A reliable method to assess the water permeability of a dialysis system: the global ultrafiltration coefficient

Enhancement of solute clearance using pulsatile push-pull dialysate flow for the Quanta SC+: A novel clinic-to-home haemodialysis system

BACKGROUND AND OBJECTIVE

The SC+ haemodialysis system developed by Quanta Dialysis Technologies is a small, easy-to-use dialysis system designed to improve patient access to self-care and home haemodialysis. A prototype variant of the standard SC+ device with a modified fluidic management system generating a pulsatile push-pull dialysate flow through the dialyser during use has been developed for evaluation. It was hypothesized that, as a consequence of the pulsatile push-pull flow through the dialyser, the boundary layers at the membrane surface would be disrupted, thereby enhancing solute transport across the membrane, modifying protein fouling and maintaining the surface area available for mass and fluid transport throughout the whole treatment, leading to solute transport (clearance) enhancement compared to normal haemodialysis (HD) operation.

METHODS

The pumping action of the SC+ system was modified by altering the sequence and timings of the valves and pumps associated with the flow balancing chambers that push and pull dialysis fluid to and from the dialyser. Using this unique prototype device, solute clearance performance was assessed across a range of molecular weights in two related series of laboratory bench studies. The first measured dialysis fluid moving across the dialyser membrane using ultrasonic flowmeters to establish the validity of the approach; solute clearance was subsequently measured using fluorescently tagged dextran molecules as surrogates for uraemic toxins. The second study used human blood doped with uraemic toxins collected from the spent dialysate of dialysis patients to quantify solute transport. In both, the performance of the SC+ prototype was assessed alongside reference devices operating in HD and pre-dilution haemodiafiltration (HDF) modes.

RESULTS

Initial testing with fluorescein-tagged dextran molecules (0.3 kDa, 4 kDa, 10 kDa and 20 kDa) established the validity of the experimental pulsatile push-pull operation in the SC+ system to enhance clearance and demonstrated a 10 to 15% improvement above the current HD mode used in clinic today. The magnitude of the observed enhancement compared favourably with that achieved using pre-dilution HDF with a substitution fluid flow rate of 60 mL/min (equivalent to a substitution volume of 14.4 L in a 4-hour session) with the same dialyser and marker molecules. Additional testing using human blood indicated a comparable performance to pre-dilution HDF; however, in contrast with HDF, which demonstrated a gradual decrease in solute removal, the clearance values using the pulsatile push-pull method on the SC+ system were maintained over the entire duration of treatment. Overall albumin losses were not different.

CONCLUSIONS

Results obtained using an experimental pulsatile push-pull dialysis flow configuration with an aqueous blood analogue and human blood ex vivo demonstrate an enhancement of solute transport across the dialyser membrane. The level of enhancement makes this approach comparable with that achieved using pre-dilution HDF with a substitution fluid flow rate of 60 mL/min (equivalent to a substitution volume of 14.4 L in a 4-hour session). The observed enhancement of solute transport is attributed to the disruption of the boundary layers at the fluid-membrane interface which, when used with blood, minimizes protein fouling and maintains the surface area.

Measuring intradialyser transmembrane and hydrostatic pressures: pitfalls and relevance in haemodialysis and haemodiafiltration

Background

Post-dilutional haemodiafiltration (HDF) with high convection volumes (HCVs) could improve survival. HCV-HDF requires a significant pressure to be applied to the dialyser membrane. The aim of this study was to assess the pressure applied to the dialysers in HCV-HDF, evaluate the influence of transmembrane pressure (TMP) calculation methods on TMP values and check how they relate to the safety limits proposed by guidelines.

Methods

Nine stable dialysis patients were treated with post-dilutional HCV-HDF with three different convection volumes [including haemodialysis (HD)]. The pressures at blood inlet (B_i), blood outlet (B_o) and dialysate outlet (D_o) were continuously recorded. TMP was calculated using two pressures (TMP2: B_o, D_o) or three pressures (TMP3: B_o, D_o, B_i). Dialysis parameters were analysed at the start of the session and at the end of treatment or at the first occurrence of a manual intervention to decrease convection due to TMP alarms.

