scholarly journals Secukinumab provides sustained low rates of radiographic progression in psoriatic arthritis: 52-week results from a phase 3 study, FUTURE 5

Rheumatology ◽  
2019 ◽  
Vol 59 (6) ◽  
pp. 1325-1334 ◽  
Author(s):  
Désirée van der Heijde ◽  
Philip J Mease ◽  
Robert B M Landewé ◽  
Proton Rahman ◽  
Hasan Tahir ◽  
...  

Abstract Objective To evaluate the effect of secukinumab on radiographic progression through 52 weeks in patients with PsA from the FUTURE 5 study. Methods Patients with active PsA, stratified by prior anti-TNF use (naïve or inadequate response), were randomized to s.c. secukinumab 300 mg load (300 mg), 150 mg load (150 mg), 150 mg no load regimens or placebo at baseline, at weeks 1, 2 and 3 and every 4 weeks starting at week 4. Radiographic progression was assessed by change in van der Heijde-modified total Sharp score (vdH-mTSS; mean of two readers). Statistical analysis used a linear mixed-effects model (random slope) at weeks 24 and 52, and observed data at week 52. Assessments at week 52 included additional efficacy endpoints (non-responders imputation and mixed-effects models for repeated measures) and safety. Results The majority (86.6%) of patients completed 52 weeks of treatment. The proportion of patients with no radiographic progression (change from baseline in vdH-mTSS ⩽0.5) was 91.8, 85.2 and 87.2% in 300, 150 and 150 mg no load groups, respectively, at week 52. The change in vdH-mTSS from baseline to week 52 using random slope [mean change (s.e.)] was –0.18 (0.17), 0.11 (0.18) and –0.20 (0.18) in 300, 150 and 150 mg no load groups, respectively; the corresponding observed data [mean change (s.d.)] was –0.09 (1.02), 0.13 (1.39) and 0.21 (1.15). Clinical efficacy endpoints were sustained, and no new or unexpected safety signals were reported through 52 weeks. Conclusion Secukinumab 300 and 150 mg with or without s.c. loading regimen provided sustained low rates of radiographic progression through 52 weeks of treatment. Trial registration ClinicalTrials.gov, http://clinicaltrials.gov, NCT02404350.

Circulation ◽  
2018 ◽  
Vol 138 (Suppl_2) ◽  
Author(s):  
Gunnar W Skjeflo ◽  
Trond Nordseth ◽  
Jan Pål Loennechen ◽  
Daniel Bergum ◽  
Eirik Skogvoll

Introduction: Pulseless electrical activity (PEA) is a frequent initial rhythm in in-hospital cardiac arrest. ECG changes during cardiopulmonary resuscitation (CPR) have been linked to prognosis; QRS complex narrowing and increase in QRS-rate/heart rate was observed before return of spontaneous circulation (ROSC). Our hypothesis was that ECG changes during CPR of patients with cardiac arrest with initial PEA could also be different depending on etiology of arrest. Methods: Patients with cardiac arrest with initial PEA at St. Olav University Hospital (Trondheim, Norway) between January 2009 and January 2012 were prospectively included. QRS duration and heart rate were measured at all pauses in chest compressions. Etiologies were categorized as ‘Cardiac’ or ‘Other’. Trends in the material were inspected using additive mixed effects models for the last 18 minutes before ROSC or end of CPR and development of QRS width was analyzed using linear mixed effects models based on these trends. Results: A total of 63 patients for whom a reliable cause of arrest could be identified retrospectively were included. 1844 combined measurements of QRS width and heart rate were made. According to the additive mixed effects model the development in heart rate was similar in both etiology groups. The change in QRS width differed in patients who obtained ROSC, with the Cardiac group narrowing more than the other group, and both groups ending at essentially the same QRS widths at ROSC (figure). By the linear mixed effects model this difference in change was statistically significant (p=0.04). In patients who did not have ROSC, the cardiac group had significantly (p=0.001, linear mixed effects model) wider QRS complexes, but the change towards ROSC was the same in both groups. Conclusion: During the last 18 minutes before ROSC ECG characteristics differed depending on etiology, with cardiac etiologies having wider QRS complexes in the no-ROSC groups and narrowing more towards ROSC in the ROSC groups.


2013 ◽  
Vol 631-632 ◽  
pp. 545-549
Author(s):  
Ya Ling Xu ◽  
Wei Wei Sui ◽  
Jun Jian Qiao

In order to explore the effect of application of J-4 micro ecological preparation, based on the data from the experiment in the farm of Yixian County, Hebei Province, the research group established a linear mixed effects model , with time as independent variables, age and different formulations as the fixed effects, using spss software for analysis and solving, the results indicate that the model has the extremely good fitting and forecasting effect and method1 is the optimal ratio. The results will shed light on the further study of the role of probiotics .


Author(s):  
Rachael A. Hughes ◽  
Michael G. Kenward ◽  
Jonathan A. C. Sterne ◽  
Kate Tilling

Linear mixed-effects models are commonly used to model trajectories of repeated measures of biomarkers of disease. Taylor, Cumberland, and Sy (1994, Journal of the American Statistical Association 89: 727–736) proposed a linear mixed-effects model with an added integrated Ornstein–Uhlenbeck (IOU) process (linear mixed-effects IOU model). This allows for autocorrelation, changing within-subject variance, and the incorporation of derivative tracking (that is, how much a subject tends to maintain the same trajectory for extended periods of time). They argued that the covariance structure induced by the stochastic process in this model was interpretable and more biologically plausible than the standard linear mixed-effects model. However, their model is rarely used, partly because of the lack of available software. In this article, we present the new command xtmixediou, which fits the linear mixed-effects IOU model and its special case, the linear mixed-effects Brownian motion model. The model is fit to balanced and unbalanced data using restricted maximum-likelihood estimation, where the optimization algorithm is the Newton–Raphson, Fisher scoring, or average information algorithm, or any combination of these. To aid convergence, xtmixediou allows the user to change the method for deriving the starting values for optimization, the optimization algorithm, and the parameterization of the IOU process. We also provide a predict command to generate predictions under the model. We illustrate xtmixediou and predict with a simulated example of repeated biomarker measurements from HIV-positive patients.


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