Year : 2008 | Volume
: 19 | Issue : 6 | Page : 895--902
Biofeedback systems and adaptive control hemodialysis treatment
Ahmad Taher Azar
Biomedical Engineering Department, Misr University for Science & Technology, 6th October City, Egypt
Ahmad Taher Azar
Ahmad Orabi Square, Azar Building, Menouf, Menoufeya
On-line monitoring devices to control functions such as volume, body temperature, and ultrafiltration, were considered more toys than real tools for routine clinical application. However, bio-feedback blood volume controlled hemodialysis (HD) is now possible in routine dialysis, allowing the delivery of a more physiologically acceptable treatment. This system has proved to reduce the incidence of intra-HD hypotension episodes significantly. Ionic dialysance and the patient«SQ»s plasma conductivity can be calculated easily from on-line measurements at two different steps of dialysate conductivity. A bio-feedback system has been devised to calculate the patient«SQ»s plasma conductivity and modulate the conductivity of the dialysate continuously in order to achieve a desired end-dialysis patient plasma conductivity corresponding to a desired enddialysis plasma sodium concentration. Another bio-feedback system can control the body temperature by measuring it at the arterial and venous lines of the extra-corporeal circuit, and then modulating the dialysate temperature in order to stabilize the patients«SQ» temperature at constant values that result in improved intra-HD cardiovascular stability. The module can also be used to quantify vascular access recirculation. Finally, the simultaneous computer control of ultrafiltration has proven the most effective means for automatic blood pressure stabilization during hemodialysis treatment. The application of fuzzy logic in the blood-pressure-guided biofeedback control of ultrafiltration during hemodialysis is able to minimize HD-induced hypotension. In conclusion, online monitoring and adaptive control of the patient during the dialysis session using the bio-feedback systems is expected to render the process of renal replacement therapy more physiological and less eventful.
|How to cite this article:|
Azar AT. Biofeedback systems and adaptive control hemodialysis treatment.Saudi J Kidney Dis Transpl 2008;19:895-902
|How to cite this URL:|
Azar AT. Biofeedback systems and adaptive control hemodialysis treatment. Saudi J Kidney Dis Transpl [serial online] 2008 [cited 2019 Sep 21 ];19:895-902
Available from: http://www.sjkdt.org/text.asp?2008/19/6/895/43462
Over the last two decades, technological innovations in dialysis equipment and new modes of therapy have improved the quality and safety of hemodialysis (HD) treatment. The traditional HD treatment is usually approached in the prescription and delivery of the dose of dialysis measured as Kt/V. , The medical staff determine the time(t) of dialysis and clearance (K) for the estimated patient volume (V) to reach the prescribed Kt/V. Disturbances such as errors in actual blood flow because of erroneous pump calibration, changes in dialyzer clearance because of fiber clotting, and reduction in effective clearance because of recirculation and compartment effects result in a difference between the delivered and the prescribed dose of dialysis. , In the forward control system, there is no means to compensate for this difference because there is no information on the actual output.
The control of the system output is improved when the controller receives information from the actual output in the so-called feedback control system.  For example; to control the dose of dialysis, effective clearance must be measured and compared to prescribed clearance.  The machine receives information from the medical staff regarding the UF volume (UFV) together with a specific UF profile and the treatment time (t) derived from the dose of dialysis (Kt/V), which depends on patient volume (V) and dialyzer clearance (K D ) determined by the dialyzer mass transfer coefficient (K 0 A), the dialysate flow (Q D ) and the effective blood flow (Q B ). From the difference between delivered and prescribed clearance (negative feedback), the automatic controller adjusts the different factors such as blood flow, dialysate flow, and treatment time to compensate for the error between output and set-point. The ultimate goal in hemodialysis is to improve patient comfort and proceed without use of additional body sensors or medication. Automatic control of hemodialysis has the potential to provide this goal to the ever increasing number of end-stage renal disease (ESRD) patients who present with more complicated co-morbid conditions.
