Extracorporeal renal replacement modeling system

A system, program product and method continuously optimize an ultrafiltration rate during an extracorporeal renal replacement process by modeling physiological and actual rate data. The system maps the sensed, physiological data to a mathematical model to assess the data in terms of the ultrafiltration rate. The model provides parameters used to predict where the treatment is headed based on current conditions. The system processes the parameters in terms of preset criteria to generate the optimized ultrafiltration rate. Where the system is networked, communication of the data may be accomplished using remote and online communication techniques.

FIELD OF THE INVENTION

The present invention generally relates to systems and methods of extracorporeal renal replacement therapy and, more particularly, to control systems and methods for operating a pump in an extracorporeal renal replacement system.

BACKGROUND OF THE INVENTION

Several extracorporeal renal replacement procedures, such as dialysis, hemodialysis, hemofiltration, hemodiafiltration, ultrafiltration, and plasmapheresis are used to provide replacement or supplementation of a patient's natural renal function in order to remove fluid and/or waste products from their blood. The specific procedure is tailored to the specific needs of the particular patient. For example, dialysis is used to remove soluble waste and solvent from blood. Hemofiltration is used to remove plasma water and dissolved waste from blood while replacing the removed volume with replacement solution. Hemodiafiltration is used to remove both unwanted solute (soluble waste) and plasma water from blood. Ultrafiltration is a species of hemofiltration where only volume and dissolved components are released; and plasmapheresis is used to remove blood plasma by means of a plasmapheresis filter.

For certain patients, renal replacement procedures may extend over hours, days, months and even years. In general, current systems for monitoring and controlling renal replacement procedures lack the flexibility and accuracy required to perform such procedures on neonates. This is mainly due to the absence of a satisfactory automatic control of the pumps employed. Because of the patient risk involved in using such equipment, health care personnel may measure the fluid removed from the patient on an hourly basis. The continuing need to monitor the fluid removed and patient responses lead to a significant increase in nursing care and, thus, increases the cost of the therapy. Therefore, there is a need to improve the level of autonomy for the systems such that the procedure is less time consuming for medical personnel, and consequently less costly. However, the enhanced autonomy must not come at the expense of patient safety.

Due to the time-varying nature of renal function replacement and supplementation systems, the dynamics of fluid pumping may change over time. For example, the characteristics of system components such as tubing, filter, and connectors may vary slowly over time due to protein deposit or as occlusion of the path for fluid flow. As the membrane changes, the pumping rate of the pump must be altered to compensate for the altered filter to maintain the same function. Current systems for monitoring and controlling renal replacement procedures lack the ability to autonomously correct these time-dependent flow rate variations with high accuracy, rapid response, and minimal overshoot or transient variations following correction. In one sense, most conventional systems, at best, tend to be reactive, rather than proactive, during a procedure.

A particular need for the ability to control fluid pumping arises in patients undergoing hemodialysis. During a hemodialysis procedure, dissolved materials are removed from the blood and added to the blood down their respective concentration gradients. In addition, plasma water and dissolved content are removed through a porous membrane down a pressure gradient in a process known as ultrafiltration. The clinical problem observed during hemodialysis is that, during the intrinsic dual treatment processes, replacement of renal function reduces the patient's intravascular or blood volume. This impacts the heart's ability to pump blood to the tissues and causes many unwanted side effects including, but not limited to, cramping, nausea, vomiting, and diaphoresis. Such cardiac function compromises can also challenge blood flow to the heart itself and cause arrhythmia or even a heart attack.

Conventional solutions to these adverse side effects is to buffer the intravascular volume reduction with effecting a change in the osmotic fluid shift. While some patients may respond, the effects are not very often consistent and, in particular, patients with intradialytic hypotension (IDH) continue to have problems. The consequences of IDH may include pain, loss of functional days and death.

Another conventional approach is to monitor the patient's hematocrit on line and use the hematocrit measurements to monitor the blood volume. The deficiency of this conventional approach is that, if one makes an adjustment based on the hematocrit, the system changes as the fluid removal rate also alters the cardiovascular physiology. Consequently, the target for alleviating the heart's inability to pump blood to the tissues will continuously shift without control. Merely reducing the fluid removal rate may paradoxially induce a state that could worsen the hypotension by interfering with the bodies physiologic response.

Therefore, there is a need for an improved hemodialysis system that can overcome these and other deficiencies of conventional hemodialysis systems.

