We investigated the value of genetic, histopathologic, and early treatment response information in prognosing long-term renal outcome in children with primary steroid-resistant nephrotic syndrome. From the PodoNet Registry, we obtained longitudinal clinical information for 1354 patients (disease onset at >3 months and <20 years of age): 612 had documented responsiveness to intensified immunosuppression (IIS), 1155 had kidney biopsy results, and 212 had an established genetic diagnosis. We assessed risk factors for ESRD using multivariate Cox regression models. Complete and partial remission of proteinuria within 12 months of disease onset occurred in 24.5% and 16.5% of children, respectively, with the highest remission rates achieved with calcineurin inhibitor-based protocols. Ten-year ESRD-free survival rates were 43%, 94%, and 72% in children with IIS resistance, complete remission, and partial remission, respectively; 27% in children with a genetic diagnosis; and 79% and 52% in children with histopathologic findings of minimal change glomerulopathy and FSGS, respectively. Five-year ESRD-free survival rate was 21% for diffuse mesangial sclerosis. IIS responsiveness, presence of a genetic diagnosis, and FSGS or diffuse mesangial sclerosis on initial biopsy as well as age, serum albumin concentration, and CKD stage at onset affected ESRD risk. Our findings suggest that responsiveness to initial IIS and detection of a hereditary podocytopathy are prognostic indicators of favorable and poor long-term outcome, respectively, in children with steroid-resistant nephrotic syndrome. Children with multidrug-resistant sporadic disease show better renal survival than those with genetic disease. Furthermore, histopathologic findings may retain prognostic relevance when a genetic diagnosis is established.
BackgroundDespite many initiatives to enhance the rational use of antibiotics, there remains substantial room for improvement. The overall aim of this study is to optimise the appropriate use of antibiotics in German ambulatory care in patients with acute non-complicated infections (respiratory tract infections, such as bronchitis, sinusitis, tonsillitis and otitis media), community-acquired pneumonia and non-complicated cystitis, in order to counter the advancing antimicrobial resistance development.MethodsA three-armed cluster randomised trial will be conducted in 14 practice networks in two German federal states (Bavaria and North Rhine-Westphalia) and an added cohort that reflects standard care. The trial is accompanied by a process evaluation.Each arm will receive a different set of implementation strategies. Arm A receives a standard set, comprising of e-learning on communication with patients and quality circles with data-based feedback for physicians, information campaigns for the public, patient information material and performance-based additional reimbursement. Arm B receives this standard set plus e-learning on communication with patients and quality circles with data-based feedback tailored for non-physician health professionals of the practice team and information material for tablet computers (culture sensitive). Arm C receives the standard set as well as a computerised decision support system and quality circles in local multidisciplinary groups.The study aims to recruit 193 practices which will provide data on 23,934 patients each year (47,867 patients in total).The outcome evaluation is based on claims data and refers to established indicators of the European Surveillance of Antimicrobial Consumption Network (ESAC-Net). Primary and secondary outcomes relate to prescribing of antibiotics, which will be analysed in multivariate regression models. The process evaluation is based on interviews with surveys among physicians, non-physician health professionals of the practice team and stakeholders. A patient survey is conducted in one of the study arms. Interview data will be qualitatively analysed using thematic framework analysis. Survey data of physicians, non-physician health professionals of the practice team and patients will use descriptive and exploratory statistics for analysis.DiscussionThe ARena trial will examine the effectiveness of large scale implementation strategies and explore their delivery in routine practice.Trial registrationISRCTN, ISRCTN58150046. Registered 24 August 2017.Electronic supplementary materialThe online version of this article (10.1186/s13012-018-0722-0) contains supplementary material, which is available to authorized users.
Background Platform trials can evaluate the efficacy of several experimental treatments compared to a control. The number of experimental treatments is not fixed, as arms may be added or removed as the trial progresses. Platform trials are more efficient than independent parallel group trials because of using shared control groups. However, for a treatment entering the trial at a later time point, the control group is divided into concurrent controls, consisting of patients randomised to control when that treatment arm is in the platform, and non-concurrent controls, patients randomised before. Using non-concurrent controls in addition to concurrent controls can improve the trial’s efficiency by increasing power and reducing the required sample size, but can introduce bias due to time trends. Methods We focus on a platform trial with two treatment arms and a common control arm. Assuming that the second treatment arm is added at a later time, we assess the robustness of recently proposed model-based approaches to adjust for time trends when utilizing non-concurrent controls. In particular, we consider approaches where time trends are modeled either as linear in time or as a step function, with steps at time points where treatments enter or leave the platform trial. For trials with continuous or binary outcomes, we investigate the type 1 error rate and power of testing the efficacy of the newly added arm, as well as the bias and root mean squared error of treatment effect estimates under a range of scenarios. In addition to scenarios where time trends are equal across arms, we investigate settings with different time trends or time trends that are not additive in the scale of the model. Results A step function model, fitted on data from all treatment arms, gives increased power while controlling the type 1 error, as long as the time trends are equal for the different arms and additive on the model scale. This holds even if the shape of the time trend deviates from a step function when patients are allocated to arms by block randomisation. However, if time trends differ between arms or are not additive to treatment effects in the scale of the model, the type 1 error rate may be inflated. Conclusions The efficiency gained by using step function models to incorporate non-concurrent controls can outweigh potential risks of biases, especially in settings with small sample sizes. Such biases may arise if the model assumptions of equality and additivity of time trends are not satisfied. However, the specifics of the trial, scientific plausibility of different time trends, and robustness of results should be carefully considered.
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