Machine control for peritoneal dialysis

Abstract

The control device (20) performs a method for generating a control signal (23) for a dialysis machine (2a; 2) configured to perform a peritoneal dialysis (PD) therapy involving one or more fluid exchange cycles with respect to a patient's peritoneal cavity to cause transport of fluids and solutes across the peritoneal membrane. The method includes obtaining target values ​​(22) of treatment parameters for the PD therapy, obtaining transport properties of the peritoneal membrane, and constructing a transport model using the at least one transport property. The transport model defines transport of fluids and solutes across the peritoneal membrane as a function of control parameters (21') for the PD therapy. The method further includes evaluating the transport model to determine set points for the control parameters (21') to achieve the target values ​​(22), and generating a control signal (23) corresponding to the set points.

Description

[Technical field]

[0001] The present disclosure relates generally to peritoneal dialysis, and more particularly to techniques for configuring and operating dialysis machines to perform peritoneal dialysis treatments on patients. [Background technology]

[0002] Dialysis therapy may be required in the treatment of individuals suffering from acute or chronic renal failure. One category of dialysis therapy is peritoneal dialysis (PD). In PD, a therapy fluid ("dialysate") is infused into an individual's peritoneal cavity via an implanted catheter during a fill phase. The peritoneal cavity is lined by a highly vascularized peritoneal membrane ("peritoneum"). The therapy fluid is allowed to reside within the peritoneal cavity during a dwell phase, after which fluid is extracted from the peritoneal cavity during a drain phase. Substances (solutes) are removed from the patient's blood primarily by diffusion into the therapy fluid across the peritoneal membrane. Excess fluid (water) is also removed by osmosis through the peritoneal membrane, due to the therapy fluid including an osmotic agent. Excess fluid removal is also known as ultrafiltration. PD treatment may include any number of fluid exchange cycles, each of which includes a fill phase, a dwell phase, and a drain phase.

[0003] In so-called automated peritoneal dialysis (APD), the PD treatment is controlled by a dialysis machine, also known as a PD cycler. A medical professional determines set-points for various control parameters of the PD cycler with the intention of achieving one or more treatment goals, e.g., regarding ultrafiltration and / or removal of solutes such as urea. Examples of control parameters include the composition of the treatment fluid, the treatment time, the number and frequency of fluid exchange cycles, and the fill volume per cycle. The determination of the set-points is done ad-hoc, based on the medical professional's previous experience, possibly taking into account general guidelines. Currently, there is no way to verify that the set-points are reasonably correct prior to PD treatment, or to guide the medical professional on the appropriate set-points to be entered into the PD cycler taking into account the treatment goals.

[0004] The difficulty of determining the appropriate set points for the PD cycler is exacerbated by the fact that there is considerable variability between patients in the solute transport and ultrafiltration capacity of the peritoneal membrane. Furthermore, continuous exposure to therapeutic fluids can result in functional changes of the peritoneal membrane over time. Therefore, it is standard procedure to perform peritoneal membrane tests to evaluate the functionality of the peritoneal membrane. Numerous options for peritoneal membrane tests exist, including peritoneal equilibration tests (PET). For example, standard PET tests can be used to classify patients into one of several transporter categories, such as high, high average, low average, and low. Based on this classification, the caregiver can qualitatively determine the set points for the PD cycler. PET tests can also be performed to quantify the transport properties of the peritoneal membrane, such as water filtration capacity. Apart from providing a crude and rather uninformative characterization of the peritoneal membrane, standard PET tests are complex and require significant time and resource expenditure, for example, to perform the test, perform laboratory analyses, interpret the data, etc. Standard PET is performed by medical staff and is time consuming for the patient, requiring the patient to spend at least half a day in a specialized clinic or hospital.

[0005] Furthermore, a technique to estimate osmolality to glucose (OCG) is presented in the article "Novel methods for osmolality to glucose in peritoneal dialysis" by Gardre et al. in Renal International Reports 5(11), pp. 1974-1981, 2020. The OCG parameter can be seen to quantify the filtering capacity of water through the peritoneal membrane. Summary of the Invention

[0006] It is an object to at least partially overcome one or more limitations of the prior art.

[0007] One objective is to facilitate the task of configuring a dialysis machine to perform PD therapy.

[0008] Another object is to improve the performance of such dialysis machines.

[0009] One or more of these objects, as well as further objects that may become apparent from the following description, are at least partly achieved by a control device for operating a dialysis machine, an apparatus for performing peritoneal dialysis, a computer-implemented method for generating control signals, and a computer-readable medium according to the independent claims, embodiments of which are defined by the dependent claims.

[0010] A first aspect is a control device for operating a dialysis machine to perform a peritoneal dialysis treatment on a patient, the dialysis machine operable to perform one or more fluid exchange cycles with respect to a peritoneal cavity of the patient to cause transport of fluids and solutes through a peritoneal membrane in the peritoneal cavity based on control signals from the control device, the control device comprising control circuitry configured to: obtain target values ​​of treatment parameters for the peritoneal dialysis treatment, obtain at least one transport characteristic of the peritoneal membrane of the patient, construct a transport model using the at least one transport characteristic, the transport model defining the transport of fluids and solutes through the peritoneal membrane as a function of a plurality of control parameters for the peritoneal dialysis treatment performed by the dialysis machine, evaluate the transport model to determine set points for the plurality of control parameters to achieve the target values, and generate control signals for the dialysis machine corresponding to the set points, the control signals causing the dialysis machine to perform the peritoneal dialysis treatment. The control circuitry is further configured to acquire first data including measurements of concentration-related parameters of fluid in the peritoneal cavity at one or more time points during the peritoneal dialysis treatment, acquire second data indicative of fluid flow of treatment fluid into and / or out of the peritoneal cavity during the peritoneal dialysis treatment, and calculate at least one updated transport characteristic of the patient's peritoneal membrane based on the first data and the second data using the transport model.

[0011] A second aspect is an apparatus for performing peritoneal dialysis comprising a fluid circuit connectable to a patient's peritoneal access for carrying a therapeutic fluid to and from the peritoneal cavity, a dialysis machine configured to operate the fluid circuit, and a control device according to the first aspect or any embodiment thereof.

[0012] A third aspect is a computer-implemented method for generating control signals to cause a dialysis machine to perform a peritoneal dialysis treatment for a patient, the peritoneal dialysis treatment including one or more fluid exchange cycles for causing transport of fluids and solutes through a peritoneal membrane in the peritoneal cavity of the patient, the method including obtaining target values ​​of treatment parameters for the peritoneal dialysis treatment, obtaining at least one transport characteristic of the peritoneal membrane of the patient, constructing a transport model with the at least one transport characteristic, the transport model defining the transport of fluids and solutes through the peritoneal membrane as a function of a plurality of control parameters for the peritoneal dialysis treatment performed by the dialysis machine, evaluating the transport model to determine set points for the plurality of control parameters to achieve the target values, and generating the control signals for the dialysis machine corresponding to the set points. The method further includes acquiring first data including measurements of concentration-related parameters of fluid in the peritoneal cavity at one or more time points during the peritoneal dialysis treatment, acquiring second data indicative of fluid flow of treatment fluid into and / or out of the peritoneal cavity during the peritoneal dialysis treatment, and calculating at least one updated transport characteristic of the peritoneal membrane of the patient based on the first data and the second data using the transport model.

[0013] A fourth aspect is a computer-readable medium comprising computer instructions that, when executed by one or more processors, cause the one or more processors to perform the method of the third aspect or any embodiment thereof.

[0014] These aspects are based on the insight that a mathematical transport model for the peritoneal membrane may be used as a link between setpoints of a dialysis machine and one or more treatment parameters that represent the effect of a peritoneal dialysis (PD) treatment as performed by the dialysis machine on a patient. This insight is applied to a technique that allows a dialysis machine to be automatically controlled to perform a PD treatment to achieve a target value of at least one treatment parameter, according to the aforementioned aspects. The target value may be input by a user (e.g., a medical professional) of the dialysis machine prior to a treatment session to define a desired change in the patient's physiological state upon completion of the treatment session. For example, the target value may represent the removal of fluid from the patient, or the removal of one or more solutes from the patient. The technique defined by the aforementioned aspects provides a technical advantage that improves the ability of a dialysis machine to achieve target values ​​throughout a PD treatment. A further advantage is that setpoints for the dialysis machine may be adapted to a patient to take into account the condition of the patient's peritoneal membrane. This may be achieved by configuring the transport model with patient-specific values ​​of one or more transport properties of the peritoneal membrane. Such patient-specific values ​​may be determined, for example, as described in the Background section or may be determined according to aspects of the patient's prior PD treatment.

[0015] Compared to conventional practice, medical professionals are at least partially relieved of the burden of determining appropriate settings for a dialysis machine, the performance of the dialysis machine is improved with respect to its ability to achieve the desired end result of the peritoneal dialysis treatment, and the effect of human error in determining or inputting settings for the dialysis machine is reduced or eliminated.

[0016] The aforementioned aspect is also based on the insight that it is possible to validate the transport model based on the first and second validation data for the PD treatment, to verify that the transport model accurately represents the transport of fluids and solutes through the peritoneal membrane, and to use the transport model to calculate an updated value of at least one transport property of the peritoneal membrane, if deemed necessary. Thus, the present aspect can essentially provide an updated patient-specific value of at least one transport property included in the transport model. This is an important technical advance, since it provides more detailed information about the state of the peritoneal membrane than conventional standard PET tests and does not involve a large effort by medical staff. The present aspect further opens the possibility of applying the updated transport property to improve the transport model. Such an improved transport model may be used to generate control signals for future PD treatments performed by the dialysis machine or by another dialysis machine according to the aforementioned aspect. Alternatively or additionally, the improved transport model may be used to determine updated set points and to generate updated control signals for the dialysis machine during an ongoing PD treatment. This improves the ability of the dialysis machine to reach the target values ​​for the PD treatment.

[0017] As explained further below, a "peritoneal dialysis treatment" or "PD treatment" may be a single PD session, a sequence of PD sessions, or a portion of a PD session, depending on the implementation.

[0018] Further objects and aspects, as well as embodiments, features, technical effects and advantages, will become apparent from the following detailed description, the appended claims, and the drawings. It should be noted that any embodiment of the first aspect found herein may be adapted and implemented as an embodiment of the second to fourth aspects. [Brief description of the drawings]

[0019] [Figure 1] 1 illustrates an exemplary device for automated peritoneal dialysis (APD). [Figure 2A] , [Figure 2B]An exemplary plot of intraperitoneal volume versus time in APD therapy. [Figure 2C] An exemplary diagram of input and output data of a control device in an apparatus for APD. [Diagram 3] Shows transport processes affecting solute concentration and fluid volume in the peritoneal cavity. [Figure 4A] A flowchart of an exemplary method for generating a control signal for a dialysis machine. [Figure 4B] , [Figure 4C] , [Figure 4D] , [Figure 4E] A flowchart of an exemplary procedure in the method of FIG. 4A. [Diagram 5] A combination of plots of intraperitoneal conductivity, glucose concentration, and fluid volume versus time calculated using a transport model. [Figure 6A] , [Figure 6B] , [Figure 6C] A flowchart of a monitoring method performed during PD treatment. [Figure 6D] An exemplary graph of a range for causing recalculation of peritoneal transport characteristics. [Figure 6E] [Figure 7A] , [Figure 7B] A block diagram of an exemplary computing device for generating a control signal. [Figure 7C] A block diagram of an exemplary implementation of a peritoneal transport model in a computing device. [Figure 7D] , [Figure 7E] A block diagram of a circuit for calculating the current value of a treatment parameter in the implementation of FIG. 7C. [Figure 8A] , [Figure 8B] A flowchart of an exemplary method for determining a reference value of the fluid volume in the peritoneal cavity for use in the method of FIGS. 4A - 4C. [Figure 9]1 is an exemplary plot of intraperitoneal volume and data samples taken over time during a test cycle. [Figure 10] FIG. 8C is a block diagram of an exemplary computing device for generating reference values ​​according to the method of FIGS. 8A-8B. [Figure 11A] , [Figure 11B] , [Figure 12A] , [Figure 12B] , [Figure 13A] , [Figure 13B] , [Figure 13C] , [Figure 13D] 4 shows the results of calculations according to an embodiment. [Figure 14] 1 is a block diagram of an example machine that may implement the methods, procedures, and functions described herein. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0020] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0023] Embodiments will now be described more fully hereinafter with reference to the accompanying drawings, in which some, but not all, embodiments are shown. Indeed, the subject matter of this disclosure may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein, but rather, these embodiments are provided so that this disclosure will satisfy applicable legal requirements.

[0021] It will also be understood that, where possible, any of the advantages, features, functions, devices, and / or operational aspects of any embodiment described and / or contemplated herein may be included in any of the other embodiments described and / or contemplated herein, and / or vice versa. Furthermore, unless expressly stated otherwise, where possible, any term expressed in the singular herein is meant to include the plural, and / or vice versa. As used herein, "at least one" means "one or more," and these phrases are intended to be interchangeable. Thus, the terms "a" and / or "an" shall mean "at least one" or "one or more," although the phrases "one or more" or "at least one" are also used herein. As used herein, unless the context otherwise requires to express the language or necessary implication, the word "comprises," or variations such as "comprises" or "comprising," are used in an inclusive sense, i.e., to specify the presence of the stated features, but not to preclude the presence or addition of additional features in various embodiments.

[0022] As used herein, the terms "multiple," "plural," and "plurality" are intended to imply the provision of two or more elements. The term "and / or" includes any and all combinations of one or more of the associated listed elements.

[0023] It will be further understood that although terms such as first, second, etc. may be used herein to describe various elements, these elements should not be limited to these terms. These terms are used only to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the present disclosure.

[0024] For the sake of brevity and / or clarity, well-known functions or constructions may not be described in detail.Unless otherwise specified, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

[0025] As used herein, "PD session," "treatment session," and "session" refer to machine-controlled PD treatment that begins with a patient being connected to a dialysis machine and ends with the patient being disconnected from the dialysis machine. Thus, a PD session is a time-discrete event.

[0026] Like numbers refer to like elements throughout.

[0027] 1 is a schematic diagram of an exemplary device 1 for peritoneal dialysis treatment. The device (or system) 1 is generally intended for on-site treatment of a patient P with a suitable treatment fluid. Specifically, the PD device (PDA) 1 is designed for treating patients suffering from renal failure by automated peritoneal dialysis (APD).

[0028] In the illustrated example, the PDA 1 comprises a dialysis management unit (DMU) 2a and a fluid supply unit (FSU) 2b. The FSU 2b is connected to the DMU 2a by a supply line 4 and is operable to supply the DMU 2a with fresh treatment fluid. In the example of FIG. 1, the FSU 2b comprises three containers 5 arranged for fluid connection to the supply line 4 and a fluid control device 6 operable to direct and pump treatment fluid of a particular composition to the supply line 4. The fluid control device 6 may comprise a combination of a fluid circuit, one or more fluid pumps, and one or more valves. The number of containers 5 in the FSU 2b may vary depending on the treatment fluid to be infused into the peritoneal cavity (PC) of the patient P during the course of PD treatment. For example, different containers may hold treatment fluids of different compositions. In an alternative example, the FSU 2b is configured for on-line preparation of treatment fluid, either batchwise or on demand, for example by mixing purified water with one or more concentrates.

[0029] A patient line 7 extends from the DMU 2a to a catheter or other access device 7' implanted in the patient P. A drain line 8 extends from the DMU 2a to a drain 9 or alternatively a receptacle for spent therapy fluid. As used herein, spent therapy fluid refers to any therapy fluid present inside the PC. Spent therapy fluid is also known in the art as "effluent."

[0030] The DMU 2a is operable to perform PD therapy including one or more fluid exchange cycles. An individual exchange cycle includes a sequence of fill, dwell and drain phases. In the fill phase, the DMU 2a operates to pump unused therapy fluid into the PC of the patient P through the patient line 7. In the dwell phase, therapy fluid resides in the PC. In the drain phase, the DMU 2a operates to pump used therapy fluid from the PC to a drain 9 via the patient line 7 and drain line 8. Although not shown, it is contemplated to have separate patient lines 7 for the inflow and outflow therapy fluid.

