Assessment of the condition of people undergoing peritoneal dialysis

Abstract

The testing method determines at least one state parameter of a person undergoing peritoneal dialysis. The state parameter may include a transport property, tonicity, or residual volume of the peritoneal membrane. The testing method is implemented by a device that inputs first data (54A) indicative of a flow rate of therapy fluid into and out of the peritoneal cavity as a function of time through the person's peritoneal access during a testing procedure that includes one or more fluid exchange cycles, and second data (54B) including measured data samples representing the concentrations of one or more solutes in the therapy fluid in the peritoneal cavity at a time point during the testing procedure. A first computing module (52) computes, based on the first data (54A) and using a mathematical transport model (52') of the peritoneal membrane, an estimated data sample representing the concentrations of one or more solutes in the therapy fluid in the peritoneal cavity at that time point. A second computing module (53) determines the state parameter in response to the measured data samples and the estimated data samples.

Description

[Technical Field]

[0001] The present disclosure relates generally to peritoneal dialysis, and more particularly to techniques for assessing the condition of a person undergoing peritoneal dialysis, for example, the functionality of a person's peritoneal membrane. [Background technology]

[0002] Dialysis therapy may be required to treat people suffering from acute or chronic kidney failure. One category of dialysis therapy is peritoneal dialysis (PD). In PD, a treatment fluid ("dialysate") is infused into a person's peritoneal cavity. This cavity is lined with a highly vascular peritoneal membrane ("peritoneum"). Substances are removed from the patient's blood primarily by diffusing across the peritoneal membrane into the treatment fluid. Excess fluid (water) is also removed by osmosis induced by the treatment fluid, which is hypertonic.

[0003] There is considerable inter- and intrapatient variability in the solute transport and ultrafiltration capacities of the peritoneal membrane. This variability makes it difficult to develop a patient-optimized regimen for PD therapy. Furthermore, continuous exposure to therapeutic fluids can lead to functional changes in the peritoneal membrane. Therefore, it is standard procedure to perform a peritoneal membrane test to assess peritoneal membrane functionality. There are numerous options for peritoneal membrane testing, including the peritoneal membrane equilibration test (PET). While there are many variations of PET, the so-called standard PET is currently the most widely used and is briefly described below.

[0004] The process may begin with a lengthy overnight dwell of 8 to 12 hours. When the patient arrives at the clinic on the morning of the PET, the overnight dwell is drained while the patient sits for at least 20 minutes. To maximize the amount of drainage, the patient may lie down and roll from side to side at the end of the drainage. For the PET, 2 L of treatment solution containing a predetermined concentration of osmotic agent is infused over 10 minutes. Every 2 minutes, the patient rolls from side to side to mix the treatment solution. Upon completion of the infusion, 200 mL of treatment solution is drained from the abdominal cavity into a bag, and the drained treatment solution is mixed by inverting the bag several times. A 10 mL sample is then obtained using aseptic technique, and the remaining treatment solution is reinfused into the patient. For the remainder of the 4-hour dwell, the patient is upright and ambulatory. At the 2-hour mark, another 10 mL sample is obtained, and a blood sample is taken for serum measurement. After the 4-hour dwell, the patient is allowed to drain completely while upright for at least 20 minutes. Drain volume is measured by weighing before collecting a 10 mL treatment fluid sample. Once the PET sample is collected, both serum and treatment fluid samples are analyzed for urea, creatinine, and glucose concentrations. For urea and creatinine, the dialysate / plasma concentration (D / P) ratio is calculated for each treatment fluid sample. For glucose, the D / D0 ratio is calculated for each treatment fluid sample. From these equilibrium ratios, the peritoneum is classified into one of four transport types: high, high average, low average, or low.

[0005] Standard PET not only results in a crude and uninformative classification of the peritoneum, but is also significantly time- and resource-intensive 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 at least half a day in a specialized clinic or hospital.

[0006] The prior art includes European Patent Application Publication No. 2623139, which proposes a peritoneal membrane functionality test that eliminates the need for blood samples and laboratory analysis. The proposed test involves extracting a first sample of equilibrated therapeutic fluid from the peritoneal cavity and measuring its conductivity; draining the peritoneal cavity; infusing fresh therapeutic fluid into the peritoneal cavity; and extracting a second sample of equilibrated therapeutic fluid from the peritoneal cavity after a set residence time, e.g., one hour, and measuring its conductivity. The difference in conductivity between the samples is used to classify transport across the peritoneal membrane as poor, normal, or optimal. While significantly simpler than standard PET, the proposed test results in a more coarse classification of peritoneal membrane functionality. Furthermore, the proposed test does not take into account residual volume, i.e., the amount of therapeutic fluid that may remain in the peritoneal cavity after draining. Variability in residual volume from patient to patient or from test to test on the same patient significantly negatively impacts the accuracy of the classification. Summary of the Invention

[0007] It is an object to at least partially overcome one or more limitations of the prior art.

[0008] One objective is to provide a technique that allows for a more detailed classification of peritoneal functionality.

[0009] Another object is to provide such a technique that eliminates the need for a serum sample.

[0010] Yet another object is to provide a technique that can quantify residual volume.

[0011] One or more of these objects, as well as further objects that may emerge from the following description, are achieved by an apparatus for determining at least one status parameter of a person undergoing peritoneal dialysis, a peritoneal dialysis device, a method for determining at least one status parameter, a computer-readable medium, and a monitoring method according to the independent claims, which are defined at least in part by the dependent claims.

[0012] A first aspect is an apparatus for determining at least one status parameter of a person undergoing peritoneal dialysis, the apparatus comprising: an input for receiving first data indicative of a flow rate as a function of time of therapeutic fluid entering and leaving the peritoneal cavity through the person's peritoneal access during one or more fluid exchange cycles, each fluid exchange cycle including a fill phase, a dwell phase, and a drain phase, and second data including measurement data samples representing concentrations of one or more solutes in the therapeutic fluid in the peritoneal cavity at two or more time points during the one or more fluid exchange cycles; a first computing module configured to compute estimated data samples representing concentrations of one or more solutes in the treatment fluid in the peritoneal cavity at two or more time points based on the first data and using a mathematical model of water and solute transport across the peritoneal membrane in the peritoneal cavity; and a second computing module configured to determine at least one state parameter as a function of the measured data samples and the estimated data samples.

[0013] A second aspect is a peritoneal dialysis device, an extracorporeal fluid circuit connectable to a person's peritoneal access for carrying therapeutic fluid to or from the peritoneal cavity; at least one sensor device disposed in the extracorporeal fluid circuit and configured to provide data samples representative of concentrations of one or more solutes in the treatment fluid; a controller configured to operate the extracorporeal fluid circuit and acquire data samples from the sensor device; and an apparatus of the first aspect connected to receive the first data and the second data from the control device.

[0014] A third aspect is a method for determining at least one status parameter of a person undergoing peritoneal dialysis, the method comprising: acquiring first data indicative of a flow rate of therapeutic fluid as a function of time into and out of a peritoneal cavity through the person's peritoneal access during one or more fluid exchange cycles, each fluid exchange cycle including a fill phase, a dwell phase, and a drain phase; acquiring second data comprising measurement data samples representative of concentrations of one or more solutes in the peritoneal cavity at two or more time points during one or more fluid exchange cycles; calculating, based on the first data and using a mathematical model of water and solute transport across the peritoneal membrane within the peritoneal cavity, estimated data samples representative of concentrations of one or more solutes in the therapeutic fluid within the peritoneal cavity at two or more time points; determining at least one state parameter in response to the measured data samples and the estimated data samples.

[0015] 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.

[0016] A fifth aspect is a monitoring method, operating the apparatus of the first aspect to determine at least one state parameter; and evaluating at least one condition parameter to detect potential failure of the peritoneum.

[0017] These aspects take a fundamentally different approach compared to the prior art. The measured concentrations of one or more solutes in peritoneal fluid in the peritoneal cavity, or equivalent characteristics such as conductivity, are used as reference data to determine one or more state parameters that affect the concentrations. Each state parameter is a human characteristic that is estimated to have a known time dependence during a fluid exchange cycle and affects the amount of treatment fluid in the peritoneal cavity and / or the concentration of one or more solutes in the treatment fluid in the peritoneal cavity. Specifically, these aspects are based on the insight that, because solute concentrations are affected by state parameters, it is possible to determine the state parameters by simulating the concentrations of solutes in the treatment fluid in the peritoneal cavity and comparing the resulting (“estimated”) concentrations of one or more solutes (or conductivities) with the measured concentrations (or conductivities). If the estimated and measured concentrations (or conductivities) substantially match, the values of the respective state parameters used in the simulation are close to the actual values. A mathematical model of solute and water transport through the peritoneal membrane is used to simulate the concentrations or conductivities in the treatment fluid in the peritoneal cavity. The simulation is performed by using first data indicating the flow rate of therapeutic fluid entering and leaving the peritoneal cavity via the peritoneal access as a function of time during a fluid exchange cycle. Those skilled in the art will appreciate that the simulation may use additional input data, such as the composition and / or conductivity of the therapeutic fluid injected into the peritoneal cavity during the filling phase, various properties of one or more solutes in the therapeutic fluid, and initial values of state parameters. For reasonable accuracy of the determined state parameters, the reference data should include at least two measurement data samples obtained in this manner at different points during the fluid exchange cycle. If data samples are measured at more points, the accuracy of the state parameters may be increased and / or additional state parameters may be determined. The points may be selected differently depending on the state parameters to be determined. Examples of state parameters include the transport properties of the peritoneal membrane, the volume (e.g., residual volume) of therapeutic fluid in the peritoneal cavity at a particular point in time, or the tonicity parameters of the person.

[0018] It will be appreciated, therefore, that the aforementioned aspects enable more detailed classification or quantification of peritoneal membrane functionality by determining status parameters. Furthermore, this can be achieved by a simple procedure of measuring the concentration or conductivity of the treatment fluid in the peritoneal cavity, for example, by extracting a fluid sample from the peritoneal cavity, eliminating the need to collect and analyze serum samples. In some embodiments, at least one of the measured data samples can be conveniently obtained for the drain phase. Furthermore, the aforementioned aspects enable the status parameters to be determined during a typical PD treatment session. The therapeutic efficiency of such a PD treatment session is only moderately impacted by the possible extraction of a fluid sample from the peritoneal cavity for concentration or conductivity measurement at one or more points during the fluid exchange cycle.

[0019] Further objects, aspects and technical effects, as well as embodiments, features 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 in this specification can be adapted and implemented as an embodiment of the second to fifth aspects. [Brief explanation of the drawings]

[0020] In the following, embodiments will be described in more detail with reference to the accompanying drawings and schematic diagrams. [Figure 1] FIG. 1 shows an exemplary device for automated peritoneal dialysis (APD). [Figure 2] FIG. 2 is an exemplary plot of intraperitoneal volume versus time during a sequence of fluid exchange cycles in APD therapy. [Figure 3] FIG. 3 illustrates the transport processes that affect the concentration of solutes and therapeutic fluids in the peritoneal cavity. [Figure 4] 4A-4C are plots of the conductivity of the treatment fluid in the peritoneal cavity as a function of time for different values of ultrafiltration coefficient, glucose permeability surface area product, and residual volume, respectively. [Figure 5A] , [Figure 5B] , [Figure 5C] 5A-5B are block diagrams of an exemplary computing device for determining state parameters of a PD patient, and FIG. 5C is a block diagram of an exemplary implementation of a peritoneal transport model in a computing device. [Figure 6] FIG. 6A is a flowchart of an exemplary method for determining status parameters of a PD patient, and FIG. 6B is an exemplary monitoring method for detecting peritoneal failure. [Figure 7] FIG. 7A is a combination of simulated plots of conductivity, sodium concentration, glucose concentration, and intraperitoneal volume versus time, showing an example of conductivity measurements during a short test procedure, and FIG. 7B is a corresponding combination of simulated plots for a long test sequence. [Figure 8A] , [Figure 8B] , [Figure 8C] , [Figure 8D] , [Figure 8E] Figure 8A is a box plot of the relative error in PSG determined based on a short test procedure of the type shown in Figure 7A for different transporter types and different glucose concentrations; Figures 8B-8D are box plots of the relative error in PSG, LpS, and fCpw, respectively, determined based on a long test procedure of the type shown in Figure 7B for different transport types and different glucose concentrations; and Figure 8E is a box plot of the absolute error in residual volume for different transporter types calculated by different estimation techniques. [Figure 9] FIG. 9 is a block diagram of a simulation module for calculating simulated output parameters of a standardized PET procedure based on state parameters determined by a computing device. [Figure 10]10A-10B are simulated plots of standard PET parameters versus time calculated by the simulation module of FIG. [Figure 11] 11A-11B are simulated plots of treatment fluid conductivity versus time during PD treatment for patients with normal and elevated PS values for low and high transporter types, respectively. [Figure 12] 12A-12B are exemplary plots of intraperitoneal volume and data samples taken over time according to the modified test procedure. [Figure 13] FIG. 13 is a plot of intraperitoneal volume versus time during an exemplary sample pull procedure. [Figure 14] FIG. 14 is a block diagram of an example machine that may implement the methods, procedures, and functions described herein. DETAILED DESCRIPTION OF THE INVENTION

[0021] 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; rather, these embodiments are provided so that this disclosure will satisfy applicable legal requirements.

[0022] It will also be understood that, where possible, any advantage, feature, function, device, and / or operational aspect of any of the embodiments described and / or contemplated herein may be included in any of the other embodiments described and / or contemplated herein, and / or vice versa. Additionally, where possible, any term expressed in the singular herein is meant to include the plural and / or vice versa, unless expressly stated otherwise. 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 requires otherwise to express the word or necessary connotation, the word "comprise" or variations such as "comprises" or "comprising" are used in an inclusive sense, i.e., to specify the presence of stated features but not to exclude the presence or addition of further features in various embodiments.

[0023] As used herein, the terms "multiple," "plural," and "plurality" are intended to mean the provision of two or more elements, whereas the term "set" of elements is intended to mean the provision of one or more elements. The term "and / or" includes any and all combinations of one or more of the associated listed elements.

[0024] A parameter or variable in square brackets ([ ]) indicates a set of values for the parameter or variable, and an asterisk (*) next to a parameter or variable indicates that the value of the parameter or variable was obtained by measurement.

[0025] Furthermore, although terms such as "first," "second," and the like may be used herein to describe various elements, it should be understood that these elements should not be limited by these terms. These terms are used only to distinguish one element from another. For example, a first element can be a second element, and similarly, a second element can be a first element, without departing from the scope of the present disclosure.

