Techniques for determining a dialysis patient profile

Abstract

Methods, apparatus, and systems are described for determining a peritoneal transport status of a patient based on mass analysis of a small amount of peritoneal dialysis (PD) effluent to generate patient information that can be evaluated using a PD effluent fingerprint to determine a peritoneal transport characteristic of the patient. For example, in one embodiment, a method of determining a transport status of a dialysis patient can include obtaining a volume of peritoneal dialysis (PD) effluent of the dialysis patient, generating patient information via mass analysis of the volume of PD effluent, and determining patient profile information based on evaluating the patient information with a profile library, the patient profile information including a peritoneal transport status classification. Other embodiments are also described.

Description

[0001] Cross-references to related applications

[0002] This application claims priority to U.S. Provisional Patent Application Serial No. 62 / 967,743, filed January 30, 2020, pursuant to 35 USC §119(e), the entire contents of which are incorporated herein by reference and are fully set forth herein. Technical Field

[0003] This disclosure generally relates to determining the physical characteristics of dialysis patients, and more specifically, to a process for determining patient dialysis profile information for patients, the patient dialysis profile information indicating the patient's health and / or the success of peritoneal dialysis (PD) patients. Background Technology

[0004] Patient success on peritoneal dialysis (PD) depends on the functional and morphological integrity of the peritoneum. In addition to peritoneal functional failure, long-term PD can lead to anatomical changes in peritoneal tissue, such as angiogenesis, vascular lesions, and fibrosis, and sometimes peritoneal sclerosis. Therefore, various patient characteristics, including peritoneal transport status (i.e., the transport of various solutes across the peritoneum), are typically monitored during PD treatment. However, conventional methods for determining peritoneal transport status (and / or other patient characteristics) are laborious, time-consuming, and require additional patient visits beyond routine PD treatment. Therefore, PD patients and healthcare providers would benefit from a process that can efficiently and effectively identify patient characteristics that may affect PD treatment without the drawbacks of conventional methods. Summary of the Invention

[0005] The present invention is provided to introduce the selection of concepts in a simplified form, which will be further described in the detailed embodiments below. The present invention is not intended to necessarily identify key or essential features of the claimed subject matter, nor is it intended to help determine the scope of the claimed subject matter.

[0006] This disclosure generally relates to methods, apparatus, and systems for determining a patient's peritoneal transport status based on quality analysis of low-volume peritoneal dialysis (PD) effluent to generate patient information that can be assessed using PD effluent fingerprints to determine the characteristics of the patient's peritoneal transport. Other embodiments are also described.

[0007] In one embodiment, a method for determining the transport status of a dialysis patient may include: obtaining the volume of peritoneal dialysis (PD) effluent from the dialysis patient, generating patient information via a quality analysis of the volume of the PD effluent, and determining patient profile information based on an assessment of the patient information using a profile database, the patient profile information including a peritoneal transport status classification.

[0008] In one embodiment, a method of performing dialysis for a dialysis patient can include performing a peritoneal dialysis (PD) process for a patient based on a peritoneal transport status determined via: obtaining a volume of peritoneal dialysis (PD) effluent for the dialysis patient, generating patient information via mass analysis of the volume of PD effluent, and determining patient profile information based on evaluating the patient information with a profile library, the patient profile information including a peritoneal transport status classification.

[0009] In one embodiment, an apparatus can include at least one memory and a logic unit coupled to the at least one memory, the logic unit to: receive patient information generated via mass analysis of a volume of peritoneal dialysis (PD) effluent for a patient, and determine patient profile information based on evaluating the patient information with a profile library, the patient profile information including a peritoneal transport status classification.

[0010] In some embodiments, the mass analysis includes one of liquid chromatography-mass spectrometry (LC-MS) or mass spectrometry (MS). In various embodiments, the volume is obtained during routine dialysis of the patient. In various embodiments, the volume includes less than or equal to about 1 milliliter (ml). In some embodiments, the peritoneal transport status classification includes a classification of solute transport characteristics of high, high average, low average, or low transporter. In various embodiments, a dialysis prescription can be determined based on the peritoneal transport status classification. In some embodiments, a dialysis treatment can be performed for the patient based on the peritoneal transport status classification. BRIEF DESCRIPTION OF DRAWINGS

[0011] Specific embodiments of the disclosed machines will now be described, by way of example only, with reference to the drawings in which:

[0012] Figure 1 A first example operating environment is shown in accordance with some embodiments.

