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Associations between dialysis modality and adherence to immunosuppression after kidney transplantation-A single-center study.
PLoS One
January 2025
Helsinki University Hospital, Abdominal Centre, Transplantation and Liver Surgery, and University of Helsinki, Helsinki, Finland.
Background: Patients with end-stage kidney disease often prefer home-based dialysis due to higher self-efficacy, which relates to improved medical treatment adherence. Kidney transplantation (KT) success depends on adhering to immunosuppressive medication post-transplant.
Objectives: To investigate whether adherence post-kidney transplantation (KT) and patients' attitudes toward immunosuppression were influenced by their prior dialysis type modality.
Measuring the effects of motion corruption in fetal fMRI.
Hum Brain Mapp
February 2025
Computational Imaging Research Lab, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria.
Irregular and unpredictable fetal movement is the most common cause of artifacts in in utero functional magnetic resonance imaging (fMRI), affecting analysis and limiting our understanding of early functional brain development. The accurate detection of corrupted functional connectivity (FC) resulting from motion artifacts or preprocessing, instead of neural activity, is a prerequisite for reliable and valid analysis of FC and early brain development. Approaches to address this problem in adult data are of limited utility in fetal fMRI.
View Article and Find Full Text PDFAdjusting for switches to multiple treatments: Should switches be handled separately or combined?
Stat Methods Med Res
January 2025
Sheffield Centre for Health and Related Research, School of Medicine and Population Health, University of Sheffield, Sheffield, South Yorkshire, UK.
Treatment switching is common in randomised controlled trials (RCTs). Participants may switch onto a variety of different treatments, all of which may have different treatment effects. Adjustment analyses that target hypothetical estimands - estimating outcomes that would have been observed in the absence of treatment switching - have focused primarily on a single type of switch.
View Article and Find Full Text PDFDetermining timeframes to death for imminently dying patients: a retrospective cohort study.
BMC Palliat Care
January 2025
Caring Futures Institute, Flinders University, Sturt Rd, Bedford Park, Adelaide, South Australia, 5042, Australia.
Background: Clinicians are frequently asked 'how long' questions at end-of-life by patients and those important to them, yet predicting timeframes to death remains uncertain, even in the last weeks and days of life. Patients and families wish to know so they can ask questions, plan, make decisions, have time to visit and say their goodbyes, and have holistic care needs met. Consequently, this necessitates a more accurate assessment of empirical data to better inform prognostication and reduce uncertainty around time until death.
View Article and Find Full Text PDFCase-Base Neural Network: Survival analysis with time-varying, higher-order interactions.
Mach Learn Appl
June 2024
McGill University Department of Biostatistics, 805 rue Sherbrooke O, Montréal, H3A 0B9, Quebec, Canada.
In the context of survival analysis, data-driven neural network-based methods have been developed to model complex covariate effects. While these methods may provide better predictive performance than regression-based approaches, not all can model time-varying interactions and complex baseline hazards. To address this, we propose Case-Base Neural Networks (CBNNs) as a new approach that combines the case-base sampling framework with flexible neural network architectures.
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