[0031] The treatment fluid may include at least one osmotic agent. As is well known in the art, the osmotic concentration of the treatment fluid relative to blood determines how much fluid is exchanged between the treatment fluid and blood. A high osmotic concentration in the treatment fluid creates a high gradient. In any of the embodiments described herein, the osmotic agent may be or include glucose (or polyglucose), L-carnitine, glycerol, icodextrin, or any other suitable substance. Alternative osmotic agents may be fructose, sorbitol, mannitol, and xylitol. It is noted that glucose is sometimes referred to as dextrose in the field of PD. The term "glucose" is intended herein to include dextrose.

[0032] The structure and functionality of the DMU 2a is implementation specific. For this reason, FIG. 1 only shows some general structural components of the DMU 2a in a schematic manner. In the illustrated example, the DMU 2a comprises a fluid controller 10 operable to direct and pump fresh therapy fluid to the patient line 7, to withdraw used therapy fluid from the patient line 7, and to pump used therapy fluid to the drain line 8. The fluid controller 10 may comprise a combination of a fluid circuit, one or more fluid pumps, and one or more valves. The DMU 2a further comprises a heater 11 operable to adjust the temperature of the fresh therapy fluid to a predetermined temperature before it is pumped to the PC. The temperature is measured by a temperature sensor 12. A flow meter 13 is positioned to measure the flow rate of fluid pumped to and from the patient line 7, and hence to and from the PC. Alternatively, if the treatment fluid is pumped to and from the PC by one or more positive displacement pumps with known stroke volumes, the flow rate may be measured by counting the number of pumping strokes performed by the individual positive displacement pumps ("volume monitoring") and the flow meter 13 may be omitted. Any reference to a flow meter 13 below may thus be replaced or supplemented by volume monitoring. A pressure sensor 14 is arranged to measure the fluid pressure in the patient line 7 or in the fluid control device 10. The measured fluid pressure may be used to control the fill and / or drain phases. This may also be used for detection of operational errors during a PD session. At least one sensor 15 is arranged to measure a property of the treatment fluid. The property measured is a concentration-related parameter CRP. In some embodiments, the CRP sensor 15 is a conductivity sensor for measuring the conductivity of the passing fluid. In some embodiments, the CRP sensor 15 is a concentration sensor for measuring the concentration of one or more substances in the passing fluid. The DMU 2a may be configured to selectively direct either fresh or withdrawn (used) treatment fluid through one and the same CRP sensor 15. Alternatively, the DMU 2a may comprise a first CRP sensor arranged to sense a property of the withdrawn treatment fluid and a second CRP sensor arranged to sense a property of the fresh treatment fluid.

[0033] In the illustrated example, the DMU 2a and the FSU 2b include respective local controllers 16a, 16b configured to operate the components of the DMU 2a and the FSU 2b, respectively, according to respective operating strategies. As indicated by the dashed arrows, the local controller 16a may also generate a control signal C2 for the local controller 16b to synchronize the FSU 2b with the DMU 2a and / or to provide a therapeutic fluid having a particular composition to the FSU 2b.

[0034] In FIG. 1, the PDA 1 further comprises a control device 20, which comprises a control circuit 20A for controlling the operation of the control device 20. The control circuit 20A may comprise one or more processors and computer memories (see FIG. 14). The control device 20 is configured to determine a so-called regimen for PD treatment. The regimen is determined in advance of a PD session, but may be updated during an ongoing session. The regimen defines the operation method to be performed by the DMU 2a during a PD session. The regimen is further described below with reference to FIG. 2C. In FIG. 1, the control device 20 is configured to generate and provide a control signal C1 representative of the regimen to the DMU 2a. The local controller 16a is thereby configured to operate according to the regimen. As indicated by the dashed arrow, the control device 20 may be configured to receive sensor data S1 from the DMU 2a. The sensor data S1 may include CRP values ​​measured by the CRP sensor 15 and / or fluid flow data measured by the flow meter 13 or obtained by volumetric monitoring. The control device 20 may be configured to determine the regimen based on the sensor data S1.

[0035] In the illustrated example, the control device 20 is electrically connected to a user interface (UI) 17. The term "user interface" is intended to include any and all devices capable of performing guided human-machine interaction, including presenting information and receiving input. Thus, the UI 17 may comprise a combination of a presentation device and data entry hardware. The presentation device may comprise one or more of a display, a speaker, a projector, a lamp, etc. The data entry hardware may include one or more of a keyboard, a keypad, a computer mouse, control buttons, a touch panel, a microphone and voice control capabilities, a camera and gesture control capabilities, etc. In one implementation, the UI 17 is or comprises a touch-sensitive display, also known as a touch screen.

[0036] Additionally, the control device 20 may be electrically connected to an external memory 18, which may be a local or remote memory device for data storage. In some embodiments, the external memory 18 is part of a cloud-based service.

[0037] It should be noted that the DMU 2a may be comprised of a reusable mechanical part and a disposable part, as is well known in the art. The mechanical part of the DMU 2a is referred to herein as a dialysis machine or "cycler". The disposable part may define the fluid circuit of the fluid control device 10 and is connected to or includes the patient line 7 and the drain line 8. The disposable part is engaged and attached to the mechanical part prior to PD treatment to "configure" the DMU 2a, and is discarded once the PD session is completed. The FSU 2b may also be comprised of a reusable mechanical part and a disposable part. For example, one or more of the containers 5 may be replaceable.

[0038] In some embodiments, the FSU 2b is integrated with the DMU 2a to form an extended DMU 2. The resulting machine part is a dialysis machine or cycler with integrated fluid production capabilities. Such a machine part may be combined with a disposable part as described above.

[0039] In some embodiments, the control device 20 is integrated as part of the DMU2a or an extended DMU2.

[0040] It is also conceivable that the local controller 16a may be omitted and the control device 20 may implement the functions of the local controller 16a. Similarly, the control device may implement the functions of the local controller 16b.

[0041] The CRP sensor 15 may be included in the mechanical or disposable part of the DMU 2a. If included in the mechanical part, the DMU 2a may comprise a cleaning unit (not shown) operable to perform rinsing and / or disinfection of the CRP sensor 15. Alternatively, the CRP sensor 15 may be located in the patient line 7 or the drain line 8 instead.

[0042] FIG. 2A illustrates a schematic representation of a portion of a PD session in terms of intraperitoneal volume IPV as a function of time. IPV indicates the amount of fluid present in the PC. IPV is also referred to herein as Vp or "fluid volume." Despite its connotation, IPV can be defined in terms of either volume or mass.

[0043] In a PD session, the PDA1 is fluidly connected to the patient P by connecting the patient line 7 to the access device 7′ (FIG. 1), and then, optionally after an initial drain phase, the PDA1 is operated to perform one or more fluid exchange cycles ("cycles"). For example, an initial drain phase may be performed if the patient holds a large fluid volume in the PC at the start of the session. This occurs if a previous session ended with a fill phase, i.e., injection of therapeutic fluid into the PC. After completion of a cycle, the patient P may be fluidly disconnected from the PDA1.

[0044] The portion of the PD session in FIG. 2A includes an initial drain phase (IDP) followed by a fluid exchange cycle (C1). The cycle C1 consists of a fill phase (FP), a dwell phase (DWP) and a drain phase (DP) executed in sequence. In the IDP, a fluid volume Vd is drawn ("extracted") from the PC by the action of the fluid control device 10 and delivered to the drain 9 (FIG. 1). At the completion of the IDP, a residual volume Vres of used therapy fluid remains in the PC. In the FP, a fluid volume Vf of unused therapy fluid is infused into the PC by the fluid control device 10 delivering unused therapy fluid produced by the FSU 2b to the patient line 7 (FIG. 1). In the DWP, therapy fluid is left as it is in the PC. In the DP, a fluid volume Vd is extracted from the PC leaving a residual volume Vres. This residual volume may be, and typically is, different from the residual volume after the IDP. Depending on the osmotic gradient, transport of fluid from the patient's blood through the peritoneum to the PC or vice versa, known as ultrafiltration (UF), can be either positive, increasing IPV, or negative, decreasing IPV. During FP, DWP and DP, UF is generally positive, increasing PC volume due to osmotic agents in the treatment fluid. For illustration purposes, the effect of UF is shown only for DWP in FIG. 2A.

[0045] The extraction of the fluid volume Vd can be controlled in various ways. In one implementation ("fixed Vd extraction"), the fluid volume Vd is deterministic and given by a set value, and the fluid controller 10 operates to extract Vd (if possible) as measured by the flow meter 13. Fixed Vd extraction is used, for example, in tidal PD or IRPC (below). In another implementation ("maximized Vd extraction"), the fluid controller 10 operates to extract as much fluid as possible from the PC. For example, the IDP / DP can be terminated when the fluid controller 10 is unable to extract any more fluid from the PC, as indicated, for example, by a low flow rate.

[0046] Figure 2B shows the IPV as a function of time during a PD session consisting of an initial drainage phase followed by six consecutive fluid exchange cycles C1-C6. At the beginning of each cycle, there is a residual volume within the PC (see Vres in Figure 2A). This residual volume may be unknown and may vary from cycle to cycle.

[0047] FIG. 2C is a schematic diagram of the PDA1 of FIG. 1. The PDA1 comprises a DMU2 or 2a that receives a control signal 23 from a control device 20. The control signal 23 corresponds to C1 of FIG. 1 and causes the DMU2 / 2A to perform a PD session, for example as shown in FIG. 2A or FIG. 2B. As mentioned in the Background section, a conventional PDA1 operates based on set values ​​determined by a medical professional, for example a physician. Traditionally, set values ​​are input to the PDA1 as a "regimen" that defines how an upcoming session is to be performed, and the PDA1 then operates in compliance with the regimen. A regimen may be considered to define set values ​​of control parameters for the PDA1. On the other hand, the present disclosure provides a technique that allows a physician to specify target values ​​of one or more treatment parameters to be achieved by an upcoming session, and then the control device 20 automatically calculates and applies a regimen that meets the target values, possibly subject to confirmation by the physician. As used herein, a treatment parameter target value ("treatment goal") defines a desired change in a patient's physiological condition. Traditionally, the effectiveness of a PD session relies on the physician's ability to determine an appropriate regimen, which is typically done based on experience and heuristics. In contrast, the proposed technology offers a significant improvement in terms of actively controlling the PD session to achieve a desired change in the patient's physiological state. It is recognized that the proposed technology has the potential to improve PD treatment, reduce the physician's workload, reduce the risk of human error, and so on. In some embodiments, the technology allows the physician to input a regimen according to conventional practice, and then the control device 20 evaluates the regimen and adjusts one or more control parameters to meet the treatment goals. In some embodiments, the technology allows the physician to specify some control parameters as fixed and other control parameters as adjustable. Once the physician inputs the set values ​​for the fixed control parameters, the control device 20 determines the set values ​​for the adjustable control parameters to meet the treatment goals, taking into account the input set values.

[0048] The control device 20 is configured to determine a regimen for the next PD session and provide corresponding control signals 23 to the DMU 2 / 2a. The regimen is determined according to a regimen definition 21′ available to the control device 20. As used herein, a “regimen definition” specifies the control parameters of the DMU 2 / 2a and a “regimen” specifies the set points of the control parameters to be applied by the DMU 2 / 2a in the next PD session.

[0049] FIG. 2C shows an example of a regimen definition 21′ including multiple control parameters: FC, Vf, NoC, CF, TT, and TM. The control parameter FC defines the composition of the fresh treatment fluid to be infused during the individual fill phases. The composition may be fixed or time-varying. The control parameter Vf defines the fill volume (see FIG. 2A), which may or may not vary between cycles. The control parameter NoC defines the number of cycles in the session. The control parameter CF defines the cycle frequency in the session, which corresponds to the duration of the individual cycles. The control parameter TT defines the total treatment time, which corresponds to the duration of the session. The control parameter TM defines the modality of PD used in the session. In one example, the control parameter TM defines whether the session should be completed by a drain phase (see FIG. 2A-2B) or a fill phase. In another example, the control parameter TM defines the drain volume Vd of each drain phase. For example, Vd can be set to result in partial drainage of PC, as used in tidal PD. In another example, the control parameter TM may specify that the session should include continuous flow PD (CFPD) or any other PD modality. FIG. 2C is given by way of example only, and the regimen definition 21′ may include fewer or additional control parameters. For example, some of the control parameters may be provided by default values ​​or may be specific to the PDA1 and do not need to be included in the regimen definition 21′. Furthermore, if the session consists of a single cycle (FIG. 2B), the control parameter CF may be omitted.

[0050] As shown in Figure 2C, the control device 20 may receive interim regimen data 21 that defines at least a portion of a regimen for an upcoming PD session and may be entered by a physician via the UI 17 (Figure 1) or retrieved from memory. The interim regimen data 21 includes values ​​of one or more of the control parameters in the regimen definition 21'. The regimen data 21 may also indicate selections of the control parameters that are modifiable by the control device 20.

[0051] The control device 20 is configured to obtain one or more therapeutic targets TTV. The therapeutic targets may be inputted into the control device 20 by a physician, for example, through the UI 17 (FIG. 1). In one example, the therapeutic target represents the net water transport through the peritoneum during a session and is specified by UFV. The therapeutic target UFV thereby specifies the total amount of fluid extracted from the patient during the session. The therapeutic target UFV effectively corresponds to the patient's weight loss during the session, assuming the patient does not urinate or ingest fluids during the session. In another example, the therapeutic target represents the cumulative amount of one or more solutes removed from the patient during the session and is specified by SRVi. The therapeutic target SRVi may be provided for any solute. In a non-limiting example, the solute is urea or creatinine, which are commonly used to quantify the effectiveness of dialysis. For example, the therapeutic target SRVi may be provided in any form conventionally used to quantify solute removal, including, but not limited to, clearance, Kt / V, urea reduction ratio (URR), etc. For example, the Kt / V for a PD session may be given by dividing the SRVi for urea (or creatinine) by the product of the patient's total body water and the patient's plasma concentration of urea (or creatinine), as is well known to those skilled in the art. The weekly Kt / V may be derived by multiplying the Kt / V by the number of PD sessions performed per week.

[0052] It will be appreciated that both UFV and SRVi respond to changes in the patient's physiological state. Depending on the implementation, the control device 20 may be configured to act on UFV or SRVi, or both, to determine the control signal 23.

[0053] As shown in Figure 2C, the control device 20 may also obtain range data 24 that defines acceptable ranges for one or more of the control parameters in the regimen definition 21'. The use of the range data 24 is described below with reference to Figure 4B.

[0054] FIG. 3 is a schematic diagram of the peritoneal membrane ("peritoneum") 30 separating the PC 31 from the blood side 32 of the patient. In FIG. 3, the concentration of the individual solute (i) on the blood side 32 as a function of time is represented by Cbi(t), the IPV as a function of time is represented by Vp(t), and the concentration of the individual solute (i) in the treatment fluid as a function of time is represented by Cpi(t). The solutes include, but are not limited to, sodium, potassium, calcium, magnesium, lactate, phosphate, albumin, bicarbonate, urea, creatinine, chloride, and the like. The solutes may also include an osmotic agent, for example, according to any of the above examples. In the following examples, the osmotic agent is assumed to be glucose. FIG. 3 also illustrates processes that may affect Vp(t) and Cpi(t). The process includes the flow of therapeutic fluid into PC 31 through access 7' during the filling phase, denoted Jf(t), the flow of therapeutic fluid out of PC 31 through access 7' during the draining phase, denoted Jd(t), and the loss of therapeutic fluid from PC 31 if a fluid sample (see below) of the therapeutic fluid in PC 31 through access 7' is taken, denoted Js(t). The process further includes fluid flows through peritoneal membrane 30, including total water flow, denoted Jv(t), solute flow of individual solutes, denoted Ji(t), and lymph flow, denoted L. It should be understood that individual substances can move in either direction across membrane 30, and the arrows in FIG. 3 merely indicate the direction corresponding to the positive sign of the individual flows. In the following, it is assumed that flow variables Jf, Jd, and Js are given in ml / min, and flow variable Ji is given in mmol / min.