The Biofeedback Approach
The concept of on-line monitoring is based on the real time and repeated measurement of chemical/physical signals coming from the patient with biosensors in order to identify as early as possible inadequate performances or physiological abnormalities induced by the treatment that may result in consequent patient discomfort and dialysis-related side effects. Once the measurement of a given signal is obtained, data are analyzed and evaluated. If the parameter is within the desired values, the treatment continues unchanged, otherwise, an action to bring it back to the desired value is needed. This action can be operated manually (by the operator), semi-automatically (authorized feedback by a nurse or a doctor) or automatically by a bio-feedback loop integrated into the machine.  This is the concept of auto matic bio-feedback. ,,
While any action performed by an operator necessarily implies a certain time lag, the automatic feedback is notable for its capability of immediate analysis of the signal coming from the patient and immediate responsive action to bring the parameter back to the desired value. The final result of automatic biofeedback systems is that the controlled variables are modulated gradually and smoothly along a pre-defined trajectory towards a pre-defined target.  Biofeedback loops could be also used to modify blood flow rates in response to inadequately predicted Kt/V by a urea sensor. Today, the different parameters for which online monitoring is possible are: blood volume (BV) changes, dialysate conductivity, urea kinetics and thermal energy balance.
1. Blood Volume Biofeedback System
Recently Santoro et al modified, along with the Gambro-Hospal research group, their first automatic BV control system based on variable ultrafiltration.  The new feedback control system is based on an adaptive controller, capable of forcing the BV trends along preselected trajectories by means of both ultrafiltration as well as sodium concentration. , This system is a software loop designed to trigger a biofeedback response based on signals derived online from the patient/machine complex. The integrated multi-input-multi-output controller (MIMO) and its software are the heart of this biofeedback system whose targets are prescribed total body weight loss, equivalent dialysate conductivity and relative BV change [Figure 1] , The monitored discrepancies between the instantaneous actual value and the instantaneous desired target for BV change, dialysate conductivity, and weight loss represent the input parameters for the controller. The control variables, used as output parameters, are the instantaneous dialysate conductivity and weight loss rate, which can vary from instant to instant to reach the desired targets. The goal of this closed-loop bio-feedback software is to reach the best compromise, according to an error-based mathematical model, between the various targets, taking into account the fact that these targets sometimes may be in reciprocal conflict. In the presence of substantial errors, the model is able to automatically update both the ultrafiltration and the conductivity with a view to minimizing any discrepancies there may be between the ideal volemia trajectories and the experimentally obtained ones, as well as any relevant errors in the patients "body weight reductions". , For greater safety during treatment, ultrafiltration and conductivity, i.e. the two independent variables, can fluctuate only within a well-defined range, established at the start of the treatment according to the patients' clinical characteristics.  To correct a large BV change, one solution could be to reduce weight loss rate (WLR), but this cannot be done below certain limits without indefinitely prolonging the duration of the session. Moreover, the overall system, apart from regulating the BV profile according to desired trajectories, makes it possible to prescribe adequate ultrafiltration to achieve the ideal body weight in the individual patients along with personalized intradialytic sodium balance. 
Apart from simple on-off techniques to more complex fuzzy logic controllers only two feedback systems are commercially available today, the Hospal and the Fresenius systems.