[0013] Figure 2 A peritoneal dialysis (PD) profiling process is shown in accordance with some embodiments.

[0014] Figure 3 A PD profile is shown in accordance with some embodiments.

[0015] Figure 4 A PD profile is shown in accordance with some embodiments.

[0016] Figure 5A Targeted and non-targeted approaches to a PD profiling process are shown in accordance with some embodiments.

[0017] Figure 5B Example mass analysis results for a PD profiling process are shown in accordance with some embodiments.

[0018] Figure 6A and 6B An example PD system is shown in accordance with some embodiments.

[0019] Figure 7 An embodiment of a computing architecture in accordance with the present disclosure is shown. DETAILED DESCRIPTION

[0020] The present embodiments will now be described more fully with reference to the accompanying drawings, in which several exemplary embodiments are shown. The subject matter of the present disclosure, however, can be embodied in many different forms and should not be construed as limited to the embodiments set forth in this disclosure. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and fully convey the scope of the subject matter to those skilled in the art. In the drawings, like numbers refer to like elements throughout.

[0021] Patient treatment success in peritoneal dialysis (PD) depends on the functional and morphological integrity of the peritoneum. In addition to peritoneal function failure, long-term PD can lead to anatomic changes of the peritoneal tissue, such as angiogenesis, vasculopathy, and fibrosis, sometimes causing peritoneal sclerosis. Especially after continuous use of non-physiologic dialysis fluid, membrane properties change. Thus, patient characteristics can be monitored over the duration of a PD patient treatment regimen to ensure the health of the patient’s peritoneal anatomy and / or the effectiveness of the PD treatment, among others. Non-limiting patient characteristics can include peritoneal transport status, dialysis adequacy, membrane properties, unexplained clinical changes, ultrafiltration failure, and / or the like. In some embodiments, treatment recommendations, dialysis prescriptions, and / or dialysis treatments can be given based on patient characteristic determinations in accordance with some embodiments.

[0022] A primary monitored characteristic can include the peritoneal transport status of a PD patient. Generally, the peritoneal transport status is a classification of membrane function by measuring the rate of solute equilibration between dialysate and body plasma. For example, the ratio of dialysate to plasma (D / P) can be used to measure the combined effect of diffusion and ultrafiltration during PD. A low solute D / P means that the transport of a given solute across the peritoneum occurs slowly, with gradual equilibration between dialysate and plasma. In contrast, a high solute D / P means that the solute transport across the membrane occurs quickly, and equilibrium is reached quickly. D / P ratios are generally assessed for various solutes, including urea, creatinine, and sodium.

[0023] Conventional tests for monitoring peritoneal transport status are generally time consuming, difficult for the patient, and lack analysis of the full suite of elements (e.g., metabolites) that can be used to form a complete assessment. For example, the standard peritoneal equilibrium test (PET) is a 4-hour test developed over 30 years ago to assess peritoneal transport status in patients on PD. The standard PET requires collection of approximately 10 ml of peritoneal effluent samples and midpoint blood samples at specific time intervals. Solute transport rates are assessed by their equilibrium rates between peritoneal capillary blood and dialysate. As a representative of all solutes, urea, creatinine, glucose, and sometimes sodium are measured in the collected samples using different analytical tests. Patients are then classified as high, high average, low average, or low transport based on their solute transport characteristics.

[0024] Since the PET is very labor intensive and the time patients spend in the clinic to complete the standard PET is long and requires multiple laboratory samplings, a mini-PET has been developed for follow-up in response to clinical changes. However, the modified version of this PET has shown inconsistencies compared to the standard PET. For both the standard PET and the mini-PET, errors can occur due to sampling, data entry, calculations, and laboratory measurements. Another drawback is that laboratory measurements of certain compounds can be affected by patient conditions that must be corrected or otherwise managed. For example, creatinine can be incorrect due to high glucose concentrations, so a correction factor is needed to calculate the true creatinine amount.

