Publications by authors named "L A Usvyat"
Background: Results from the CONVINCE clinical trial suggest a 23% mortality risk reduction among patients receiving high-volume (> 23 L) hemodiafiltration. We assessed the real-world effectiveness of blood-based kidney replacement therapy (KRT) with hemodiafiltration vs. hemodialysis in a large, unselected patient population treated prior to and during the COVID-19 pandemic.
View Article and Find Full Text PDFIntroduction: Chronic kidney disease-associated pruritus (CKD-aP) is a common, yet underdiagnosed condition among patients on hemodialysis. Considering the lack of established treatment pathways, we sought to evaluate the use of antidepressant, systemic antihistamines, or gabapentinoid medications among patients with CKD-aP in the year following pruritus assessment.
Methods: We included 6209 patients on hemodialysis in the analysis.
Background: Fluid overload remains critical in managing patients with end-stage kidney disease. However, there is limited empirical understanding of fluid overload's impact on mortality. This study analyzes fluid overload trajectories and their association with mortality in hemodialysis patients.
View Article and Find Full Text PDFIntroduction: The management of anemia in chronic kidney disease (CKD-An) presents significant challenges for nephrologists due to variable responsiveness to erythropoietin-stimulating agents (ESAs), hemoglobin (Hb) cycling, and multiple clinical factors affecting erythropoiesis. The Anemia Control Model (ACM) is a decision support system designed to personalize anemia treatment, which has shown improvements in achieving Hb targets, reducing ESA doses, and maintaining Hb stability. This study aimed to evaluate the association between ACM-guided anemia management with hospitalizations and survival in a large cohort of hemodialysis patients.
View Article and Find Full Text PDFArticle Synopsis
- Researchers assessed the potential of machine learning, specifically using XGBoost and logistic regression, to predict the 180-day risk of gastrointestinal bleeding (GIB) hospitalizations in patients on hemodialysis.
- The study analyzed a large dataset from the US involving over 450,000 patients between 2017-2020, identifying risk factors such as age and various health indices.
- XGBoost demonstrated better predictive ability compared to logistic regression, suggesting machine learning could improve early detection of GIB risk, but further validation is required to confirm these findings.