Endovascular salvage of failing arterio-venous fistulas utilising sirolimus eluting balloons: Six months results from the ISABELLA trial

Background: Aim of this pilot clinical study was to evaluate the safety and efficacy of the Selution Sustained Limus Release (SLR)™ sirolimus-eluting balloon (SEB) for improving failing arterio-venous fistulas (AVF) patency in Asian haemodialysis patients. Methods: Prospective single-centre, multi-investigator, non-consecutive, non-blinded single arm trial. Forty end-stage renal failure Asian patients with a dysfunctional AVF underwent SEB angioplasty between May and November 2020. All stenotic lesions were prepared with high pressure non-compliant balloon angioplasty prior to SEB angioplasty. Endpoints of interest included target lesion primary patency and circuit access patency and safety through 30 days. All patients received dual antiplatelet therapy for 1 month and were followed up with Duplex ultrasound at 6 months. Results: There was one subject dropout so final n = 39 patients (mean age 65.0 ± 11.9; males = 26 (66.7%)) and n = 43 target lesions treated. Main indication for intervention was dropping access flow (24/39; 61.5%) and most common target lesion was in the juxta-anastomosis (24/43; 54.5%). There was 100% technical and procedural success. There were no adverse events related to the SEB. Target lesion primary patency rates at 3 and 6 months were 39/41 (95.1%) and 28/39 (71.8%) respectively. Access circuit patency rates at 3 and 6 months were 35/37 (94.6%) and 22/35 (62.9%) respectively. There were 3 (7.7%) deaths all attributable to patients’ underlying co-morbidities. Conclusions: Fistuloplasty using the novel Selution SLR™ SEB for dysfunctional AVF circuits seems a safe and effective modality in Asian haemodialysis patients at 6 months but larger randomised controlled studies are required now to determine its true efficacy against plain balloon angioplasty.

Robust Identification of Temporal Biomarkers in Longitudinal Omics Studies

Longitudinal studies increasingly collect rich 'omics' data sampled frequently over time and across large cohorts to capture dynamic health fluctuations and disease transitions. However, the generation of longitudinal omics data has preceded the development of analysis tools that can efficiently extract insights from such data. In particular, there is a need for statistical frameworks that can identify not only which omics features are differentially regulated between groups but also over what time intervals. Additionally, longitudinal omics data may have inconsistencies, including nonuniform sampling intervals, missing data points, subject dropout, and differing numbers of samples per subject. In this work, we developed a statistical method that provides robust identification of time intervals of temporal omics biomarkers. The proposed method is based on a semi-parametric approach, in which we use smoothing splines to model longitudinal data and infer significant time intervals of omics features based on an empirical distribution constructed through a permutation procedure. We benchmarked the proposed method on five simulated datasets with diverse temporal patterns, and the method showed specificity greater than 0.99 and sensitivity greater than 0.72. Applying the proposed method to the Integrative Personal Omics Profiling (iPOP) cohort revealed temporal patterns of amino acids, lipids, and hormone metabolites that are differentially regulated in male versus female subjects following a respiratory infection. In addition, we applied the longitudinal multi-omics dataset of pregnant women with and without preeclampsia, and the method identified potential lipid markers that are temporally significantly different between the two groups. We provide an open-source R package, OmicsLonDA (Omics Longitudinal Differential Analysis): https://bioconductor.org/packages/OmicsLonDA to enable widespread use.

Incremental intervention effects in studies with dropout and many timepoints#

Modern longitudinal studies collect feature data at many timepoints, often of the same order of sample size. Such studies are typically affected by dropout and positivity violations. We tackle these problems by generalizing effects of recent incremental interventions (which shift propensity scores rather than set treatment values deterministically) to accommodate multiple outcomes and subject dropout. We give an identifying expression for incremental intervention effects when dropout is conditionally ignorable (without requiring treatment positivity) and derive the nonparametric efficiency bound for estimating such effects. Then we present efficient nonparametric estimators, showing that they converge at fast parametric rates and yield uniform inferential guarantees, even when nuisance functions are estimated flexibly at slower rates. We also study the variance ratio of incremental intervention effects relative to more conventional deterministic effects in a novel infinite time horizon setting, where the number of timepoints can grow with sample size and show that incremental intervention effects yield near-exponential gains in statistical precision in this setup. Finally, we conclude with simulations and apply our methods in a study of the effect of low-dose aspirin on pregnancy outcomes.

How e-learning engagement time affects academic achievement in e-learning environments. A large-scale study of open and distance learners

The literature is considerably rich about engagement and academic achievement in the context of open and distance learning. However, there is limited research that investigates these variables with large scale participants. In this regard, the aim of this research was to investigate causal correlations between e-learning engagement time and academic achievement of open and distance learners according to course subject, dropout, and bounce rate variables. The participants of this study were 323,264 open and distance learners from Anadolu University, Turkey. Throughout this research, open and distance learners’ engagement time levels and their academic achievements are compared. Academic achievement was found to increase significantly when learners engaged more with e-learning materials.