Results

During HD sessions, TMP2 and TMP3 remained stable. During HCV-HDF, TMP2 remained stable while TMP3 clearly increased. For the same condition, TMP3 could be 3-fold greater than TMP2. This shows that the TMP limit of 300 mmHg as recommended by guidelines could have different effects according to the TMP calculation method. In HCV-HDF, the pressure at the B_i increased over time and exceeded the safety limits of 600 mmHg provided by the manufacturer, even when respecting TMP safety limits.

Conclusions

This study draws our attention to the dangers of using a two-pressure points TMP calculation, particularly when performing HCV-HDF.

Artificial Intelligence for the Artificial Kidney: Pointers to the Future of a Personalized Hemodialysis Therapy

Background

Current dialysis devices are not able to react when unexpected changes occur during dialysis treatment or to learn about experience for therapy personalization. Furthermore, great efforts are dedicated to develop miniaturized artificial kidneys to achieve a continuous and personalized dialysis therapy, in order to improve the patient's quality of life. These innovative dialysis devices will require a real-time monitoring of equipment alarms, dialysis parameters, and patient-related data to ensure patient safety and to allow instantaneous changes of the dialysis prescription for the assessment of their adequacy. The analysis and evaluation of the resulting large-scale data sets enters the realm of "big data" and will require real-time predictive models. These may come from the fields of machine learning and computational intelligence, both included in artificial intelligence, a branch of engineering involved with the creation of devices that simulate intelligent behavior. The incorporation of artificial intelligence should provide a fully new approach to data analysis, enabling future advances in personalized dialysis therapies. With the purpose to learn about the present and potential future impact on medicine from experts in artificial intelligence and machine learning, a scientific meeting was organized in the Hospital Universitari Bellvitge (L'Hospitalet, Barcelona). As an outcome of that meeting, the aim of this review is to investigate artificial intel ligence experiences on dialysis, with a focus on potential barriers, challenges, and prospects for future applications of these technologies.

Summary and Key Messages

Artificial intelligence research on dialysis is still in an early stage, and the main challenge relies on interpretability and/or comprehensibility of data models when applied to decision making. Artificial neural networks and medical decision support systems have been used to make predictions about anemia, total body water, or intradialysis hypotension and are promising approaches for the prescription and monitoring of hemodialysis therapy. Current dialysis machines are continuously improving due to innovative technological developments, but patient safety is still a key challenge. Real-time monitoring systems, coupled with automatic instantaneous biofeedback, will allow changing dialysis prescriptions continuously. The integration of vital sign monitoring with dialysis parameters will produce large data sets that will require the use of data analysis techniques, possibly from the area of machine learning, in order to make better decisions and increase the safety of patients.

Consequences of increasing convection onto patient care and protein removal in hemodialysis

INTRODUCTION

Recent randomised controlled trials suggest that on-line hemodiafiltration (OL-HDF) improves survival, provided that it reaches high convective volumes. However, there is scant information on the feasibility and the consequences of modifying convection volumes in clinics.

METHODS

Twelve stable dialysis patients were treated with high-flux 1.8 m2 polysulphone dialyzers and 4 levels of convection flows (QUF) based on GKD-UF monitoring of the system, for 1 week each. The consequences on dialysis delivery (transmembrane pressure (TMP), number of alarms, % of achieved prescribed convection) and efficacy (mass removal of low and high molecular weight compounds) were analysed.

RESULTS

TMP increased exponentially with QUF (p<0.001 for N >56,000 monitoring values). Beyond 21 L/session, this resulted into frequent TMP alarms requiring nursing staff interventions (mean ± SEM: 10.3 ± 2.2 alarms per session, p<0.001 compared to lower convection volumes). Optimal convection volumes as assessed by GKD-UF-max were 20.6 ± 0.4 L/session, whilst 4 supplementary litres were obtained in the maximum situation (24.5 ± 0.6 L/session) but the proportion of sessions achieving the prescribed convection volume decreased from 94% to only 33% (p<0.001). Convection increased high molecular weight compound removal and shifted the membrane cut-off towards the higher molecular weight range.

CONCLUSIONS

Reaching high convection volumes as recommended by the recent RCTs (> 20L) is feasible by setting an HDF system at its optimal conditions based upon the GKD-UF monitoring. Prescribing higher convection volumes resulted in instability of the system, provoked alarms, was bothersome for the nursing staff and the patients, rarely achieved the prescribed convection volumes and increased removal of high molecular weight compounds, notably albumin.