[0055] The peritoneal membrane 30 may be characterized by its transport properties. FIG. 3 shows two such transport properties, LpS and PS. The property LpS is the hydraulic conductance of the membrane 30, also known as the fluid permeability or ultrafiltration coefficient. The property LpS may be expressed, for example, in mL / min / mmHg. The property PS, also known as the mass transfer area coefficient or the diffusion mass transfer area coefficient, is the permeability-surface area product that describes the flow of solutes (molecules) through the membrane 30. The property PS may be expressed, for example, in mL / min. The property PS generally differs between solutes and is therefore expressed herein as PSi. In some embodiments, it is assumed that there is a predetermined relationship between PSi for all solutes, and therefore PSi for any solute can be calculated if PSi for one solute is known. This assumption may facilitate the computation, since determining PSi for a number of different solutes may be reduced to determining a scaling factor fPS that scales a global set PSiG of PS values ​​for the different solutes according to PSi=fPS·PSiG, where PSiG is the global PS value for solute i. The global set represents the predetermined relationship described above and may be considered to include PS values ​​that represent a "typical patient." Once fPS has been determined, any PSi may be determined by scaling the corresponding PSiG by fPS.

[0056] FIG. 3 shows a further scaling factor fCpw, which represents the tonicity of the patient and may represent the patient's deviation from an isotonic state. In some embodiments, to avoid the need to take a patient serum sample, the blood concentration of the solute Cbi(t) may be set to a fixed (time-invariant) value for a standard (nominal) patient. To account for the fact that tonicity may vary between patients, fCpw may be applied to scale the nominal blood concentration value. It can thus be seen that the scaling factor fCpw takes into account the patient's actual plasma water fraction.

[0057] In some embodiments, the regimen for the PD session is determined using a mathematical model of the composition of the fluid in the PC 31. The mathematical model is a kinetic model that depends on the transport properties of the peritoneal membrane 30 and describes the time dynamics of the flow of the therapeutic fluid represented by Jf(t), Jd(t) and Js(t) in FIG. 3, as well as the time dynamics of the fluid flow Jv(t) and solute flow Ji(t) through the membrane 30. These embodiments rely on the insight that the composition of the fluid in the PC is influenced by the regimen and that the physiological state of the patient is influenced by the composition of the fluid in the PC 31 during the course of the session. Based on this insight, the kinetic model is constructed as a transport model that specifies the transport of fluids and solutes through the peritoneal membrane as a function of control parameters in the regimen for the PD session. Such a transport model can be processed to determine a regimen that brings about a desired change in the physiological state of the patient.

[0058] FIG. 4A is a flow chart of an exemplary method 400 for generating control signals to cause a PDA to perform PD therapy on a patient. Method 400 may be performed by control device 20 of FIGS. 1 and 2C. Control method 400 includes a set-up procedure 400A that is performed to determine set-point values ​​for the PDA. The set-point values ​​are applied to generate control signals for operating the PDA to perform PD therapy. Control method 400 also includes a verification procedure 400B that is performed during PD therapy to verify the transport characteristics used by set-up procedure 400A and optionally take appropriate action. Set-up procedure 400A is now described in detail. Verification procedure 400B is further described below.

[0059] An exemplary setup procedure 400A of FIG. 4A includes steps 401-405. In step 401, one or more treatment targets TTV are obtained (see 22 in FIG. 2C). In step 402, transport properties of the peritoneum are obtained. The transport properties may include LpS and PSi, as described above. The transport properties may be input by a user via the UI 17 or may be retrieved by the control device 20 from an internal memory or from an external memory (see 18 in FIG. 1). At least one of the transport properties is patient-specific. The individual patient-specific transport properties may be determined by any known technique based on patient measurements, for example as described in the Background section. Further techniques for estimating patient-specific transport properties by using kinetic models are further described below. In step 403, a transport model is configured by the transport properties obtained in step 402. The model is "configured" by specifying values ​​of variables in the model, here the variables representing the transport properties. It should be understood that step 403 may configure the transport model with further values, for example a scaling factor fCpw, which may also be patient-specific and obtained in step 402. In step 404, the transport model is evaluated to determine the setpoints of a number of control parameters ("control parameter values") that achieve the treatment goal TTV obtained in step 401. The model is "evaluated" by adjusting the setpoints according to an optimization procedure until the model achieves the individual treatment goal. Any conventional optimization procedure may be used in step 404. In step 405, the PDA1 operates according to the setpoints. In the context of Figures 1 and 2C, step 405 includes generating a control signal 23 for the DMU2 / 2a corresponding to the setpoints. Step 405 may further include presenting the setpoints to the user via the UI 17. The user may also be given the option to accept or reject the proposed setpoints.

[0060] In some embodiments, step 401 includes a user inputting the UFV and / or SRVi via the UI 17 (FIG. 1). Alternatively, the UFV may be calculated based on patient weight data input by the user via the UI 17 or otherwise obtained by the control device 20. The weight data may include the patient's initial weight before the session and the patient's target weight at the end of the session, and the UFV may be calculated as a function of the difference between the current weight and the target weight. If the initial and target concentrations of the relevant solutes are known or input, it is conceivable that the SRVi is calculated in a corresponding manner.

[0061] As shown by the dashed lines in Figure 4A, step 404 may include obtaining 404A reference values ​​for fluid volume and solute concentration in the PC, and calculating 404B set points based on the reference values ​​using a transport model. The reference values ​​are provided at distinct reference time points. In step 404B, the reference values ​​form boundary conditions used to "anchor" the transport model to values ​​of fluid volume and solute concentration in the PC at the reference time points.

[0062] The reference value may be measured or estimated, or may be a predefined value. The predefined value may be global or patient-specific. In the example of solute concentration, the reference value may be obtained by extracting fluid samples from PC1 at one or more selected time points, measuring the CRP value of the individual sample using the CRP sensor 15 (FIG. 1), and estimating the solute concentration at the reference time point based on the CRP value. In the example of fluid volume, the remaining volume Vres may be determined and the reference value used. For example, the remaining volume after the initial drain phase may be estimated using a dilution scheme and based on Vf, e.g., conductivity may be measured before and after the filling phase, as described in EP 2 623 139 A1. Alternative techniques for estimating the reference value of fluid volume are described below with reference to FIGS. 8A-B.

[0063] FIG. 4B is a flow chart of an exemplary procedure that may be part of step 404B. In step 410, for candidate settings of the control parameters, time-resolved values ​​of fluid volume and solute concentration in the PC are calculated by using a transport model. It is recalled that the fluid volume corresponds to Vp(t) in FIG. 3 and the solute concentration corresponds to Cpi(t) in FIG. 3. The time-resolved values ​​represent the fluid volume and solute concentration at discrete times during the PD session to be performed, given the candidate settings. The candidate settings used in step 410 define a provisional regimen and may be at least partially entered by the user and / or given by default values ​​stored in the memory. The time-resolved values ​​are calculated by applying the transport model to the candidate settings as configured in step 403, taking into account the boundary conditions defined by the reference values ​​as obtained in step 404A. In step 411, the time-resolved values ​​are processed for the calculation of the resulting ("current") target values ​​of the treatment parameters. In step 412, the difference ("error") between the current target value and the corresponding treatment target TTV obtained in step 401 is evaluated with respect to an error threshold TH. If the difference exceeds TH, then in step 413, one or more of the candidate set points are changed (updated) according to an optimization procedure. If multiple current target values ​​(for different target parameters) are calculated in step 411, step 412 may calculate the corresponding multiple differences and compare the aggregate of the calculated differences with the error threshold TH. The resulting candidate set points are then processed again in step 410. Steps 410-413 are repeated until the difference does not exceed TH. At this time, processing proceeds to step 414, where the last candidate set point generated in step 413 is output as the set point for use in step 405.

[0064] As mentioned above, some control parameters are designated as modifiable and other control parameters are designated as fixed. For example, method 400 may include a step (not shown) of allowing a user to select control parameters as fixed and modifiable, respectively, via UI 17. Alternatively or additionally, one or more control parameters may be predefined in control device 20 as fixed or modifiable. In some embodiments, only candidate setting values ​​of modifiable control parameters are changed in step 413, while candidate setting values ​​of fixed control parameters remain unchanged. Furthermore, in some embodiments, method 400 includes a step (not shown) of allowing a user to input tolerance ranges for one or more control parameters (see range data 24 in FIG. 2C). Alternatively or additionally, one or more tolerance ranges may be predefined for control device 20. Adjustment of candidate setting values ​​in step 403 may thereby be limited to the tolerance range for the associated control parameter. The tolerance ranges provide the advantage of allowing settings to be tailored to clinic requirements, individual physician or patient needs, etc.

[0065] Procedure 404B may be automatically terminated if steps 410-413 are repeated for longer than a timeout period. If the timeout period is exceeded, method 400 may inform the user, via UI 17, of the need to change the treatment goal and / or control parameters designated as changeable (if specified) and / or the acceptable range of one or more control parameters (if specified).

[0066] FIG. 5 is a plot of the time resolved values ​​calculated in step 404B for a PD session performed by PDA1. The bottom graph in FIG. 5 shows the fluid volume in the peritoneal cavity as a function of time. As can be seen, the session includes an initial drain phase followed by five fluid exchange cycles. The session ends with a drain phase. The middle graph in FIG. 5 shows the concentration of glucose in the fluid in the peritoneal cavity as a function of time. The top graph is included to show that when time resolved values ​​of concentration for different solutes are calculated in step 404B, it is also possible to calculate time resolved values ​​of conductivity for the fluid in the peritoneal cavity as a function of time. In particular, the conductivity at individual time steps can be calculated by aggregating the contributions of different solutes at the time steps.

[0067] In some embodiments, the kinetic model used in the control method 400 is based on the well-known three-pore model (TPM) of the peritoneum. The TPM is a transport model that assumes that the peritoneal vascular wall has three types of pores with different pore radii, allowing passive transport of molecules with different size characteristics. The smallest pore type, called aquaporin-1 (AQP-1), is a water-selective pore structure. AQP-1 pores allow passive transport of water, i.e., UF, driven by osmotic forces. The medium pore types allow transport of fluids and smaller solutes. The majority of protein transport across the membrane is enabled by the large pore types. Examples of TPM can be found in WO 2018 / 041760 and in the article "Optimization of Automated Peritoneal Dialysis Using an Extended Three-Pore Model" by C. Eberg and B. Lippe, published in Kidney International Reports 2(5), pp. 943-951, 2017, both of which are incorporated by reference in their entirety.

[0068] In some embodiments, the peritoneal transport model is configured to take into account ion transport by electrostatic forces across the peritoneal membrane, caused by differences in the amount of dissolved ions on either side of the peritoneal membrane and the reflection of highly charged solutes by the peritoneal membrane. An example of such a mathematical transport model is given in Appendix A. Further examples of the incorporation of electrostatic forces in transport models are found in Chapter 17 (pp. 33-36) of the publication "Analysis of Transport Phenomena in the Glomeruli and Peritoneal Microcirculation" by Øberg, Karl, (1st Edition), Lund: Lund University School of Medicine, ISBN 978-91-7619-372-3. It is currently believed that taking electrostatic forces into account will give more accurate results. It should be noted that there are alternative and / or simpler techniques for describing ion transport by electrostatic forces, for example using so-called Donnan factors.

[0069] The equations describing the process of Figure 3 are detailed in Appendix A. These equations are based on the TPM, but as would be readily understood by one of ordinary skill in the art, other kinetic models may be used instead, such as a two-pore model, a double-barrier membrane model, or a distribution model.

[0070] Equations 1 through 4 in Appendix A represent the fluid flow through the peritoneum, J v (t) and the flow of solute i through the peritoneum J i (t) and the time change in intraperitoneal volume dV p / dt and the time change in concentration of solute i in the treatment solution in PC, dC pi / dt, can be generalized to the basic time-dependent governing functions. TIFF2025508082000002.tif3991 where C (p1..N) (t) specifies the collection of concentration values ​​of all included solutes at time t, and C Ii specifies the concentration of solute i in the unused treatment solution, and P 1..N specifies a collection of PS values ​​for all included solutes.

[0071] As can be seen and understood from Appendix A, the governing functions have complex and mixed dependencies on the time-dependent variables and the transport properties LpS and PSi shown in Figure 3. As further illustrated below with reference to Figures 7A-7C, the governing functions f1, f2, f3, f4 can be combined to define a peritoneal transport model.

[0072] The above-mentioned scale factor fCpw is included in Cbi (see Figure 3), which is J v (t) and J i Note that the governing functions f1, f2 are used in the calculation of both fCpw and fCpw (see Equations 2 and 4 in Appendix A). Thus, the governing functions f1, f2 also depend on fCpw, if used.

[0073] Also, function f2 is PS (i,m) It can also be noted from Appendix A that this represents a pore type specific value. In some embodiments, the PS value for large pores (PS (i,3) ) is set to a global value, for example given by the global setting described above, and the scaling factor fPS is the PS value for small pores (PS (i,2) ) only. The PS value for a particular solute may then be calculated by scaling the sum of the corresponding generic PS values ​​for small and large pores by a scaling factor fPS. This simplification has been found to have little effect on the accuracy of the results. However, in other embodiments, the small pore (PS (i,2) ) and large pores (PS (i,3) A scaling factor fPS may be applied to both PS values, PSiG and PSiG. As a further simplification, the PS value for a particular solute may be calculated by scaling the corresponding PSiG for the small pore by the scaling factor fPS, thus omitting the contribution from the large pore. This further simplification is at least applicable for small solutes, such as glucose or sodium, where the contribution to PSi from the large pores is effectively negligible.

[0074] Returning to the control method 400 of FIG. 4A, step 403 of configuring the transport model involves applying known values ​​of LpS to function f1 and PS to function f2. (1..N) and applying a known value of

[0075] Given the notation in Figure 3, the treatment target UFV is the duration of the session (J v (t)-L), and the treatment target SRVi is given by the J i (t). Furthermore, the control parameters in the regime definition 21′ in FIG. 2C are represented by variables in functions f1 to f4. The fluid composition (FC) is C Ii and the filling volume (Vf) is given by J f (t), and the number of cycles (NoC), cycle frequency (CF), treatment time (TT) and modality (TM) are all in J f (t) and J d (t) whereby it is possible to calculate set points for the control parameters so as to achieve one or more therapeutic goals during a session based on the transport model given by the functions f1-f4.

[0076] FIG. 7A is a block diagram of an exemplary control device 20 for operating a PD device, such as the PDA1 of FIG. 1. The control device 20 is configured to execute the setup procedure 400A according to FIGS. 4A-4B. In the illustrated example, the control device 20 is configured to receive input data including interim regimen data 21, one or more treatment goals (TTV) 22, treatment history data 76A, comprehensive patient data 76B, patient-specific data 76C, and solute characteristic data 76D. The control device 20 comprises a first calculation module 72 and a second calculation module 73, which cooperatively operate to determine a regimen including set values ​​of control parameters. The set values ​​are provided to a signal generator 74 configured to generate and output corresponding control signals 23 for the DMU2 / 2a in the PDA1 (see FIG. 2C). The first calculation module 72 defines or includes a transport model (PTM) 72′. For example, the PTM 72′ may be an implementation of governing functions f1-f4. The first calculation module 72 is configured to operate the PTM 72' on at least a portion of the input data to calculate candidate setpoints (see step 410). The second calculation module 73 defines or includes a parameter fitting algorithm (PFA) 73', which is configured to operate on the candidate setpoints from the first calculation module 72 to calculate current setpoints (see step 411). The first calculation module 72 and the second calculation module 73 are configured to alternately generate candidate setpoints and current setpoints (see step 412) until the PFA 73' finds that the current setpoints meet a convergence criterion or a time limit expires. The control device 20 then operates the signal generator 74 to generate and output the control signal 23. The PFA 73' can be any algorithm ("optimization procedure") capable of solving a nonlinear optimization problem and fitting experimental data to simulated data. Such algorithms include any nonlinear programming (NLP) algorithm, such as an algorithm for least-squares minimization. The results presented herein were generated using the standard function Lsqnonlin in MATLAB®.