SUMMARY OF THE INVENTION

According to the principles of the present invention and in accordance with the described embodiments, one aspect of the invention provides a system, program product and method for optimizing an ultrafiltration rate during a blood filtration process. The ultrafiltration rate may be optimized by modeling physiological and actual flow rate data. The system maps the sensed, physiological data to a mathematical model to assess the data in terms of the ultrafiltration rate. The model provides model parameters used to predict where the treatment is headed based on current conditions. The system may process the parameters in terms of preset criteria to generate the optimized ultrafiltration rate.

Embodiments of the invention more particularly include an extracorporeal renal replacement system for fluid removal from the blood of a patient. The system includes pumps for pumping liquid such as dialysate or infusate, drained fluid, and blood in the hemofiltration system. A flow rate sensor measures the flow rate of fluid in the system generated by the pump and provides flow rate data signals to a controller. Patient sensor, measures physiological conditions of the patient and generates patient sensor data signals that are also communicated to the controller. The controller communicates with the pump and analyzes the flow rate data signals and the patient parameter data signals using the model. The controller then initiates generation an output signal for the pump to adjust the flow rate of the liquid. The adjustment may be continuously and dynamically accomplished, and the communication with the controller may be networked and/or wireless.

Processes of the invention may include receiving and using a model to process flow rate data and physiological condition to determine a model parameter. The model parameter may be used to generate an output signal configured to adjust the flow rate realized by a pump. The model parameter may be compared or otherwise processed in conjunction with criteria to determine if the ultrafiltration rate can be optimized. For instance, if the model parameter fails to conform with the criteria, then the ultrafiltration rate may be reduced. If the model parameter alternatively conforms with the criterion, then the ultrafiltration rate may be increased or otherwise further optimized.

Features of the invention also include program code configured to cause the controller to receive flow rate and physiological condition data. The program code executed by the controller uses the model to process the flow rate data and the physiological condition data to determine a model parameter, which is used to generate an output signal configured to adjust the flow rate realized by a pump. A signal bearing medium bears the program code.

Ultrafiltration may be accomplished in manner that mitigates the problems of the prior art. The system is proactive in nature, rather then merely reactive, anticipating and correcting potential problems before they occur. Moreover, the system and method of the invention are advantageous because of the multipurpose nature thereof, the repeatability and accuracy of the processes, and the simultaneous, continuous flow of fluids in an extracorporeal blood circuit, while being equally applicable to adult, pediatric and neonatal patients.

Implementation of either or both of the aforementioned adaptive or supervisory control may increase the autonomy of an extracorporeal renal replacement system. Various advantages follow from the enhanced autonomy. For example, the continuous monitoring and control reduces medical costs and improves the quality of medical care by reducing the need for intermittent supervision of the extracorporeal renal replacement procedure by clinical staff.

These and other benefits and advantages of the present invention will become more readily apparent during the following detailed description taken in conjunction with the drawings herein.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1shows a system10configured to continuously optimize an ultrafiltration rate during a filtration process by modeling physiological and actual rate data. The system10maps the sensed, physiological data to a mathematical model to assess the data in terms of the ultrafiltration rate. The model provides parameters used to predict where the treatment is headed based on current conditions. The system10may process the parameters in terms of preset criteria to generate the optimized ultrafiltration rate.

Turning more particularly toFIG. 1, a extracorporeal renal replacement system10generally includes an extracorporeal hydraulic circuit with a filter, such as a filter12and a blood flow pump14that directs a flow of blood to be cleaned from the circulatory system of a patient (not shown), which may be an adult, pediatric or neonatal patient, to the filter12. An arterial blood line defined by convention in the form of an inlet conduit16is connected with the patient with a suitable catheter (not shown) providing an access site to an artery of the patient's cardiovascular system or to a port on a catheter. The inlet conduit16includes an internal lumen through which blood is pumped by the blood flow pump14.

Blood flow pump14withdraws blood from the patient by a pumping action that causes blood to flow from the access site through the inlet conduit16toward the filter12and establishes a continuous flow during system operation to the filter12. The blood flow rate established in the inlet conduit16may range from about 30 ml/min to about 700 ml/min. Blood flow pump14may be of the roller or peristaltic type that comprises a track for receiving a section of the inlet conduit16and a rotor that intermittently applies pressure to this section to cause flow. Blood flow pump14has a drive unit15that is electrically coupled with controller18over a communication link20, such as a wire, radiofrequency (RF) link, or infrared (IR) link.