[0026] Well-known functions or configurations may not be described in detail for the sake of brevity and / or clarity. Unless otherwise defined, 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.

[0027] Like numbers refer to like elements throughout.

[0028] 1 is a schematic diagram of an exemplary apparatus 1 for peritoneal dialysis treatment. The apparatus (or system) 1 is generally intended for on-site treatment of a patient P with an appropriate treatment fluid. Specifically, the PD apparatus 1 is designed to treat patients suffering from impaired renal function, particularly using an automated peritoneal dialysis (APD) cycler 3.

[0029] The APD cycler 3 comprises a peritoneal dialysis (PD) unit 3a and a corresponding disposable unit 3b, sometimes referred to as a "disposable line set." The PD unit 3a includes a control system 3' connected to an actuator 5 that forms a pumping mechanism for moving fluid in the fluid circuit of the disposable unit 3b.

[0030] The disposable unit 3b is connectable to the PD unit 3a for engagement by the pump mechanism 5 of the PD unit 3a. A patient line 12 is included in or connected to the disposable unit 3b and is configured to connect to a catheter or other access device 12' implanted in the patient P. The access device 12' is hereinafter referred to as "access."

[0031] 1, the PD device 1 includes a source S of treatment fluid, which includes three containers 6 connected via respective supply lines to corresponding processing ports 7, 8, 9 of the disposable unit 3b. The number of containers 6 of the source S may vary depending on the treatment fluid to be infused into the peritoneal cavity (PC) during the course of PD treatment. For example, different containers may hold treatment fluids of different compositions. Alternatively, the source S may comprise a device for online preparation of the treatment fluid, for example by mixing purified water with one or more concentrated fluids.

[0032] The treatment solution may comprise at least one osmotic agent. As is well known in the art, the osmotic concentration of the treatment solution relative to blood determines how much fluid is exchanged between the treatment solution and blood. A high osmotic concentration in the treatment solution 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 agent. Alternative osmotic agents may be fructose, sorbitol, mannitol, and xylitol. Note that glucose is sometimes referred to as dextrose in the PD field. The term glucose is intended to include dextrose herein.

[0033] In the example shown in FIG. 1 , the disposable unit 3b further includes or is connected to a delivery container 11, sometimes referred to as a “heating bag,” configured to receive unused therapeutic fluid from a supply S. The delivery container 11 is disposed on a heater (not shown) of the PD unit 3a. The heater is operated to adjust the temperature of the therapeutic fluid to a predetermined temperature before being pumped to the PC of the patient P. The disposable unit 3b further includes or is connected to a drain line 13 configured to receive used therapeutic fluid through a drain port 10 on the disposable unit 3b. The drain line 13 extends to a drain 15. Alternatively, not shown, the drain line 13 may extend to a receptacle for used therapeutic fluid.

[0034] The PD device 1 further includes at least one sensor 14 for detecting a property of a liquid flowing within the disposable unit 3b or within a line or container fluidly connected to the disposable unit 3b, such as the drain line 13. In some embodiments, the sensor 14 is a conductivity sensor for measuring the conductivity of the passing liquid. In some embodiments, the sensor 14 is a concentration sensor for measuring the concentration of one or more substances in the passing liquid. The sensor 14 need not be attached to the drain line 14 but can be attached elsewhere in the disposable unit or the aforementioned source S for online generation of treatment liquid. Furthermore, the PD device 1 may include two or more such sensors 14. In some embodiments, the PD device 1 includes one such sensor 14 arranged to detect a property of treatment liquid drained from the patient's PC. In some embodiments, the PD device 1 includes another such sensor 14 arranged to detect a property of unused treatment liquid intended to be infused into the patient's PC. In some embodiments, the sensor 14 arranged to sense the properties of the drained treatment fluid is also arranged to sense the properties of the unused treatment fluid by pumping a sample of the unused treatment fluid through the sensor 14.

[0035] Although not shown in FIG. 1, the PD device 1 may include additional sensors for detecting other properties of the fluid, such as one or more flow sensors for measuring the flow rate of the treatment fluid into or out of the patient P's PC, a temperature sensor for measuring the temperature of the treatment fluid, etc. The flow sensors may have any location within the PD device and may determine the fluid flow rate, for example, by weight change, volumetric throughput, or dead reckoning of the pump stroke. However, it should be understood that the flow rate used for calculations need not be measured by one or more flow meters but may be provided by a set value for the PD device 1 (see test regimen data 54A in FIG. 5A).

[0036] The PD device 1 is operable, via the control unit 3', to perform PD therapy including one or more fluid exchange cycles. Each exchange cycle includes a sequence of a fill phase, a dwell phase, and a drain phase. During the fill phase, the PD device 1 operates to pump unused therapy fluid from the delivery container 11 to the patient P's PC via the patient line 12 and the access 12'. During the dwell phase, the therapy fluid resides in the PC. During the drain phase, the PD device 1 operates to pump used therapy fluid from the PC via the access 12' and the lines 12, 13 to the drain 15. The used therapy fluid is also known in the art as "waste fluid."

[0037] Figure 2 schematically illustrates PD therapy in terms of intraperitoneal volume (IPV) as a function of time. IPV indicates the amount of therapy fluid present in the PC and is also referred to herein as Vp. The illustrated PD therapy involves six consecutive exchange cycles. The fill, dwell, and drain phases, designated F, DW, and D, are shown for the third exchange cycle. The duration of the dwell phase, which may vary between exchange cycles, is indicated by ΔT1 through ΔT6. In the example shown, the patient has a residual volume in the PC at the start, designated Vres. During the first exchange cycle, a volume of therapy fluid, Vf, is infused into the PC. During phases F, DW, and D, the volume of therapy fluid in the PC increases due to the transport of fluid from the patient's blood through the peritoneal membrane into the PC, known as ultrafiltration (UF). Depending on the osmotic gradient, both positive and negative UF are possible. For illustrative purposes, UF is shown only for the dwell phase DW in Figure 2. During the drain phase, spent therapy fluid is extracted from the PC, leaving a residual volume. This residual volume may differ from the residual volume at the start of PD treatment and may vary from drain to drain.

[0038] In the example of FIG. 2 , PD treatment is completed with a drain phase, and the next PD treatment begins with a fill phase. However, although not shown in FIG. 2 , PD treatment is typically completed with a fill phase, which leaves treatment fluid in the PC. The patient may then disconnect from the PD device 1 and carry the treatment fluid within the PC until the next treatment session or until a manual drain is performed after a selected dwell time. APD is typically performed at night while the patient is sleeping, allowing the patient to move freely during the day. For this reason, PD treatment may begin with a drain phase ("first drain"), not shown in FIG. 2 , which leaves a residual volume Vres.

[0039] FIG. 3 is a schematic diagram of the peritoneal membrane ("peritoneum") 30 separating the PC 31 from the patient's blood side 32. In FIG. 3, the concentration of each solute (i) in the blood side 32 as a function of time is designated Cbi(t), the intraperitoneal volume as a function of time is designated Vp(t), and the concentration of each solute (i) in the treatment fluid as a function of time is designated Cpi(t). Solutes include, but are not limited to, sodium, potassium, calcium, magnesium, lactate, phosphate, albumin, bicarbonate, urea, creatinine, chloride, etc. Solutes may also include an osmotic agent, for example, according to any of the examples described above. In the following example, the osmotic agent is assumed to be glucose. FIG. 3 also illustrates processes that may affect Vp(t) and Cpi(t). The process includes fluid flow through access 12', including the flow of therapeutic fluid into PC 31 through access 12' during the fill phase, designated Jf(t), the flow of therapeutic fluid from PC 31 through access 12' during the drain phase, designated Jd(t), and the loss of therapeutic fluid from PC 31 when a fluid sample (see below) of the therapeutic fluid in PC 31 is taken through access 12', designated Js(t). The process further includes fluid flow through peritoneal membrane 30, including total fluid flow, designated Jv(t), the flow of each solute, designated Ji(t), and lymph flow, designated L. It should be understood that each substance can move in either direction across membrane 30, and the arrows in FIG. 3 merely indicate the direction corresponding to the positive sign of each flow.

[0040] The peritoneal membrane 30 can be classified by its transport properties. Figure 3 shows two such transport properties, LpS and PS. The LpS property is the hydraulic conductance of the membrane 30, also known as the liquid permeability or ultrafiltration rate. The LpS property may be expressed, for example, in mL / min / mmHg. The PS property, also known as the overall mass transfer area coefficient or the diffusional overall mass transfer area coefficient, is the permeability surface area product that describes the flow of solutes (molecules) through the membrane 30. The PS property may be expressed, for example, in mL / min. The PS property generally varies between solutes and is therefore referred to herein as PSi. In some embodiments, a predetermined relationship is assumed between PSi for all solutes; therefore, if PSi for one solute is known, PSi for any solute can be calculated. This assumption simplifies the computation because determining PSi for multiple different solutes can be reduced to determining a scale factor, fPS, that scales a common set of PS values for different solutes, PSiG, according to PSi = fPS · PSiG, where PSiG is the common PS value for solute i. The common set can be considered to contain PS values that represent the predetermined relationship described above and are representative of a "typical patient." Once fPS is determined, any PSi can be determined by scaling the corresponding PSiG by fPS.

[0041] 3 also shows an additional scale factor, fCpw, which represents the patient's tonicity and may indicate the patient's deviation from an isotonic state. In some embodiments, to avoid the need to draw patient serum samples, the solute's blood concentration, 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 scale factor, fCpw, accounts for the patient's actual plasma water fraction.

[0042] The described embodiments provide techniques for estimating one or more state parameters of patient P by using a dynamic model of the concentrations of solutes in the treatment fluid in PC 31. The dynamic model relies on the one or more state parameters and takes into account the time dynamics of the treatment fluid flow, shown 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 membrane 30. Such embodiments rely on the insight that it is possible to estimate the state parameters by comparing estimated data samples representing the concentrations of one or more solutes in the treatment fluid as estimated by the dynamic model at a series of time points with measured data samples representing the actual concentrations of the solutes measured at a series of time points.

[0043] It should be noted that "representing a concentration" in this context includes any and all equivalent characteristics. In one example, the data samples include concentration values of at least one solute, such as an osmotic agent or sodium, in the treatment fluid. In another example, the data samples include conductivity values measured by one or more conductivity sensors (see 14 in FIG. 1). For simplicity, the following description assumes that the data samples are conductivity values. Corresponding embodiments based on concentration values will be readily apparent to those skilled in the art.

[0044] The utility of the proposed embodiment is illustrated in Figures 4A-4C, which are simulated graphs of the conductivity of the treatment fluid in a PC over a 300-minute period. Figure 4A shows the conductivity for different values of LpS with a fixed PSG of 15.27 mL / min, where PSG is the PS value for glucose. Figure 4B shows the conductivity for different values of PSG with a fixed LpS of 0.074 mL / min / mmHg. Figure 4C shows the conductivity for different values of residual volume Vres with fixed values of LpS and PSG. As can be seen from Figures 4A-4C, the transport properties of the peritoneal membrane and the residual volume of the treatment fluid in the PC each have a significant impact on the resulting conductivity of the treatment fluid (and the concentrations of various solutes therein) over time. A similar effect can be demonstrated for the scale factor fCpw.

[0045] In some embodiments, the mechanical model is based on the well-known Three-Pore Model (TPM) of the peritoneal membrane. 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 water transport, i.e., UF, driven by osmotic force. Intermediate pore types allow transport of liquids and smaller solutes. The majority of membrane protein transport is enabled by large pore types. Examples of the TPM can be found in International Publication No. 2018 / 041760 and in the article "Optimizing Automated Peritoneal Dialysis Using an Extended 3-pore Model," Kidney Int Rep, 2(5):943-951 (2017), both of which are incorporated herein by reference in their entireties.

[0046] The equations describing the process of Figure 3 are detailed in Appendix A. These equations are based on the TPM, but as will be readily understood by those skilled in the art, other mechanical models, such as the Two-Pore Model, Dual Barrier Membrane Model, or Distributed Model, can be used instead.

[0047] Equations 1-4 in Appendix A represent the flow of fluid through the peritoneum, J. v (t), the flow of solute i through the peritoneum Ji(t), and the temporal change in intraperitoneal volume dV p / dt, and the time change in the concentration of solute i in the treatment solution in PC, dC pi can be generalized to basic time-dependent governing functions for / dt.

number

[0048] As can be seen and understood from Appendix A, the control functions have complex and mixed dependencies on the time-dependent variables and the transport properties LpS and PSi shown in Figure 3. The control functions f1, f2, f3, and f4 can be combined to define a peritoneal transport model, as exemplified below with reference to Figures 5A-5C.

[0049] The above scale factor fCpw is J v (t) and J i Note that (t) is included in Cbi (see Figure 3) used in both calculations (see Equations 2 and 4 in Appendix A). Therefore, the control functions f1 and f2, if used, also depend on fCpw.

[0050] From Appendix A, the function f2 is a PS i,mNote that in some embodiments, large pores (PS i,3 ) is set to a typical value given, for example, by the typical set mentioned above, and the scale factor fPS is set to a value corresponding to the small pore (PS i,2 ) values. The PS value for a particular solute may then be calculated by scaling the sum of the corresponding general PS values for small and large pores by the scale factor fPS. This simplification has been found to have little effect on the accuracy of the results. However, in other embodiments, the scale factor fPS is applied only to the small pore (PS i,2 ) and large pores (PS i,3 ) can be applied to both PS values. As a further simplification, the PS value for a particular solute can be calculated by scaling the corresponding PSiG for the small pore by a scale factor fPS, thus omitting the contribution from the large pore. This further simplification is at least applicable to small solutes, such as glucose or sodium, for which the contribution to PSi from the large pores is virtually negligible.

