor study equaled 0.02, whereas fewer than 20% of the bias analysis iterations yielded a p-value of 0.02 or lower. Similarly, the conventional fully-adjusted result associating glyphosate exposure with multiple myleoma equaled 2.6 with 95% confidence interval of 0.7 to 9.4. The frequency distribution generated by the bias analysis yielded a median hazard ratio equal to 1.5 with 95% simulation interval of 0.4 to 8.9, which was 66% wider than the conventional interval. Conclusion Bias analysis provides a more complete picture of true uncertainty than conventional frequentist statistical analysis accompanied by a qualitative description of study limitations. The latter approach is likely to lead to overconfidence regarding the potential for causal associations, whereas the former safeguards against such overinterpretations. Furthermore, such analyses, once programmed, allow rapid implementation of alternative assignments of probability distributions to the bias parameters, so elevate the plane of discussion regarding study bias from characterizing studies as "valid" or "invalid" to a critical and quantitative discussion of sources of uncertainty.