The Hospal system proposes two bio-feedback systems, one focuses on BV management (Hemocontrol) and the other on sodium management (Diacontrol). Hemocontrol: Integra dialysis machines are equipped with the Hemocontrol software coupled to a blood volume variation monitor. The blood volume monitor (Hemoscan, Hospal-Gambro) consists of an optical probe located on the arterial line of the extracorporeal circuit and measures blood volume variations through changes in hemoglobin concentration. Thus an increase in hemoglobin concentration reflects a proportional decrease in circulating blood volume and vice versa. The Hemocontrol software analyzes the data on blood volume variations and, through a feedback mechanism, continuously adjusts both ultrafiltration rate and dialysate conductivity.  The main objective of the system is to maintain the blood volume above a critical level below which hypotension occurred in previous hemodialysis sessions.  Diascan and Diacontrol: The dialysis monitor Integra (Monitral S; Hospal, Medolla, Italy) is equipped with a specially designed "Diascan Module" (COT; Hospal, Meyzeu, France) connected to the dialysate line between the dialyzer and the dialysis machine. This module consists of (i) the implementation in the dialysis machine of a conductivity probe at the dialysate outlet of the dialyzer; (ii) appropriate software (Diacontrol). Each 30 min, the Diascan software records values X1 and Y1 of the dialysate conductivity at the dialyzer inlet and outlet, changes the conductivity of the dialysate delivered by the dialysis machine during about 2 min, records values X2 and Y2 and then calculates the values of ionic dialysance and effective plasma conductivity (see appendix).  Diacontrol is a specific software specially designed for the Integra dialysis monitor. From the values of ionic dialysance, a reflection of sodium dialysance and plasma conductivity measured by the Diascan monitor, Diacontrol determines automatically the optimal dialysate conductivity for each individual patient in order to reach the desired value of the effective plasma conductivity at the end of the session. , Thus the automatic optimization of dialysate sodium by Diacontrol can be considered safer for the patient than a prescription based on empirical and intuitive knowledge.
On the other hand, the Fresenius control system is based on the definition of a critical current blood volume and on declining UF rates. However, the system does not track a pre-defined BV trajectory and does not aim to reach a target blood volume reduction. Controlled fluid removal is achieved by a continuous determination of UF rates according to the following rules: a) volume must be removed within the treatment time; b) initial UF rate is set at twice the constant UF rate; c) if RBV drops more than half of the distance between the current relative blood volume (RBV) and the critical RBV, UF rate is linearly decreased.  Thus, in a treatment without a drop of RBV below half of the distance between the current and critical RBV, the algorithm provides a linear decrease in UF rate, with UF starting at twice the constant UF rate and reaching zero UF rate at the end of the treatment. However, when RBV falls below half of the distance between the current and critical RBV, UF rate is further reduced such as defined by rule b) with the consequence that UF at the end of the treatment can no longer be zero. However, the system does not include control of dialysate conductivity, but it can operate in combination with the temperaturecontrol system offered by a blood temperature monitor. Even though both systems are grounded on measuring the same system output, i.e., relative blood volume changes, they are based on different approaches. However, a combined control of the three main aspects of hemodialysis including control of fluid removal, control of dialysate temperature, dialysate composition, has not yet been realized. 
2. Blood Temperature Biofeedback
The rationale of temperature control is to prevent heat accumulation, which increases body temperature in the patient during hemodialysis.  The dialysate temperature should be individualized with regard to actual patient temperature, blood flow, and treatment mode such as hemodialysis and hemodiafiltration. Furthermore, it is not sufficient to maintain a constant dialysate temperature throughout the treatment, but it should be adjusted to a defined patient temperature. Body temperature and thermal balance during HD can be controlled by the blood temperature monitor (BTM) available in some recent dialysis machines such as those made by Fresenius® .  Short sections of the extracorporeal circulation are inserted into arterial and venous measuring heads equipped with sensors to measure arterial and venous blood temperatures. The arterial and venous tube sections, which have to meet certain specifications to fit into the measuring heads, are located within a given distance from the access. The blood temperature at the access is then calculated for given insulation and environmental conditions using the extracorporeal blood flow measured by the dialysis machine. The precision of the temperature measurement is better than 0.1°C for blood flows above 120 ml/min.  Arterial temperature (Tart) and venous temperature (Tven) are measured with a sampling period of 15s. Recirculation is measured in 30 minute intervals utilizing the automatic thermodilution option. The thermodilution used in the BTM is based on changing dialysate temperature levels, which change the venous blood temperature returning to the patient.