[0051] FIG. 5A is a block diagram of an exemplary peritoneal testing device 50 ("PT device") for determining one or more condition parameters of a person based on measurements made during a testing procedure performed, for example, by a PD device 1 such as that shown in FIG. 1 for patient P. The testing procedure includes one or more fluid exchange cycles. The PT device 50 includes an input 51, which may include any type of hardware and / or software structure configured to receive input data in any format. For example, the input 51 may be one or more sensors (see 14 in FIG. 1), a controller (see 3' in FIG. 1), a storage memory, or an interface configured to receive signals from an input such as a keyboard, touchpad, touchscreen, computer mouse, or the like. In the illustrated example, the PT device 50 is configured to receive test regimen data 54A, measurement data 54B, treatment history data 56A, general patient data 56B, patient-specific data 56C, and solute characteristic data 56D via the input 51. The PT device 50 further comprises a first computing module 52 and a second computing module 53, cooperatively operated to determine one or more state parameters 55, which may be output for presentation, storage, further processing, etc. The first computing module 52 defines or comprises a peritoneal transport model (PTM) 52′, which is a mathematical model of water and solute transport through the peritoneum in a person's PC, and also describes transport of therapeutic fluids through the access 12′ and into and out of the PC. For example, the PTM 52′ may be an implementation of management functions f1-f4. The first computing module 52 is configured to operate the PTM 52′ on at least a portion of the input data received via the input 51 to generate estimated data samples corresponding to the measurement data 54B. The second computing module 53 defines or comprises a parameter fitting algorithm (PFA) 53′, which is configured to operate on the estimated data samples from the first computing module 52 and the measurement data 54B to determine candidate characteristic data for the person. The candidate characteristic data includes one or more parameters that are processed by the PTM 52' to generate estimated data samples.The first and second calculation modules 51, 52 are configured to alternately generate estimated data samples and candidate characteristic data until the PFA 53' determines that the candidate characteristic data meets a convergence criterion or expires. The PT device 50 then generates state parameters 55 based on the candidate characteristic data. The PFA 53' can be any algorithm capable of solving nonlinear optimization problems 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.

[0052] Looking more closely at the input data presented in FIG. 5A, test regimen data 54A represents the flow rate of treatment fluid into and out of the PC via access 12' as a function of time during the test procedure. Thus, in the notation of FIG. 3, test regimen data 54A indicates Jf(t), Jd(t), and Js(t), as well as the start and end times of the test procedure. The test regimen data can also indicate the composition of the unused treatment fluid (C in Equation 3 of Appendix A). Ii (See ). Note that the composition of the unused treatment fluid may vary between fluid exchange cycles.

[0053] In the following example, measurement data 54B represents the measured conductivity of the treatment fluid inside the PC at a series of points in time during one or more fluid exchange cycles. Measurement data 54B may also identify the series of points in time, if not predefined. In the example of FIG. 1, the conductivity may be measured by sensor 14, as described in more detail below with reference to FIG. 7.

[0054] Treatment history data 56A may include test regimen data for one or more fluid replacement cycles performed on the person in the preceding period, for example, within 12-48 hours of the test procedure.

[0055] The typical patient data 56B may include a typical set of PSi values as described above, as well as typical values for plasma concentrations of solutes (see Cbi in FIG. 3). In some embodiments, all solutes expected to be present in blood or therapeutic fluids at concentrations of at least about 0.5 mmol / L are included in PTM 52' and therefore also included in the typical patient data 56B. The typical values may be given as population means. The typical patient data 56B may be calculated using a m It is understood that the parameter values may also include other parameter values used by PTM 52', such as (see Appendix A).

[0056] The patient-specific data 56C may include any known or estimated characteristics of the patient relevant to calculations by the PT device 50. For example, the patient-specific data may include the concentration of one or more solutes in the patient's blood, or previously determined values of Vres, LpS, PSi, or fCpw (see FIG. 3). It will be appreciated that the provision of the patient-specific data 56C may reduce the complexity of calculations by the PT device 50 and / or may increase the accuracy of the calculations.

[0057] Solute property data 56D may include any known property data of the solute included in the governing function used by PTM 52'. In Appendix A, solute property data 56D includes the osmotic coefficient (φ i ), the charge of the solute (z i ) etc.

[0058] The input data shown in FIG. 5A and described above are not intended to be limiting and are provided by way of example only. It is contemplated that additional input data may be used and / or one or more of data items 56A-56D may be omitted. For example, patient-specific data 56C may not be available. In another example, all or part of solute characteristic data 56D may be integrated into PTM 52'. Additionally, treatment history data 56A may be omitted entirely.

[0059] FIG. 5B is a block diagram of a more detailed example of the first and second computing modules 52, 53 in the PT device 50 (FIG. 5A). In FIG. 5B, the PT device 50 is configured to receive input data 54 including a set of measured data samples [Kp*] representing the conductivity of the treatment fluid in the PC at the aforementioned series of time points. The input data 54 may be part of the measured data 54A of FIG. 5A. The PTM 52′ is configured to receive an initial data set 52A including initial values of variables in the control function and to operate on the initial data set 52A to generate a data set 57A including a series of estimated values for the concentration [Cpi] of the solute in the treatment fluid in the PC. The estimates are generated for time points that at least approximately correspond to the series of time points of [Kp*]. In the illustrated example, the PT device 50 further includes a conversion module 57 configured to convert the estimated concentration [Cpi] of the solute at each time point to a corresponding estimated conductivity [Kp] of the treatment fluid. The conversion module 57 may be configured to aggregate the conductivity contributions of all charged solutes given their concentrations, potentially also considering the effects of uncharged solutes such as glucose and urea (if present). Thus, the conversion module 57 may be configured according to well-known standard formulas, such as those described in U.S. Patent Application Publication No. 2012 / 0018379 and International Publication No. 2016 / 188950, both of which are incorporated herein by reference. The output data 57B of the conversion module 57, including [Kp], is received by a subtraction module 58, which is configured to calculate the difference between corresponding values in [Kp] and [Kp*], i.e., the difference between the estimated conductivity value and the conductivity value measured at the series of time points. The result is an array of difference values for the series of time points, represented as residual data 53A in FIG. 5B. The PFA 53′ is configured to operate on the residual data 53A to generate the aforementioned candidate characteristic data 53B, exemplified in FIG. 5B as LpS and PSi. The PTM 52' is then configured to operate on the candidate characteristic data 53B, and possibly on at least a portion of the initial dataset 52A, to generate a new dataset 57A that includes an updated [Cpi].As will be appreciated from the above, the computation and flow of data may continue until the PFA 53' finds that the convergence criteria are met, e.g., the residual data 53A is sufficiently small. It can be seen that the PT apparatus of Figure 5B represents a feedback control system, where the first computation module 52 corresponds to the system to be controlled, the input data 54 corresponds to the setpoint, the output data 57B corresponds to the actual value, and the PFA 53' corresponds to the controller.

[0060] In some embodiments, the candidate characteristic data 53B may be assumed to be time-invariant (constant) during the testing procedure, facilitating computation. However, one or more parameters in the candidate characteristic data 53B may be time-varying by including a predefined time dependency for each parameter. For example, modeling the decreasing time dependency of PSi and / or LpS during PD has been known for some time, as described, for example, in the article "Diffusive Mass Transfer Coefficients Are Not Constant During a Single Exchange In Continuous A Continuous Ambulatory Peritoneal Dialysis" by Waniewski et al., published in ASAIO J 1996;42:M518-523, which is incorporated herein by reference in its entirety.

[0061] It should also be understood that the candidate characteristic data 53B may include any unknown characteristic included in the management function of the PTM 52'. In the example of FIG. 5B, fCpw is assumed to be known, and LpS and PSi are assumed to be fitted by the PFA 53'. In another example, the candidate characteristic data 53B includes fCpw, LpS, and PSi. In another example, only one of LpS, PSi, and fCpw is fitted by the PFA 53', with the others set to fixed and known values.

[0062] In some embodiments, as described above, PSi may be represented by a scale factor fPS, which may then be fitted by the PFA 53' or set to a fixed, known value.

[0063] The conversion module 57 and the subtraction module 58 do not have to be included in the first calculation module 52 as shown in FIG. 5B, but one or both of the modules 57, 58 may instead be included in the second calculation module 53 or the third calculation module.

[0064] FIG. 5C is a block diagram of a more detailed example of PTM 52′. Generally, PTM 52′ is configured to implement management functions f1 through f4. In the example shown, PTM 52′ includes a differential equation solving submodule (DES) 71 configured to operate on values of derivatives (“time-varying”) of variables at one or more previous time steps to generate values of the variables at the current time step. For example, DES submodule 71 may implement any known recursive method for obtaining numerical solutions to differential equations, such as a linear multistep method, a Runzi-Kutta method, or a general linear method (GLM). The results presented herein were generated by implementing a conventional ODE (general differential equation) solving method, specifically the ode45 function in MATLAB, in PTM 52′.

[0065] In the illustrated example, the PTM 52' is configured to generate a time series of values of intraperitoneal volume Vp and a corresponding time series of values of the concentration Cpi of a solute in the treatment fluid within the PC. To this end, the PTM 52' further comprises management submodules 72-75 that implement respective management functions f1-f4. During operation, the DES submodule 71 generates a data set 71B containing Vp and Cpi for the current time step based on data sets 74B, 75B containing derivatives of Vp and Cpi for the previous time step. The management submodule 72 operates on Vp and Cpi for the current time step to generate Jv for the current time step. The management submodule 73 operates on Jv, Vp, and Cpi for the current time step to generate Ji for the current time step. The management submodule 74 operates on Jv for the current time step to generate the derivative of Vp for the current time step. The management sub-module 75 operates on Jv, Ji, Cpi, and Vp to generate the derivative of Cpi with respect to the current time step. It will be appreciated that by operating the FTM 52' from a start time (t=0) to an end time, a time series of values for each of Vp and Cpi is generated. Based on the time series of Cpi values, the FTM 52' extracts Cpi values for a series of time points, resulting in [Cpi], which is output as a data set 57A for use by the transformation module 57 (see FIG. 5B).

[0066] When the operation of the PTM 52' is first initiated, the DES sub-module 71 obtains the intraperitoneal volume Vp(0) at the start time and the concentration Cpi(0) of the solute in the therapeutic fluid in the PC at the start time from the initial data set 52A. In the example of FIG. 5C, the initial data set 52A is provided by the start data module 76. The values of Vp(0) and Cpi(0) are then provided as data set 71B at the start time (t=0). As can be seen from the management functions f1-f4, the sub-module 72 operates on LpS (and possibly fCpw), and the sub-module 73 operates on LpS and PS. 1..N(and possibly fCpw). LpS, PSi, and fCpw are included in candidate characteristic data 53B, and therefore, to the extent determined by PT device 50, initial data set 52A may include initial values for LpS, PSi, and fCpw used by sub-modules 72, 73, 75 when operation of PTM 52′ is first initiated. In the example of FIG. 5C, each sub-module 72, 73 may retrieve initial values LpS0 and PSi0 from initial data set 52A. The initial values LpS0, PSi0 may be obtained by starting data module 76, for example, from general patient data 56B or patient-specific data 56C (FIG. 5A).

[0067] The initial values Vp(0) and Cpi(0) may also be obtained by the starting data module 76 from the general patient data 56B or the patient-specific data 56C (FIG. 5A). However, if one or more of the initial values Cpi(0) differ significantly from the patient's actual values, using the general patient data 56B may reduce the accuracy of the resulting state parameters. In some embodiments, to mitigate this potential problem, the starting data module 76 is configured to perform a simulation based on information about one or more recent regimens in the treatment history data 56A (FIG. 5A). The simulation may be performed by calculating Cpi(0) based on the patient's expected residual volume, taking into account the recent regimen, and using the governing functions f1-f4. The initial values of the solute concentrations in the residual volume for this simulation may optionally be taken as plasma water concentrations slightly modified from plasma water, for example, by reducing the content of large solutes and / or adjusting sodium and chloride according to Donnan's equilibrium.

[0068] Alternatively, the starting data module 76 may set the initial value Cpi(0) equal to the plasma water concentration, optionally applying a reduction factor for large solutes such as albumin. Alternatively, the starting data module 76 may set the initial value Cpi(0) equal to the concentration of each solute in the unused treatment fluid.

[0069] When the PFA 53' has calculated the candidate characteristic data 53B based on the data set 57A generated by the PTM 52' for the initial data set 52A, the PTM 52' is again operated to generate time series values for each of Vp and Cpi. The PTM 52' can again use Vp(0) and Cpi(0) as initial values, but now use LpS and PSi in the candidate characteristic data 53B from the PFA 53' (FIG. 5B).

[0070] As shown in FIG. 4C, residual volume can have a significant effect on the conductivity of the treatment fluid in the PC. This means that an error in the initial value Vp(0) can significantly affect the accuracy of the estimated concentration value [Cpi] and, thereby, the estimated conductivity [Kp]. In some embodiments, to mitigate the effect of such errors, the PFA 53′ is configured to include the intraperitoneal volume at one or more time points among the fitted parameters. In other words, Vp at one or more time points is included in the candidate characteristic data 53B. In some embodiments, the intraperitoneal volume at the end of the drain cycle, i.e., the residual volume Vres, is included in the candidate characteristic data 53B.

[0071] FIG. 6A is a flowchart of an exemplary method 600 for determining at least one condition parameter of a person undergoing PD dialysis. The exemplary method 600 may be performed by the exemplary PT device 50 shown in FIGS. 5A-5C and described above in this specification. In step 601, first data is input. The first data indicates the flow rate of therapy fluid as a function of time into and out of the peritoneal cavity 31 (FIG. 3) via the person's peritoneal access 12′ (FIG. 1) during one or more fluid exchange cycles. The first data may correspond to or be included in test regimen data 54A (FIG. 5A). In step 602, second data is input. The second data includes measurement data samples [Kp*] (FIG. 5B) representing the conductivity of therapy fluid in the peritoneal cavity 31 at two or more time points during one or more fluid exchange cycles. The second data may correspond to or be included in measurement data 54B (FIG. 5A). Step 603 includes a sub-step 603A of evaluating the mathematical peritoneal transport model 52' (FIGS. 5A-5C) based on the first data and calculating estimated data samples [Kp] (FIG. 5B) representing the conductivity of the treatment fluid in the peritoneal cavity 31 at two or more time points. Step 604 determines at least one state parameter depending on the measured data samples [Kp*] provided by the second data and the estimated data samples [Kp] provided by step 603.

[0072] It is understood that the example method 600 may be performed by PT devices that differ significantly from the example PT device 50 of FIGS. 5A-5C. Accordingly, the present disclosure is not limited to the particular combination of features presented with reference to FIGS. 1-5. For example, it is contemplated that the complexity of the management functions f1-f4 may be reduced to simplify the operations performed by the PT device 50, possibly at the expense of accuracy. It may even be possible to define the management functions to allow algebraic operations of the state parameters based on measured and estimated data samples.

[0073] However, it should be appreciated that the above examples include features that, alone or in combination, may provide distinct technical advantages, such as for increased accuracy, increased processing efficiency, etc.