Upstream from the blood flow pump14is a pressure transducer22that is electrically coupled with controller18over a communication link24, such as a wire, radiofrequency (RF) link, or infrared (IR) link. The pressure transducer22monitors the arterial pressure, which typically represents the negative pressure created by the suction of blood flow pump14, by sensing the fluid pressure inside the conduit16at a location in the hydraulic circuit between the patient and the blood flow pump14.

Upstream and downstream from the filter12are a pair of pressure transducers26,28. Pressure transducer26senses the venous fluid pressure of the blood stream flowing in inlet conduit16downstream from the blood flow pump14and upstream from the filter12. Similarly, pressure transducer28senses the arterial fluid pressure of the blood stream flowing in inlet conduit16downstream from the filter12. The pressure transducers26,28may communicate pressure measurements to the controller18over respective communication links30,32, such as a wire, RF link, or IR link.

The controller18may use the downstream and upstream pressure indications received from the pressure transducers26,28to determine a pressure drop across the filter12from the pressure differential. The pressure drop arises from the flow restriction represented by the filter12and increases as the filter12ages with use. If the pressure drop reaches a set upper level, this may indicate that the filter12needs regeneration or replacement. The pressure transducers26,28may each be any conventional type of pressure sensing device capable of sensing or measuring fluid pressure, generating an analog or digital signal indicating the sensed fluid pressure, and communicating an indication of the fluid pressure as a digital or analog electrical signal to the controller18. Pressure transducers26,28may be configured to measure either total pressure or static pressure, and may be any one of numerous pressure sensing devices known in the art including, but not limited to, a capacitance sensor, a strain gauge sensor, a piezoresistive sensor, and a thermal sensor. Drip chambers (not shown) may also be used to facilitate the pressure measurements.

Upstream from the filter12is a medicament source34that permits the injection or infusion of desired fluids, including drugs, medications, and anticoagulants such as heparin or citrate into the stream of the patient's blood being pumped through the inlet conduit16. The injection or infusion of such medicament fluids to the blood stream flowing in inlet conduit16may be accomplished in any conventional manner as understood by a person having ordinary skill in the art and maybe quite close to the arterial inlet.

The filter12includes a semi-permeable membrane36that is housed within a container38having an inlet40coupled hydraulically with the inlet conduit16and an outlet42. The membrane36, which may have the form of a large number of semi-permeable hollow fiber membranes, divides the container38into a blood compartment44and a dialysate compartment46. When the system10is operating, a continuous blood stream is directed from the inlet40into blood compartment44on one side of the membrane36and, simultaneously, a continuous dialysate stream is supplied to dialysate compartment46on the opposite side of the membrane36.

The filter12removes toxic substances normally eliminated in a healthy patient's urine from the stream of blood by a diffusion mechanism established by a concentration gradient across the membrane36created by the flowing blood and dialysate. Substances containing plasma water are also filtered by a pressure gradient established across the semi-permeable membrane36from the blood stream flowing in blood compartment44to the dialysate flowing in dialysate compartment46. The dialysate, which is typically a water-based solution, absorbs the substances transported through the membrane36and removes those substances as a component of a spent dialysate stream for subsequent disposal.

A mixer48generates a continuous supply of dialysate for use in the filter12by combining and blending a dialysate concentrate with water. A warmer50receives an output stream of dialysate from the mixer48and elevates the temperature of the fresh dialysate supplied to compartment46to near body temperature. The dialysate is moved through the filter12using a dialysate inflow pump52located on an inlet side of the filter12and a dialysate outflow pump54located on an output side of the filter12. These pumps52,54, which have adjustable flow rates, regulate the pressure of the dialysate, as monitored by a pressure transducer56on the dialysate outflow from dialysate compartment46. A blood detector58monitors for the presence of blood as a contaminant in the spent dialysate, which is routed to a sanitary drain for disposal. The pumps52,54and blood detector58are coupled electrically with the controller18by respective communications links60,62,64.

The outlet42from the blood compartment44of the filter12is coupled hydraulically with a venous bloodline or outlet conduit68. The outlet conduit68is connected with the circulatory system of the patient with a suitable catheter (not shown) providing an access site to a vein of the patient's cardiovascular system. The outlet conduit68includes an internal lumen through which cleaned or dialyzed blood is pumped by the blood flow pump14and returned to the patient's circulatory system.

