Version 1
: Received: 2 July 2019 / Approved: 3 July 2019 / Online: 3 July 2019 (09:42:07 CEST)
How to cite:
Achakzai, M.; Argyropoulos, C.; Roumelioti, M.E. Predicting Residual Function in Hemodialysis and Hemodiafiltration – A Population Kinetic, Decision Analytic Approach. Preprints2019, 2019070059. https://doi.org/10.20944/preprints201907.0059.v1
Achakzai, M.; Argyropoulos, C.; Roumelioti, M.E. Predicting Residual Function in Hemodialysis and Hemodiafiltration – A Population Kinetic, Decision Analytic Approach. Preprints 2019, 2019070059. https://doi.org/10.20944/preprints201907.0059.v1
Achakzai, M.; Argyropoulos, C.; Roumelioti, M.E. Predicting Residual Function in Hemodialysis and Hemodiafiltration – A Population Kinetic, Decision Analytic Approach. Preprints2019, 2019070059. https://doi.org/10.20944/preprints201907.0059.v1
APA Style
Achakzai, M., Argyropoulos, C., & Roumelioti, M.E. (2019). Predicting Residual Function in Hemodialysis and Hemodiafiltration – A Population Kinetic, Decision Analytic Approach. Preprints. https://doi.org/10.20944/preprints201907.0059.v1
Chicago/Turabian Style
Achakzai, M., Christos Argyropoulos and Maria Eleni Roumelioti. 2019 "Predicting Residual Function in Hemodialysis and Hemodiafiltration – A Population Kinetic, Decision Analytic Approach" Preprints. https://doi.org/10.20944/preprints201907.0059.v1
Abstract
In this study, we introduce a novel framework for the estimation of residual renal function (RRF), based on the population compartmental kinetic behavior of Beta 2 Microglobulin (B2M) and its dialytic removal. Using this model, we simulated a large cohort of patients with various levels of RRF receiving either conventional high-flux hemodialysis or on-line hemodiafiltration. These simulations were used to estimate a novel population kinetic (PK) equation for RRF (PK-RRF) that was validated in an external public dataset of real patients. We assessed the performance of the resulting equation(s) against their ability to estimate urea clearance using cross-validation. Our equations derived entirely from computer simulations and advanced statistical modeling, and had extremely high discrimination (AUC 0.808 – 0.909) when applied to a human dataset of measurements of RRF. A clearance-based equation that utilized pre and post dialysis B2M measurements, patient weight, treatment duration and ultrafiltration had higher discrimination than an equation previously derived in humans. Furthermore, the derived equations appeared to have higher clinical usefulness as assessed by Decision Curve Analysis, potentially supporting decisions that for individualizing dialysis frequency in patients with preserved RRF.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