[0025] Accordingly, some embodiments can provide a dialysis profiling process that can be used to determine a patient profile that can include a peritoneal transport status in a more efficient and effective manner than conventional methods such as PET. The dialysis profiling process according to some embodiments can provide a variety of technical advantages and technical improvements including computational techniques compared to conventional systems. Among non-limiting technical advantages, the dialysis profiling process according to some embodiments can provide a more practical and personalized tool to assess dialysis adequacy, membrane characteristics, unexplained clinical changes, ultrafiltration failure, and / or the like. Among non-limiting technical advantages, the dialysis profiling process according to some embodiments can use PD effluent collected from a patient at routine checkups and / or the like at a clinic; thus, no additional visits are needed, such as those required for PET. Furthermore, the patient and medical team do not need to undergo a four-hour protocol. Rather, the dialysis profiling process according to some embodiments can use PD effluent that can be routinely collected at scheduled monthly or quarterly visits. In some embodiments, a large number of molecules (i.e., hundreds or more molecules) including, but not limited to, urea, creatinine, and glucose can be analyzed in less than 1 ml of PD effluent using mass analysis such as liquid chromatography (LC)-mass spectrometry (MS). In some embodiments, the dialysis profiling process can classify a patient as, for example, a high transporter, a high average transporter, an average transporter, a low average transporter, or a low transporter based on the patient’s molecular fingerprint (e.g., see Figure 3 and 4 ). In various embodiments, the patient can be classified using non-targeted and targeted LC-MS, MS, and / or similar methods (e.g., see Figure 5A and 5B ). In various embodiments, the dialysis profiling process can provide a personalized metabolomics-based transport test for peritoneal dialysis.

[0026] Accordingly, the dialysis profiling process according to some embodiments can minimize the impact and disruption of treatment on a patient by reducing the number of additional clinic visits for determining a transport status and providing accurate measurements of physical characteristics important for PD health and effectiveness. Furthermore, the dialysis profiling process according to some embodiments can allow for regular and routine monitoring of a patient’s transport status rather than only when there are warning signs as with traditional methods. Accordingly, the reduction in maintenance and intervention of disease can reduce the risk of infection, which is the second leading cause of death and other complications for dialysis patients. Thus, the dialysis profiling process according to some embodiments can be used to improve the quality of life for PD patients. Other technical advantages are described. Embodiments are not limited in this context.

[0027] Furthermore, the dialysis profile process according to some embodiments can be integrated into multiple practical applications. In one non-limiting practical application, the dialysis profile process can be combined with providing personalized metabolomics-based transport tests for peritoneal dialysis. In another non-limiting practical application, the dialysis profile process can be combined with providing treatment recommendations, dialysis prescriptions, and / or can be based on patient characteristics determined according to some embodiments to administer dialysis treatment. Other practical applications are also described. The embodiments are not limited to this context.

[0028] Table 1 below provides the advantages of the dialysis profile procedure compared to PET testing according to some embodiments:

[0029]

[0030] Table 1

[0031] Figure 1 An example of an operating environment 100, which may represent some embodiments, is shown. For example... Figure 1 As shown, the operating environment may include computing device 110. In various embodiments, the functions, operations, configurations, data storage functions, applications, logical units, and / or such described with respect to computing device 110 may be performed and / or stored therein by one or more other computing devices (not shown), for example, coupled to computing device 110 via network 150 (i.e., network nodes 152a-n). A single computing device 110 is depicted for illustrative purposes only to simplify the figures. For example, operating environment 100 may include multiple computing devices 110 configured independently or in combination to perform various aspects of the embodiments described herein. The embodiments are not limited to this context.

