Related Experiment Video
Updated: Jun 6, 2026
09:20
Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
Published on: February 13, 2021
Linear parameter varying system based modeling of hemodynamic response to profiled hemodialysis
Faizan Javed1, Andrey V Savkin, Gregory S H Chan
1School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney, 2052, Australia. faizan@student.unsw.edu.au
Summary
This study developed a linear parameter varying (LPV) model to predict hemodynamic changes in end-stage renal failure patients undergoing profiled hemodialysis (PHD). The model accurately estimates relative blood volume and blood pressure variations during treatment.
Area of Science:
- Biomedical Engineering
- Physiological Modeling
- Renal Medicine
Background:
- End-stage renal failure (ESRD) patients undergoing hemodialysis experience significant hemodynamic fluctuations.
- Maintaining hemodynamic stability during dialysis is crucial for patient safety and treatment efficacy.
- Current methods for monitoring and managing hemodynamic responses during dialysis may lack precision for individual patient needs.
Purpose of the Study:
- To develop a novel linear parameter varying (LPV) system model.
- To accurately predict the hemodynamic response of end-stage renal failure patients during profiled hemodialysis (PHD).
- To provide a foundation for designing advanced control systems for hemodialysis.
Main Methods:
- A linear parameter varying (LPV) system was proposed to model hemodynamic responses.
- Ultrafiltration rate (UFR) and dialysate sodium concentration (Na) were used as control inputs.
- Model parameters were estimated using a least squares approach with data from 12 ESRD patients undergoing 4 PHD sessions each.
- Key hemodynamic variables including relative blood volume (RBV), percentage change in heart rate (ΔDHR(%)), and percentage change in systolic blood pressure (ΔDSBP(%)) were computed.
Main Results:
- The LPV model demonstrated good accuracy in estimating individual patient hemodynamic behavior.
- Parameter identification yielded an average mean square error of 0.11 for RBV, 0.24 for ΔDHR, and 0.43 for ΔDSBP across profiled sessions.
- The model effectively captured the dynamic changes in RBV, ΔDHR, and ΔDSBP during PHD.
Conclusions:
- The developed LPV model provides a reliable method for estimating patient-specific hemodynamic responses during PHD.
- This model can be instrumental in designing robust control systems for automated regulation of UFR and Na.
- The research facilitates maintaining hemodynamic stability within safe ranges for ESRD patients undergoing dialysis.
More Related Videos
Related Concept Videos
01:25Hemodialysis I: Introduction
Hemodialysis (HD) is a medical treatment that artificially removes waste products, excess fluids, and toxins from the blood when the kidneys are no longer able to perform these functions effectively. In this process, blood is filtered through a semipermeable membrane, allowing for the selective removal of waste while preserving necessary components like blood cells and proteins. Hemodialysis is typically performed in patients with end-stage renal disease (ESRD) or severe kidney...
01:24Hemodialysis II: Procedure and Complications
DialyzersA hemodialysis (HD) dialyzer is a plastic cartridge containing thousands of parallel hollow fibers, which serve as semipermeable membranes. These fibers are typically made from cellulose-based or other synthetic materials. During HD, blood is pumped into the top of the cartridge and distributed among these fibers. Simultaneously, dialysis fluid, known as dialysate, is introduced into the bottom of the cartridge, bathing the outside of the fibers. Across the semipermeable membrane,...
01:25Hemodialysis III: Nursing Management
The nursing management of a patient undergoing hemodialysis includes several critical steps, starting with a thorough assessment before the procedure.Before the Hemodialysis ProcedureFirst, record the patient's vital signs—blood pressure, heart rate, respiratory rate, and temperature—to establish a baseline. This baseline is essential for detecting conditions such as hypotension that could impact the patient's response to dialysis. Document the patient's pre-dialysis weight, as this measurement...
01:14Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model
The link model is a fundamental pharmacokinetic-pharmacodynamic (PK–PD) approach to account for delayed drug responses when the observed effect does not immediately correlate with the drug's plasma concentration peak. This delay is mathematically addressed by introducing an effect compartment concentration, Ce, which is kinetically linked to the plasma concentration, Cp, via a first-order rate constant, ke0. The linkage allows for a more accurate prediction of drug effects over time. A higher...
01:29Pharmacodynamic Models: Direct Effect Model and Indirect Response Model
Pharmacodynamic models are essential tools in understanding the relationship between drug concentrations and their effects on biological systems. By characterizing the dynamics of drug action, these models guide dose selection, optimize therapeutic efficacy, and inform the development of new drugs. Two major classes of pharmacodynamic models include direct effect and indirect response models.Direct Effect ModelsDirect effect models describe the immediate relationship between drug concentration...
01:06Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...