[0074] In some embodiments, the at least one condition parameter comprises one or more transport properties of the peritoneal membrane, knowledge of such transport properties permitting a detailed assessment of the condition of the peritoneal membrane.

[0075] In some embodiments, the at least one condition parameter includes the diffusion capacity of a solute through the peritoneal membrane and / or the filtration capacity of water through the peritoneal membrane. Both of these transport properties are indicators related to the condition of the peritoneal membrane. As understood above, the diffusion capacity may include the product of the permeability coefficient of a drug in the treatment solution and the surface area, PSi. The drug may be any solute present in the unused or used treatment solution, such as an osmotic agent. As understood above, the filtration capacity may include, for example, the hydraulic conductance given by the ultrafiltration rate.

[0076] In some embodiments, the at least one condition parameter includes the volume of treatment fluid in the peritoneal cavity at a selected time point. For example, the selected time point may be the completion of the drain phase of at least one of one or more fluid exchange cycles, resulting in a residual volume. As described above with reference to FIG. 5C, the intraperitoneal volume at one or more time points may be included as a parameter determined based on measured and estimated data samples. Thus, by appropriately selecting the time points, an improved estimate of the residual volume may be obtained. The residual volume is a relevant feature for assessing the condition of the peritoneum and may also be used to optimize PD treatment.

[0077] In some embodiments, the at least one condition parameter includes a human tonicity parameter, such as the scale factor fCpw described above. Such tonicity parameters can be used to assess the patient's condition.

[0078] In some embodiments, as illustrated in Figures 5A-5C, the PT device 50 includes a parameter fitting algorithm 53' operable to determine candidate values for each of a set of parameters included in a mathematical peritoneal transport model 52' to minimize the difference between the measured data samples and the estimated data samples. The set of parameters includes at least one state parameter. Thus, each state parameter can be given a corresponding candidate value that minimizes the difference, at least to the extent that the difference is below a threshold. The use of the parameter fitting algorithm 53' allows for the use of a more complex or more accurate peritoneal transport model 52'.

[0079] In some embodiments, as illustrated in FIG. 5B, the set of parameters represents the diffusion capacity of one or more solutes through the peritoneal membrane (e.g., PSi or fPS) and the filtration capacity of water through the peritoneal membrane (e.g., LpS).

[0080] In some embodiments, the set of parameters further represents the person's tonicity (eg, fCpw), as described with reference to FIG. 5B.

[0081] 5A-5C, in some embodiments, the first computing module 52 is configured to alternately compute estimated data samples [Kp] based on respective candidate values of a set of parameter values (see candidate characteristic data 53B) alternately determined by the second computing module 53. Thus, the second computing module 53 may be configured to alternately determine respective candidate values of the set of parameters based on the estimated data samples [Kp] from the first computing module 52. The second computing module 53 may be configured to output at least one state parameter when a convergence criterion is met or when a time limit is reached. The first and second computing modules 52, 53 are thereby configured to define the above-described feedback control system that operates to iteratively find best values for the set of parameters, and thereby the best value for the at least one state parameter.

[0082] 5C, the first calculation module 52 is configured to calculate a time sequence of estimated volumes of therapeutic fluid in the peritoneal cavity at points during one or more fluid exchange cycles based on the first data (see test regimen data 54A) through the use of a mathematical peritoneal transport model 52'. As can be seen from the above description, calculating a time series of Vp values allows for highly accurate calculation of corresponding Cpi values.

[0083] In some embodiments, the mathematical peritoneal transport model 52' is the three-pore model TPM for transport through the peritoneal membrane. The TPM is an established and reliable model.

[0084] In some embodiments, the mathematical peritoneal transport model 52' is configured to consider ion transport across the peritoneal membrane due to electrostatic forces, which are caused by differences in the amount of dissolved ions on both sides of the membrane and the repulsion of highly charged solutes by the peritoneal membrane. An example of such a mathematical peritoneal transport model 52' is shown in Appendix A. Further examples of incorporating electrostatic forces in peritoneal transport models are found in Chapter 17 (pp. 33-36) of the publication "Analysis of Transvascular Transport Phenomena in the Glomerular and Peritoneal Microcirculation" by Oeberg, Carl, (1 ed.), Lund University, Faculty of Medicine, Lund, ISBN 978-91-7619-372-3. It is currently believed that accounting for electrostatic forces will yield more accurate results. Note that alternative and / or simpler techniques exist for considering ion transport due to electrostatic forces, for example, by using so-called Donnan coefficients.

[0085] 5C , the first computing module 52 includes a differential equation solving (DES) sub-module 71 configured to calculate, for the current time step, the amount of treatment fluid in the peritoneal cavity (see 71B) based on a preceding temporal change in the amount of treatment fluid in the peritoneal cavity (see 74B). The DES sub-module 71 may be further configured to calculate, for the current time step, the concentration of one or more solutes in the treatment fluid in the peritoneal cavity (see 71B) based on a preceding temporal change in the concentration of one or more solutes in the treatment fluid in the peritoneal cavity (see 75B). As can be understood from the above description, this allows the time series of Cpi values to be calculated with high accuracy.

[0086] 5C , the first calculation module 52 further includes a first change calculation system configured to calculate a current temporal change (see 74B) in the amount of therapeutic fluid in the peritoneal cavity according to the current concentrations (see 71B) of one or more solutes in the therapeutic fluid in the peritoneal cavity calculated by the DES sub-module 71 and the current amount (see 71B) of therapeutic fluid in the peritoneal cavity calculated by the DES sub-module 71. As can be understood from the above description, this enables the time series of Cpi values to be calculated with high accuracy.

[0087] 5C , the first change calculation system includes a first flow rate calculation sub-module 72 configured to calculate a current flow rate of water through the peritoneal membrane (see 72B) in response to the current amount of therapeutic fluid in the peritoneal cavity (see 71B) calculated by the DES sub-module 71. The first change calculation system may further include a first change calculation sub-module 74 configured to calculate a current temporal change in the amount of therapeutic fluid in the peritoneal cavity (see 74B) in response to the current flow rate of water through the peritoneal membrane (see 72B). As can be understood from the above description, this allows the time series of Cpi values to be calculated with high accuracy.

[0088] 5C , the first calculation module 52 further includes a second change calculation system configured to calculate a current temporal change (see 75B) in the concentration of one or more solutes in the treatment fluid in the peritoneal cavity, depending on the current concentration (see 71B) of one or more solutes in the treatment fluid in the peritoneal cavity calculated by the DES sub-module 71 and the current volume (see 71B) of the treatment fluid in the peritoneal cavity calculated by the DES sub-module 71. As can be understood from the above description, this enables the time series of Cpi values to be calculated with high accuracy.

[0089] 5C , the second change calculation system includes a second flow rate calculation sub-module 73 configured to calculate a current flow rate (see 73B) of one or more solutes through the peritoneal membrane in response to the current concentration (see 71B) of one or more solutes in the therapeutic fluid in the peritoneal cavity calculated by the DES sub-module 71 and the current volume of the therapeutic fluid in the peritoneal cavity calculated by the DES sub-module 71. The second change calculation system may further include a second change calculation sub-module 75 configured to calculate a current temporal change (see 75B) in the concentration of one or more solutes in the peritoneal cavity in response to the current flow rate (see 73B) of one or more solutes through the peritoneal membrane, the current flow rate (see 72B) of water through the peritoneal membrane, the current concentration (see 71B) of one or more solutes in the therapeutic fluid in the peritoneal cavity calculated by the DES sub-module 71, and the current volume of the therapeutic fluid in the peritoneal cavity calculated by the DES sub-module 71. As can be seen from the above explanation, this allows the time series of Cpi values to be calculated with high accuracy.

[0090] 5B, the first computing module 52 is configured to generate a time sequence of estimated concentration values Cpi of at least one solute in the therapeutic fluid within the peritoneal cavity, convert the time sequence of estimated concentration values to a time sequence of conductivity values, and determine estimated data samples Kp from the time sequence of conductivity values. The conversion from concentration values to conductivity values allows the first computing module 52 to apply a dynamic model of solute concentrations within the peritoneal cavity, which in turn allows for physically accurate modeling of the effect of state parameters on the conductivity of the therapeutic fluid within the peritoneal cavity. Ultimately, this protects the accuracy of the determined state parameters.

[0091] In some embodiments illustrated in FIG. 5C, the PT device 50 is further arranged to receive fluid exchange data for the person for a preceding time period prior to one or more fluid exchange cycles (see FIG. 56A in FIG. 5A). The PT device 50 may be further configured to estimate an initial concentration Cpi(0) of one or more solutes in the therapeutic fluid in the peritoneal cavity at the start of the evaluation based on the fluid exchange data. The first calculation module 52 may be configured to calculate an estimated data sample Kp based on the initial concentrations. As understood from the above, estimating the initial concentration value allows for improved accuracy of the estimated data sample Kp, because initial concentration values estimated from the person's fluid exchange data at a preceding time point are likely to be more representative of the patient at the start of the evaluation than typical concentration values.

[0092] The operation and use of the above described techniques will now be described with reference to the simulation data shown in Figures 7-11.

[0093] 7A-7B are graphs of example variables generated by the first computing module 52 for a simulated patient with known values of PSi, LpS, fCpw, and Vres. FIG. 7A shows a short test sequence including, in sequence, a fill phase, a dwell phase, a drain phase, a fill phase, and a partial dwell phase. FIG. 7B shows a longer test sequence including six complete fluid exchange cycles followed by a fill phase and a partial dwell phase. In each of FIGS. 7A and 7B, the graphs show, from top to bottom, the conductivity (K) of the peritoneal therapy fluid, the concentration of sodium (CpNa) in the peritoneal therapy fluid, the concentration of glucose (CpG) in the peritoneal therapy fluid, and the intraperitoneal volume (Vp). The simulation is performed with a test sequence that begins at the end of the first drain phase (not shown) and ends with the period into the final dwell phase. Vertical dotted lines in FIG. 7A have been added to indicate the transitions between the different phases. The variables Vp, CpNa and CpG are generated by the PTM 52' in the first calculation module 52 using CpNa and CpG, which are examples of Cpi, and the variable K is generated by the conversion module 57 based on Cpi (including CpNa and CpG).

[0094] 7A-7B also show examples of time points at which conductivity is measured during a test procedure. In the short test procedure of FIG. 7A, data samples are shown at five time points. Data sample Kp0* is taken at the end of the first drain phase, i.e., before the start of the first fill phase. Data sample Kp1* is taken during the next dwell phase at a predetermined time (Δt1) after the completion of the first fill phase. Data sample Kp2* is acquired during the next drain phase. This data sample can be taken at any well-defined time during the drain phase, or at two or more times. In FIG. 7A, data sample Kp2* is shown at the start and completion of the drain phase. Data sample Kp3* is taken at a predetermined time Δt2 after the completion of the next fill phase, i.e., during the next dwell phase. In non-limiting examples, Δt1 and Δt2 can range from 0 to 20 minutes or 0 to 10 minutes. FIG. 7A also shows conductivity data samples Kd0* and Kd1* measured with fresh treatment fluid infused into the peritoneal cavity during each filling phase. As noted above, PTM 52' may operate on a nominal solute concentration in the fresh treatment fluid (C in Equation 3 of Appendix A). Ii (See, for example, ). However, the accuracy of the state parameter can be improved by working with actual concentrations, if available, or by estimating the actual concentrations in unused treatment fluid. For example, ready-to-use treatment fluids are produced with relatively large tolerance limits for their components. For example, the concentration of sodium may have a tolerance limit of ±2.5%. This means that the conductivity will vary from batch to batch of treatment fluid, introducing error into the calculation of the state parameter. By measuring the conductivity (Kd0*) of unused treatment fluid and knowing its nominal conductivity, the nominal concentration can be adjusted to better represent the actual concentration. This adjustment may be performed in different ways, as will be readily understood by those skilled in the art. In one example, the percent water content in unused treatment fluid can be calculated to minimize the difference between the nominal and measured conductivity while ensuring that the sum of the charges in the treatment fluid is zero, and the nominal concentration is then scaled by the percent water content.

[0095] Returning to PD device 1 of Figure 1, data samples Kd0*, Kd1*, etc. may be measured by sensor 14 or other conductivity sensors in the PD device. If the same treatment fluid is used in all fluid exchange cycles, it may be sufficient to obtain only one measurement data sample of the conductivity of the unused treatment fluid.

[0096] Figure 7B shows an example of data samples for a long test procedure. Compared to Figure 7A, Kp3* has been moved from the dwell phase after the first fluid exchange cycle to the dwell phase after the last completed fluid exchange cycle. Data samples Kd12*, Kd13*, Kd14*, Kd15*, and Kd16* correspond to Kd1* and may be omitted if the treatment fluid is the same between fluid exchange cycles. Data samples Kp22*, Kp23*, Kp24*, Kp25*, and Kp26* correspond to Kp2* and are measured at each fluid exchange cycle.

[0097] Returning to the PD device 1 of FIG. 1 , data samples may be measured by sensor 14. It will be appreciated that data sample Kp2* during the drain phase can be readily obtained by having sensor 14 measure the conductivity of the treatment fluid (waste fluid) while it is being pumped from the peritoneal cavity through drain line 13. The same is true for data sample Kp0*. Other data samples Kp1* and Kp3* may be obtained by operating APD cycler 3 to draw small amounts of treatment fluid from the peritoneal cavity via patient line 12 into drain line 13 for measurement by sensor 14. Alternatively, data samples Kp0*, Kp1*, and Kp3* may be measured by other conductivity sensors in the fluid circuit of PD device 1. The extraction of treatment fluid results in a slight loss of treatment fluid within the peritoneal cavity, represented by Js(t) in FIG. 3 and shown as a slight decrease in Vp at each measurement time in FIGS. 7A-7B.

[0098] The number of data samples acquired during the test procedure and used to calculate the state parameters depends on the required accuracy (confidence) of the state parameters, the number of state parameters, and possibly the type of state parameter being calculated. If a single state parameter is being calculated, two data samples may be sufficient. The timing of the data samples may depend on the state parameter being calculated. It is currently believed that the use of data samples Kp2* and Kp3* allows for the calculation of any one of the state parameters PSi, LpS, fCpw, or Vres, as shown, for example, in FIG. 7A. By including one or both of Kp0* and Kp1*, for example, as shown in FIG. 7A, accuracy may be significantly improved and / or additional state parameters may be calculated. It will also be appreciated that accuracy may be improved by performing calculations for a test procedure that includes more than one complete exchange phase, as shown, for example, by one or more of data samples Kd12*, Kd13*, Kd14*, Kd15*, Kd16*, Kp22*, Kp23*, Kp24*, Kp25*, Kp26* in Figure 7B, and by taking additional data samples.