[0032] The computing device 110 can include a transceiver 170, a display 172, an input device 174, and / or a processor circuit 120 that can be communicably coupled to the memory unit 130. The processor circuit 120 can be, can include, and / or can access various logic units for performing processes in accordance with some embodiments. For example, the processor circuit 120 can include and / or can access a dialysis profile logic unit 122. The processing circuit 120 and / or the dialysis profile logic unit 122 and / or portions thereof can be implemented in hardware, software, or a combination thereof. As used in this application, the terms "logic unit," "means," "layer," "system," "circuit," "decoder," "encoder," "control loop," and / or "module" are intended to refer to computer-related entities, either hardware, a combination of hardware and software, software, or software in execution, examples of which are provided by the example computing architecture 700. For example, a logic unit, circuit, or module can be and / or can include, without limitation, a process running on a processor, a processor, a hard disk drive, multiple storage drives (optical and / or magnetic storage media), an object, an executable, a thread of execution, a program, a computer, hardware circuitry, an integrated circuit, an application specific integrated circuit (ASIC), a programmable logic device (PLD), a digital signal processor (DSP), a field programmable gate array (FPGA), a system-on-chip (SoC), a memory unit, a logic gate, a register, a semiconductor device, a chip, a microchip, a chipset, a software means, a program, an application, firmware, a software module, computer code, a control loop, a proportional-integral-derivative (PID) controller, a combination of any of the foregoing, and / or the like.

[0033] Although the dialysis profile logic unit 122 is depicted as being within the processor circuit 120 in Figure 1 Embodiments are not limited thereto, however. For example, the dialysis profile logic unit 122 and / or any means thereof can be located within an accelerator, a processor core, an interface, a separate processor die, implemented entirely as a software application (e.g., the dialysis profile application 140), and / or the like.

[0034] Memory cell 130 may include various types of computer-readable storage media and / or systems in the form of one or more higher-speed memory cells, such as read-only memory (ROM), random access memory (RAM), dynamic RAM (DRAM), dual data rate DRAM (DDRAM), synchronous DRAM (SDRAM), static RAM (SRAM), programmable ROM (PROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, polymer memory such as ferroelectric polymer memory, austenite memory, phase change or ferroelectric memory, silicon oxide-nitride-oxide The storage unit 130 may include SONOS memory, magnetic or optical cards, arrays of devices such as RAID drives, solid-state storage devices (e.g., USB storage, SSDs), and any other type of storage medium suitable for storing information. Furthermore, the storage unit 130 may include various types of computer-readable storage media in the form of one or more low-speed storage units, including internal (or external) hard disk drives (HDDs), floppy disk drives (FDDs), and optical disc drives for reading from or writing to removable optical discs (e.g., CD-ROMs or DVDs), SSDs, and / or the like.

[0035] Memory unit 130 may store profile database information 132, patient profile information 134, and / or patient information 136. In some embodiments, profile database information 132 may include information used to determine an individual patient profile (e.g., see [link to relevant documentation]). Figure 2 and 3 The patient profile includes baseline information (or "fingerprint") of the peritoneal transport status. In various embodiments, the patient profile may include peritoneal transport status, dialysis adequacy, membrane characteristics, unexplained clinical changes, ultrafiltration failure information, and / or its classification. For example, the patient profile may include a classification of peritoneal transport status, such as categories like: high, high average, average, low average, or low transport. Embodiments are not limited to these categories, as various systems can be used to classify patient profiles and / or peritoneal transport status, such as numerical categories, grades (i.e., AF), symbols, and / or the like. In some embodiments, the patient profile and its associated information (i.e., peritoneal transport status) may be stored as patient profile information 132.

[0036] In various embodiments, the profile library information 132 can include quality analysis information for patients having known patient profiles. For example, the profile library information 132 can include MS data for metabolites of patients having known peritoneal transport status. In various embodiments, the profile library information 132 can include fingerprints, libraries, and / or the like generated from a population of patients such that, for example, patient information can be compared to the same or similar population of patients (i.e., based on age, gender, disease progression, and / or the like) to determine a patient profile.

[0037] In some embodiments, the patient information 136 can include information about a patient obtained via analysis of a patient sample, e.g., blood or PD effluent. For example, in some embodiments, the patient information can include MS data resulting from LC-MS and / or MS analysis of a volume of PD effluent. While LC-MS and MS are used as examples, embodiments are not so limited. In some embodiments, for example, the patient information 136 and / or the profile library information 132 can be generated via various analytical instrument systems, including but not limited to liquid chromatography (LC) systems, gas chromatography (GC) systems, mass analyzer systems, mass spectrometer (MS) systems, ion mobility spectrometer (IMS) systems, high performance liquid chromatography (HPLC) systems, ultra-high performance liquid chromatography (UHPLC) systems, ultra-high performance liquid chromatography (UHPLC) systems, or any combination thereof.

