**6. Other biofeedback systems**

Three other biofeedback systems were designed to reduce the occurrence of hypotensive episodes during HD. Rather than focusing on BV changes during HD, these devices use other targets (e.g. arterial BP) or other means of action (e.g. thermal balance, plasma conductivity). They are reviewed here briefly.

## **6.1 Arterial pressure biofeedback**

Arterial pressure biofeedback aimed at stabilizing BP during HD uses repetitive measurement of arterial blood pressure as the monitored parameter and a fuzzy-logic system as the controller of fluid removal. Created by B. Braun and implemented on the Dialog Advanced machines, the APBS® (Automatic Blood Pressure Stabilization system) puts blood pressure itself as the main input to the automatic fuzzy controller rather than a surrogate marker (e.g. blood volume). Fuzzy logic is a problem-solving system, rather than

Automated Blood Volume Regulation During Hemodialysis 43

compatibility of membranes, and in part by decreased blood flow to the skin, with subsequent heat retention. This phenomenon can contribute to hemodynamic instability. Cooler dialysate, by inducing vasoconstriction, is known to enhance vascular stability, but

Blood temperature control (BTC®, Fresenius) is a biofeedback system aimed at keeping body temperature stable throughout the session, with progressive decline of dialysate temperature in response to progressive increased in heat production, resulting in « isothermic dialysis » (Mercadal & Petitclerc, 2009). Designed by Fresenius, the blood temperature monitor (BTM) is composed of sensors in the arterial and venous lines and monitors blood temperature change by a thermodilution technique. Thermal balance can be maintained through the automatic modulation of the dialysate temperature (the output) by the BTC® software (the controller) in response to BTM measurements compared to the set

A systematic review, published in 2006, reviewed the most pertinent publications on the clinical effects of cool dialysate (Selby & McIntyre, 2006). Six of them, which were all crossover studies of relatively short duration, evaluated the use of BTM compared to various control groups. Overall, there was a significant decrease in IDH frequency with reduction in dialysate temperature using BTM, with a rate of IDH 2.0 (95% CI, 1.9-2.1) times less than

This biofeedback system was designed to allow variation in the dialysate sodium (Na) concentration to better suit the patient's initial plasmatic value and parallel the changes in plasma Na concentration occurring during dialysis. Instead of a fixed dialysate Na concentration, a target final plasma conductivity (as a surrogate for Na) is rather prescribed, and thus the patient's post-dialysis sodium concentration is independent of the initial status. The goal of this system is to maximise sodium removal individually for each session, but avoiding large gap between plasmatic and dialysate sodium, which can produce patient's discomfort, hypotension and cramps (if dialysate Na is lower) or sodium loading, thirst and

The Diascan® (Gambro) module monitors the patient's plasma conductivity every 15 minutes through conductivity probes located at the dialysate inlet and outlet. The software Diacontrol® (Gambro) computes this information and softly and gradually modulates the dialysate conductivity in order to reach the prespecified target plasmatic conductivity at the end of the session. The curve of the conductivity trajectory is pre-defined and minimizes large variations to avoid rapid shift of plasmatic osmolality and disequilibrium syndrome. Again, large randomized trials are lacking to evaluate the utility of this feedback system, and results of two small prospective studies published recently are conflicting. Both compared Diacontrol® to standard dialysis in stable patients to assess whether gradual decrease in target conductivity, and consequent increased ionic mass balance (meaning increased sodium removal) could be achieved. Manlucu and colleagues (2010) found a significant reduction in end plasmatic conductivity and in ionic mass balance, with consequent reduction in IDWG and BP. On the contrary, Selby and al. (2007) found a lower final conductivity with fixed dialysate conductivity, and no difference in BP, IDH frequency or dialysis tolerability. Hence, demonstration of a Diacontrol® beneficial effect remains to be

poor tolerance due to chills and discomfort is a major drawback to its use.

target temperature (the input).

**6.3 Plasma conductivity biofeedback system** 

worsening hypertension (if dialysate Na is higher).

control group.

proven.

a mathematical model, and is reported to be better suited to analyse and compute non-linear data systems. It mimics how a person would make a decision, based on judgments such as: « if X, then Y», according to the rules pre-set by the operator. In a practical manner, the operator has to set two parameters for each patient: the BP set point and the maximal UF rate. The set-point is the critical BP level at which the patient experiences symptomatic hypotension, or simply the BP threshold at which the nurse or the physician would consider stopping the UF in that particular patient. The maximal UF rate is defined as the maximal rate of fluid removal that can be applied at any time, since this system is designed to maximise UF rate at the start of the HD session and to minimize it towards the end. With a specialized arm cuff that takes BP measurements every five minutes, three variables are calculated (Mancini et al., 2007): 1) Relative difference between actual systolic BP and the pre-adjusted set point; 2) Short-term pressure trend (15 min); 3) Long-term pressure trend (25 min). These input data are then computed by the fuzzy controller through several steps that involve probabilistic reasoning according to specific rule bases, and ultimately result in modulation of the UF rate, in a closed feedback loop (figure 5). This system allows gradual and continuous variations of the UF rate, as it varies proportionally to the changes of the BP trends.

Fig. 5. Fuzzy control of the UF rate.

Technical scheme of the closed-loop system for the fuzzy control of the UF rate. (Adapted from Mancini et al., 2007)

Literature on the use of fuzzy logic control in preventing IDH is still scarce, but a prospective multi-center study published in 2007 (Mancini et al., 2007) showed a significant decrease of 25% in IDH incidence in 55 hypotension-prone patients. The authors emphasized the need to introduce correct critical BP for each patient for the fuzzy controller to perform adequately (Mancini et al., 2007).

### **6.2 Thermal balance system**

During HD, body temperature usually rises due in part to an increased production of heat secondary to inflammatory reactions induced by imperfect dialysate water and bio-

a mathematical model, and is reported to be better suited to analyse and compute non-linear data systems. It mimics how a person would make a decision, based on judgments such as: « if X, then Y», according to the rules pre-set by the operator. In a practical manner, the operator has to set two parameters for each patient: the BP set point and the maximal UF rate. The set-point is the critical BP level at which the patient experiences symptomatic hypotension, or simply the BP threshold at which the nurse or the physician would consider stopping the UF in that particular patient. The maximal UF rate is defined as the maximal rate of fluid removal that can be applied at any time, since this system is designed to maximise UF rate at the start of the HD session and to minimize it towards the end. With a specialized arm cuff that takes BP measurements every five minutes, three variables are calculated (Mancini et al., 2007): 1) Relative difference between actual systolic BP and the pre-adjusted set point; 2) Short-term pressure trend (15 min); 3) Long-term pressure trend (25 min). These input data are then computed by the fuzzy controller through several steps that involve probabilistic reasoning according to specific rule bases, and ultimately result in modulation of the UF rate, in a closed feedback loop (figure 5). This system allows gradual and continuous variations of the UF rate, as it varies proportionally to the changes of the BP

Technical scheme of the closed-loop system for the fuzzy control of the UF rate. (Adapted

prospective multi-center study published in 2007 (Mancini et al., 2007) showed a significant

emphasized the need to introduce correct critical BP for each patient for the fuzzy controller

During HD, body temperature usually rises due in part to an increased production of heat secondary to inflammatory reactions induced by imperfect dialysate water and bio-

Literature on the use of fuzzy logic control in preventing IDH is still scarce, but a

decrease of 25% in IDH incidence in 55 hypotension-prone patients. The authors

trends.

Fig. 5. Fuzzy control of the UF rate.

to perform adequately (Mancini et al., 2007).

from Mancini et al., 2007)

**6.2 Thermal balance system**

compatibility of membranes, and in part by decreased blood flow to the skin, with subsequent heat retention. This phenomenon can contribute to hemodynamic instability. Cooler dialysate, by inducing vasoconstriction, is known to enhance vascular stability, but poor tolerance due to chills and discomfort is a major drawback to its use.

Blood temperature control (BTC®, Fresenius) is a biofeedback system aimed at keeping body temperature stable throughout the session, with progressive decline of dialysate temperature in response to progressive increased in heat production, resulting in « isothermic dialysis » (Mercadal & Petitclerc, 2009). Designed by Fresenius, the blood temperature monitor (BTM) is composed of sensors in the arterial and venous lines and monitors blood temperature change by a thermodilution technique. Thermal balance can be maintained through the automatic modulation of the dialysate temperature (the output) by the BTC® software (the controller) in response to BTM measurements compared to the set target temperature (the input).

A systematic review, published in 2006, reviewed the most pertinent publications on the clinical effects of cool dialysate (Selby & McIntyre, 2006). Six of them, which were all crossover studies of relatively short duration, evaluated the use of BTM compared to various control groups. Overall, there was a significant decrease in IDH frequency with reduction in dialysate temperature using BTM, with a rate of IDH 2.0 (95% CI, 1.9-2.1) times less than control group.

### **6.3 Plasma conductivity biofeedback system**

This biofeedback system was designed to allow variation in the dialysate sodium (Na) concentration to better suit the patient's initial plasmatic value and parallel the changes in plasma Na concentration occurring during dialysis. Instead of a fixed dialysate Na concentration, a target final plasma conductivity (as a surrogate for Na) is rather prescribed, and thus the patient's post-dialysis sodium concentration is independent of the initial status. The goal of this system is to maximise sodium removal individually for each session, but avoiding large gap between plasmatic and dialysate sodium, which can produce patient's discomfort, hypotension and cramps (if dialysate Na is lower) or sodium loading, thirst and worsening hypertension (if dialysate Na is higher).

The Diascan® (Gambro) module monitors the patient's plasma conductivity every 15 minutes through conductivity probes located at the dialysate inlet and outlet. The software Diacontrol® (Gambro) computes this information and softly and gradually modulates the dialysate conductivity in order to reach the prespecified target plasmatic conductivity at the end of the session. The curve of the conductivity trajectory is pre-defined and minimizes large variations to avoid rapid shift of plasmatic osmolality and disequilibrium syndrome.

Again, large randomized trials are lacking to evaluate the utility of this feedback system, and results of two small prospective studies published recently are conflicting. Both compared Diacontrol® to standard dialysis in stable patients to assess whether gradual decrease in target conductivity, and consequent increased ionic mass balance (meaning increased sodium removal) could be achieved. Manlucu and colleagues (2010) found a significant reduction in end plasmatic conductivity and in ionic mass balance, with consequent reduction in IDWG and BP. On the contrary, Selby and al. (2007) found a lower final conductivity with fixed dialysate conductivity, and no difference in BP, IDH frequency or dialysis tolerability. Hence, demonstration of a Diacontrol® beneficial effect remains to be proven.

Automated Blood Volume Regulation During Hemodialysis 45

Déziel, C., Bouchard, J., Zellweger, M. & Madore, F. (2007). Impact of Hemocontrol on

Franssen, CFM., Dasselaar, JJ., Sytsma, P., Burgerhof, JGM., De Jong, PE. & Huisman, RM.

Gabrielli, D., Kristal, B., Katzarski, K., Youssef, M., Hachache, T., Lopot, F., Lasseur, C.,

Garzoni, D., Keusch, G., Kleinoeder, T., Martin, H., Dhondt, A., Cremaschi, L., Tatsis, E.,

Locatelli, F., Buoncristiani, U., Canaud, B., Köhler, H., Petitclerc, T. & Zucchelli, P. (2005).

Mancini, E., Mambelli, E., Irpinia, M., Gabrielli, D., Cascone, C., Conte, F., Meneghel, G.,

Manlucu, J.,Gallo, K., Heidenheim, PA. & Lindsay, RM. (2010). Lowering postdialysis

McIntyre, CW., Lambie, SH. & Fluck, RJ. (2003). Biofeedback controlled hemodialysis

Mercadal, L. & Petitclerc, T. (2009). Technical advances in haemodialysis. *Néphrologie &* 

Mitra, S., Chamney, P., Greenwood, R. & Farrington, K. (2004). The relationship between

Mizumasa, T., Hirakata, H., Yoshimitsu, T., Kubo, M., Kashiwagi, M., Tanaka, H., Kanai, H.,