[0077] Looking more closely at the input data presented in FIG. 7A, the regimen data 21 defines an interim regimen that may be entered at least in part by a user. In some embodiments, the interim regimen includes fixed control parameter settings and interim settings for modifiable control parameters. The treatment history data 76A defines dialysis treatments performed on the patient during a prior period, e.g., within 12-48 hours of a PD session. For example, the treatment history data 76A may include regimen data for one or more fluid exchange cycles performed on the patient during the preceding period. The global patient data 76B may include the global settings of PSi values ​​discussed above, as well as global values ​​of plasma concentrations of solutes (see Cbi in FIG. 3). In some embodiments, all solutes expected to be present in blood or treatment fluid at a concentration of at least about 0.5 mmol / L are included in the PTM 72′ and therefore also in the global patient data 76B. The global values ​​may be provided as population mean values. The global patient data 76B may include, for example, α m It will be appreciated that the patient specific data 76C may also include other parameter values ​​used by the PTM 72', such as values ​​of LpS and PSi. The patient specific data 76C may also include the concentration of one or more solutes in the patient's blood, or previously determined values ​​of Vres or fCpw (see FIG. 3). The solute characteristic data 76D may include any known characteristic data of the solutes included in the governing function used by the PTM 72'. In the example of Appendix A, the solute characteristic data 76D includes the osmotic coefficient (φ i ), the charge of the solute (z i ), etc.

[0078] The input data shown in FIG. 7A and described above is not intended to be limiting and is provided by way of example only. It is contemplated that additional input data may be used and / or one or more of the data sets 76A-76D may be omitted. For example, the patient-specific data 76C may not be available. In another example, all or a portion of the solute characteristic data 76D may be integrated into the PTM 72'. Additionally, the treatment history data 76A may be omitted altogether.

[0079] FIG. 7B is a block diagram of a more detailed example of the first calculation module 72 and the second calculation module 73. The PTM 72′ is configured to receive target data 22 (see step 401) including the treatment target TTV, characteristic data 72B (see step 402) including the values ​​of the transport characteristics LpS and PSi, and reference data 72A (see step 404A) including reference values ​​of fluid volumes and solute concentrations. The characteristic data 72B may be included in the patient-specific data 76C (FIG. 7A) or may be input by a user. In the example of FIG. 7B, the PTM 72′ is also configured to receive fixed regimen data (RDf) 21A that specifies the values ​​of all fixed control parameters (if present) and initial regimen data 21B (RDm(0)) that specifies the initial values ​​of all changeable control parameters (if present). The first regimen data 21A and the second regimen data 21B may be included in the provisional regimen data 21 of FIG. 7A.

[0080] As shown in FIG. 7B, the PTM 72′ is operable to calculate a current target value (TTc) 77A of the treatment parameter based on the reference data 72A, the characteristic data 72B, and the regimen data 21A, 21B (see steps 410-411). The current target value TTc 77A is received by a subtraction module 78 configured to calculate a difference between TTV and TTc (see step 412) to yield error data (ΔTT) 73A. If TTV and TTc include more than one treatment parameter, a difference is calculated for each treatment parameter and included in the error data 73A. The error data 73A is received by a second calculation module 73. The PFA 73′ is configured to operate on the error data 73A to generate candidate set points for the control parameters (see step 413), as represented by RDm 73B in FIG. 7B. In the example of FIG. 7B, taking into account RDf21A, PFA'73 refrains from modifying fixed control parameters and generates RDm73B to include only candidate set values ​​of modifiable control parameters. Upon receiving RDm73B, PTM72' is configured to operate on RDm73B and RDf21A, as well as reference data 72A and characteristic data 72B, to calculate a new current target value TTc77A. As will be understood from the above, the calculation and flow of data may continue until PFA73' finds that a convergence criterion is met, e.g., the error data 73A is sufficiently small. It can be seen that the control device 20 of FIG. 7B represents a feedback control system, with the first calculation module 72 corresponding to the controlled system, TTV22 corresponding to the set value, TTc77A corresponding to the actual value, and PFA73' corresponding to the controller.

[0081] In some embodiments, the characteristic data 72B is assumed to be time-invariant (constant) during a PD session, which may facilitate computation. However, it is also possible that one or more transport properties in the characteristic data 72B vary in time by including a predetermined time dependency for the individual properties. It has been previously known to model the decreasing time dependency of PSi and / or LpS during PD, as described, for example, in the article "Diffusion mass transfer coefficients are not constant during a single exchange in continuous ambulatory peritoneal dialysis" by Waniski et al., published in ASAIO Journal, 1996, Vol. 42, pp. M518-523, which is incorporated herein by reference in its entirety.

[0082] The subtraction module 78 does not have to be included in the first calculation module 72 as shown in FIG. 7B, but may instead be included in the second calculation module 73 or in a separate (third) calculation module.

[0083] FIG. 7C is a block diagram of a more detailed example of the PTM 72′ of FIG. 7B. In general, the PTM 72′ is configured to implement governing functions f1-f4. In the illustrated example, the PTM 72′ includes a differential equation solver (DES) submodule 171 configured to operate on values ​​of derivatives ("time changes") of variables at one or more previous time steps to generate values ​​of the variables at a current time step. For example, the DES submodule 171 may implement any known regression method for obtaining a numerical solution to a differential equation, such as a linear multi-step method, a Runge-Kutta method, or a generalized linear method (GLM). The results presented herein were generated by implementing the PTM 12′ with a conventional ODE (ordinary differential equation) solver, specifically the ode45 function in MATLAB.

[0084] In the illustrated example, the PTM 72′ is configured to generate a time series of values ​​of the intraperitoneal volume Vp and a corresponding time series of values ​​of the concentration Cpi of the solute in the fluid in the PC. To this end, the PTM 72′ further includes management submodules 172-175 implementing the respective governing functions f1-f4. In operation, the DES submodule 171 generates a data set 171B including Vp, Cpi for the current time step based on data sets 174B, 175B including derivatives of Vp and Cpi for the previous time step. The governing submodule 172 operates on Vp, Cpi for the current time step to generate Jv for the current time step. The governing submodule 173 operates on Jv, Vp, Cpi for the current time step to generate Ji for the current time step. The governing submodule 174 operates on Jv for the current time step to generate the derivative of Vp for the current time step. The governing submodule 175 operates on Jv, Ji, Cpi, and Vp to generate the derivative of Cpi for the current time step. It is understood that by running the FTM 72' from a start time (t=0) to an end time, a separate time series of values ​​of Vp and Cpi is generated. The PTM 72' is configured to generate a separate time series of the treatment period, which is given by the control parameter TT (FIG. 2C) in the regimen data 21A or 21B.

[0085] As will be appreciated from the above and shown in FIG. 7C, PTM 72′ operates on reference data 72A, regimen data 21A, 21B, and characteristic data 72B in generating the two time series. As noted above, the control parameter TT in the regimen data defines the treatment duration. As noted above, the regimen data also defines other variables in governing functions f1-f4 and is used by at least sub-modules 174, 175. Similarly, characteristic data 72B is used by at least sub-modules 172, 173.

[0086] The DES submodule 171 derives from the reference data 72A the intraperitoneal volume at a reference time RVp and the concentration of solutes in the PC at a reference time RCpi. The reference time may or may not be the start time of the session. If the reference time is equal to the start time, then RVp and RCpi may be used as initial seed values ​​in the data set 171B when the PTM 172' starts to calculate Vp and Cpi. If the reference time is different from the start time, the operation of the PTM 172' is slightly modified as will be easily understood by those skilled in the art.

[0087] The DES submodule 171 may retrieve the reference data 72A from the memory. As mentioned above, RVp and RCpi may be determined from previous measurements or estimated by calculation. In the example of FIG. 7C, the reference data 72A is provided by the reference data module 176. In some embodiments, the reference data module 176 is configured to calculate RVp and / or RCpi from the global patient data 76B or the patient-specific data 76C (FIG. 7A). The use of the global patient data 76B may reduce the accuracy of the resulting set point if RCpi or RVp is significantly inaccurate. In some embodiments, to mitigate this potential problem, the reference data module 176 is configured to perform a simulation based on information in the treatment history data 76A (FIG. 7A) relating to dialysis treatments performed in previous periods. The simulation may be performed using the governing functions f1 to f4, taking into account these regimens. The initial values ​​of solute concentrations for this simulation may be taken as plasma water concentrations, optionally slightly modified from plasma water, for example by reducing the content of large solutes and / or adjusting sodium and chloride according to Donnan equilibrium. Alternatively, the reference data module 176 may be configured to set RCpi equal to the plasma water concentration, optionally applying a reduction factor for large solutes such as albumin. In another alternative, the reference data module 176 may be configured to set RCpi equal to the concentration of the individual solutes in the unused treatment solution.

[0088] Once the time series of values ​​for Vp and Cpi have been generated, the PTM 72' calculates a current target value TTc as a function of at least one of the time series. The calculated TTc is then output for receipt by the subtraction module 78 (FIG. 7B). Once the PFA 73' has calculated RDm 73B based on ΔTTc 73A as generated by the subtraction module 78, the PTM 72' again operates to generate separate time series for Vp and Cpi. Here, the PTM 72' may again use the reference data 72A, the characteristic data 72B, and RDf21A.

[0089] FIG. 7D shows an exemplary structure in the PTM 72′ for calculating a current target value 77A of fluid removal, denoted as UFVc. This structure comprises an accumulation module 177 configured to receive and accumulate instantaneous values ​​of Jv generated by the governing submodule 172. Functionally, the accumulation module 177 performs an integration of Jv(t) (FIG. 3) over the treatment period, resulting in a total water volume. The accumulation module 177 may also be configured to calculate a cumulative lymphatic water absorption for the treatment period and subtract the cumulative lymphatic water absorption from the total water volume to obtain a net water volume. If lymph flow (L in FIG. 3) is assumed to be constant, the cumulative lymphatic water absorption may be calculated as the product of treatment time and lymphatic flow. Depending on the implementation, UFVc may be given as a total water volume or a net water volume.

[0090] FIG. 7E shows an exemplary structure in the PTM 72′ for calculating a current target value 77A of solute removal from the patient during a session, denoted as SRVic. This structure comprises an accumulation module 177 configured to receive and accumulate instantaneous values ​​of Ji generated by the governing submodule 173. Functionally, the accumulation module 177 performs an integration of Ji(t) (FIG. 3) over the treatment time, resulting in the total amount of solute i that has passed through the peritoneum and entered the PC ("total solute transport"). In the illustrated example, the accumulation module 177 is also configured to receive and process instantaneous values ​​of Cpi generated by the governing submodule 171. Functionally, the accumulation module 177 performs an integration of L·Cpi(t) (FIG. 3) over the treatment time, resulting in the cumulative lymphatic solute absorption. The current target value SRVic is calculated by subtracting the cumulative lymphatic solute absorption from the total solute transport. In a variant not shown, SRVic may instead be given as the total solute transport.

[0091] Applicant has found that the transport model may also be used to validate one or more of the transport properties used in the transport model, and optionally to validate the performance of the control method 400, by using CRP values ​​measured for the fluid in the PC during the session. Thus, in the embodiment of Figure 4A, the control method 400 includes a verification procedure 400B that may be performed during or after the PD session initiated by the set-up procedure 400A. In the example of Figure 4A, the verification procedure 400B includes steps 406-409.

[0092] In step 406, first validation data ("first data") is obtained. The first validation data includes CRP values ​​measured at one or more time points during the session. In FIG. 1, the control device 20 obtains measured CRP values ​​from the CRP sensor 15. In one example, the CRP values ​​may be measured during the drain phase by the control device 20 directing the outflow through the CRP sensor 15. In another example, the CRP values ​​may be measured during the dwell phase by the PDA 1 operating to draw a small sample of fluid from the PC via the patient line 7 and direct the fluid sample through the CRP sensor 15. Such a fluid sample is represented in FIG. 3 by Js(t). Each measured CRP value in the first validation data may be provided by a single measurement from the CRP sensor 15 or may be provided as a time average of multiple measurements.

[0093] In step 407, second validation data ("second data") is obtained. The second validation data represents the fluid flow into and out of the PC via the access 7' (Figure 1) during the session. For example, the control device 20 may obtain the second validation data from the flow meter 13 or based on volume monitoring. Alternatively or additionally, the second validation data may be provided by set values ​​applied by the control device 20 to run the session up to the time of validation, for example set values ​​calculated in step 404 of the setup procedure 400A. The second validation data specifies the fluid flow as a function of time during the session. In some embodiments, the second validation data includes values ​​of the fill volume, the drain volume and (if relevant) the sample volume of the individual cycles, as well as the start and end times of the phases of the individual cycles. Such phases include a fill phase, a drain phase and an optional sampling phase for extracting a fluid sample from the PC, and the values ​​of the fill volume, the drain volume and the sample volume specify the cumulative volumes for the individual phases. In another embodiment, the second validation data comprises a time series of instantaneous flow values ​​for fluid flow through the access 7' during the session.

[0094] In step 408, at least one of the transport properties is recalculated using the transport model. Thus, step 408 results in at least one updated transport property of the peritoneum. The recalculation is based on the first validation data and the second validation data, and typically results in at least one value of LpS or PSi. Step 408 can be performed at any time during the session, at the completion of the session, or at any time after the completion of the session. Returning to the governing functions f1-f4, the skilled person will understand that LpS and / or PSi can be calculated if the other variables are known or estimated. The flow related variables Jf(t), Jd(t), Js(t) are known from the second validation data. It can be shown that it is sufficient for the first validation data to include CRP values ​​measured at two different time points to calculate one updated transport property. A larger number of measured CRP values ​​improves the accuracy (reliability) and / or allows the calculation of another updated transport property. The measured CRP values ​​may be obtained during at least two different phases during a PD session, such as during an emptying phase and a successive dwell phase, or during a dwell phase and a successive emptying phase. Calculation of the updated transport properties is further described below in connection with FIG. 10.

[0095] In step 409, one or more dedicated actions are performed based on the one or more updated transport properties calculated in step 408. Examples of such dedicated actions are shown in Figures 4C-4E.

[0096] In the example of FIG. 4C, the dedicated action is step 409A of displaying and / or storing the calculated value of the individual updated transport characteristic. In the example of FIG. 1, the calculated value may be stored in memory 18 (local or external) and / or presented on UI 17, for example as part of an electronic medical record (EMR) or in a remote patient monitoring system. It is understood that the calculated value from step 408, given the first and second validation data for the ongoing session, is more likely to accurately represent the patient's peritoneum than the previous value entered in step 402. Thus, step 409A provides the caregiver with a better understanding of the current state of the patient's peritoneum. The calculated value may be used for any suitable purpose, including improving the performance of future PD treatments for the same patient. For example, during future executions of the control method 400, the calculated value may be entered by the caregiver or retrieved from memory in step 402.

[0097] In the example of FIG. 4D, the dedicated actions include steps 409B, 409C, and 409D, which are performed during ongoing PD treatment. In step 409B, the transport model is reconstructed using the individual updated transport properties. Step 409B may be performed similar to step 403 by replacing the current values ​​of the individual transport properties with the calculated values ​​from step 408. For example, step 403 may include applying recalculated values ​​of LpS in function f1 and / or PS in function f2. (1..N)In step 409C, the setpoints of the control parameters are recalculated. Step 409C may include a calculation procedure in which the reconstructed transport model is evaluated to determine updated setpoints that achieve the treatment goal (TTV from step 401) for the PD session. The calculation procedure of step 409C is performed to take into account the second validation data from step 407 and possibly the first validation data from step 406. In one implementation of the calculation procedure, an intermediate value of the treatment parameter is calculated based on the second validation data. The intermediate value represents the amount of the treatment goal that is achieved at the current time point. An updated treatment goal is then calculated by subtracting the intermediate value from the treatment goal. The calculation procedure then performs step 404 by using the reconstructed transport model from step 409B to determine updated setpoints for PDA1 that achieve the updated treatment goal. The reference data 72A may also be updated based on the first validation data and the second validation data according to method 800 (FIGS. 8A-8B below). In step 409D, updated control signals for PDA1 are generated based on the updated settings, similar to step 405. PDA1 then operates according to the updated settings to achieve the TTV entered in step 401 for the entire session.