An air detector66communicates with the outlet conduit68to check for the presence of air bubbles or foam in the flow of dialyzed blood. Air detector66, which is located downstream from the filter12and which may be any conventional air detector suitable for this purpose, is coupled electrically with the controller18by a communications link69.

Typically, the inlet and outlet conduits16,68are transparent or translucent such that the stream of blood at any given time is visible for perceiving irregularities in flow. For example, the inlet and outlet conduits16,68may be made of flexible polyvinylchloride tubing.

The controller18exercises supervisory control over the operation of the system10. The controller18may be a programmable logic controller (“PLC”) or another microprocessor-based controller capable of executing software and carrying out the functions described herein, as is described below in greater detail. The controller18includes a suitable user interface (not shown), such as a touch screen display, an alphanumeric keyboard and/or a pointing device, capable of accepting commands or input from the operator and transmitting the input to the data processing unit of controller18. The controller18may display information, such as the current operating status of the system10and includes a video display. The controller18may further include push buttons to manually initiate or halt certain machine functions and one or more alarms or indicators that warn the operator of the existence of an alarm condition, such as abnormal operation or component failure, in the system10. The controller18communicates with the various sensors of the system10and controls the operation of the pumps in the system10.

FIG. 2shows a block diagram of a controller such that may be used in connection with the system ofFIG. 1. The controller ofFIG. 2more particularly comprises a networked computer system70having one or more client computer(s)72coupled to a network80. Network80represents a networked interconnection, including, but not limited to local-area, wide-area, wireless, and public networks (e.g., the Internet). Moreover, any number of computers and other devices may be networked through network80, e.g., multiple servers (not shown). Computer system70will hereinafter also be referred to as a “controller,” “apparatus,” “microchip,” “computer,” or “processing system,” although it should be appreciated that the terms may respectively include many other controller configurations. Moreover, while only one computer72is shown inFIG. 1, any number of computers and other devices may be networked through network80. In still another embodiment, the system could be implemented in a stand-alone configuration, i.e., disconnected from another computer or computer network. Moreover, applicable connections between components of the system70may be wireless, where desired.

Computer72typically includes at least one processor86coupled to a memory74. Processor86may represent one or more processors (e.g., microprocessors), and memory74may represent the random access memory (RAM) devices comprising the main storage of computer72, as well as any supplemental levels of memory, e.g., cache memories, non-volatile or backup memories (e.g., programmable or flash memories), read-only memories, etc. In addition, memory74may be considered to include memory storage physically located elsewhere in computer72, e.g., any cache memory present in processor86, as well as any storage capacity used as a virtual memory, e.g., as stored within a database84, or on another computer coupled to computer72via network80.

Computer72also may receive a number of inputs and outputs for communicating information externally. For interface with a user, computer72typically includes one or more input devices76(e.g., a keyboard, a mouse, a trackball, a joystick, a touch pad, iris/fingerprint scanner, and/or a microphone, among others).

The computer72additionally includes a display78(e.g., a CRT monitor, an LCD display panel, and/or a speaker, among others). It should be appreciated, however, that with some implementations of the computer72, direct user input and output may not be supported by the computer, and interface with the computer may be implemented through a computer or workstation networked with the computer72.

For additional storage, computer72may also include one or more mass storage devices82configured to store, for instance, a database84. Exemplary devices82can include: a floppy or other removable disk drive, a flash drive, a hard disk drive, a direct access storage device (DASD), an optical drive (e.g., a CD drive, a DVD drive, etc.), and/or a tape drive, among others. Furthermore, computer72may include an interface with one or more networks (e.g., a LAN, a WAN, a wireless network, and/or the Internet, among others) to permit the communication of information with other computers coupled to the network80. It should be appreciated that computer72typically includes suitable analog and/or digital interfaces between processor86and each of components74,76,82,78and80.

Computer72operates under the control of an operating system92, and executes various computer software applications, components, programs, modules, e.g., a model and associated program94, a filtration program95and stored criteria96, among others. A model for purposes of this specification may include a theoretical construct that represents a physical or biological process, with a set of variables and a set of logical and quantitative relationships between them. Embodiments of the present invention use a mathematical model, which includes mathematical language to describe the behavior of a system. For instance, the system70is configured to use a mathematical model comprising a series of mathematical equations descriptive of hemodynamic parameters.