[0099] Because data samples are simplest to acquire during the drain phase, it may be desirable to use Kp2* whenever possible. As discussed above, two or more time-separated Kp2* samples may be used in the calculation. Surprisingly, it has been found that the accuracy of the state parameter can be increased by generating residual data 53A (FIG. 5B) to include not only the difference between one Kp2* sample and the corresponding simulated Kp2 sample, but also the difference between the measured temporal change in conductivity during the drain phase and the corresponding simulated temporal change. In one example, the measured temporal change is given by the difference in conductivity between two Kp2* samples divided by the time between the Kp2* samples.

[0100] Note that Kp1* does not have to be taken at the first exchange cycle, as shown in FIG. 7B, but may be taken at any subsequent exchange cycle. However, as seen in the top graph of FIG. 7B, for example, the change in measured conductivity between the drain phase and the subsequent dwell phase is expected to decrease during the test procedure. It is currently believed that the calculation can be improved by obtaining Kp1* at the first exchange cycle, thereby maximizing the change in measured conductivity.

[0101] Figures 8A-8D present simulation results generated for the implementation shown in Figures 5A-5C using candidate property data including PSi, LpS, fCpw, and Vres, where PSi is represented by the scale factor fPS described above. Simulations were performed for three different glucose compositions of virgin treatment solution: 1.36% w / v, 2.27% w / v, and 3.86% w / v glucose, respectively. Simulations were performed with a random error of 0.01 mS / cm (standard deviation) added to the simulated measured conductivity and a random error of 0.45% (standard deviation) added to the nominal flow rate. Simulations were performed for the short test procedure (Figure 7A) and the long test procedure (Figure 7B) for patients belonging to four different transporter types: H, HA, LA, and L.

[0102] The box plots in Figure 8A show the relative error (RE) of PSi for glucose for each transporter type when calculated for a short test procedure. For each transporter type, the boxes represent unused treatment solutions with glucose contents of 1.36%, 2.27%, and 3.86%, from left to right. Reasonable precision is achieved for all transporter types and all glucose contents, with better precision for the H and HA transporters. The box plots in Figure 8B correspond to Figure 8A but are calculated for a longer test procedure. As can be seen, precision in PSi improves significantly with longer test procedures.

[0103] The box plots in Figure 8C show the relative error (RE) in LpS for each transporter type when calculated for the long test procedure. As can be seen, good precision can be achieved for all transporter types and glucose contents.

[0104] The box plots in Figure 8D show the relative error (RE) in fCpw for each transporter type when calculated for the long test procedure. As can be seen, good precision can be achieved for all transporter types and glucose contents.

[0105] The box plots in Figure 8E show the absolute error (mL) in Vres for each transporter type when calculated for a short test procedure (indicated by I) and a long test procedure (indicated by II). As can be seen, good accuracy can be achieved for all transporter types and glucose contents in both scenarios. The box plots also show, in Figure 8E, the absolute error (mL) in Vres when calculated according to the conventional dilution formula for different transporter types and glucose contents. The dilution formula calculates Vres according to the measured conductivity of the treatment solution during the drain phase, the measured conductivity of the treatment solution at the completion of the subsequent fill phase, the conductivity of the unused treatment solution, and the amount of treatment solution infused during the fill phase. Examples of such calculations can be found, for example, in the aforementioned European Patent Application Publication No. 2623139. The dilution formula does not account for the fact that transport across the peritoneal membrane is always ongoing, i.e., even during the fill phase, and is therefore very sensitive to transporter type and glucose content. This dependence is clearly seen in Figure 8E, making the dilution equation largely unreliable for determining Vres.

[0106] The testing procedures and calculation techniques described herein have many advantages. The testing procedures and calculation techniques may be automated and performed at the point of care. Furthermore, the testing procedures may be performed as part of a routine, prescribed treatment. This is in stark contrast to standard PET, which is a four-hour procedure that requires a specific composition of virgin treatment fluid, regardless of the patient's prescription.

[0107] Computational techniques can determine small solute transport properties of the peritoneal membrane, such as the permeability coefficient and surface area (PSi). Standard PET produces small solute transport properties such as D / D0 glucose and D / P creatinine for classification into H / HA / LA / L, which provides significantly less information about the state of the peritoneal membrane. Furthermore, computational techniques can be configured to account for both charged and uncharged solutes, whereas standard PET measures only glucose and creatinine, both of which are uncharged.

[0108] Calculation techniques can determine the filtration capacity of water through the peritoneal membrane, such as the ultrafiltration rate (LpS). Standard PET has limited ability to consider ultrafiltration characteristics when classifying a patient's fluid transport as H / HA / LA / L. Standard PET involves measuring the total drained volume, which, after subtracting the infused volume, can be used to determine the patient's fluid transport rate. However, due to the lack of accurate information regarding residual volume and measurement error in determining the infused and withdrawn volumes of fluid, the margin of error is large. Similarly, standard PET does not provide information regarding residual volume unless additional, non-traditional procedures, such as measuring component concentrations before and after infusion, are added to the standard PET test. However, the calculation techniques presented herein can determine the volume of therapeutic fluid in the peritoneal cavity at any time, for example, at the completion of any drain cycle.

[0109] The testing procedures and calculation techniques may be performed frequently, for example, daily. Daily testing allows for assessment of the progression of the peritoneal condition over both long and short time scales, for example, to detect possible membrane failure. Standard PET is performed at most twice a year.

[0110] Computational techniques can determine a patient's tonicity, which is a measure of overall plasma electrolyte concentration. Tonicity can be a useful property for diagnosing and / or classifying patients, and for optimizing prescriptions or detecting shifts. Standard PET does not provide such information.

[0111] FIG. 9 is a block diagram of an additional module 90 that may be included in the PT device 50 to simulate a standard PET procedure based on the state parameters 55 calculated by the PT device 50. The standard PET procedure may be any PET procedure provided by a standardized protocol (regimen, sampling, etc.), including, but not limited to, a standard PET. The simulation module 90 may include a PTM 52′, which may be configured to operate on test regimen data specific to a standard PET procedure, including the timing and number of exchange cycles, the flow rate as a function of time through the access 12′, and the composition of unused therapy fluid, for a patient whose characteristics are according to the state parameters calculated by the PT device 50. The PTM 52′ in the module 90 thereby operates to simulate the concentrations of solutes in the therapy fluid in the peritoneal cavity, extract concentration values of specific solutes at specific time points, and calculate output parameters of the standard PET procedure, such as D / D and D / P of the standard PET. The parameter D / D indicates the relative change in glucose concentration in the therapy fluid in the peritoneal cavity between a starting time point and a specific time point. The parameter D / P may indicate the concentration ratio of creatinine at a particular time point between the intraperitoneal treatment fluid and the patient's plasma. Alternatively, or in addition, the parameter D / P may be calculated for urea. The calculated parameter values of D / D and / or D / P at one or more particular time points may be presented to a physician wishing to evaluate the status of a patient and their peritoneal membrane based on a testing procedure, for example, in lieu of or in addition to status parameters. Figures 10A-10B are plots of D / D and D / P for creatinine generated by simulation module 90 including PTM 52' described herein for a standard PET testing regimen for H, HA, LA, and L transporter types.

[0112] In some embodiments, the PT device 50 is further configured to process one or more state parameters calculated for the test procedure to detect potential failure of the peritoneal membrane, for example, due to inflammation and / or infection, also known as peritonitis. Early detection of membrane failure allows medical personnel to address peritonitis early and avoid dropout from PD therapy, which would be beneficial to the patient's health. Upon detection of a potential failure, the PT device 50 can be configured to generate a dedicated alarm, for example, by activating a visual, audio, or tactile feedback device, and / or generate an alert, such as, for example, an electronic message to a physician, via any suitable communication channel. Any one of PSi, LpS, Vres, and fCpw, or any combination thereof, can be monitored to detect membrane failure.

[0113] In some embodiments, the PT device 50 is configured to analyze trends in one or more condition parameters to detect changes and evaluate the changes to detect potential failures. Trends can extend over several test procedures, and changes can be sudden, e.g., gradual, or appear over a longer period of time, e.g., a week or more.

[0114] In some embodiments, a potential failure may be detected whenever the value of the state parameter exceeds a threshold, which may be set relative to one or more previously calculated values of the state parameter, e.g., relative to a moving average. In one example, a potential failure may be detected when the value of the state parameter exceeds the moving average by at least 20%, 30%, 40%, or 50%. To illustrate the effect of a 50% increase in PSi, FIGS. 11A-11B show simulated conductivity values of intraperitoneal treatment fluid, with a normal PSi value designated K1 and a 50% increase in the normal PSi value designated K2, where FIG. 11A is given for the H transporter and FIG. 11B is given for the L transporter. It is understood that membrane failure is likely to result in a significant change in the measured conductivity of the treatment fluid, which is therefore likely to be detectable from the state parameter calculated by PT device 50.

[0115] FIG. 6B is a flowchart of an exemplary monitoring method 610 for membrane failure. The exemplary method 610 can be performed by the PT device 50 and includes a two-stage evaluation. In the first stage, one or more state parameters of the current test procedure are determined according to the exemplary method 600 of FIG. 6A, but only for a subset of the fluid exchange cycles of the current test procedure. Step 611 evaluates the trend of the state parameter to detect a first temporal change. If a first temporal change is not detected, step 612 proceeds to the end of method 610. If a first temporal change is detected, step 612 proceeds to a second stage evaluation, in which the same or any other state parameter is determined according to the exemplary method 600 of FIG. 6A, including conductivity measurements in additional exchange cycles of the current test procedure. As will be appreciated from the above, increasing the number of fluid exchange cycles improves the accuracy of the calculated state parameter. Step 613 evaluates the trend of the state parameter to detect a second temporal change using criteria that may be the same or different from the criteria used to detect the first temporal change in step 611. If the second temporal change is not detected, step 614 proceeds to end method 610. Otherwise, step 614 proceeds to generate an alarm. In some embodiments, step 611 is configured to detect the first temporal change as a gradual change in the state parameter occurring in the current test procedure. Because such gradual changes may be caused by measurement error, the second stage of evaluation reduces the risk of generating a false alarm.

[0116] As can be seen from the above discussion, the computational techniques described herein, using mathematical peritoneal transport models, can be applied to estimate IPV during PD treatment. This information can be monitored to ensure IPV is maintained within limits during PD treatment. Given that the residual volume within the PC can change after a complete drain or if a complete drain is not performed, such as during tidal therapy, IPV is largely unknown in modern APDs. Therefore, there is a risk of overfilling the patient. To mitigate this risk, complete drains are frequently performed during PD treatment. However, this can increase the incidence of so-called "drain pain," a common complication of APD. The cause of drain pain is not fully understood by the scientific community. Some believe that drain pain is caused by the application of negative pressure to the highly sensitive peritoneal membrane of the body cavity wall shortly before the end of each drain cycle. This results in associated pain, often felt very uncomfortable, in the rectal or genital area. Others speculate that drain pain is related to negative suction of the external intestinal wall. Regardless of the cause, managing drain pain is challenging.

[0117] Referring to FIG. 7A , the applicant has found that it is possible to determine the IPV after a fill phase based on at least a data sample collected during the drain phase (see Kp0* or Kp2*) and a data sample collected after a subsequent fill phase, e.g., a subsequent dwell phase (see Kp1* or Kp3*). Based on the IPV, the residual volume after the preceding drain phase can be determined. Through this type of calculation, for example, in the test procedure of FIG. 7B , the residual volume after one drain phase can be estimated and taken into account in the subsequent fill phase by adjusting the infusion volume of the APD cycler to avoid overfilling, and by adjusting the drain volume of the APD cycler in the subsequent drain phase to reduce the risk of drain pain. This calculation may be repeated for each sequence of drain and fill phases in a series of exchange cycles. This makes it possible to intermittently estimate the residual volume, for example, during a test procedure including a series of exchange cycles for each exchange cycle. The applicant has also found that it is possible to omit all but one of the data samples collected during the dwell phase. Thus, the residual volume after each drain phase in a series of exchange cycles can be estimated intermittently during the test procedure by using data samples taken during the drain phase (e.g., Kp0*), data samples taken during a dwell phase after a subsequent fill phase (e.g., Kp1*), and data samples taken during at least one subsequent drain phase in the series of exchange cycles (e.g., Kp2*, Kp22*, Kp23*, etc.).

[0118] Thus, in some embodiments, the PT device (50 in FIG. 5A) can be configured to provide an estimated IPV, e.g., Vres, during an exchange cycle for receipt by the APD cycler (3 in FIG. 1), and thereby perform adjustments of at least one of the drain and fill phases of the subsequent exchange cycle based on the estimated IPV. The adjustments can involve decreasing or increasing the infusion rate of the therapy fluid.

[0119] As described above, PD therapy may begin with an initial drain if the patient is assumed to have a large volume of therapy fluid in the PC when connected to the APD cycler. In some embodiments, the residual volume is estimated by the above-described calculation based at least on data samples taken during the initial drain and data samples taken after the subsequent fill phase. This may further reduce the risk of overfilling and / or drain pain during PD therapy.

[0120] The applicant has further realized that the risk of overfilling and / or drain pain can be further reduced by providing an initially limited probing cycle, hereinafter abbreviated as IRPC. IRPC, in turn, includes a drain phase and a fill phase. IRPC may be performed by an APD cycler prior to PD treatment and is "limited" in that only a portion of the maximum fill volume is extracted from the peritoneal cavity. The maximum fill volume may be measured for the patient or a general value. Based on two or more data samples of conductivity (or concentration) collected during IRPC, the intraperitoneal volume (IPV) may be calculated by use of a mathematical peritoneal transport model, for example, based on the formula in Appendix A and / or the governing function described above. An estimate of IPV is thereby available at the very beginning of PD treatment. This allows the APD cycler to automatically adjust its operation based on the thus estimated IPV, reducing the risk of drain pain and / or overfilling.