[0098] The dedicated action of FIG. 4D improves the performance of the PD therapy in terms of its ability to reach therapeutic goals by automatically considering the effect of any differences between the values ​​of the individual transport properties originally entered in step 402 and the values ​​calculated in step 408.

[0099] In the example of FIG. 4E, the dedicated actions include steps 409B, 409F, 409G, and are performed during the ongoing PD treatment. In step 409B, the transport model is reconstructed using the individual updated transport properties as described above. In step 409F, the final values ​​of the treatment parameters at the completion of the ongoing PD session are calculated using the updated transport model and based on the second validation data from step 407. Thus, the final values ​​represent the actual TTV achieved by the ongoing session ("actual values ​​at the end of treatment"). The final values ​​may be calculated to represent at least one of fluid removal or solute removal. In step 409G, the final values ​​of fluid removal and / or solute removal are stored and / or presented to the user. In the example of FIG. 1, the final values ​​may be stored in memory 18 (local or external) and / or presented on the UI 17.

[0100] It is understood that the final value indicates the actual change in the patient's physiological state as a result of the session. The final value allows the physician to verify the validity of the session. The final value may also be used by the physician in determining the treatment goal for the next session. In a further variant, the control device 20 may comprise a calculation function for automatically calculating the treatment goal for the next session based on the final values ​​of the preceding sessions, for example to arrive at an overall treatment goal for multiple sessions. The final value may be manually entered into the calculation function by a caregiver via the UI 17 or may be automatically retrieved by the control device 20 from the memory 18.

[0101] It should be noted that one or more of the dedicated actions represented by Figures 4C-4E may be combined.

[0102] Another dedicated action, not shown, is to perform step 409B to reconfigure the transport model with the individual updated transport characteristics from step 408, thereby preparing the reconfigured (updated) transport model for use the next time control method 400 is executed. Such a dedicated action assumes that one and the same patient is treated by a PDA and may be applicable, for example, to home dialysis.

[0103] 6A is a flow chart of an exemplary method 600A that includes an embodiment of the verification procedure 400B. The method 600A is performed while the PDA1 is operating according to step 405 according to settings determined by the setup procedure 400A.

[0104] The method 600A includes steps 406, 407 of obtaining first validation data including CRP values ​​measured at one or more time points during a session according to the validation procedure 400B. In step 601, individual expected CRP values ​​are calculated at the time points of the individual measured CRP values. The expected CRP values ​​are calculated based on the second validation data using the transport model from step 403 (FIG. 4A). Returning to FIG. 7C, it is understood that the PTM 72′ is operable to generate a time series of Cpi values ​​if all necessary input data to the PTM 72′ is known. Since the PDA1 operates according to the settings determined by the setup procedure 400A, all required input data is essentially known. Thus, with the transport model known, it is a straightforward task to calculate the Cpi value of an individual solute at any given time point during an ongoing session. Depending on the CRP, step 603 may further include converting one or more calculated Cpi values ​​at a time point into an expected CRP value. For example, if the CRP value represents conductivity, then the Cpi value needs to be converted to conductivity. The conversion can be performed as described above in connection with FIG.

[0105] In steps 602-603, the measured CRP value and the expected CRP value are evaluated with respect to a deviation criterion. In the illustrated embodiment, in step 602, an evaluation parameter (first deviation, DEV1) is calculated, which represents the difference between the corresponding measured and expected values. If the measured CRP values ​​are available at multiple time points, DEV1 may be an aggregate of such differences. In step 603, DEV1 is evaluated with respect to the deviation criterion. If DEV1 is found to represent a sufficiently large difference (deviation), step 603 proceeds to step 408, where at least one transport characteristic is recalculated (updated). Thus, method 600A selectively recalculates at least one transport characteristic based on DEV1. If in step 603, DEV1 is found to be sufficiently small, method 600A returns to step 405, where control unit 20 continues to operate PDA1 according to the set-up values ​​determined by set-up procedure 400A. As indicated by dashed lines, step 408 may be followed by action step 409, for example according to any one of FIGS. 4C-4E, and / or method 600A may return to step 405 after step 408 or step 409.

[0106] The control device 20 may be configured to repeatedly perform the sequence of steps 406, 407, 601, 602, 603 during a PD session, for example at fixed intervals or at other selected times.

[0107] The combination of steps 601-603 is implemented to trigger a recalculation of at least one transport property whenever a sufficient deviation between the modeled and actual conditions is detected. By properly defining the deviation conditions, unnecessary recalculation of transport properties by step 408 can be avoided, and unnecessary actions by step 409 can be avoided. Thus, the embodiment of FIG. 6A can help to reduce the processing load and energy consumption of the control device 20.

[0108] In the foregoing example, the modeled conditions and the actual conditions are represented by CRP values. Thus, the expected value is calculated using the transport model and the first verification data (step 601), and the deviation between the expected value and the corresponding value given by the second verification data is determined (step 602). This is thought to provide a simple and robust detection of the deviation between the modeled conditions and the actual conditions. As an alternative, the expected value is calculated using the transport model and the second verification data (step 601), and the deviation between the expected value and the corresponding value given by the first verification data is determined (step 602). For example, the expected value may be the expected discharge volume for the discharge phase (Vd in FIG. 2A), and the deviation may be determined between the expected discharge volume and the actual discharge volume given by the second verification data.

[0109] FIG. 6B is a flowchart of a procedure that may be part of the evaluation step 603 of FIG. 6A. The procedure of FIG. 6B includes comparing DEV1 with a plurality of ranges. An example of such a range is shown in FIG. 6D. In the illustrated example, the range is defined by a first positive limit L1p, a second positive limit Lp2, a first negative limit L1n, and a second negative limit L2n, where L2n < L1n and L2p > L1p. Specifically, a first range R1 is defined between L1n and L1p, and two second ranges R2 are defined between L2n and L1n and between Lp1 and Lp2, respectively. Thus, the first range R1 is located closer to DEV1 = 0 than the second range R2. Note that the absolute values of L2n and L2p may or may not be the same. Similarly, the absolute values of L1n and L1p may or may not be the same.

[0110] In the example of FIG. 6D, the limits of the ranges R1, R2 change over time. Specifically, the spread (width) of R1 decreases monotonically with time from the start to the end of the PD session. Narrowing R1 in this way may be beneficial if the transport model is repeatedly updated during the PD session conditional on the outcome of step 603, e.g., if step 409 of FIG. 6A includes step 409B. The underlying rationale is that the accuracy of the transport model should improve with each update, and DEV1 becomes smaller over time. By gradually narrowing the range R1, it becomes more likely that the method 600A will be continuously triggered (at step 603) to improve the transport model by updating one or more transport properties (step 408) and by reconfiguring the transport model with the updated one or more transport properties (step 409B). The outer limits of the range R2 (L2p, L2n) may gradually converge to improve the sensitivity of the method 600A to PDA malfunctions. The extent (width) of the individual range R2 may remain the same or gradually narrow over time, depending on the implementation. It is understood that FIG. 6D is a non-limiting example. In general, the control device 20 may be configured to narrow the range R1 (the difference between L1p and L1n) over time by modifying at least one of L1p, L1n. Similarly, the control device 20 may be configured to reduce the difference between L2n, L2p over time by modifying at least one of L2n, L2p.

[0111] Returning to FIG. 6B, the illustrated procedure includes a first evaluation step 603A and a second evaluation step 603B. In step 603A, DEV1 is evaluated with respect to a first range R1. If DEV1 falls within R1, step 603A proceeds to step 405, which means that the method 600A refrains from calculating any updated transport properties. R1 is thus defined to include the tolerable deviation between the modeled conditions as given by steps 601-602 (FIG. 6A) and the actual conditions. If DEV1 falls outside R1, step 603A proceeds to step 603B, where DEV1 is evaluated with respect to a second range R2. If DEV1 falls within one of the second ranges R2, step 603B proceeds to step 408, which means that at least one transport property is recalculated (updated). On the other hand, if DEV1 falls outside the second range R2, step 603B proceeds to step 603C where an alert to the user is generated, for example via UI 17 (FIG. 1). The alert may, for example, instruct the user to check that PDA1 is properly installed, that the tubes are not kinked, etc. In some embodiments, step 603C may also cause the control device 20 to shut down the PDA or terminate the PD session. The limits L2n and L2p are set to define the minimum and maximum deviations that are allowed to occur as a result of inaccuracies in the transport model, with the understanding that deviations beyond L2n and L2p are likely to be the result of a PDA malfunction. Thus, the procedure of FIG. 6(b) reduces the processing load of the control device 20 while also performing a safety function to detect operational errors or malfunctions of the PDA.

[0112] It should be noted that the procedure of Fig. 6B refrains from performing step 408 if DEV1 causes an alert to be generated by step 603C. This saves processing resources and also avoids the risk of a potentially erroneous value of the transport property being calculated based on the first and second validation data from steps 406, 407. However, in a variant, step 408 is performed in conjunction with step 603C.

[0113] FIG. 6C is a flow chart of an exemplary method 600B implemented to selectively generate updated control signals for the PDA corresponding to the method 600A of FIG. 6A. Similar to the method 600A, the method 600B is executed while the PDA1 is operating according to the current settings according to step 405. In the embodiment of FIG. 6C, step 408 is followed by step 409B in which the transport model is reconfigured with the individual updated transport characteristics, and step 409F in which the actual TTV for the ongoing session is calculated using the updated transport model and based on the second validation data from step 407. Steps 409B, 409F may be performed as described above. Then, in step 604, a second evaluation parameter (second deviation, DEV2) is calculated. DEV2 represents the difference between the corresponding actual TTV and the TTV entered in step 401 (FIG. 4A). In step 605, DEV2 is evaluated with respect to the second deviation criterion. If DEV2 is found to be sufficiently small in step 605, method 600B returns to step 405, where control unit 20 continues to operate PDA1 according to the current setpoint. If DEV2 is found to represent a sufficiently large difference, step 605 proceeds to step 409C, where updated setpoints are determined to achieve the therapeutic goal. Thereafter, in step 409D, updated control signals for PDA1 are generated based on the updated setpoints. Method 600B returns to step 405, where control unit 20 operates PDA1 according to the setpoints determined by step 409C. Steps 409C and 409D may be performed as described above. It is understood that method 600B selectively evaluates an updated transport model to determine updated setpoints based on DEV2, and generates updated control signals for PDA corresponding to the updated setpoints. Method 600B provides a process-efficient way of ensuring that TTV is achieved during a PD session. In a variant, steps 409F, 604 and 605 are omitted.

[0114] The applicant has realized that a transport model may be used to estimate a reference value RVp that may be used in step 404 of method 400. In some embodiments, the intraperitoneal volume at the end of the previous PD session is calculated and this final Vp is stored in memory (see 18 in FIG. 1). When the set point is calculated according to step 404 of method 400 (FIG. 4A), the final Vp is read from memory and RVp is set equal to or based on the final Vp. However, it may be desirable to obtain a current value of RVp. This need is addressed by an exemplary calculation method 800 shown in FIG. 8A. Method 800 may be executed by control device 20.

[0115] In step 801, corresponding to step 402 (FIG. 4A), transport properties of the peritoneum are obtained. In step 802, corresponding to step 403 (FIG. 4A), a transport model is constructed by the transport properties obtained in step 801. In step 803, the PDA 1 is operative to initiate a test cycle, which in turn includes a drain phase and a fill phase. Step 803 may be performed according to a predefined test regimen that defines the composition of the treatment fluid, the drain volume, the fill volume, and the duration of the sequence. In step 804, the fluid flow to and / or from the PC via the access 7′ is monitored during a measurement period ("test period") that includes the test cycle. For example, the fluid flow may be monitored by the flow meter 13 or by volume monitoring. According to step 804, the control device 20 obtains first measurement data indicative of the fluid flow through the access 7′ during the test period. Similar to the second validation data (step 407 in FIG. 4A), the first measurement data may include the fill, drain, and sample volumes of the test cycle, as well as the start and end times of phases of the test cycle. In a variant, the first measurement data may include a time series of instantaneous flow values ​​for the fluid flow through the access 7′ during the test period.

[0116] In step 805, second measurement data is obtained. The second measurement data includes CRP values ​​("data samples") of the fluid in the PC at two or more time points ("measurement times") during the test period. In the example of FIG. 1, the CRP values ​​are measured by the CRP sensor 15. In step 806, the fluid volume in the PC at a reference time point is calculated by using a transport model configured according to step 802 and based on the first and second measurement data. The calculated fluid volume is RVp. In step 807, the calculated RVp is output, for example for use in method 400. The reference time point can be any time point or a later time during the test cycle. To facilitate the calculations in method 400, the reference time point can be selected to correspond to the end of the drainage phase, such that RVp represents the residual volume Vres.

[0117] As indicated by the dashed line in FIG. 8A, step 806 may include steps 806A and 806B. In step 806A, an estimated CRP value is calculated at the measurement time based on the first measurement data using a transport model. The estimated CRP value is determined from the solute concentration value (Cpi) generated by the transport model. As mentioned above, depending on the CRP, the estimated CRP value may be given directly by the solution concentration value or may be calculated, for example, by a conversion operation from the solute concentration value to conductivity. In step 806B, the fluid volume in the PC at the reference time is determined as a function of the measured CRP value and the estimated CRP value.

[0118] FIG. 4B is a flow chart of an exemplary procedure that may be part of step 806 in method 800. In step 810, using a transport model, time-resolved values ​​of fluid volume (Vp) and solute concentration (Cpi) in the PC are calculated based on the first measurement data and based on candidate values ​​of Vp and Cpi at a selected time point. The selected time point may be any time point during the test period. If the selected time point coincides with the start of the test period (t=0), the calculation may be facilitated. In step 811, an estimated CRP value at the measurement time is determined based on the time-resolved values ​​of solute concentration. Step 811 may be performed similarly to step 601. In step 812, the estimated and measured CRP values ​​are evaluated with respect to a convergence criterion. The convergence criterion may be met if the (aggregated) difference between the estimated and measured CRP values ​​is below a predefined limit. If the convergence criterion is not met, step 813 is performed. In step 813, the candidate Vp value is changed (updated) according to an optimization procedure. The calculations in step 810 are then repeated for the updated candidate Vp value. Thus, steps 810-813 are repeated until a convergence criterion is met or a time limit is exceeded. If the convergence criterion is met, the procedure proceeds to step 814, where RVp is determined. RVp may be given by the fluid volume at a reference time point in the time-resolved values ​​of the fluid volume calculated by step 810, for example in the last execution of step 810.

[0119] FIG. 10 is a block diagram of an exemplary structure for implementing the method 800 according to FIGS. 8A-8B, for example in the control device 20. For the sake of brevity, the description of elements also included in FIG. 7B is not repeated. In FIG. 10, the input data to the control device 20 includes the first measurement data 75A (see step 804), the second measurement data 75B (see step 805), the initial data set 872A, and the characteristic data 72B. As shown, the second measurement data 75B includes the CRP value [Kp*] measured at the measurement time mentioned above. The initial data set 872A includes candidate values ​​of Vp and Cpi. The PTM 72′ may be configured according to FIG. 7C and is therefore operable to generate a data set 877A including an estimate of the solute concentration [Cpi] in the fluid in the PC. The estimate is generated for a time point that at least approximately corresponds to the measurement time of [Kp*]. In the illustrated example, the control device 20 further comprises a conversion module 877 configured to convert the estimated solute concentration [Cpi] at each time point into a corresponding estimated conductivity [Kp]. The conversion module 877 may be configured to aggregate the conductivity contributions of all charged solutes, given their concentrations, possibly also taking into account the effects of uncharged solutes such as glucose and urea (if present). Thus, the conversion module 877 may be configured according to well-known standard formulas, for example as described in US Patent Application Publication No. 2012 / 0018379 and WO 2016 / 188950, which are incorporated herein by reference. The output data 877B of the conversion module 877, including [Kp], is received by a subtraction module 878 configured to calculate the difference between the corresponding values ​​in [Kp] and [Kp*], i.e., the difference between the estimated conductivity value and the measured conductivity value at the measurement time. The result is a sequence of difference values, represented in FIG. 10 as error data 873A. The PFA 73' is configured to operate on the error data 873A to generate candidate data 873B including updated candidate Vp values, represented as Vpt in Figure 10. The updated candidate Vp values ​​replace corresponding values ​​in the initial data set 872A.The PTM 72' is then configured to operate on the candidate data 873B, and possibly on the candidate values ​​for [Cpi] in the initial data set 872A, to generate a new data set 877A including an updated [Cpi]. As will be appreciated from above, the computation and flow of data may continue until the PFA 73' finds that a convergence criterion is met, e.g., the error data 873A is sufficiently small.

[0120] In some embodiments, the test cycle performed in step 803 of FIG. 8A is an "Initial Limited Probing Cycle", hereinafter abbreviated as IRPC. IRPC includes, in order, a drain phase, a fill phase, and a dwell phase. IRPC may be performed by the control device 20 at the beginning of a PD session and is "limited" in that only a portion of the maximum fill volume is drawn from the PC. The maximum fill volume may be measured for the patient or a global value. Based on two or more CRP values ​​measured during IRPC in step 805, RVp may be determined according to step 806.

[0121] An example of IRPC and its use is described with reference to FIG. 9 showing intraperitoneal volume (Vp) as a function of time. Assume that Vp is unknown before the start of IRPC. IRPC begins with a drain phase, where the PDA1 operates to drain a limited amount of fluid from the PC, specified by ΔVp. In some embodiments, ΔVp is set to limit the risk of drainage pain. For example, ΔVp may be set to less than or equal to a nominal residual volume, which may be determined for a patient or group of patients, for example, based on historical data. In some embodiments, ΔVp is set to less than 10%-25% of the maximum fill volume. In some embodiments, ΔVp is in the range of 50-400 mL or 100-300 mL. The drain phase is followed by a fill phase, where unused therapeutic fluid is infused into the PC. To avoid the risk of overfilling, the amount of fluid infused may be set to less than or equal to ΔVp. During IRPC, a first data sample Kpi0* is provided by one or more measurements in the drain phase. After completion of the filling phase, a second data sample Kpi1* is acquired at a predefined time period Δti. The time period Δti may be 0-20 minutes or 0-10 minutes. The second data sample Kpi1* may be acquired by the control device 20 operating the PDA1 to draw a small fluid sample from the PC. Thus, as shown in FIG. 9, Kpi1* results in a small reduction in the fluid volume in the PC. After a short calculation time, at time T1, RVp has been calculated based on the extracted volume (ΔVp), the injected volumes Kpi0* and Kpi1* (see step 806). As shown in FIG. 9, the control device 20 may then proceed to perform an initial drain phase (IDP), followed by one or more fluid exchange cycles as indicated by a subsequent filling phase (PD).

[0122] In Fig. 9, it is assumed that Vp is sufficient at the start of IRPC. If not, and ΔVp cannot be extracted from the PC, the initial drain phase of IRPC will fail. If so, the control device 20 may execute an initial fill phase, in which an initial fill volume of therapy fluid is infused into the PC. The initial fill volume may be at least equal to ΔVp. After a predetermined period of time, e.g. Δti (above), from the completion of the initial fill phase, IRPC as shown in Fig. 9 is resumed.

[0123] A test cycle, e.g., IRPC in FIG. 9, may or may not be considered part of a session. If considered part of a session, the calculation of set points in method 400 takes into account the contribution to the treatment goal from fluid exchange during the test period. This may be done in a manner similar to step 409C (FIG. 4D) described above. For example, step 401 of method 400 may use the transport model (configured in step 802) to calculate an intermediate value of the treatment parameter based on the first measurement data (obtained in step 804). The intermediate value represents the amount of the treatment goal achieved during the test period. An updated treatment goal is then calculated by subtracting the intermediate value from the treatment goal TTV obtained in step 401. Method 400 is then performed with the updated treatment goal.

[0124] In the above, the variable C of the function f4 is determined by the nominal concentration of the solute in the unused treatment solution of a particular composition (according to the control parameter FC, FIG. 2C). Iiis assumed to be given. However, accuracy can be improved by working against the actual concentration, if available, or by estimating the actual concentration in unused treatment liquid. For example, ready-made treatment liquids are produced with components with relatively large tolerance limits. For example, the concentration of sodium may have a tolerance limit of ±2.5%. This means that the conductivity may vary from batch to batch of treatment liquid. Thus, the use of nominal solute concentrations introduces errors into the calculations performed in methods 400, 600A, 600B, and 800. By measuring the CRP value of unused treatment liquid and knowing the corresponding nominal CRP value, the nominal concentration can be adjusted to better represent the actual concentration. If the CRP value represents conductivity, this adjustment can be performed in various ways, as will be readily understood by those skilled in the art. In one example, the percent water in unused treatment liquid may be calculated to minimize the difference between the nominal conductivity and the measured conductivity while ensuring that the sum of the charges in the treatment liquid is zero, so that the nominal concentration is scaled by the percent water. The CRP value of the fresh treatment fluid may be obtained by the control device 20 directing the fresh treatment fluid through the CRP sensor 15 .

[0125] It is also conceivable to calculate RCpi based on the measured CRP value for the therapeutic fluid extracted from the PC, for example during the drainage phase. By measuring the CRP value and knowing the corresponding nominal CRP value, the nominal concentration can be adjusted to better represent the actual concentration, resulting in RCpi. If the CRP value represents the conductivity, this adjustment can be done by adjusting the C Ii This can be performed similarly to the above adjustment for the determination of

[0126] Returning to the calculation of the updated transport characteristic in step 408 (FIG. 4A), this calculation may be performed by a structure similar to that of FIG. 10. To calculate the updated transport characteristic, e.g. LpS or PSi, the PFA 73′ of FIG. 10 is modified to generate a candidate data set 873B to include candidate values ​​of the transport characteristic, which is then used by the PTM 72′ to generate the data set 877A. Furthermore, in comparison with FIG. 10, the initial data set 872A includes initial values ​​for the reference values ​​of Vp and Cpi as well as the characteristic data. These initial values ​​are used by the submodules 172, 173 (FIG. 7C) when the operation of the PTM 72′ is first started. The initial values ​​may be obtained, for example, from the global patient data 76B or the patient-specific data 76C (FIG. 7A). Further details regarding the calculation of the updated transport characteristic are given in the applicant's patent application, International Application PCT / EP2021 / 074288, which is incorporated herein by reference in its entirety.

[0127] In the above, it was assumed that the setup procedure 400A of FIG. 4A is performed to determine the setpoints for the entire PD session, with the aim of achieving the treatment goal at the end of the session. This type of "session control" may be modified to include multiple sessions, meaning that the treatment goal is given by the final value to be achieved after multiple sessions. In one implementation, the setup procedure 400A is performed to calculate the setpoints for each session of the multiple sessions, and the setpoints are applied to control the PDA1 to perform the respective session. Optionally, the setpoints may be recalculated before each session to take into account the performance of the previous session of the multiple sessions.

[0128] In a different variant denoted "Cycle Control", the setup procedure 400A is performed for a subset of upcoming sessions ("session subsets"), for example for individual cycles or groups of cycles. Here, the therapy goal represents the desired change in the patient's physiological state during the upcoming session subset, and the setup procedure 400A is repeated for successive session subsets throughout the entire session. The therapy goal for the individual session subsets may still be defined to achieve the overall therapy goal for the complete session. One reason for performing cycle control is to ensure that the Vp is kept within limits during the session. For example, as shown below with reference to Figures 13A-13D, errors in the value of the transport characteristic (step 402) may cause the actual Vp to deviate from the calculated Vp. If, for example, a deterministic Vd extraction is performed during tidal PD, significant and unintended changes in Vres may occur during the PD session. By dividing the PD session into subsets, the deviation between the actual Vp and the calculated Vp is reduced, and Vd may be dynamically determined to achieve a predetermined Vres after each drainage phase. Cycle control may be further improved by using method 800 to calculate RVp for each session subset, allowing the actual Vres to be determined for each individual cycle.

[0129] 11-13 are presented to demonstrate the utility of the techniques described herein.

[0130] 11A-11B represent a first scenario in which the cG in the unused therapy fluid is determined to achieve a target UF (UFV) of 1300 ml in a PD session, assuming that the unused therapy fluid has the same concentration of glucose (cG) throughout the session. All other control parameters are fixed. Reference values ​​RVp and RCpi are calculated by treating the initial drain and the first cycle as a test cycle. The reference time for the reference values ​​is set at the end of the initial drain, i.e. RVp=Vres. The calculation assumes that Vres is the same for all cycles. In the test cycle, the therapy fluid has a glucose concentration (cG) of 2.27%. The number of cycles, including the test cycle, is 5. The fill volume for each cycle is 2000 ml. The calculation starts with cG=2.27% therapy fluid. The top graph of FIG. 11A shows the UF as a function of time during a session, in the following denoted as "UF Progression". The dashed line 1101 shows the UF progression resulting from the first execution of step 410 (FIG. 4B). As can be seen, the total UF at the end of the session deviates from the target UF (UFV=1300 ml). Steps 410-413 are therefore repeated. The solid line 1102 shows the UF progression resulting from the last execution of step 410, i.e. when the UF target 1103 is reached. The bottom graph of FIG. 11A shows the corresponding progression of solute removal for urea, creatinine and sodium. FIG. 11B is a graph of the fluid volume (Vp) in the PC during the session. The dashed line 1105 represents the fluid volume resulting from the first execution of step 410, and the solid line 1106 represents the fluid volume resulting from the last execution of step 410. The sudden drop in fluid volume after 35 minutes is caused by the extraction of a fluid sample (100 ml) from the PC for the purpose of measuring the CRP value (see Kpi1* in FIG. 9).

[0131] 11A-11B demonstrate that it is possible to determine the glucose concentration in the therapy solution that results in a target UF for a PD session. In the example shown, cG is 2.27% in the test cycle and 2.46% in the subsequent cycle.

[0132] 12A-12B represent a second scenario in which the fill volume (Vf) is determined to achieve a target UF (UFV) of 1300 ml in a PD session, assuming that the same fill volume is used in all cycles after the test cycle, defined and used similarly to the first scenario. All other control parameters are fixed. The unused therapy fluid has a cG=2.27%. The calculation starts for a fill volume of 2000 ml. FIG. 12A corresponds to FIG. 11A. The dashed line 1201 shows the UF progression resulting from the first execution of step 410. As can be seen, the total UF at the end of the session deviates from the target UF (UFV=1300 ml). The solid line 1202 shows the UF progression resulting from the last execution of step 410, i.e. when the UF target 1203 is reached. FIG. 12B corresponds to FIG. 11B. The dashed line 1205 represents the fluid volume (Vp) resulting from the first execution of step 410 and the solid line 1206 represents the fluid volume resulting from the last execution of step 410.

[0133] 12A-12B demonstrate that it is possible to determine the fill volume that results in a target UF for a PD session. In the illustrated example, the fill volume is 2000 ml for the test cycle and 2580 ml for the subsequent cycle. 13A-13D are provided to show the sensitivity of the transport model calculations to changes in the transport properties of the peritoneum. The calculations underlying 13A-13D are the same as those in 11A-11B, which means that the concentration of glucose (cG) in the unused process fluid is determined to achieve a target UF (UFV) of 1300 ml. 13A shows the fluid volume (Vp) in the PC as a function of time, and 13B shows the CRP value (conductivity) in the fluid in the PC as a function of time during the session. The black bars in 13B show the conductivity measured during the drain phase. Line 1301 represents a first calculation based on default values ​​for PSi and LpS, while line 1302 represents a second calculation for a 50% increase in PSi and a 25% decrease in LpS. As can be seen, such a change in transport properties results in a decrease in Vp and an increase in the measured conductivity. Note that the initial drain phase is used as a test cycle to essentially match the calculated conductivity to the measured conductivity in this drain phase. The second calculation results in a total UF of 802 ml for the session. Figure 13C corresponds to Figure 13B, differing in that a second calculation is made for a 50% increase in PSi and default LpS. As can be seen, the increase in the measured CRP value is almost the same as in Figure 13B. Although not shown, the second calculation also results in a decrease in Vp, but the decrease is smaller than in Figure 13(a). The second calculation results in a total UF of 1067 ml for the session. Figure 13D corresponds to Figure 13B, except that a second calculation is made for a 25% decrease in the default PSi and LpS. As can be seen, the increase in measured CRP values ​​is much smaller than in Figure 13B. Although not shown, the second calculation also reduces Vp, but the decrease is smaller than in Figure 13(a). The second calculation results in a total UF of 986 ml for the session.

[0134] The calculations represented by Figs. 13A-13D reveal that a PD session performed according to the calculated setpoints may not actually meet its UFV if large changes in LpS and / or PSi between sessions go unnoticed. Such large changes may occur as a result of a patient infection, e.g., peritonitis. Figs. 13B-13D show that one or more measured CRP values, e.g., measured during one or more drain phases, may be used to detect the need to recalculate the transport properties, e.g., as described in relation to step 408 of method 400 (Fig. 4A). Furthermore, Fig. 13A shows that deviations in fluid volume (Vp) may also be used to detect the need to recalculate the transport properties. For example, if the control device 20 implements a maximized Vd extraction, the need for recalculation may be triggered if Vres is found to increase between cycles. According to Fig. 13A, the actual Vp in the PC will be lower than expected, which means that the actual Vd extracted during the drain phase will be lower than expected. This means that Vres will increase for each cycle performed during the session. A temporary increase in Vres is not uncommon and may have other causes, such as a temporary dislocation of the access device 7'. Thus, if Vres increases (or, equivalently, Vd decreases) for two or more consecutive cycles, a recalculation of the transport characteristics may be triggered. In the context of method 600A of FIG. 6A, such a change in Vres (or Vd) may be represented by DEV1 and may trigger an update in step 603 in addition to or in lieu of one or more deviant CRP values ​​according to step 408.

[0135] The structures and methods disclosed herein are applicable to any modality of automated peritoneal dialysis (APD), including, but not limited to, continuous cyclic peritoneal dialysis (CCPD), intermittent peritoneal dialysis (IPD), tidal peritoneal dialysis (TPD), and continuous flow peritoneal dialysis (CFPD), all of which include at least one fluid exchange cycle that includes a fill phase, a dwell phase, and a drain phase.

[0136] The structures and methods disclosed herein may be implemented by hardware or a combination of software and hardware. In some embodiments, the hardware comprises one or more software-controlled computer resources. FIG. 14 shows a schematic diagram of a control device 20 comprising one or more processors 201, a computer memory 202, and a communication interface or circuit 203 for input and / or output of data. The processor 201 and the computer memory 202 may be part of the control circuit 20A (FIGS. 1 and 2C). The communication interface 203 may be configured for wired and / or wireless communication. As will be understood from the above, the control device 20 may or may not be integrated into the dialysis machine. The processor 201 may include, for example, one or more of a CPU ("Central Processing Unit"), a DSP ("Digital Signal Processor"), a GPU ("Graphics Processing Unit"), a microprocessor, a microcontroller, an ASIC ("Application Specific Integrated Circuit"), a combination of discrete analog and / or digital components, or some other programmable logic device such as an FPGA ("Field Programmable Gate Array"). A control program 202A including computer instructions is stored in the memory 202 and executed by the processor 201 to implement logic to perform any of the methods, procedures, functions or steps described above. The control program 202A may be provided to the control device 20 on a computer readable medium 205, which may be a tangible (non-transitory) product (e.g., magnetic media, optical disk, read-only memory, flash memory, etc.) or by a propagated signal. As shown in Fig. 14, the memory 202 may also store all or part of control data 202B used by the processor 201, such as the characteristic data 72B, the treatment history data 76A, the comprehensive patient data 76B, the patient-specific data 76C or the solute characteristic data 76D (Fig. 7A).

[0137] While the subject matter of this disclosure has been described in connection with what are presently considered to be the most practical embodiments, it is to be understood that the subject matter of this disclosure is not limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and equivalents of the appended claims.

[0138] Additionally, although acts are shown in the figures in a particular order, this should not be understood as requiring that such acts be performed in the particular order or sequential order shown, or that all of the shown acts be performed, to achieve desirable results.

[0139] Below, sections are listed to summarize certain aspects and embodiments disclosed above.

[0140] C1. A control device for operating a dialysis machine to perform a peritoneal dialysis treatment on a patient, the dialysis machine being operable to perform one or more fluid exchange cycles on a peritoneal cavity of the patient to cause transport of fluids and solutes through a peritoneal membrane in the peritoneal cavity based on control signals from the control device, the control device obtaining target values ​​of treatment parameters for the peritoneal dialysis treatment, obtaining at least one transport characteristic of the peritoneal membrane of the patient, and constructing a transport model using the at least one transport characteristic, the transport model defining the transport of fluids and solutes through the peritoneal membrane as a function of a plurality of control parameters for the peritoneal dialysis treatment performed by the dialysis machine, and controlling the control device to achieve the target values. a control circuit configured to: evaluate the transport model to determine set points for the plurality of control parameters; and generate a control signal for the dialysis machine corresponding to the set points, the control signal causing the dialysis machine to perform the peritoneal dialysis therapy; the control circuit further configured to: acquire first data including measurements of a concentration-related parameter of a fluid in the peritoneal cavity at one or more time points during the peritoneal dialysis therapy; acquire second data indicative of fluid flow of treatment fluid into and / or out of the peritoneal cavity during the peritoneal dialysis therapy; and calculate at least one updated transport characteristic of the peritoneum of the patient based on the first data and the second data using the transport model.

[0141] C2. A control device as described in C1, wherein the control circuitry is further configured to reconfigure the transport model according to the at least one updated transport characteristic.

[0142] A control device as described in C3.C2, wherein the control circuit is further configured to evaluate the thus reconstructed transport model to determine updated setting values ​​of the plurality of control parameters for achieving the target value, and to generate updated control signals for the dialysis machine corresponding to the updated setting values.

[0143] C4. A control device as described in C2 or C3, wherein the control circuitry is further configured to present the at least one updated transport characteristic on a user interface of the dialysis machine and / or store the at least one updated transport characteristic in a memory associated with the control device.

[0144] C5. A control device as described in any one of C2 to C4, wherein the control circuit is further configured to calculate a final value of the treatment parameter based on the second data using the transport model thus reconstructed, and to present the final value on a user interface of the dialysis machine, and / or to store the final value in a memory associated with the control device.

[0145] A control device as described in C6.C1, wherein the control circuit is further configured to calculate an expected value of an evaluation parameter for the fluid in the peritoneal cavity using the transport model based on the first data and / or the second data, determine a first deviation between the expected value and a corresponding value provided by the first data or the second data, and selectively calculate the at least one updated transport characteristic of the peritoneum of the patient based on the first deviation.

[0146] A control device as described in C7.C6, wherein the control circuit is configured to calculate an expected value representing the concentration-related parameter of fluid in the peritoneal cavity at the one or more time points during the peritoneal dialysis treatment based on the second data, and to determine the first deviation between the expected value and the measured value in the first data.

[0147] C8. The control device of C6 or C7, wherein the control circuitry is configured to refrain from calculating the at least one updated transport characteristic when the first deviation is between a first positive limit and a first negative limit, to calculate the at least one updated transport characteristic when the first deviation is greater than the first positive limit or less than the first negative limit, and to generate an alert to an operator of the dialysis machine when the first deviation is greater than a second positive limit greater than the first positive limit or less than a second negative limit less than the first negative limit.

[0148] A control device as described in C9.C8, wherein the control circuitry is further configured to refrain from calculating the at least one updated transport characteristic if the first deviation causes the alert to be generated.

[0149] A control device as described in C10.C8 or C9, wherein the control circuit is configured to perform at least one of reducing a first difference between the first positive limit and the first negative limit and reducing a second difference between the second positive limit and the second negative limit over time during the peritoneal dialysis treatment.

[0150] A control device as described in any one of C11.C6 to C10, wherein the control circuit is further configured to: reconfigure the transport model according to the at least one updated transport characteristic, calculate a final value of the therapy parameter using the thus reconfigured transport model, determine a second deviation between the final value and the target value, selectively evaluate the thus reconfigured transport model based on the second deviation to determine updated setting values ​​of the plurality of control parameters to achieve the target value, and generate updated control signals for the dialysis machine corresponding to the updated setting values.

[0151] A control device as described in any one of C12.C1 to C11, wherein the control circuitry is further configured to, when evaluating the transport model, repeatedly perform the following: use the transport model to calculate time-resolved values ​​of fluid volume and solute concentration in the peritoneal cavity for candidate setting values ​​of the plurality of control parameters; calculate result values ​​of the treatment parameters based on the time-resolved values ​​of fluid volume and solute concentration; and change one or more of the candidate setting values ​​until the result values ​​satisfy a termination criterion or a time limit is reached.

[0152] A control device as described in C13.C12, wherein the control circuit is configured to designate a first subset of the candidate setting values ​​as changeable and a second subset of the candidate setting values ​​as fixed, the one or more of the candidate values ​​being included in the first subset.

[0153] A control device as described in C14.C12 or C13, wherein the control circuit is configured to obtain an individual tolerance range for the one or more of the candidate setting values, and to change the one or more of the candidate setting values ​​to fall within the individual tolerance range.

[0154] A control device as described in any one of C15.C1 to C14, wherein the plurality of control parameters include a composition of therapeutic fluid supplied to the peritoneal cavity during the peritoneal dialysis therapy, a number of fluid exchange cycles performed during the peritoneal dialysis therapy, an amount of therapeutic fluid supplied to the peritoneal cavity in each fluid exchange cycle during the peritoneal dialysis therapy, and a duration of the peritoneal dialysis therapy.

[0155] C16.A control device as described in any one of C1 to C15, wherein the treatment parameter represents a cumulative amount of fluid removed from the patient during the peritoneal dialysis treatment, or a cumulative amount of one or more solutes removed from the patient during the peritoneal dialysis treatment.

[0156] A control device as described in C17.C16, wherein the control circuit is configured to obtain an initial weight of the patient before the peritoneal dialysis treatment and a target weight of the patient at the end of the peritoneal dialysis treatment to obtain the target value, and to calculate the target value for the cumulative amount of fluid based on the current weight and the target weight.

[0157] C18. The control device of C16, wherein the one or more solutes include urea or creatinine.

[0158] A control device as described in any one of C19.C1 to C18, wherein the control circuit is further configured to obtain reference data including fluid volume and solute concentration in the peritoneal cavity at a reference time when evaluating the transport model, and to calculate the setting value using the transport model further based on the reference data.

[0159] A control device as described in C20.C19, wherein the control circuit is configured to operate the dialysis machine to perform a sequence of drain and fill phases during a measurement period to obtain the fluid volume in the peritoneal cavity at the reference time; obtain first measurement data indicative of fluid flow into and / or out of the peritoneal cavity via the patient's peritoneal access during the measurement period; obtain second measurement data including measurement data samples representative of solute concentrations in the fluid volume in the peritoneal cavity at two or more time points during the measurement period; calculate estimated data samples representative of the solute concentrations in the fluid volume at the two or more time points based on the first measurement data using the transport model; and determine the fluid volume in the peritoneal cavity at the reference time as a function of the measurement data samples and the estimated data samples.

[0160] A control device as described in C21.C20, wherein the control circuit is configured to repeatedly calculate time-resolved values ​​of fluid volume and solute concentration in the peritoneal cavity based on the first measurement data and a candidate value of the fluid volume and the solute concentration in the peritoneal cavity at a selected time point using the transport model until a convergence criterion is met or a time limit is reached, determine the estimated data samples at the two or more time points based on the time-resolved value of solute concentration, and modify the candidate value of the fluid volume in the peritoneal cavity at the selected time point based on a difference between the estimated data samples and the measurement data samples, and the control circuit is configured to determine the fluid volume in the peritoneal cavity at the reference time point based on the calculated time-resolved value of fluid volume when the convergence criterion is met or the time limit is reached.

[0161] C22. A control device according to C20 or C21, wherein the reference time corresponds to the completion of the discharge phase.

[0162] C23. A control device as described in any one of C20 to C22, wherein the measurement data samples include a first data sample acquired during the discharge phase and a second data sample acquired following the fill phase.

[0163] A control device as described in C24.C23, wherein the first and second data samples are acquired during an initial probing cycle including the drain phase and the fill phase, wherein a limited amount of fluid in the peritoneal cavity is extracted in the drain phase, the limited amount corresponding to a portion of a maximum filling volume of the peritoneal cavity.

[0164] C25. The control device according to C24, wherein the limited amount is less than 10%-25% of the maximum fill volume.

[0165] C26. The control device according to C24 or C25, wherein the limited amount is within the range of 50 to 400 mL or 100 to 300 mL.

[0166] C27. A control device as described in any one of C24 to C26, wherein the measurement period is part of the peritoneal dialysis treatment.

[0167] C28.A control device as described in any one of C1 to C27, wherein the at least one transport property includes a diffusion capacity of one or more solutes through the peritoneal membrane and a filtration capacity of water through the peritoneal membrane.

[0168] C29. A control device according to any one of C1 to C28, wherein the transport model is a three-pore model for transport through the peritoneal membrane.

[0169] A control device as described in any one of C30.C1 to C29, wherein the transport model is configured to take into account ion transport due to electrostatic forces across the peritoneal membrane caused by differences in the amount of dissolved ions on either side of the peritoneal membrane and reflection of large charged solutes by the peritoneal membrane.

[0170] A control device as described in any one of C31.C1 to C30, wherein the control circuitry includes a differential equation solver sub-module configured to calculate a fluid volume in the peritoneal cavity from an initial time point to an end time point, including intermediate time steps, and to calculate a solute concentration in the fluid volume from the initial time point to the end time point, including intermediate time steps.

[0171] A control device as described in C32.C31, wherein the differential equation solver submodule is configured to calculate the fluid volume in the peritoneal cavity based on preceding time changes in the fluid volume for each individual time step, and to calculate a solute concentration in the fluid volume based on preceding time changes in the solute concentration for each individual time step.

[0172] A control device as described in C33.C32, wherein the control circuit further comprises a first change calculation system configured to calculate a temporal change in the fluid volume for the individual time steps as a function of the solute concentration calculated by the differential equation solver submodule for the individual time steps and as a function of the fluid volume in the peritoneal cavity calculated by the differential equation solver submodule for the individual time steps.

[0173] A control device as described in C34.C33, wherein the first change calculation system comprises a first flow rate calculation submodule configured to calculate a flow rate of water through the peritoneum for the individual time step as a function of the fluid volume calculated by the differential equation solver submodule for the individual time step, and the first change calculation system further comprises a first change calculation submodule configured to calculate the time change in the fluid volume as a function of the flow rate of water through the peritoneum for the individual time step.

[0174] A control device as described in C35.C33 or C34, wherein the control circuit further comprises a second change calculation system configured to calculate a time change in the solute concentration for the individual time step as a function of the solute concentration calculated by the differential equation solver sub-module for the individual time step and the fluid volume calculated by the differential equation solver sub-module for the individual time step.

[0175] A control device as described in C36.C35, wherein the second change calculation system comprises a second flow rate calculation submodule configured to calculate a flow rate of one or more solutes through the peritoneum for the individual time step as a function of the solute concentration calculated by the differential equation solver submodule for the individual time step and the fluid volume calculated by the differential equation solver submodule for the individual time step, and the second change calculation system further comprises a second change calculation submodule configured to calculate the time change in the solute concentration for the individual time step as a function of the flow rate of the one or more solutes through the peritoneum for the individual time step, the flow rate of water through the peritoneum for the individual time step, the solute concentration calculated by the differential equation solver submodule for the individual time step, and the fluid volume calculated by the differential equation solver submodule for the individual time step.

[0176] C37. An apparatus for performing peritoneal dialysis, comprising: a fluid circuit connectable to a patient's peritoneal access for carrying therapeutic fluid to and from the peritoneal cavity; a dialysis machine configured to operate the fluid circuit; An apparatus comprising: a control device according to any one of claims C1 to C36.

[0177] C38. A computer-implemented method for generating control signals to cause a dialysis machine to perform a peritoneal dialysis treatment for a patient, the peritoneal dialysis treatment comprising one or more fluid exchange cycles for causing transport of fluids and solutes through a peritoneal membrane in a peritoneal cavity of the patient, the method comprising: obtaining target values ​​of treatment parameters for the peritoneal dialysis treatment; obtaining at least one transport characteristic of the peritoneal membrane of the patient; and constructing a transport model with the at least one transport characteristic, the transport model defining the transport of fluids and solutes through the peritoneal membrane as a function of a plurality of control parameters for the peritoneal dialysis treatment performed by the dialysis machine. ; evaluating the transport model to determine set points for the plurality of control parameters to achieve the target values; and generating the control signal for the dialysis machine corresponding to the set points, the method further comprising: acquiring first data including measurements of a concentration-related parameter of fluid in the peritoneal cavity at one or more time points during the peritoneal dialysis treatment; acquiring second data indicative of fluid flow of therapy fluid into and / or out of the peritoneal cavity during the peritoneal dialysis treatment; and calculating at least one updated transport characteristic of the peritoneum of the patient based on the first data and the second data using the transport model.

[0178] C39. A computer-readable medium comprising computer instructions that, when executed by one or more processors in a control device described in any one of C1 to C36, cause the one or more processors to perform the method described in C38.

[0179] Appendix A The change in intraperitoneal volume is TIFF2025508082000003.tif13142, where J f (t) is the flow rate during the filling phase, J d (t) is the flow rate during the discharge phase, and J s(t) is the fluid loss during sample extraction and L is the lymphatic absorption which can be assumed to be constant, e.g., 0.3 mL / min.

[0180] The total fluid flow across the peritoneum is the sum of the flows through aquaporins (m1), small pores (m2) and large pores (m3), TIFF2025508082000004.tif21153, alpha m is the ratio of hydraulic conductance for individual pore types, which is 1 = 0.02, α 2 = 0.90, α 3 = 0.08, where R is the ideal gas constant, T is the correspondence, and N is the number of different solutes.

[0181] Area factor A f (t) can be introduced to take into account the fact that the effective area for exchange depends on the volume of fluid in the peritoneal cavity. The area factor is: The image can be given by TIFF2025508082000005.tif1578.

[0182] The hydrostatic pressure difference ΔP(t) between the peritoneal cavity and the capillaries is ΔP(t)=P cap (t)-IPP(t), where IPP(t) is the intraperitoneal pressure, which is It is assumed to be a function of intraperitoneal volume by TIFF2025508082000006.tif1447.

[0183] Capillary pressure P cap (t) may be set to be dependent on the mean arterial pressure MAP and the venous pressure, which may be assumed to be equal to IPP(t). TIFF2025508082000007.tif1467

[0184] Different solutes φ i The osmotic coefficient for may be given by tabulated values, and the reflection coefficients σ for different solutes m,i teeth, TIFF2025508082000008.tif25116, where λ m,i is the quotient between the hydrodynamic radius of solute i and the pore radius of the distinct pore types (m = 1, 2, 3). The hydrodynamic radii of different solutes are a useful measure of their size, which depends on the molecular mass / geometry as well as charge. Also, for aquaporins, σ 1,i It may also be noted that =1.