Various applications, components, programs, markers, modules, etc. may also execute on one or more processors in another computer coupled to computer72via a network80, e.g., in a distributed or client-server computing environment, whereby the processing required to implement the functions of a computer program may be allocated to multiple computers over a network.

The memory74shown inFIG. 2includes various data components that may be utilized by the programs to accomplish a system design. As with other memory components described herein, the data may be stored locally as shown inFIG. 2, or may alternatively be remotely accessed. Examples of such data include equations comprising the model, as well as cached model parameters.

Though not shown inFIG. 1, one skilled in the art will appreciate that a server computer may include many of the same or similar components as included in the computer72, where a networked design processes implementation is desired. In such a situation, for example, the server computer may be remote, e.g., at a nurses' station, while computer72may be proximate the pump52.

The discussion hereinafter will focus on the specific routines utilized to automatically design dispensing systems. In general, the routines executed to implement the embodiments of the invention, whether implemented as part of an operating system or a specific application, component, program, marker, module or sequence of instructions will be referred to herein as “programs,” or simply “program code.” The programs typically comprise one or more instructions that are resident at various times in various control device memory and storage devices. When a program is read and executed by a processor, the program causes the access control device to execute steps or elements embodying the various aspects of the invention.

Moreover, while the invention has and hereinafter will be described in the context of fully functioning access control devices, such as computer systems, those skilled in the art will appreciate that the various embodiments of the invention are capable of being distributed as a program product in a variety of forms, and that the invention applies equally regardless of the particular type of computer readable signal bearing media used to actually carry out the distribution. Examples of computer readable signal bearing media include but are not limited to recordable type media such as volatile and non-volatile memory devices, floppy and other removable disks, hard disk drives, optical disks (e.g., CD-ROM's, DVD's, etc.), among others, and transmission type media such as digital and analog communication links.

In addition, various programs described hereinafter may be identified based upon the application for which they are implemented in a specific embodiment of the invention. However, it should be appreciated that any particular program nomenclature that follows is used merely for convenience, and thus the invention should not be limited to use solely in any specific application identified and/or implied by such nomenclature.

FIG. 3shows a flowchart100having a sequence of steps for automatically determining an ultrafiltration rate. The flowchart100more particularly shows processes that may be executed by the controllers18and/or70ofFIGS. 1 and 2, respectively, to continuously optimize the ultrafiltration rate.

At block102ofFIG. 3, the controller70receives hemodynamic input signals. The hemodynamic input signals include patient driven, physiological data received from one or more sensors, and/or input from the system, such as from the transducers26,28ofFIG. 1. Exemplary hemodynamic input signals may convey physiological data indicative of blood pressure, heart rate, arterial pressure, a temperature differential, and hematocrit, for instance. Where the system10is networked, such monitoring may be accomplished using remote and online communication and monitoring techniques.

These input signals are processed using the hemodynamic model at block104, along with any current ultrafiltration rate data. That is, the controller70maps the physiological data from the input signals to a series of equations comprising the model94to assess the physiological data of the patient in terms of the ultrafiltration rate. The model94, in a sense, provides a perspective on where the treatment is headed based on current conditions. The controller70then initiates at block106generation of the ultrafiltration rate that will achieve the desired output at block110, and the processes continuously repeats. That is, the controller70will then generate at block108output signals configured to realize the desired ultrafiltration rate, which is delivered to the patient at block110ofFIG. 3.

FIG. 4shows a flowchart120having steps suited to optimize the control signals driving the ultrafiltration rate. As such, the processes of the flowchart120may have application in the context of block106ofFIG. 3. At block122ofFIG. 4, the controller70may receive flow rate, or ultrafiltration rate data, i.e., the speed at which the pump52delivers the infusate or dialysate, in addition to hematocrit readings.

In addition to the ultrafiltration rate data, the controller70may receive patient physiological condition data at block124. As discussed herein, such data may include a patient's heart rate, blood pressure, heart rate, plasma return rate and changes associated therewith. This physiological data may be monitored locally or remotely, i.e., online via a computer network, and generally indicates how a patient is handling the extracorporeal renal replacement process. To this end, a more comprehensive (though non-exhaustive) list of physiological data may be monitored online includes: systemic arterial pressure, pulse pressure, pulse rate, estimated cardiac ejection fraction, estimated stroke volume, estimate stroke volume index, estimated cardiac output, estimated cardiac index, large artery elasticity index (capacitive arterial compliance), small artery elasticity index, systemic vascular resistance, and total vascular impedance.