[0121] An example of an IRPC and its use is described with reference to FIG. 12A, which shows IPV (Vp) as a function of time. When a patient is connected to an APD cycler, the residual volume is assumed to be unknown. IRPC begins with a drain phase, in which the APD cycler is activated to drain a limited amount of therapy fluid, indicated by ΔVp, from the PC. In some embodiments, ΔVp is set to limit the risk of drain pain. For example, ΔVp can be set equal to or less than a nominal residual volume, which can be determined for a patient or group of patients, e.g., based on historical data. In some embodiments, ΔVp is set to 10% to 25% less than the maximum fill volume. In some embodiments, ΔVp is in the range of 50 to 400 mL or 100 to 300 mL. The drain phase is followed by a fill phase, in which unused therapy fluid is infused into the PC. To avoid the risk of overfilling, the amount of therapy fluid infused may be set to be equal to or less than ΔVp. During IRPC, the first data sample Kpi0* is provided by one or more measurements during the drain phase, similar to Kp0* and Kp2* described above. The second data sample Kpi1* is taken at a predetermined time period Δti after completion of the fill phase. Δti may range from 0 to 20 minutes or from 0 to 10 minutes. The second data sample Kpi1* may be obtained by operating the APD cycler to drain a small amount of therapy fluid from the PC, similar to Kp1* and Kp3* described above. Kpi1* thus results in a small loss of therapy fluid in the PC, as shown in FIG. 12A. After a short calculation time, at time C1, the IPV (Vp) is calculated based on the extracted volume (ΔVp), the infused volumes Kpi0* and Kpi1*. In the illustrated example, the IPV is found to be large, and the APD cycler is operated to perform an initial drain according to conventional practice. However, the initial drain is controlled to reduce drain pain, for example, by setting the drain volume to ensure that the PC is not completely emptied by the initial drain.Similarly, the injection volume in the subsequent fill phase is set based on the estimated IPV and drain volume to avoid overfilling. As shown in FIG. 12A, the APD cycler may then proceed with the test procedure according to the example of FIGS. 7A-7B by performing one or more exchange cycles while acquiring additional data samples, e.g., Kp0*, Kp1*, etc. The calculations for the test procedure may generate one or more of the state parameters described above. As noted above, it may be sufficient to acquire data samples during the drain phases, e.g., Kp0*, Kp2*, Kp22*, etc., to determine the residual volume after each drain phase.

[0122] FIG. 12B illustrates another example of the use of IRPC. FIG. 12B differs from FIG. 12A in that the patient begins with a small IPV. This is detected at time C1 by the same calculation as in FIG. 12A. It is understood that the collection of a limited volume (ΔVp) reduces the risk of drain pain during IRPC. In the illustrated example, because the IPV is found to be small, the APD cycler operates to perform the fill phase. Furthermore, because the IPV at time C1 is known, the fill phase can be controlled to prevent overfilling. The fill phase may be part of a testing procedure as described above, and at least one of the aforementioned state parameters may be generated based on a set of data samples collected during the testing procedure. For example, Kp1* in FIG. 12B may (but need not) be included in the set of data samples.

[0123] As can be seen from FIGS. 12A-12B, operation of the APD cycler can be controlled based on the IPV estimated for IRPC. In some embodiments, the APD cycler is configured to perform a fill phase after IRPC if the estimated IPV is below a limit value (see FIG. 12B) and to perform a drain phase if the estimated IPV exceeds the limit value (see FIG. 12A). In one example, the limit value is in the range of 300-700 mL. The limit value may be patient-specific, e.g., set by a caregiver.

[0124] Thus, in some embodiments, the PT device (50 in FIG. 5A) is configured to provide output data representing the estimated IPV, e.g., Vres, for receipt by the APD cycler (3 in FIG. 1), thereby selectively initiating a drain phase or a fill phase after the IPRC depending on the estimated IPV.

[0125] As described above, the calculation of IPV can be repeated during the testing procedure, resulting in calculated IPV values at discrete points during the testing procedure. Such an embodiment allows IPV to be tracked over time, for example, to ensure that IPV remains within limits. This can be important when the transport properties of the peritoneal membrane are not yet known or when the transport properties are suspected to be drifting, for example, as a result of infection.

[0126] It should be understood that the calculation of IPV may further be based on Kd0* (see FIGS. 7A-7B), i.e., the measured conductivity / concentration of the unused treatment fluid, instead of the nominal conductivity / concentration.

[0127] In some embodiments, the calculation of IPV for IRPC is performed as described above, but with one or more transport properties of the peritoneal membrane set to fixed values. Thus, in some embodiments, the PT device is configured to set one or more transport properties of the peritoneal membrane to fixed values for inclusion in the mathematical peritoneal transport model (52′ in FIG. 5A) when calculating IPV. Those skilled in the art will appreciate that assuming a fixed value for at least one of Psi, LpS, and fCpw significantly simplifies the calculation. For example, the complexity of the parameter fitting algorithm (53′ in FIG. 5B) may be reduced. The fixed value may be provided by historical values for the patient, e.g., generated by calculation of one or more prior test procedures. Alternatively, or additionally, the fixed value may be provided by a general transport property, e.g., an average value for a patient group. It is also conceivable to adjust such general transport properties for the patient using information from a standard PET performed on the patient.

[0128] Instead of using a mathematical peritoneal transport model to calculate residual volume, as described above, residual volume may be estimated by using a conventional dilution equation. Such estimation may be performed, for example, for any sequence of drain and fill phases during IRPC or during conventional PD therapy. As described above, such an equation calculates residual volume as a function of three conductivity values: the conductivity of the therapy solution during the drain phase, the conductivity of the therapy solution at the completion of the subsequent fill phase (optionally after a delay period), and the conductivity of the unused therapy solution.

[0129] Applicants also envision the further use of IPV, estimated by the use of mathematical peritoneal transport models or dilution equations, for either IRPC prior to PD treatment or the first exchange cycle of PD treatment.

[0130] One such additional application is modifying the composition of unused therapy fluid based on the residual volume Vres given by the estimated IPV. The unused therapy fluid mixes with the residual volume in the PC, diluting the therapy fluid and potentially reducing the efficacy of the therapy. In some embodiments, the APD cycler includes an algorithm that calculates an adjusted composition of the therapy fluid based on the estimated Vres, which, when mixed with the residual volume inside the PC, results in approximately the prescribed composition. The APD cycler may further be configured to acquire and infuse therapy fluid using the adjusted composition during one or more fill phases after the IRPC. Accordingly, in some embodiments, the PT device (50 in FIG. 5A) is configured to provide output data representing Vres for receipt by the APD cycler (3 in FIG. 1), thereby adjusting the composition of unused therapy fluid produced by the APD cycler to account for the dilution of the unused therapy fluid by the residual fluid in the PC given by Vres. The composition may be adjusted to at least partially address the dilution.

[0131] Another application is detecting peritoneal access problems based on the residual volume (Vres) given by the estimated IPV after complete drainage. An access problem results in an abnormal increase in Vres, and therefore, if Vres exceeds a patient safety limit, e.g., a range of 600-900 mL, it can be detected and alerted by the APD cycler. Access problems can be caused by catheter blockage or migration, or by constipation. Note that detection of an access problem can be, but need not be, based on the IPV determined for IRPC. In a variant, an access problem is detected based on Vres determined for a test procedure, e.g., as described with reference to FIGS. 7A-7B. Thus, in some embodiments, the PT device (50 in FIG. 5A) can be configured to evaluate the IPV for the detection of peritoneal access problems and output a warning signal to alert a caregiver upon detection of an access problem.

[0132] FIG. 13 schematically illustrates a technique for sampling therapeutic fluid from the peritoneal cavity with minimal impact on intraperitoneal volume. Sampling includes an extraction phase D', a measurement phase, and a return phase F'. The duration of sampling, Δt, can be approximately 1 to 5 minutes. During extraction phase D', therapeutic fluid is drawn from the peritoneal cavity through the lines of the PD device to the conductivity sensor (see FIG. 1). The extracted volume of therapeutic fluid is ΔV1 + ΔV2, e.g., 200 mL. During return phase F', at least a portion of the extracted volume is pumped back into the peritoneal cavity, as indicated by ΔV2 in FIG. 13. By performing return phase F', only ΔV1 is removed, thereby minimizing the impact of sampling on intraperitoneal volume. Returning to FIG. 3, note that Js(t) can represent the flow rate of therapeutic fluid as a function of time during sample extraction when Js(t) is input into the PT device 50 as part of the test regimen data (54A in FIG. 5A).

[0133] The PT device 50 described herein may be part of a PD device integrated with, for example, an APD cycler. Alternatively, the PT device 50 may be implemented on a device separate from the PD device 1. Such a device may be a local or remote computing device, and may or may not be located in the cloud. Furthermore, the PT device 50 may be configured to automatically acquire at least some of the input data shown in FIG. 5A. In some embodiments, the PT device 50 may be configured and connected to acquire measurement data (54B in FIG. 5A) from the conductivity sensor 14 or the APD cycler 3. In other embodiments, the measurement data 54B may be manually input by an operator via an input unit connected to the PT device 50.

[0134] 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 involve at least one fluid exchange cycle that includes a fill phase, a dwell phase, and a drain phase.

[0135] 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 schematically illustrates such a computer resource 200, comprising a processing system 201, computer memory 202, and a communication interface or circuitry 203 for data input and / or output. The communication interface 203 may be configured for wired and / or wireless communication. As understood from the above, the computer resource 200 may or may not be part of an APD cycler. The processing system 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 comprising computer instructions is stored in memory 202 and executed by processing system 201 to implement logic that performs any of the methods, procedures, functions, or steps described above. Control program 202A may be provided to computer resource 200 on computer-readable medium 205, which may be a tangible (non-transitory) article of manufacture (e.g., magnetic media, optical disk, read-only memory, flash memory, etc.) or a propagated signal. As shown in FIG. 14, memory 202 may also store control data 202B used by processing system 201, such as all or a portion of test regimen data 54A, treatment history data 56A, general patient data 56B, patient-specific data 56C, or solute characteristic data 56D (FIG. 5A).

[0136] 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 to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements that come within the meaning and range of equivalents of the appended claims.

[0137] Additionally, although acts are shown in the figures in a particular order, this should not be understood as requiring such acts to be performed in the particular order shown, or in any sequential order, or that all of the shown acts be performed, to achieve desirable results.

[0138] Below, items are listed to summarize some aspects and embodiments disclosed above.

[0139] Item 1 1. An apparatus for determining at least one state parameter of a person (P) undergoing peritoneal dialysis, the apparatus receiving first data (54A) indicative of a flow rate as a function of time of a therapy fluid entering and leaving a peritoneal cavity (31) via a peritoneal access of the person (P) during one or more fluid exchange cycles, each fluid exchange cycle including a fill phase, a dwell phase, and a drain phase, and second data (54B) including measurement data samples ([Kp*]) representing concentrations of one or more solutes in the therapy fluid in the peritoneal cavity (31) at two or more points during the one or more fluid exchange cycles. a first computing module (52) configured to compute estimated data samples ([Kp]) representative of the concentrations of the one or more solutes in the therapeutic fluid in the peritoneal cavity (31) at the two or more time points based on the first data (54A) and using a mathematical model (52') of water and solute transport through a peritoneal membrane (30) in the peritoneal cavity (31); and a second computing module (53) configured to determine the at least one state parameter in response to the measured data samples ([Kp*]) and the estimated data samples ([Kp]).

[0140] Item 2 2. The device according to item 1, wherein the at least one state parameter comprises a transport property of the peritoneum (30).

[0141] Item 3 3. The apparatus according to claim 1, wherein the second calculation module (53) comprises a parameter fitting algorithm (53′) and is operable to determine candidate values (53B) for each of a set of parameters included in the mathematical model (52′) in order to minimize one or more differences between the measured data samples ([Kp*]) and the estimated data samples ([Kp]) by using the parameter fitting algorithm (53′), the set of parameters including the at least one state parameter.

[0142] Item 4 Item 3. The apparatus according to item 3, wherein the second calculation module (52) is further configured to calculate measured and estimated temporal changes in conductivity during the drain phase of the one or more liquid exchange cycles according to the measured data samples ([Kp*]) and the estimated data samples ([Kp]), and the second calculation module (52) is operable to determine the respective candidate values (53B) to further minimize the difference between the measured and estimated temporal changes by using the parameter fitting algorithm (53′).

[0143] Item 5 5. The device according to claim 3 or 4, wherein the set of parameters represents the diffusion capacity of one or more solutes through the peritoneal membrane (30) and the filtration capacity of water through the peritoneal membrane (30).

[0144] Item 6 6. The device according to item 4 or 5, wherein the set of parameters further represents the tonicity (fCpw) of the person (P).

[0145] Item 7 7. The apparatus according to any one of items 2 to 6, wherein the first calculation module (52) is configured to iteratively calculate the estimated data sample ([Kp]) based on the respective candidate values (53B) of the set of parameter values determined by the second calculation module (53), the second calculation module (53) is configured to iteratively determine the respective candidate values (53B) of the set of parameters based on the estimated data sample ([Kp]) from the first calculation module (52), and the second calculation module (53) is configured to output the at least one state parameter when a convergence criterion is met or a time limit is reached.

[0146] Item 8 8. The device according to any one of claims 1 to 7, wherein the first calculation module (52) is further configured to calculate a time sequence of an estimated volume of therapeutic fluid in the peritoneal cavity (31) for a period during the one or more fluid exchange cycles based on the first data (54A) and by using the mathematical model (52′).

[0147] Item 9 9. The device according to any one of items 1 to 8, wherein the mathematical model (52') is a three-pore model for transport through the peritoneum (30).

[0148] Item 10 10. The apparatus according to any one of claims 1 to 9, wherein the mathematical model (52') is configured to take into account electrostatic force-driven ion transport across the peritoneal membrane (30) due to differences in the amount of dissolved ions on either side of the peritoneal membrane (30) and repulsion of highly charged solutes by the peritoneal membrane (30).

[0149] Item 11 11. The device according to any one of claims 1 to 10, further configured to evaluate the at least one condition parameter for detection of a potential insufficiency of the peritoneum (30) and to generate an alarm or warning upon detection of the potential insufficiency.

[0150] Item 12 Item 12. The apparatus of item 11, configured to analyze trends in the at least one condition parameter for detection of changes and evaluate the changes for detection of the potential failure.

[0151] Item 13 Item 12. The device of item 11, configured to analyze a trend of the at least one condition parameter to detect a first temporal change, determine the at least one condition parameter with high accuracy upon detection of the first temporal change, include the at least one condition parameter with the high accuracy in the trend, analyze the trend to detect a second temporal change, and evaluate the second temporal change to detect the potential failure.

[0152] Item 14 Item 14. The apparatus according to item 13, configured to determine the at least one state parameter with increased accuracy by increasing the number of exchange cycles included in the first data (54A) and used by the first calculation module (52) to calculate the estimated data sample ([Kp]).