Moret, K., Aalten, J., van den Wall Bake, W., Gerlag, P., Beerenhout, C., van der Sande, F.,

hemodialysis. *J Am Soc Nephrol.*Vol.15, No.2, pp. 463-469

trial. *Clin J Am Soc Nephrol*. Vol.2, No.4, pp. 661-668

*Hemodialysis International.* Vol.9, No.4, pp. 383-392

ultrafiltration. *Int J Artif Organs.* Vol.30, No.1, pp. 16-24

*Nephrol.* Vol.22, No.2, pp. 232-240

*Transplant*. Vol.20, No.1, pp. 22-33

*Transplant.*Vol.22, No.5, pp. 1420-1427

*Kidney Dis.* Vol.56, No.1, pp. 69-76

*Thérapeutique*. Vol.5, No.2, pp. 109-113

*Nephron Clin Pract*. Vol.97, No.1, pp. 23–30

105-112

144

hypertension, nursing interventions, and quality of life: A randomized, controlled

(2005). Automatic feedback control of relative blood volume changes during hemodialysis improves blood pressure stability during and after dialysis.

Gunne, T., Draganov, B., Wojke, R. & Gauly, A. (2009). Improved intradialytic stability during haemodialysis with blood volume-controlled ultrafiltration. *J* 

Ibrahim, N., Boer, W., Kuehne, S., Claus, M., Zahn, M., Schuemann, E., Engelmann, J., Hickstein, H., Wojke, R., Gauly, A. & Passlick-Deetjen, J. (2007). Reduced complications during hemodialysis by automatic blood volume controlled

Haemodialysis with on-line monitoring equipment: tool or toys? *Nephrol Dial* 

Cavatorta, F., Antonelli, A., Villa, G., Dal Canton, A., Cagnoli, L., Aucella, F., Fiorini, F., Gaggiotti, E., Triolo, G., Nuzzo, V. & Santoro, A. (2007). Prevention of dialysis hypotension episodes using fuzzy logic control system. *Nephrol Dial* 

plasma sodium (conductivity) to increase sodium removal in volume-expanded hemodialysis patients: A pilot study using a biofeedback software system. *Am J* 

reduces symptoms and increases both hemodynamic tolerability and dialysis adequacy in non-hypotension prone stable patients. *Clin Nephrol.* Vol.60, No.2, pp.

systemic and whole-body hematocrit is not constant during ultrafiltration on

Fujimi, S. & Iida, M. (2004). Dialysis-related hypotension as a cause of progressive frontal lobe atrophy in chronic hemodialysis patients: a 3-year prospective study.

Leunissen, K. & Kooman, J. (2006). The effect of sodium profiling and feedback technologies on plasma conductivity and ionic mass balance: a study in hypotension-prone dialysis patients. *Nephrol Dial Transplant.* Vol.21, No.1, pp. 138-

## **7. Conclusions**

IDH is the most frequent complication of dialysis, and is associated with significant patient morbidity. Although pathogenesis is multifactorial, blood volume reduction appears to be central in the development of such events, especially when cardio-vascular compensatory mechanisms are impaired. In an attempt to reduce hypotensive episodes, blood volume biofeedback devices have been developed. The underlying premise of such devices is to automatically adjust dialysis parameters such as UF rate and dialysate conductivity, in response to variations of monitored patient's characteristics, in order to make dialysis sessions more physiological and to prevent IDH by acting on subclinical signs of hemodynamic instability. Evidence supports BV biofeedback in hypotensive prone patients to reduce occurrence of IDH, nursing interventions, and probably intra- and inter- dialytic symptoms, although no large scale randomized trial has been published to date. BV biofeedback may also be helpful to enhance vascular tolerance in stable patients, but literature is limited. Data concerning improvement of hypertension and volume overload, as well as improvement of dialysis delivery, is conflicting. Finally, there is no large randomized trial that assessed the impact of automatic BV control on morbidity and mortality. Data suggesting that Crit-line® based algorithm of hypertension management is associated with higher hospitalisation and mortality rates are of concern. Larger and longterm randomized trials comparing BV biofeedback devices to standard HD are needed to better define the impact of these novel technologies on patient outcomes.