The ultrafiltration data and physiological data inputs are mapped to the mathematical model94at block126ofFIG. 4. The mathematical model94includes equations and calculations used to generate data indicative of a predicted status for parameters. The results of the model analysis, i.e., the model parameters, are then used at block128to determine if the modeled results are within a predetermined ultrafiltration rate criteria96. For instance, the controller70at block128may determine if the modeled parameters determined at block126are within a window or range of values associated with an accepted, predetermined ultrafiltration rate. This criteria96may be input as a function of the size and cardiovascular state of a patient, for instance. The criteria96, in one sense, acts as a comparator against which the modeled parameters are evaluated. The criteria96comprises safe bounds in which modeled parameter should reside during a successful ultrafiltration procedure. In one sense, features of the invention determine at block128whether the predicted model parameters are within a body's ability to respond to changes.

If the controller70determines at block128that the ultrafiltration rate is not within acceptable limits, then the ultrafiltration rate may be too fast. As such, the controller70may reduce the ultrafiltration rate at block130in accordance with the modeled results. For instance, the controller may determine that the ultra-filtration rate excess line and will overwhelm the normal physiological response. In response, the controller70may access a lookup table or algorithm used to determine by what percentage the ultrafiltration rate should be reduced in order to bring the parameter within the acceptable limit. A modeled comparison may involve one or more parameters, and a resultant ultrafiltration rate determination typically accounts for the multiple parameters. For example, the physiologic data may reveal an increasing hematocrit outside of acceptable criteria, increasing heart rate with or without a softening of the blood pressure. The model would predict intradialytic hypotension would eventually result if no change in ultrafiltration occurs. The model would provide a reduction in the ultrafiltration rate that would optimize ultrafiltration while circumventing hypotension.

Should the ultrafiltration rate at block128alternatively be determined to be within acceptable criteria limits, then the controller70at block132determines if the ultrafiltration rate can be further optimized. That is, features of the invention determine whether a faster ultrafiltration rate may be achieved without causing a harmful effect. If not, then the process will continue at block134.

Alternatively, the ultrafiltration rate may be increased at block136based on the modeled results. For instance, the controller70may determine that the model parameters were a certain percentage under a target value/criterion. A target value may comprise a ceiling or other range of the criteria used to evaluate the ultrafiltration rate at block128, for instance. If so, then the controller70may increase the ultrafiltration rate by that an amount determined by a stored algorithm or lookup table. If the hematocrit rate of change is static or at an optimisable rate, for example, substantiated by a stable blood pressure and heart rate; the mathematical model may be used to predict on increase for the ultrafiltration rate that would be optimal. The change will be made and continued monitoring will assess the ability to sustain the new fluid removal rate. Alternatively, the controller70may increment the ultrafiltration rate by a small, predetermined speed. In any case, the new ultrafiltration rate will be continuously evaluated as part of real time analysis. The above described processes may be fully automated, or may be augmented with manual inputs and confirmation where desired.

In practice, the system10removes the required amount of fluid in the shortest amount of time without causing hypotension. The system10estimates a patient's physiological data using an online estimation scheme, and uses the data to regulate the ultrafiltration rate blood pressure. The control system then uses the model with the data to derive an ultrafiltration rate schedule to fulfill the control system requirement. The control system uses the online data to update the ultrafiltration rate schedule as the patient's physiological conditions change.

Features of the present invention thus proactively optimize filtration rates. This contrasts prior art systems, which have been largely reactive in nature. For instance, if a sensed blood pressure was too low and a heart rate too high, a clinician would make manual adjustments. In another example prior art example, the ultrafiltration rate would be automatically blindly/unintelligently decreased over time. For instance, the ultrafiltration rate would be reduced by one-half in the first hour and then decremented according to a predetermined scheme. Such conventional schemes would not account for real time physiological fluid flux rate data. Features of the present invention use this data to intelligently adjust ultrafiltration rates. Features of the present invention use the model94as a comparator to address the filtration rate before the blood pressure and heart rate become problematic. The model94is in this manner used as a prediction tool to present expected trends and results to a clinician or controller. The filtration rate may then be adjusted automatically or manually based on the predictions.