[0185] The concentration difference of each solute depends on the solute flow and dilution from the fluid flow over the membrane, as well as dilution during the loading phase. TIFF2025508082000009.tif15140

[0186] Here, C Ii is the concentration of solute i in the unused treatment solution. The flow of solute i is TIFF2025508082000010.tif12138, where Pe i,m (t) is is the dimensionless Péclet number (convective vs. diffusive rate) for solute i through pore type m, calculated by TIFF2025508082000011.tif1691, where σ i,m is the reflection coefficient of solute i in pore type m, which can be given as a tabulated value. The last term is the charge of the solute z across the membrane 30. i and the electrostatic effect from the potential ΔE(t). However, since there is no current between the peritoneal cavity 31 and the blood side 32, TIFF2025508082000012.tif1768, where J i,m2 (t) and J i,m3 (t) are the flows of solute i through the small pore (m2) and the large pore (m3), respectively. This means that at each time point, there exists a potential ΔE(t) that satisfies zero current. The magnitude of the potential, i.e., Pe i,m (t) may be determined by using any suitable route-finding algorithm, for example a bracketing method such as a bisection method, or an iterative method such as Newton's method or a Newton-like method.

Claims

1. A control device (20) for operating a dialysis machine (2a; 2) to perform a peritoneal dialysis treatment on a patient, the dialysis machine (2a; 2) being operable to perform one or more fluid exchange cycles on a peritoneal cavity (31) of the patient to cause transport of fluids and solutes through a peritoneal membrane (30) in the peritoneal cavity based on control signals (23) from the control device, the control device comprising: obtaining target values ​​(22) of treatment parameters for said peritoneal dialysis treatment; obtaining at least one transport characteristic (72B) of the peritoneum (30) of the patient; constructing a transport model (72') using said at least one transport characteristic (72B), said transport model (72') defining said transport of fluids and solutes through said peritoneal membrane (30) as a function of a plurality of control parameters (21') for said peritoneal dialysis treatment performed by said dialysis machine (2a; 2); evaluating the transport model (72') to determine settings of the plurality of control parameters (21') to achieve the target value (22); generating a control signal (23) for said dialysis machine (2a; 2) in response to said setpoint, said control signal (23) causing said dialysis machine (2a; 2) to perform said peritoneal dialysis treatment; The control circuit (20A) obtaining first data including measurements of concentration-related parameters of fluid within the peritoneal cavity (31) at one or more time points during the peritoneal dialysis treatment; obtaining second data indicative of fluid flow of treatment fluid into and / or out of the peritoneal cavity (31) during the peritoneal dialysis treatment; and calculating at least one updated transport characteristic (72B) of the patient's peritoneum (30) based on the first data and the second data using the transport model (72').

2. 2. The control device of claim 1, wherein the control circuit (20A) is further configured to reconfigure the transport model (72') with the at least one updated transport characteristic (72B).

3. 3. The control device according to claim 2, wherein the control circuit (20A) is further configured to evaluate the thus reconstructed transport model (72') to determine updated set values ​​of the plurality of control parameters (21') for achieving the target value (22), and to generate an updated control signal (23) for the dialysis machine (2a; 2) corresponding to the updated set values.

4. A control device as described in claim 2 or 3, wherein the control circuit (20A) is further configured to present the at least one updated transport characteristic (72B) on a user interface (17) of the dialysis machine (2a; 2) and / or to store the at least one updated transport characteristic (72B) in a memory (18; 202) associated with the control device.

5. 5. A control device according to claim 2, wherein the control circuit (20A) calculating a final value of said treatment parameter based on said second data using said reconstructed transport model (72'); A control device further configured to present the final value on a user interface (17) of the dialysis machine (2a; 2) and / or to store the final value in a memory (18; 202) associated with the control device.

6. 2. A control device according to claim 1, wherein the control circuit (20A) comprises: calculating an expected value of an evaluation parameter for the fluid in the peritoneal cavity based on the first data and / or the second data using the transport model; determining a first deviation (DEV1) between the expected value and a corresponding value provided by the first data or the second data; and selectively calculating the at least one updated transport characteristic (72B) of the patient's peritoneum (30) based on the first deviation (DEV1).

7. 7. The control device of claim 6, wherein the control circuit (20A) is configured to calculate an expected value representing the concentration-related parameter of fluid in the peritoneal cavity (31) at the one or more time points during the peritoneal dialysis treatment based on the second data, and to determine the first deviation (DEV1) between the expected value and the measured value in the first data.

8. 8. A control device according to claim 6 or 7, wherein the control circuit (20A) comprises: refraining from calculating the at least one updated transport characteristic (72B) when the first deviation (DEV1) is between a first positive limit (L1p) and a first negative limit (L1n); calculating said at least one updated transport characteristic (72B) when said first deviation (DEV1) is greater than said first positive limit (L1p) or less than said first negative limit (L1n); generating an alert to an operator of the dialysis machine (2a; 2) when the first deviation (DEV1) is greater than a second positive limit (L2p) that is greater than the first positive limit (L1p) or is less than a second negative limit (L2n) that is less than the first negative limit (L1n).

9. 9. The control device of claim 8, wherein the control circuit (20A) is further configured to refrain from calculating the at least one updated transport characteristic (72B) if the first deviation (DEV1) causes the alert to be generated.

10. 10. A control device as described in claim 8 or 9, wherein the control circuit (20A) is configured to perform at least one of reducing a first difference between the first positive limit (L1p) and the first negative limit (L1n) and reducing a second difference between the second positive limit (L2p) and the second negative limit (L2n) over time during the peritoneal dialysis therapy.

11. A control device according to any one of claims 6 to 10, wherein the control circuit (20A) reconstructing the transport model (72') according to the at least one updated transport characteristic (72B) and calculating a final value of the treatment parameter using the thus reconstructed transport model (72'); determining a second deviation between the final value and the target value (22); a control device further configured to selectively evaluate the thus reconstructed transport model (72') based on the second deviation to determine updated setting values ​​of the plurality of control parameters (21') for achieving the target value (22), and to generate an updated control signal (23) for the dialysis machine (2a; 2) corresponding to the updated setting values.

12. 12. A control device according to claim 1, wherein the control circuit (20A) when evaluating the transport model (72') comprises: calculating time-resolved values ​​of fluid volume and solute concentration in the peritoneal cavity for candidate settings of the plurality of control parameters using the transport model; calculating a resultant value of the treatment parameter based on the time resolved values ​​of fluid volume and solute concentration; and iteratively modifying one or more of the candidate setting values ​​until the resulting value meets a termination criterion or a time limit is reached.

13. 13. The control device of claim 12, wherein the control circuitry (20A) is configured to designate a first subset of the candidate setting values ​​as changeable and a second subset of the candidate setting values ​​as fixed, the one or more of the candidate values ​​being included in the first subset.

14. 14. A control device as claimed in claim 12 or 13, wherein the control circuit (20A) is configured to obtain an individual tolerance range (24) for the one or more of the candidate setting values, and to change the one or more of the candidate setting values ​​to within the individual tolerance range (24).

15. A control device as described in any one of claims 1 to 14, wherein the plurality of control parameters include a composition (FC) of a therapeutic fluid supplied to the peritoneal cavity during the peritoneal dialysis therapy, a number of fluid exchange cycles (NoC) performed during the peritoneal dialysis therapy, a volume (Vf) of therapeutic fluid supplied to the peritoneal cavity in each fluid exchange cycle during the peritoneal dialysis therapy, and a duration (TT) of the peritoneal dialysis therapy.

16. A control device according to any one of claims 1 to 15, wherein the therapy parameter represents a cumulative amount of fluid removed from the patient during the peritoneal dialysis therapy or a cumulative amount of one or more solutes removed from the patient during the peritoneal dialysis therapy.

17. 17. The control device of claim 16, wherein the control circuit (20A) is configured to obtain the target value (22) by obtaining an initial weight of the patient before the peritoneal dialysis therapy and a target weight of the patient at the end of the peritoneal dialysis therapy, and to calculate the target value for the cumulative amount of fluid based on the current weight and the target weight.

18. 17. The control device of claim 16, wherein the one or more solutes include urea or creatinine.

19. A control device as described in any one of claims 1 to 18, wherein the control circuit (20A) is further configured to obtain reference data (72A) including fluid volume and solute concentration in the peritoneal cavity at a reference time when evaluating the transport model (72'), and to calculate the set value using the transport model (72') further based on the reference data (72A).

20. 20. The control device according to claim 19, wherein the control circuit (20A) performs the following steps to obtain the fluid volume in the peritoneal cavity at the reference time: operating the dialysis machine to perform a sequence of drain and fill phases during a measurement period; obtaining first measurement data (75A) indicative of fluid flow into and / or out of the peritoneal cavity via the patient's peritoneal access during the measurement period; obtaining second measurement data (75B) including measurement data samples representative of solute concentrations in the fluid volume within the peritoneal cavity at two or more time points during the measurement period; calculating estimated data samples (877A; 877B) representative of the solute concentration in the fluid volume at the two or more time points based on the first measured data (75A) using the transport model (72'); determining the fluid volume in the peritoneal cavity at the reference time as a function of the measured data sample (75B) and the estimated data sample (877A; 877B).

21. 21. A control device as claimed in claim 20, wherein the control circuit (20A) performs the following steps until a convergence criterion is met or a time limit is reached: calculating time resolved values ​​of fluid volume and solute concentration in the peritoneal cavity using the transport model (72') based on the first measurement data (75A) and candidate values ​​of the fluid volume and solute concentration in the peritoneal cavity at selected time points; determining the estimated data samples (877A; 877B) at the two or more time points based on the time-resolved values ​​of solute concentration; and iteratively modifying the candidate value (873B) of the fluid volume in the peritoneal cavity at the selected time point based on a difference between the estimated data sample and the measured data sample (75B); The control circuit (20A) is configured to determine the fluid volume in the peritoneal cavity at the reference time based on the calculated time-resolved value of fluid volume when the convergence criterion is met or the time limit is reached.

22. 22. A control device according to claim 20 or 21, wherein the reference point in time corresponds to the completion of the discharge phase.

23. A control device as claimed in any one of claims 20 to 22, wherein the measurement data samples (75B) include a first data sample (Kpi0*) acquired during the discharge phase and a second data sample (Kpi1*) acquired following the fill phase.

24. 24. The control device of claim 23, wherein the first and second data samples are acquired during an initial probing cycle (IRPC) including the drain phase and the fill phase, wherein a limited amount (ΔVp) of fluid in the peritoneal cavity is extracted in the drain phase, the limited amount (ΔVp) corresponding to a portion of the maximum filling volume of the peritoneal cavity.

25. 25. The control device of claim 24, wherein the limited amount is less than 10% to 25% of the maximum fill volume.

26. A control device according to claim 24 or 25, wherein the limited volume is in the range of 50 to 400 mL or 100 to 300 mL.

27. 27. A control device according to any one of claims 24 to 26, wherein the measurement period is part of the peritoneal dialysis treatment.

28. A control device according to any one of claims 1 to 27, wherein the at least one transport property includes a diffusion capacity of one or more solutes through the peritoneal membrane (30) and a filtration capacity of water through the peritoneal membrane (30).

29. 29. The control device of any one of claims 1 to 28, wherein the transport model (72') is a three-pore model for transport through the peritoneal membrane (30).

30. A control device as described in any one of claims 1 to 29, wherein the transport model (72') is configured to take into account ion transport due to electrostatic forces across the peritoneal membrane (30) caused by differences in the amount of dissolved ions on either side of the peritoneal membrane (30) and reflection of highly charged solutes by the peritoneal membrane (30).

31. A control device as described in any one of claims 1 to 30, wherein the control circuit (20A) comprises a differential equation solver sub-module (171) configured to calculate a fluid volume (171B) in the peritoneal cavity from an initial time point to an end time point, including intermediate time steps, and to calculate a solute concentration (171B) in the fluid volume from the initial time point to the end time point, including intermediate time steps.

32. 32. The control device of claim 31 , wherein the differential equation solver sub-module (171) is configured to calculate the fluid volume (171B) in the peritoneal cavity based on preceding time changes (174B) in the fluid volume for each individual time step, and to calculate the solute concentration (171B) in the fluid volume based on preceding time changes (175B) in the solute concentration for each individual time step.

33. 33. The control device of claim 32, wherein the control circuit (20A) further comprises a first change calculation system (172, 174) configured to calculate a temporal change in fluid volume (174B) for the individual time steps as a function of the solute concentration (171B) calculated by the differential equation solver submodule (171) for the individual time steps and as a function of the fluid volume (171B) in the peritoneal cavity calculated by the differential equation solver submodule (171) for the individual time steps.

34. 34. The control device of claim 33, wherein the first change calculation system (172, 174) comprises a first flow calculation sub-module (172) configured to calculate a water flow rate (172B) through the peritoneum (30) for the individual time steps as a function of the fluid volume (171B) calculated by the differential equation solver sub-module (171) for the individual time steps, and the first change calculation system (172, 174) further comprises a first change calculation sub-module (174) configured to calculate the time change (174B) in the fluid volume (171B) as a function of the water flow rate (172B) through the peritoneum (30) for the individual time steps.

35. A control device as described in claim 33 or 34, wherein the control circuit (20A) further comprises a second change calculation system (173, 175) configured to calculate a time change (175B) in the solute concentration for the individual time step as a function of the solute concentration (171B) calculated by the differential equation solver sub-module (171) for the individual time step and the fluid volume (171B) calculated by the differential equation solver sub-module (171) for the individual time step.

36. 36. The control device of claim 35, wherein the second change calculation system (173, 175) comprises a second flow calculation sub-module (173) configured to calculate, for the individual time steps, a flow rate (173B) of one or more solutes through the peritoneum (30) as a function of the solute concentration (171B) calculated by the differential equation solver sub-module (71) for the individual time steps and the fluid volume (171B) calculated by the differential equation solver sub-module (171) for the individual time steps, and the second change calculation system (173, 175) is configured to calculate, for the individual time steps, a flow rate (173B) of one or more solutes through the peritoneum (30) as a function of the solute concentration (171B) calculated by the differential equation solver sub-module (171) for the individual time steps. and a second change calculation submodule (175) configured to calculate, for the individual time steps, the change in solute concentration over time (175B) as a function of the flow rates (173B) of the one or more solutes through the peritoneal membrane (30) for the individual time steps, the flow rate (172B) of water through the peritoneal membrane (30) for the individual time steps, the solute concentrations (171B) calculated by the differential equation solver submodule (171) for the individual time steps, and the fluid volumes (171B) calculated by the differential equation solver submodule (171) for the individual time steps.

37. A device (1) for performing peritoneal dialysis, comprising: a fluid circuit (7, 10) connectable to a patient's peritoneal access (7') for conveying therapeutic fluids to and from the peritoneal cavity; a dialysis machine (2a; 2) configured to operate said fluid circuit (7, 10); An apparatus comprising a control device (20) according to any one of the preceding claims.

38. 1. A computer-implemented method for generating control signals to a dialysis machine to perform a peritoneal dialysis treatment on a patient, the peritoneal dialysis treatment including one or more fluid exchange cycles on a peritoneal cavity of the patient to cause transport of fluids and solutes across a peritoneal membrane in the peritoneal cavity, the method comprising: Obtaining (401) target values ​​for treatment parameters for the peritoneal dialysis treatment; Obtaining (402) at least one transport property of the peritoneum of the patient; constructing (403) a transport model with the at least one transport property, the transport model defining the transport of fluids and solutes through the peritoneal membrane as a function of a plurality of control parameters for the peritoneal dialysis treatment performed by the dialysis machine; evaluating (404) the transportation model to determine settings of the plurality of control parameters to achieve the target value; generating (405) the control signal for the dialysis machine in response to the set point; The method comprises: acquiring (406) first data including measurements of a concentration-related parameter of fluid in the peritoneal cavity at one or more time points during the peritoneal dialysis treatment; acquiring (407) second data indicative of fluid flow of treatment fluid into and / or out of the peritoneal cavity during the peritoneal dialysis treatment; calculating (408) at least one updated transport property of the peritoneum of the patient based on the first data and the second data using the transport model.

39. A computer-readable medium comprising computer instructions (202A) that, when executed by one or more processors (201) in a control device described in any one of claims 1 to 36, cause the one or more processors (201) to perform the method described in claim 38.

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