BTM thermodilution yields results are consistent with the ultrasound dilution technique used to measure recirculation and access blood flow.  The BTM can be operated in two control modes. The T-control mode is used to control body temperature. Mixed venous blood temperature draining from all tissues can be considered a good representative of core temperature. However, the temperature of blood drawn from the patient's access does not necessarily reflect mixed venous blood temperature because of possible access and cardiopulmonary recirculation. The effects of recirculation on arterial line temperature are comparable to the effects of recirculation on arterial line urea concentration during dialysis and depend on the type and function of the access used.  The T-control mode requires the prescription of an hourly change in body temperature (in °C/hr) [Figure 2]. In the temperature control system, the target change in body temperature is compared to the measured change in body temperature (sensor output) by the BTM (sensor). The error signal obtained from this comparison (negative feedback) is entered into the controller to determine the dialysate temperature to be set by the dialysis machine (actuator) and to change the temperature in the patient (plant). Even if the actual body temperature (output) is disturbed by external effects (disturbance signal or noise) not accounted for by the control system such as vasoconstriction or increased metabolic rate, the information provided by the actual output (negative feedback) allows to compensate for such effects. The degree of compensation depends on the type of control (proportional, integral, and differential control).  For example, to control for a constant body temperature throughout a dialysis treatment and deliver an isothermic dialysis, the temperature change rate has to be set to ± 0.00°C/hr. The BTM controller uses the error signal between the desired and actual change in body temperature to trigger a bounded change in dialysate temperature that changes the temperature of the venous blood returning to the patient, thereby changing the extracorporeal heat flow. Body temperature is a physiologic variable. The control of this variable by the BTM may therefore be called a physiologic feedback control system. 
The BTM can also be operated in an Econtrol mode, which controls for the rate of thermal energy removal (in kJ/hr). To control for a treatment, where thermal energy is neither removed from nor delivered to the patient (extracorporeal thermoneutral dialysis), the thermal energy flow rate has to be set to 0 kJ/hr. Even if this type of control has indirect effects on patient temperature, it actually controls thermal flow rate, which is not a physiologic variable. Therefore, it is not a physiologic feedback control system. While the BTM can be used to measure fistula temperatures and extracorporeal heat balance (Jex) in almost any extracorporeal circulation where Q b is greater than 120 ml/min, the measureement of recirculation (R), the calculation of body temperature, and the operation of the two control modes requires the presence of ongoing dialysis. 
3. Arterial blood pressure biofeedback
Symptomatic hypotension occurs in up to 30% of hemodialysis treatments and represents one of the most severe intradialytic complications.  Therefore, in order to exert a feed-back control online, the essential prerequisite is the continuous measurement of the arterial pressure. The feedback control provides for the measurement of arterial pressure and its trend during the treatment and an accurate regulation of ultrafiltration. The system controller is based on fuzzy logic (FL). FL is a problem-solving control system that provides a simple method to define conclusion based on vague, ambiguous, imprecise, noisy or missing input information. It may control non-linear systems that would be difficult or impossible to model mathematically. Incorporating a simple, rulebased "if X and Y then Z" approach to solving a control problem rather than attempting to model a system mathematically, it mimics how a person would make decisions. ,, Probabilistic reasoning and fuzzy logic were used to transfer medical knowledge into a closed-loop system providing blood pressure control in hemodialysis patients prone to hypotension. The fuzzy controller allows the modulation of ultrafiltration proportionally to the variation trend in the arterial pressure and so small variations in blood pressure are matched by small variations in ultrafiltration or maintenance of constant ultrafiltration, while large pressure variations are matched by large variations in ultrafiltration. When the controller highlights a negative trend in the blood pressure, it reduces the ultrafiltration to almost zero when there is no pressure recovery. Obviously, a system limitation maybe having to respond categorically to given constraints such as duration of the session and the specified ultrafiltration. If rigid limits are set upon these parameters, then in some patients the control by the system becomes more difficult.  In 1995, Nordio et al, first described the results of their experience with the adaptive fuzzy control on a simulation model of blood pressure (BP) and blood volume implemented in a personal computer with satisfactory results in stabilizing BP as well as in achieving the desired dry weight.  In 2001, Schmidt R et al first tested this system in seven hypotensionprone patients in 237 treatments achieving less frequent and less severe hypotension events as compared with control group patients who were treated by conventional hemodialysis.  The closed-loop system for biofeedback control of blood pressure is shown in [Figure 3]. Recent study by Santoro A et al, was conducted by using automatic system (ABPS, automatic blood pressure stabilization) for BP control by fluid removal feedback regulation.  The system was implemented on a dialysis machine (Dialog Advanced, Braun). A fuzzy logic (FL) control runs in the system, using instantaneous BP as the input variable governing the ultrafiltration rate (UFR) according to the BP trend. The system is user-friendly and just requires the input of two data: critical BP (individually defined as the possible level of dialysis hypotension (DH) risk) and the highest UFR applicable (percentage of the mean UFR). At the start of hemodialysis, the UFR is at its highest level (so-called UFR max), which is subsequently retroactively reduced and adapted depending on the BP instant variation and on how much and how fast the BP approaches the BP set point. If the BP set point is reached, then the UFR automatically stops, after which it re-starts, once the critical hemodynamic moment has been overcome. The multicenter study included hypotension-prone patients alternately treated with ABPS-controlled BP or with conventional hemodialysis, and each patient was his/her own control. The results confirmed that the FL fits to analyze BP trends during dialysis, provided a correct critical BP parameter has been introduced. The automatic ABPS system based on this logic has allowed for an overall 25% reduction of intradialytic hypotensive episodes and 40% of severe episodes in the hypotension-prone subjects. The system is quite straightforward to use and only a few specific parameters are needed.  Moreover, FL may be suited to interpreting and controlling the trend of a determined multi-variable parameter such as BP.
4. Ionic dialysance bio-feedback
Effective ionic dialysance can be calculated automatically from dialysate conductivity measurements carried out by electrodes integrated into HD machines. ,, Ionic dialysance is equivalent to urea clearance corrected for recirculation.  Therefore, it is a valuable tool to monitor dialysis efficiency within a single HD session and from session to session, without the need for blood or dialysate sampling and at no extra cost. ,, The implementation of the conductivity kinetic model, replacing the previous sodium kinetic model that was unsuitable for routine application because it required blood sampling, permits the achievement of a zero sodium balance at each HD session. , It has been demonstrated that the conductivity kinetic model can significantly improve intra-HD cardiovascular stability. , A bio-feedback system depending on the calculation of the patient's plasma conductivity is able to modulate the conductivity of the dialysate continuously in order to achieve a desired end-dialysis patient plasma conductivity, corresponding to the desired enddialysis plasma sodium concentration. Ionic dialysance is also useful for the determination of the vascular access blood flow, therefore provides a tool for surveillance of vascular access function. ,
The technological innovation that has come about in the dialysis field in the past few years has allowed for the realization of sophisticated biofeedback systems based on the continuous measurement of physical variables such as body temperature or hemodynamic variables such as blood volume and arterial pressure. These systems have been implanted and used successfully in day-to-day dialysis practice and management of "difficult" and unstable patients. These feedback systems control for one or two patient variables at their best. However, for the full benefit the feedback control system would have to integrate all aspects of a controlled perturbation such as UFR, T D , [Na + ] D , Q D and K D . However, even such a system will be far from exhibiting the full benefits of feedback control. For example, current systems still require an input regarding the UF volume, the relative blood volume limit or the desired target volume reduction. An integrated feedback system should be able to autonomically prescribe and remove fluid depending on the degree of overhydration in patients and under the constraints of hemodynamic stability and considering the time dependence of internal parameters and their variations, such as aging, temperature, and physical condition.
The author thanks all medical staff at the nephrology department in Ahmad Maher Teaching Hospital, Cairo, Egypt for their invaluable support and special thanks to the maintenance department in the hospital.
Ionic dialysance (ID) can be determined by the following equation provided by Eq. 1 : ,,
Where Q D and Q f are dialysate and ultrafiltration flow rates, respectively.
The same parameters may also be used to estimate plasma water conductivity (C pw ) by means of Eq. 2: 
|1||Depner TA. Assessing adequacy of hemodialysis: Urea modeling. Kidney Int ;45:1522-35.|
|2||Gotch FA. Kt/V is the best dialysis dose parameter. Blood Purif 2000;18:276-85.|
|3||Schneditz D, Daugirdas JT. Compartment effects in hemodialysis. Semin Dial 2002;14: 271-7.|
|4||Paolini F, Bosetto A. Biofeedback systems architecture. Adv Renal Replace Ther 1999; 6:255-64.|
|5||Lindsay RM, Sternby J. Future directions in dialysis quantification. Semin Dial 2001;14: 300-7.|
|6||Ronco C, Ghezzi PM, La Greca G. The role of technology in hemodialysis. J Nephrol 1999;12 (Suppl 2):S68-81.|
|7||Santoro A, Mambelli E, Canova C, et al. Biofeedback in dialysis. J Nephrol 2003;16 (Suppl 7):S48-56.|
|8||Pilgram R, Schneditz D. Feedback Control Of Hemodialysis". 2 nd international congress of nephrology in internet, November, 5-30, 2001.|
|9||Locatelli F. Haemodialysis with on-line monitoring equipment: Tools or toys? Nephrol Dial Transplant 2005;20:22-33.|
|10||Santoro A, Mancini E, Paolini F, Spongano M, Zucchelli P. Automatic control of blood volume trends during hemodialysis. ASAIO J 1994;40 (3):M419-422.|
|11||Santoro A, Mancini E. Biofeedback in blood volume regulation during hemodialysis. Nefrologia 1997;17(suppl 1):56-60.|
|12||Santoro A, Mancini E, Paolini F, et al. Blood volume regulation during hemodialysis. Am J Kidney Dis 1998;32(5):739-48.|
|13||Santoro A, Mambelli E, Canova C, et al. Biofeedback in dialysis. J Nephrol 2003;16(Suppl 7):S48-56.|
|14||Ronco C, Brendolan A, Milan M, et al. Impact of biofeedback-induced cardiovascular stability on hemodialysis tolerance and efficiency. Kidney Int 2000;58(2):800-8.|
|15||Franssen CF, Dasselaar JJ, Sytsma P, et al. Automatic feedback control of relative blood volume changes during hemodialysis improves blood pressure stability during and after dialysis. Hemodial Int 2005;9(4):383-92.|
|16||Begin V, Deziel C, Madore F. Biofeedback regulation of ultrafiltration and dialysate conductivity for the prevention of hypotension during hemodialysis. ASAIO J 2002;48(3): 312-5.|
|17||Basile C, Giordano R, Vernaglione L. Efficacy and safety of hemodialysis treatment with the Hemocontrol biofeedback system: A prospective medium-term study. Nephrol Dial Transplant 2001;16(2):328-34.|
|18||Petitclerc T. Recent developments in conductivity monitoring of haemodialysis session. Nephrol Dial Transplant 1999;14(11):2607-13.|
|19||Petitclerc T, Goux N, Hamani A, et al. Biofeedback technique through the variations of the dialysate sodium concentration. Nefrologia 1997;17(Suppl. 1)50-5.|
|20||Moret K, Aalten J, van den Wall Bake W, et al. 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 2006;21(1):138-44.|
|21||Schneditz D. Temperature and thermal balance in hemodialysis. Semin Dial 2001;14(5):357-64.|
|22||Schneditz D, Ronco C, Levin N. Temperature control by the blood temperature monitor. Semin Dial 2003;16(6):477-82.|
|23||Schneditz D, Wang E, Levin NW. Validation of hemodialysis recirculation and access blood flow measured by thermodilution. Nephrol Dial Transplant 1999;14(2):376-83.|
|24||Sherman RA. Intradialytic hypotension: An overview of recent, unresolved and overlooked issues. Semin Dial 2002;15(3):141-3.|
|25||Berbardinis LA. Clear thinking on fuzzy logic. Machine Design 1992;46-52.|
|26||Cox E. Fuzzy fundamentals. IEEE Spectrum 1992;29(10):58-61|
|27||Lee CC. Fuzzy logic in control systems. IEEE T Syst Man Cy SMC 1990;20:404-35.|
|28||Nordio M, Giove S, Lorenzi S, Marchini P, Saporiti E. A new approach to blood pressure and blood volume modulation during hemodialysis: An adaptive fuzzy control module. Int J Artif Organs 1995;18:513-7.|
|29||Schmidt R, Roeher O, Hickstein H, Korth S. Prevention of hemodialysis-induced hypotension by biofeedback control of ultrafiltration and infusion. Nephrol Dial Transplant 2001;16(3): 595-603.|
|30||Santoro A, Mancini E, Mambelli E, et al. Prevention of dialysis hypotension episodes using fuzzy logic control system. Nephrol DialTransplant 2007;22(5):1420-7.|
|31||Kuhlmann U, Goldau R, Samadi N, et al. Accuracy and safety of online clearance monitoring based on conductivity variation. Nephrol Dial Transplant 2001;16(5):1053-8.|
|32||Petitclerc T, Coevoet B. "Ionic dialysance and quality control in hemodialysis". Nephrologie 2001; 22 (5):191-7.|
|33||Petitclerc T. Recent developments in conductivity monitoring of haemodialysis session. Nephrol Dial Transplant 1999;14(11):2607-13.|
|34||Mercadal L, Du Montcel ST, Jaudon MC, et al. Ionic dialysance vs urea clearance in the absence of cardiopulmonary recirculation. Nephrol Dial Transplant 2002;17(1):106-11.|
|35||Mercadal L, Peticlerc T, Jaudon MC, Bene B, Goux N, Jacobs C. Is ionic dialysance a valid parameter for quantification of dialysis efficiency?. Artif Organs 1998;22(12):1005-9.|
|36||Manzoni C, Di Filippo S, Corti M, Locatelli F. Ionic dialysance as a method for the on-line monitoring of delivered dialysis without blood sampling. Nephrol Dial Transplant 1996;11: 2023-30.|
|37||Ridel C, Osman D, Mercadal L, et al. Ionic dialysance: A new valid parameter for quantification of dialysis efficiency in acute renal failure? Intensive Care Med 2007;33(3):460-5.|
|38||Locatelli F, Di Filippo S, Manzoni C. Relevance of the conductivity kinetic model in the control of sodium pool. Kidney Int Suppl 2000;76:S89-95.|
|39||Effectiveness of sodium and conductivity kinetic models in predicting end-dialysis plasma water sodium concentration: Preliminary results of a single-center experience. Hemodial Int 2007;11(2):169-77.|
|40||Gotch FA, Rosemary B, Panlilio F, Folden T. Measurement of blood access flow rate during hemodialysis from conductivity dialysances. ASAIO J 1999;45:139-46.|
|41||Mercadal L, Hamani A, Bene B, Petitclerc T. Determination of access blood flow from ionic dialysance: Theory and validation. Kidney Int 1999;56(4):1560-5 .|
|42||Polaschegg HD. Automatic, non-invasive intradialytic clearance measurement. Int J Artif Organs 1993;16:185-91.|
|43||Peticlerc T, Benet B, Jacobs C, Jaudon MC, Goux N. Non-invasive monitoring of effective dialysis dose delivered to the hemodialysis patient. Nephrol Dial Transplant 1995;10:212-6.|