The model94mitigates IDH and other problems by incorporating an automatic feedback control system that constantly evaluates the patient hemodynamic physiological conditions and appropriately adjusts the ultrafiltration rate. Features of the present invention achieve a critical balance between the ultrafiltration rate and the compensatory rate. In order to accomplish this balance, quantitative knowledge of hemodynamics are realized using the hemodynamic model94.

The model94includes all significant dynamics, including blood pressure, transcapillary fluid transfer, interstitial pressure-volume relationship, lymphatic flow and a vascular stress-relaxation property. From this model, quantitative predictions are made regarding the change in blood volume and blood pressure due to hemodialysis, and this information is used to determine the optimum ultrafiltration rate. Exemplary hemodynamic and modeled parameters include arteriole resistance, venous compliance and interstitial space compliance. These parameters may change from person to person and moment to moment, and are expected to continuously change during hemodialysis. As such, the model94incorporates a parameter estimation feature, and takes into account pressure dynamic disregarded by the prior art.

The model94describes the dynamics of blood and plasma volume during ultrafiltration and incorporates the dynamics of fluid exchange through capillary wall and the dynamics of protein concentration. The dynamics of blood and plasma volume during ultrafiltration are realized by fitting the model to online blood volume changes data in order to determine the initial blood volume and filtration coefficient. The blood volume may be used to estimate the volume overload, while the filtration coefficient might be used to determine the rate at which the excess volume can be removed. With known techniques that permit analyzing the response of blood volume to ultrafiltration within a short period of time, where exponential conditions might well be controlled, it is possible to prescribe adequate ultrafiltration for subsequent treatment phases or even for the whole remaining treatment session.

A comprehensive mathematical model94of the hemodynamic response to hemodialysis accounts for, among other dynamics: the dynamics of sodium, urea and potassium in the intracellular and extracellular pool; fluid balance equations for the intracellular, interstitial and plasma volume; systemic and pulmonary hemodynamics (pressures); and the action of several short term arterial pressure control mechanisms. The input to the controller70include information coming from both arterial and cardiopulmonary pressoreceptors to accommodate systemic arterial resistance, heart rate and volume data.

In order to predict blood pressure, the model includes a set of dynamic equations used to determine model parameters for blood pressure in each compartment. One set of such dynamic algorithms and associated model parameters can be expressed as:

Parameters in the above model parameter equations include: systemic vascular resistance (RS), the systemic arterial compartment compliance (CAS), the systemic venous compartment compliance (CVS), the pulmonary arterial compartment compliance (CAP), KLand KRmay be determined as a function of the compliance of the relaxed left and right ventricles, CLand CR, total viscous resistance of the filling of the left and right ventricles RLand RR, cardiac frequency (f), as well as the strength and compliance of the left and right ventricles SL, SR, CLand CR, respectively. QULTis the ultrafiltration rate of fluid from the systemic venous compartment, and QINFis the infusion rate of fluid into the systemic venous compartment.

To predict the blood volume and blood pressure change due to ultrafiltration, the mathematical model94includes the dynamic of plasma refilling, among others. Capillary, interstitial and lymphatic systems help regulate fluid volume in the circulatory circuit, which in turn, helps regulate blood pressure. Excessive fluid filters from circulatory system through capillary wall into interstitial space and hence reduces increase in blood pressure. Interstitial space fluid, returning through lymphatic system helps restore blood volume against blood loss, and therefore reduces blood pressure drop.

The below equations may be used to determine model parameters that include the fluid filtration rate from the vascular compartment to the interstitial space (e.g., the capillary filtration rate, QF), as well as systemic arterial and venous resistance (RASand RVS, respectively):

Newly introduced variables in the above equations include: the hydrostatic pressure of the fluid inside the capillary and in the interstitial space (PCand PI, respectively), the plasma colloid osmotic pressure of the fluid in the interstitial space and inside the capillary (IILand IIP, respectively), and the reciprocal of the filtration coefficient of the capillary membrane (RF).

The below equation may be used to determine a model parameter that includes the hydrostatic pressure of the fluid in the interstitial space (PI). The equation includes the lymph flow rate (QLYMPH) as a variable.