[0153] Item 15 15. The apparatus according to any one of claims 1 to 14, further comprising a simulation module (90) configured to calculate at least one of a concentration ratio of at least one of urea and creatinine between the treatment fluid in the peritoneal cavity (30) and the person's plasma (D / P) and a relative change in concentration of glucose in the treatment fluid in the peritoneal cavity (30) from a start of the one or more fluid exchange cycles (D / D0) in response to the at least one state parameter provided by a standardized PET procedure and one or more time points.

[0154] Item 16 16. The device according to any one of claims 1 to 15, wherein the second data (54B) further comprises one or more measurement data samples (Kd0*, Kd1*, Kd12*, Kd13*, Kd14*, Kd15*, Kd16*) representing concentrations of the one or more solutes in the therapeutic fluid infused into the peritoneal cavity (30) during the filling phase of the one or more fluid exchange cycles.

[0155] Item 17 17. The apparatus according to any one of items 1 to 16, wherein the first calculation module (52) comprises a differential equation solving sub-module (71) configured to calculate a volume (71B) of treatment fluid in the peritoneal cavity (31) from an initial time point to an end time point, including intermediate time steps, and to calculate concentrations (71B) of one or more solutes in the treatment fluid in the peritoneal cavity (31) from the initial time point to the end time point, including intermediate time steps.

[0156] Item 18 Item 18. The apparatus according to item 17, wherein the differential equation solving sub-module (71) is configured to calculate, for each time step, the amount (71B) of the treatment fluid in the peritoneal cavity (31) based on a preceding temporal change (74B) of the amount of the treatment fluid in the peritoneal cavity (31), and to calculate, for each time step, the concentrations (71B) of the one or more solutes in the treatment fluid in the peritoneal cavity (31) based on a preceding temporal change (75B) of the concentrations of the one or more solutes in the treatment fluid in the peritoneal cavity (31).

[0157] Item 19 Item 19. The apparatus according to item 18, wherein the first calculation module (52) further comprises a first change calculation system (72, 74) configured to calculate a temporal change (74B) in the amount of the therapeutic fluid in the peritoneal cavity (31) for each time step in response to the concentrations (71B) of the one or more solutes in the therapeutic fluid in the peritoneal cavity (31) calculated by the differential equation solving sub-module (71) for each time step and the amount (71B) of the therapeutic fluid in the peritoneal cavity (31) calculated by the differential equation solving sub-module (71) for each time step.

[0158] Item 20 20. The apparatus according to item 19, wherein the first change calculation system (72, 74) comprises a first flow rate calculation sub-module (72) configured to calculate a water flow rate (72B) through the peritoneal membrane (30) for each time step, depending on the amount (71B) of the therapeutic fluid in the peritoneal cavity (31) calculated by the differential equation solving sub-module (71) for each time step, and the first change calculation system (72, 74) further comprises a first change calculation sub-module (74) configured to calculate the temporal change (74B) of the amount of the therapeutic fluid in the peritoneal cavity (31) depending on the water flow rate (72B) through the peritoneal membrane (30).

[0159] Item 21 21. The device according to item 19 or 20, wherein the first calculation module (52) further comprises a second change calculation system (73, 75) configured to calculate a temporal change (75B) in the concentration of the one or more solutes in the treatment fluid in the peritoneal cavity (31) for each time step, in response to the concentration (71B) of the one or more solutes in the treatment fluid in the peritoneal cavity (31) calculated by the differential equation solving sub-module (71) for each time step and the amount (71B) of the treatment fluid in the peritoneal cavity (31) calculated by the differential equation solving sub-module (71) for each time step.

[0160] Item 22 Item 21. The apparatus according to item 21, wherein the second change calculation system (73, 75) comprises a second flow rate calculation sub-module (73) configured to calculate a flow rate (73B) of the one or more solutes through the peritoneum (30) for each time step in response to the concentration (71B) of the one or more solutes in the treatment fluid in the peritoneal cavity (31) calculated by the differential equation solving sub-module (71) for each time step and the amount (71B) of the treatment fluid in the peritoneal cavity (31) calculated by the differential equation solving sub-module (71), and the second change calculation system (73, 75) the flow rate (73B) of the one or more solutes through the peritoneal membrane (30), the flow rate (72B) of water through the peritoneal membrane (30), the concentration (71B) of the one or more solutes in the treatment fluid in the peritoneal cavity (31) calculated by the differential equation solving submodule (71) for each time step, and the amount (71B) of the treatment fluid in the peritoneal cavity (31) calculated by the differential equation solving submodule (71) for each time step.

[0161] Item 23 23. The apparatus of any one of items 1 to 22, wherein the measurement data sample ([Kp*]) includes a measured conductivity value.

[0162] Item 24 24. The apparatus according to any one of claims 1 to 23, wherein the first computing module (52) is configured to generate a time sequence of estimated concentration values ([Cpi]) of at least one solute in the treatment fluid in the peritoneal cavity (31), convert the time sequence of estimated concentration values ([Cpi]) into a time sequence of conductivity values, and determine the estimated data sample ([Kp]) from the time sequence of conductivity values.

[0163] Item 25 25. An apparatus according to any one of items 1 to 24, wherein the one or more liquid exchange cycles include two consecutive exchange cycles, and the two or more points in time include a point in the drain phase of a first liquid exchange cycle and a point in the dwell phase of a second liquid exchange cycle following the first exchange cycle.

[0164] Item 26 26. The device of claim 25, wherein the two or more time points further include at least one of a time point during the residence phase of the first liquid exchange cycle and a time point during a drain phase preceding the first liquid exchange cycle.

[0165] Item 27 27. The device according to any one of items 1 to 26, wherein the input (51) is further arranged to receive fluid exchange data of the person (P) in a preceding period prior to the one or more fluid exchange cycles, the device being further configured to estimate an initial concentration of one or more solutes in the treatment fluid in the peritoneal cavity (31) at a start time of evaluation based on the fluid exchange data, and the first calculation module (52) is configured to calculate the estimated data sample ([Kp]) based on the initial concentrations.

[0166] Item 28 28. The device according to any one of claims 1 to 27, wherein the at least one state parameter comprises at least one of a diffusion capacity of solutes through the peritoneal membrane (30) and a filtration capacity of water through the peritoneal membrane (30).

[0167] Item 29 29. The device of claim 28, wherein the solute diffusion capacity comprises the product of the permeability coefficient and surface area (PSi) of the drug in the therapeutic solution, and the water filtration capacity comprises the hydraulic conductance (LpS).

[0168] Item 30. 30. The apparatus according to any one of the preceding claims, wherein the at least one condition parameter comprises a volume of therapeutic fluid in the peritoneal cavity (31) at a selected time.

[0169] Item 31 31. The apparatus of claim 30, wherein the selected time point corresponds to the completion of the drain phase of at least one of the one or more liquid exchange cycles.

[0170] Item 32 Item 32. The apparatus according to item 30 or 31, wherein the measurement data samples of the second data (53B) include a first data sample taken during a drain phase and a second data sample taken after completion of a fill phase following the drain phase, and wherein the second calculation module (53) is configured to determine a volume of the therapeutic fluid in the peritoneal cavity (31) at the selected time point.

[0171] Item 33 Item 33. The device according to item 32, wherein the first data sample and the second data sample are collected during an initial probing cycle (IRPC) that includes the drain phase and the fill phase and in which a limited volume (ΔVp) of therapeutic fluid within the peritoneal cavity (31) is extracted during the drain phase, the limited volume (ΔVp) corresponding to a portion of a maximum filling volume of the peritoneal cavity (31).

[0172] Item 34 Item 34. The device according to item 33, wherein the limited amount is less than 10% to 25% of the maximum fill volume.

[0173] Item 35 35. The device according to item 33 or 34, wherein the limited volume is in the range of 50 to 400 mL or 100 to 300 mL.

[0174] Item 36 36. The apparatus according to any one of claims 33 to 35, wherein a peritoneal dialysis machine (3) operative to perform the peritoneal dialysis is configured to provide output data representative of the volume of the therapy fluid in the peritoneal cavity (31) for receipt by the peritoneal dialysis machine (3), such that a drain phase is initiated after the first probing cycle (IPRC) if the volume of the therapy fluid is above a limit value, and a fill phase is initiated after the first probing cycle (IPRC) if the volume of the therapy fluid is below a limit value.

[0175] Item 37 37. The apparatus according to any one of items 32 to 36, wherein the measurement data samples of the second data (54B) further include data samples taken during a subsequent drain phase, and wherein the second computing module (53) is further configured to determine a volume of the treatment fluid in the peritoneal cavity (31) at the end of the subsequent drain phase.

[0176] Item 38 38. The apparatus according to any one of items 30 to 37, configured to set one or more transport properties of the peritoneal membrane (31) included in the mathematical model (52') to a fixed value when determining the volume of the therapeutic fluid in the peritoneal cavity (31) at the selected time point.

[0177] Item 39 39. The device according to any one of claims 30 to 38, configured to determine and output a volume of the therapy fluid for each fluid exchange cycle intermittently during the peritoneal dialysis.

[0178] Item 40 40. The apparatus of claim 39, wherein a peritoneal dialysis machine (3) operative to perform the peritoneal dialysis is configured to provide output data representing the volume of the therapy fluid for each fluid exchange cycle for receipt by the peritoneal dialysis machine (3) so as to adjust at least one of a drain phase and a fill phase of a fluid exchange cycle subsequent to the respective fluid exchange cycle based on the volume of the therapy fluid for the respective fluid exchange cycle.

[0179] Item 41 41. The device according to any one of items 30 to 40, further configured to evaluate the volume of the therapeutic fluid in the peritoneal cavity (31) at the selected time point to detect a problem with the peritoneal access, and to output a warning signal when the problem is detected.

[0180] Item 42 42. The apparatus according to any one of claims 30 to 41, wherein a peritoneal dialysis machine (3) operating to perform the peritoneal dialysis is configured to provide output data representative of the volume of the therapy fluid in the peritoneal cavity (31) after completion of a drain phase for receipt by the peritoneal dialysis machine (3), such that the composition of the unused therapy fluid produced by the peritoneal dialysis machine (3) is adjusted to account for dilution of the unused therapy fluid by the volume of the therapy fluid in the peritoneal cavity (31).

[0181] Item 43 43. The apparatus according to any one of items 1 to 42, wherein the at least one state parameter includes a tonicity parameter (fCpw) of the person (P).

[0182] Item 44 32. A peritoneal dialysis machine comprising: an extracorporeal fluid circuit (3b) connectable to a peritoneal access of a person (P) for carrying a treatment fluid to or from a peritoneal cavity (31); at least one sensor device (14) disposed within the extracorporeal fluid circuit (3b) and configured to provide data samples representative of concentrations of one or more solutes in the treatment fluid; a control unit (3a) configured to operate the extracorporeal fluid circuit (3b) and to obtain the data samples from the sensor device (14); and the apparatus according to any one of items 1 to 31 connected to receive the first data and the second data from the control unit (3a).

[0183] Item 45 1. A method for determining at least one state parameter of a person undergoing peritoneal dialysis, the method comprising: acquiring (601) first data indicative of a flow rate of a therapy fluid as a function of time into and out of a peritoneal cavity through a peritoneal access of the person during one or more fluid exchange cycles, each fluid exchange cycle including a fill phase, a dwell phase, and a drain phase; acquiring (602) second data including measured data samples representative of concentrations of one or more solutes in the therapy fluid in the peritoneal cavity at two or more time points during the one or more fluid exchange cycles; calculating (603, 603A) estimated data samples representative of the concentrations of the one or more solutes in the therapy fluid in the peritoneal cavity at the two or more time points based on the first data and using a mathematical model of water and solute transport across the peritoneal membrane in the peritoneal cavity; and determining (605) the at least one state parameter in response to the measured data samples and the estimated data samples.

[0184] Item 46 A computer-readable medium comprising computer instructions (202A) that, when executed by one or more processors (201), cause the one or more processors (201) to perform the method described in item 45.

[0185] Item 47 44. A monitoring method comprising: operating a device according to any one of items 1 to 43 to determine the at least one status parameter; and evaluating the at least one status parameter to detect a potential failure of the peritoneum.

[0186] Appendix A The change in intraperitoneal volume is

number

[0187] The total fluid flow in the peritoneum is the sum of the flows through aquaporins (m1), small pores (m2), and large pores (m3).

number

[0188] Area coefficient A f (t) can be introduced to account for the dependence of the effective area for exchange on the volume of fluid in the abdominal cavity. The area coefficient is:

number

[0189] 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 intra-abdominal pressure, which may be assumed to be a function of intra-abdominal volume;

number

[0190] Capillary pressure P cap (t) can be set to depend on the mean arterial pressure MAP and the venous pressure, which can be assumed to be equal to IPP(t).

number

[0191] Osmotic coefficient φ for different solutes i may be given as a table of values, and the restitution coefficients σ for different solutes m,i teeth,

number

[0192] The concentration difference of each solute depends on the solute flow and dilution from the liquid flow over the membrane, as well as dilution during the loading phase,

number

number

number

number

Claims

1. 1. An apparatus for determining at least one status parameter of a person (P) undergoing peritoneal dialysis, comprising: an input (51) for receiving first data (54A) indicative of a flow rate as a function of time of a therapeutic fluid entering or leaving a peritoneal cavity (31) via the person's (P) peritoneal access during one or more fluid exchange cycles, each fluid exchange cycle including a fill phase, a dwell phase, and a drain phase; and second data (54B) including measurement data samples ([Kp*]) representing concentrations of one or more solutes in the therapeutic fluid within the peritoneal cavity (31) at two or more points during the one or more fluid exchange cycles; a first calculation module (52) configured to calculate, based on the first data (54A) and using a mathematical model (52') of water and solute transport through the peritoneal membrane (30) in the peritoneal cavity (31), estimated data samples ([Kp]) representing the concentrations of the one or more solutes in the therapeutic fluid in the peritoneal cavity (31) at the two or more time points; a second calculation module (53) configured to determine the at least one state parameter in response to the measurement data samples ([Kp*]) and the estimated data samples ([Kp]).

2. The device of claim 1 , wherein the at least one state parameter comprises a transport property of the peritoneal membrane (30).

3. 3. The apparatus according to claim 1, wherein the second calculation module (53) comprises a parameter fitting algorithm (53') and is operable to determine candidate values (53B) for each of a set of parameters included in the mathematical model (52') in order to minimize one or more differences between the measured data samples ([Kp*]) and the estimated data samples ([Kp]) by using the parameter fitting algorithm (53'), the set of parameters including the at least one state parameter.

4. 4. The apparatus of claim 3, wherein the first calculation module (52) is further configured to calculate measured and estimated temporal changes in conductivity during the drain phase of the one or more liquid exchange cycles according to the measurement data samples ([Kp*]) and the estimated data samples ([Kp]), and the second calculation module (53) is operable to determine the respective candidate values (53B) to further minimize the difference between the measured and estimated temporal changes by using the parameter fitting algorithm (53').

5. 5. The device according to claim 3 or 4, wherein the set of parameters represents the diffusion capacity of one or more solutes through the peritoneal membrane (30) and the filtration capacity of water through the peritoneal membrane (30).

6. 6. The device according to claim 4 or 5, wherein the set of parameters further represents the tonicity (fCpw) of the person (P).

7. 7. The apparatus according to claim 3, wherein the first calculation module (52) is configured to iteratively calculate the estimated data samples ([Kp]) based on the respective candidate values (53B) of the set of parameters determined by the second calculation module (53), the second calculation module (53) is configured to iteratively determine the respective candidate values (53B) of the set of parameters based on the estimated data samples ([Kp]) from the first calculation module (52), and the second calculation module (53) is configured to output the at least one state parameter when a convergence criterion is met or a time limit is reached.

8. 8. The device of claim 1, wherein the first calculation module (52) is further configured to calculate a time sequence of an estimated amount of therapeutic fluid in the peritoneal cavity (31) for a period during the one or more fluid exchange cycles based on the first data (54A) and by using the mathematical model (52').

9. 9. The device of any one of claims 1 to 8, wherein the mathematical model (52') is a three-pore model for transport through the peritoneal membrane (30).

10. 10. The apparatus of claim 1, wherein the mathematical model is configured to account for electrostatic ion transport across the peritoneal membrane due to differences in the amount of dissolved ions on either side of the membrane and the repulsion of highly charged solutes by the membrane.

11. 11. The device of any one of claims 1 to 10, further configured to evaluate the at least one condition parameter for detection of a potential insufficiency of the peritoneum (30), and to generate an alarm or warning upon detection of the potential insufficiency.

12. 12. The apparatus of claim 11, configured to analyze trends in the at least one condition parameter for detection of changes and evaluate the changes for detection of the potential failure.

13. 12. The apparatus of claim 11, configured to analyze a trend of the at least one condition parameter to detect a first temporal change, determine the at least one condition parameter upon detection of the first temporal change, include the at least one condition parameter in the trend, analyze the trend to detect a second temporal change, and evaluate the second temporal change to detect the potential failure.

14. 14. The apparatus of claim 13, configured to determine the at least one state parameter by increasing the number of exchange cycles included in the first data (54A) and used by the first calculation module (52) to calculate the estimated data sample ([Kp]).

15. 15. The apparatus according to claim 1, further comprising a simulation module (90) configured to calculate at least one of a concentration ratio (D / P) of at least one of urea and creatinine between the treatment fluid in the peritoneal cavity (30) and the person's plasma and a relative change (D / D0) in the concentration of glucose in the treatment fluid in the peritoneal cavity (30) from the start of the one or more fluid exchange cycles, depending on the at least one state parameter provided by a standardized PET procedure and one or more time points.

16. 16. The device of claim 1, wherein the second data further comprises one or more measurement data samples (Kd0*, Kd1*, Kd12*, Kd13*, Kd14*, Kd15*, Kd16*) representing concentrations of the one or more solutes in the therapeutic fluid infused into the peritoneal cavity during the filling phase of the one or more fluid exchange cycles.

17. 17. The apparatus according to claim 1, wherein the first calculation module (52) comprises a differential equation solving sub-module (71) configured to calculate the amount (71B) of treatment fluid in the peritoneal cavity (31) from an initial time point to an end time point, including intermediate time steps, and to calculate the concentration (71B) of one or more solutes in the treatment fluid in the peritoneal cavity (31) from the initial time point to the end time point, including intermediate time steps.

18. 18. The apparatus of claim 17, wherein the differential equation solving sub-module (71) is configured to calculate, for each time step, the amount (71B) of the therapeutic fluid in the peritoneal cavity (31) based on preceding temporal changes (74B) of the amount of the therapeutic fluid in the peritoneal cavity (31), and to calculate, for each time step, the concentrations (71B) of the one or more solutes in the therapeutic fluid in the peritoneal cavity (31) based on preceding temporal changes (75B) of the concentrations of the one or more solutes in the therapeutic fluid in the peritoneal cavity (31).

19. 19. The device according to claim 18, wherein the first calculation module (52) further comprises a first change calculation system (72, 74) configured to calculate a temporal change (74B) in the amount of the therapeutic fluid in the peritoneal cavity (31) for each time step in response to the concentration (71B) of the one or more solutes in the therapeutic fluid in the peritoneal cavity (31) calculated by the differential equation solving sub-module (71) for each time step and the amount (71B) of the therapeutic fluid in the peritoneal cavity (31) calculated by the differential equation solving sub-module (71) for each time step.

20. 20. The device according to claim 19, wherein the first change calculation system (72, 74) comprises a first flow rate calculation sub-module (72) configured to calculate a water flow rate (72B) through the peritoneal membrane (30) for each time step in response to the amount (71B) of the therapeutic fluid in the peritoneal cavity (31) calculated by the differential equation solving sub-module (71) for each time step, and the first change calculation system (72, 74) further comprises a first change calculation sub-module (74) configured to calculate the temporal change (74B) of the amount of the therapeutic fluid in the peritoneal cavity (31) in response to the water flow rate (72B) through the peritoneal membrane (30).

21. 21. The apparatus according to claim 19 or 20, wherein the first calculation module (52) further comprises a second change calculation system (73, 75) configured to calculate a temporal change (75B) in the concentration of the one or more solutes in the treatment fluid in the peritoneal cavity (31) for each time step, depending on the concentration (71B) of the one or more solutes in the treatment fluid in the peritoneal cavity (31) calculated by the differential equation solving sub-module (71) for each time step and the amount (71B) of the treatment fluid in the peritoneal cavity (31) calculated by the differential equation solving sub-module (71) for each time step.

22. The apparatus according to claim 21 dependent on claim 20, wherein the second change calculation system (73, 75) comprises a second flow rate calculation sub-module (73) configured to calculate a flow rate (73B) of the one or more solutes through the peritoneum (30) for each time step in response to the concentration (71B) of the one or more solutes in the treatment fluid in the peritoneal cavity (31) calculated by the differential equation solving sub-module (71) for each time step and the amount (71B) of the treatment fluid in the peritoneal cavity (31) calculated by the differential equation solving sub-module (71) for each time step, and the second change calculation system (73, 75) comprises a second flow rate calculation sub-module (73) configured to calculate a flow rate (73B) of the one or more solutes through the peritoneum (30) for each time step in response to the concentration (71B) of the one or more solutes in the treatment fluid in the peritoneal cavity (31) calculated by the differential equation solving sub-module (71) for each time step, ) further comprises a second change calculation sub-module (75) configured to calculate the temporal change (75B) of the concentration of the one or more solutes in the treatment fluid in the peritoneal cavity (31) in response to the flow rates (73B) of the one or more solutes through the peritoneal membrane (30), the flow rate (72B) of water through the peritoneal membrane (30), the concentrations (71B) of the one or more solutes in the treatment fluid in the peritoneal cavity (31) calculated by the differential equation solving sub-module (71) for each time step, and the amount (71B) of the treatment fluid in the peritoneal cavity (31) calculated by the differential equation solving sub-module (71) for each time step.

23. 23. The apparatus of any one of claims 1 to 22, wherein the measurement data sample ([Kp*]) comprises a measured conductivity value.

24. 24. The apparatus according to claim 1, wherein the first computing module (52) is configured to generate a time sequence of estimated concentration values ([Cpi]) of at least one solute in the treatment fluid in the peritoneal cavity (31), convert the time sequence of estimated concentration values ([Cpi]) into a time sequence of conductivity values, and determine the estimated data samples ([Kp]) from the time sequence of conductivity values.

25. 25. An apparatus according to any one of claims 1 to 24, wherein the one or more liquid exchange cycles include two consecutive exchange cycles, and the two or more points in time include a point in the drain phase of a first liquid exchange cycle and a point in the dwell phase of a second liquid exchange cycle following the first liquid exchange cycle.

26. 26. The device of claim 25, wherein the two or more points in time further include at least one of a point in time during the dwell phase of the first liquid exchange cycle and a point in time during a drain phase preceding the first liquid exchange cycle.

27. 27. The device according to any one of claims 1 to 26, wherein the input (51) is further arranged to receive fluid exchange data of the person (P) in a preceding period prior to the one or more fluid exchange cycles, the device being further configured to estimate an initial concentration of one or more solutes in the therapeutic fluid in the peritoneal cavity (31) at a start time of evaluation based on the fluid exchange data, and the first calculation module (52) is configured to calculate the estimated data sample ([Kp]) based on the initial concentrations.

28. 28. The device of any one of claims 1 to 27, wherein the at least one state parameter comprises at least one of a diffusion capacity of solutes through the peritoneal membrane (30) and a filtration capacity of water through the peritoneal membrane (30).

29. 29. The device of claim 28, wherein the solute diffusion capacity comprises a permeability coefficient times a surface area (PSi) of a drug in the treatment solution, and the water filtration capacity comprises a hydraulic conductance (LpS).

30. 30. The device of any one of claims 1 to 29, wherein the at least one condition parameter comprises a volume of therapeutic fluid in the peritoneal cavity (31) at a selected time.

31. 31. The apparatus of claim 30, wherein the selected time point corresponds to the completion of the drain phase of at least one of the one or more liquid exchange cycles.

32. 32. The apparatus of claim 30 or 31, wherein the measurement data samples of the second data (54B) include a first data sample taken during a drain phase and a second data sample taken after completion of a fill phase following the drain phase, and wherein the second computing module (53) is configured to determine the volume of the therapeutic fluid in the peritoneal cavity (31) at the selected time point.

33. 33. The device of claim 32, wherein the first data sample and the second data sample are collected during an initial probing cycle (IRPC) that includes the drain phase and the fill phase and in which a limited volume (ΔVp) of therapeutic fluid in the peritoneal cavity (31) is extracted during the drain phase, the limited volume (ΔVp) corresponding to a portion of the maximum filling volume of the peritoneal cavity (31).

34. 34. The device of claim 33, wherein the limited amount is less than 10% to 25% of the maximum fill volume.

35. 35. The device of claim 33 or 34, wherein the limited volume is in the range of 50 to 400 mL or 100 to 300 mL.

36. 36. The apparatus of any one of claims 33 to 35, wherein a peritoneal dialysis machine (3) operative to perform the peritoneal dialysis is configured to provide output data representative of the volume of the therapy fluid in the peritoneal cavity (31) for receipt by the peritoneal dialysis machine (3), such that a drain phase is initiated after the first probing cycle (IPRC) if the volume of the therapy fluid is above a limit value, and a fill phase is initiated after the first probing cycle (IPRC) if the volume of the therapy fluid is below a limit value.

37. 37. The device of claim 32, wherein the measurement data samples of the second data (54B) further include data samples taken during a subsequent drain phase, and wherein the second computing module (53) is further configured to determine a volume of the therapeutic fluid in the peritoneal cavity (31) at the end of the subsequent drain phase.

38. 38. The device according to any one of claims 30 to 37, configured to set one or more transport properties of the peritoneal membrane (31) included in the mathematical model (52') to a fixed value when determining the volume of the therapeutic liquid in the peritoneal cavity (31) at the selected time point.

39. 39. The device of any one of claims 30 to 38, configured to determine and output the volume of therapy fluid for each fluid exchange cycle intermittently during the peritoneal dialysis.

40. 40. The apparatus of claim 39, wherein a peritoneal dialysis machine (3) operative to perform the peritoneal dialysis is configured to provide output data representative of the volume of the therapy fluid of each fluid exchange cycle for receipt by the peritoneal dialysis machine (3) so as to adjust at least one of a drain phase and a fill phase of a fluid exchange cycle after each fluid exchange cycle based on the volume of the therapy fluid for the each fluid exchange cycle.

41. 41. The device according to any one of claims 30 to 40, further configured to assess the volume of the therapeutic fluid in the peritoneal cavity (31) at the selected time to detect a problem with the peritoneal access, and to output a warning signal when the problem is detected.

42. 42. The apparatus of any one of claims 30 to 41, wherein a peritoneal dialysis machine (3) operative to perform the peritoneal dialysis is configured to provide output data representative of the volume of the therapy fluid in the peritoneal cavity (31) after completion of a drain phase for receipt by the peritoneal dialysis machine (3), so as to adjust the composition of the unused therapy fluid produced by the peritoneal dialysis machine (3) to account for dilution of the unused therapy fluid by the volume of the therapy fluid in the peritoneal cavity (31).

43. 43. Apparatus according to any one of the preceding claims, wherein said at least one state parameter comprises a tonicity parameter (fCpw) of said person (P).

44. 1. A peritoneal dialysis device comprising: an extracorporeal fluid circuit (3b) connectable to a peritoneal access of a person (P) for carrying therapeutic fluids to or from the peritoneal cavity (31); at least one sensor device (14) disposed within the extracorporeal fluid circuit (3b) and configured to provide data samples representative of concentrations of one or more solutes in the treatment fluid; a control unit (3a) configured to operate the extracorporeal fluid circuit (3b) and to acquire the data samples from the sensor device (14); and a device according to any one of claims 1 to 31 connected to receive the first data and the second data from the control device (3a).

45. 1. A computer-readable medium comprising computer instructions (202A) that, when executed by one or more processors (201), cause the one or more processors (201) to perform a method for determining at least one status parameter of a person undergoing peritoneal dialysis, the method comprising: acquiring (601) first data indicative of a flow rate of therapeutic fluid into and out of a peritoneal cavity of the person via the person's peritoneal access as a function of time during one or more fluid exchange cycles, each fluid exchange cycle including a fill phase, a dwell phase, and a drain phase; acquiring (602) second data comprising measurement data samples representative of concentrations of one or more solutes in the treatment fluid in the peritoneal cavity at two or more time points during the one or more fluid exchange cycles; calculating (603, 603A) estimated data samples representing the concentrations of the one or more solutes in the therapeutic fluid in the peritoneal cavity at the two or more time points based on the first data and using a mathematical model of water and solute transport across the peritoneal membrane in the peritoneal cavity; and determining (605) the at least one state parameter in response to the measured data samples and the estimated data samples.

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