[0038] In some embodiments, the volume of PD effluent required to generate the patient information 132 can be about 1 milliliter (ml) or less. In various embodiments, the volume of PD effluent can be about 0.001 ml, about 0.005 ml, about 0.01 ml, about 0.05 ml, about 0.1 ml, about 0.2 ml, about 0.3 ml, about 0.4 ml, about 0.5 ml, about 1.0 ml, about 1.5 ml, and / or any value or range between any two of these values, inclusive of the endpoints.

[0039] In example embodiments, the profile library information 132, patient profile information 134, and / or patient information can be obtained from remote data sources such as data stores 154a-n and / or via network nodes 152a-n.

[0040] In some embodiments, the dialysis profile logic unit 122 can determine a patient profile for a patient based on the patient information 136 and the profile library information 132, e.g., alone or via the dialysis profile application 140. For example, the dialysis profile logic unit 122 can receive patient information 132 in the form of MS analysis results for a volume of PD effluent from a patient. The dialysis profile logic unit 122 can compare the MS analysis results to corresponding profile library information 132 (e.g., see FIG. 1) to determine a patient profile. Figures 2-4 ​The results are compared to determine the matching profile. For example, patient A's MS analysis results may be matched with a high peritoneal transport status. Examples are not limited to this context.

[0041] Figure 2 An example of an operating environment 200, which may represent some embodiments, is shown. For example... Figure 2 As shown, the operating environment 200 depicts a process diagram for a dialysis profile procedure 205 according to some embodiments.

[0042] At box 210, the dialysis profile procedure 205 may include obtaining a patient sample. In some embodiments, the patient sample may include PD effluent. For example, the dialysis profile procedure may use less than 1 ml of PD effluent collected from patients coming to the clinic for routine checkups (i.e., without scheduling additional appointments compared to conventional methods). At box 212, analytical methods such as LC-MS and / or MS may be used to analyze a large number of molecules, including urea, creatinine, and glucose, in less than 1 ml of PD effluent. At box 214, analysis of the results (i.e., from box 212) may be performed. For example, the dialysis profile procedure 205 may include monitoring patient characteristics via molecular signatures and / or advanced data analysis such as these.

[0043] Figure 3 A PD profile according to some embodiments is shown. For example... Figure 3 As shown, PDF profile 305 (e.g., a profile library or a portion thereof) can be generated by analyzing small amounts of PD effluent (0.005 ml) using LC-MS at 0, 1, 2, 3, and 4 hours of standard PET. Hundreds of molecules of varying abundance were detected. These molecular fingerprints can be used to classify PD patient profiles (e.g., peritoneal transport status, dialysis adequacy, membrane characteristics, and / or the like) via dialysis profile procedures according to some embodiments.

[0044] Figure 4 A PD profile according to some embodiments is shown. For example... Figure 4 As shown, PD profile 405 may include, for example, a profile library or a portion thereof. In some embodiments, metabolomics may be used to characterize PD effluent over time and correlate individual temporal changes with transport status to trigger adjustments to dialysis prescriptions or other interventions. For example, the dialysis profile creation process according to some embodiments may provide a molecular fingerprinting platform operable to detect metabolites, including, for example, unknown and / or previously unclassified metabolites.

[0045] exist Figure 4In the example of FIG. 4, seven conventionally collected PD effluent samples were analyzed, five of which had known transporter type “fast” or “slow” (425). For example, a small amount of PD effluent (0.005 ml) from a routine visit was analyzed using LC-MS. As shown in FIG. 4, hundreds of different abundance molecules were detected. Based on analysis of known metabolic fingerprints, two unknown samples were assigned to the “fast” or “slow” categories (430). Non-limiting examples of analysis of unknown samples with known metabolic fingerprints can include hierarchical clustering. For example, in 430, hierarchical clustering analysis can delineate differences in molecular fingerprints with peritoneal transport status. Figure 4

[0046] Figure 5A 5B A method of dialysis profile establishment process is delineated in accordance with some embodiments. Conventionally, hypothesis-driven approaches have been used to classify transporter status for known solutes such as urea, creatinine, and glucose. In a non-targeted approach, all molecules, including previously unknown, present in PD effluent can be used to generate and / or assess patient profile information, such as dialysis adequacy, transport characteristics, and / or the like.