The below dynamic equations may be additionally or alternatively used to determine model parameters that include the respective blood pressures in the pulmonary arterial and venous compartments (PAPand PVP) the systemic and venous arterial compartments (PASand PVS), as well as the hydrostatic pressure of the fluid in the interstitial space (PI):

The hemodynamic model94of another or the same embodiment may include stress-relaxation properties and factors and equations to determine the pulmonary arterial and venous compartments (PAPand PVP), the systemic and venous arterial compartments (PASand PVS) the hydrostatic pressure of the fluid in the interstitial space (PI), as well as the volume in the systemic venous compartment (VVS).

As will be appreciated by one of skill in the art, any of the algorithms used by the system10can be represented graphically, as well as in the above listed mathematical format. Furthermore, mathematical equations may be approximated in a discrete-time format.FIGS. 5 and 6graphically show parameters predicted by the modeling processes of the controller ofFIG. 2. These parameters include the capillary filtration rate, QF, the lymph return rate, QLYMPH, and the plasma return rate, QPR=QLYMPH−QF.

Turning more particularly toFIG. 5, the graph140shows the ability of the model94to predict the distribution of the fluid between the vascular compartment, Vb(denoted by solid line144), and the interstitial fluid compartment, Vl(shown as dashed line142). During infusion, the controller70uses the model to predict a rise in both Vband Vl. When the infusion stops, Vbfalls slightly before settling down to a new equilibrium. The rise in Vlalso stops and settles to a new equilibrium, but without falling. During ultrafiltration, both Vband Vlfall. When the ultrafiltration stops, Vbrises slightly, then settles up to the equilibrium point before infusion. The fall in Vlalso stops and settles to the equilibrium before infusion, but without rising.

FIG. 6shows a graphical representation150of capillary filtration and lymph return rate parameters (QFand QLYMPH) modeled by the controller70. During the infusion, the model94predicts a rise in both QFand QLYMPH, with QFrising faster than QLYMPH. When the infusion stops, QF(shown as solid line152) decays slightly, then settles to a new equilibrium. QLYMPH(shown as dashed line154) slows down in rising and settles at a new equilibrium, which is the same as the equilibrium point of QF. During ultrafiltration, the model predicts a fall in both QFand QLYMPH. QFfalls faster than QLYMPHand eventually goes negative towards the end of the ultrafiltration period. When ultrafiltration stops, QFrises slightly, then settles to an equilibrium point before infusion. QLYMPHslows down in falling and settles at the equilibrium point before infusion, which is the same as the equilibrium point of QF.

FIG. 7is a graphical representation160of the response of the plasma refilling rate parameter modeled by the controller70. Nephrologists use the term “plasma refilling rate,” to refer to the mechanism of the restoration of the blood volume during an ultrafiltration procedure. Because the plasma return rate QPRis equal to QLYMPH−QF, during infusion, the model predicts a fall in QPR(charted as line162) with a decreasing rate of falling with infusion time. When the infusion stops, QPRgradually rises to an equilibrium point of zero. During the ultrafiltration, the model predicts a rise in Qpr, with a decreasing rate of rising with ultrafiltration time. When the ultrafiltration stops, QPRgradually falls down to the equilibrium point of zero again, which is the same as the equilibrium point before infusion.

While the present invention has been illustrated by a description of various preferred embodiments and while these embodiments have been described in considerable detail in order to describe the best mode of practicing the invention, it is not the intention of applicant to restrict or in any way limit the scope of the appended claims to such detail. For instance, while the embodiments described above focus mainly on ultrafiltration processes, one skilled in the art will recognize that features of the present invention may have equal application in other areas of extracorporeal renal replacement and hemofiltration, to include hemodialysis, hemofiltration, hemodiafiltration and plasmapheresis processes. As used herein, the term “infusate” is defined to include dialysate fluid or any other replacement fluids which may be supplied to the patient as a part of the extracorporeal renal replacement procedures.

Additional advantages and modifications within the spirit and scope of the invention will readily appear to those skilled in the art. For example, the model and criteria of an embodiment may include actual clinical data, as opposed or in addition to algorithms. Such data may be downloaded from a clinical source. Furthermore the equations included herein are not meant as an exhaustive list of all equations comprising a model. One skilled in the art will appreciate that many additional known algorithms may be used to supplant or augment those equations included in the specification. In any case, the invention itself should only be defined by the appended claims, wherein We claim: