Publications by authors named "A Chakera"
Background And Hypothesis: A static predictive model relying solely on baseline clinicopathological data cannot capture the heterogeneity in predictor trajectories observed in the progression of chronic kidney disease (CKD). To address this, we developed and validated a dynamic survival prediction model using longitudinal clinicopathological data to predict end-stage kidney disease (ESKD), with death as a competing risk.
Methods: We trained a sequence of random survival forests using a landmarking approach and optimized the model with a pre-specified prediction horizon of 5 years.
HLA-compatibility remains an important triage test for deceased donor kidney allocation. Low-intermediate resolution donor HLA-typing is typically available at allocation, but its accuracy in assigning pre-transplant donor-specific anti-HLA antibody (DSA) and HLA mismatches compared to 2-field high-resolution typing is poorly characterised. Consecutive deceased donor/recipient pairs from a single centre between 2016 and 2020 were included.
View Article and Find Full Text PDFArticle Synopsis
- Chronic kidney disease (CKD) poses a major global health challenge, with the study focusing on estimating direct healthcare costs in Australia for non-dialysis patients.
- Utilizing a comprehensive dataset from Western Australia, the research compared healthcare costs for individuals with CKD to those without, covering various healthcare services and expressing costs in 2023 AUD.
- Findings revealed that in 2019, CKD accounted for an $8.3 billion cost in Australia, with the highest yearly costs linked to later stages of CKD but a significant number of early-stage cases contributing to the overall financial burden, underscoring the need for effective prevention strategies.