[0047] In some embodiments, the dialysis profile process can be combined with machine learning (ML) techniques, including but not limited to artificial intelligence (AI) processes, neural networks (NNs), and / or the like. For example, the dialysis profile process, patient information, profile information, library information, fingerprints, and / or the like can be used in ML / AI applications to analyze, predict, and / or the like patient profiles (e.g., peritoneal transport status and / or classification thereof) and / or determine recommended treatments or other action plans based on patient profiles. In various embodiments, the library information can be or can include patient profile computational models (e.g., ML processes, AI processes, neural networks (NNs), convolutional neural networks (CNNs), and / or the like. For example, in some embodiments, the ML / AI processes can associate specific molecular patterns with peritoneal transport status.

[0048] ​​For example, in some embodiments, the best parameters of a predictive model can be learned using ML / AI algorithms, processes, and / or the like by studying past examples with known inputs and known outputs. After training, the predictive model can be used to make predictions on unpracticed inputs (i.e., generalization). For example, the dialysis profiling process can involve a classification supervised learning problem, where the output belongs to a set of different classes (e.g., the type of transport for a PD patient). According to some embodiments, non-limiting machine learning algorithm types used to build the predictive model can include, but are not limited to, logistic regression, tree-based methods, random forest methods, gradient boosting methods, deep learning (DL) algorithms such as recurrent neural networks (RNNs) that process input sequences, and / or the like. Embodiments are not limited to this context.

[0049] Figures 6A-6B One example of a peritoneal dialysis (PD) system 601 configured according to one example embodiment of the systems described herein is shown. In some implementations, the PD system 601 can be a home PD system, e.g., a PD system configured for use in a patient’s home. The dialysis system 601 can include a dialysis machine 600 (e.g., a peritoneal dialysis machine 600, also referred to as a PD cycler), and in some embodiments, the machine can be housed on a cart 634.

[0050] The dialysis machine 600 can include a housing 606, a door 608, and a cartridge interface including pump heads 642, 644 for contacting a disposable cartridge or cartridge 615, where the cartridge 615 is positioned within a compartment formed between the cartridge interface and the closed door 608 (e.g., a cavity 605). Fluid lines 625 can be coupled to the cartridge 615 in a known manner, e.g., via connectors, and can also include valves for controlling the flow of fluid into and out of fluid bags including fresh dialysate and warming fluid. In another embodiment, at least a portion of the fluid lines 625 can be integrated with the cartridge 615. Prior to operation, a user can open the door 608 to insert a new cartridge 615 and remove a used cartridge 615 after operation.

[0051] The cartridge 615 can be placed in the cavity 605 of the machine 600 for operation. During operation, dialysis fluid can be pumped into a patient’s abdomen via the cartridge 615, and used dialysate, waste, and / or excess fluid can be removed from the patient’s abdomen via the cartridge 615. The door 608 can be securely closed onto the machine 600. Peritoneal dialysis for a patient can include a total treatment of approximately 10 to 30 liters of fluid, where approximately 2 liters of dialysis fluid is pumped into the patient’s abdomen and held for a period of time, e.g., about an hour, and then pumped out of the patient’s body. The process is repeated until the full treatment volume is reached, and typically occurs overnight while the patient is asleep.

[0052] A heater tray 616 may be positioned on top of the housing 606. The heater tray 616 may be of any size and shape to accommodate dialysate bags (e.g., 5L dialysate bags) for batch heating. The dialysis machine 600 may also include a user interface, such as a touchscreen 618 and a control panel 620 operated by a user (e.g., a healthcare provider or patient), to allow, for example, setting, initiating, and / or terminating dialysis treatment. In some embodiments, the heater tray 616 may include a heating element 635 for heating the dialysate before it is delivered to the patient.

[0053] The dialysate bag 622 can be suspended from a hook on the side of the trolley 634, and the heater bag 624 can be positioned in the heater tray 616. Suspending the dialysate bag 622 improves air management because the air content is held in place by gravity at the top of the dialysate bag 622. Although in Figure 6B Four dialysate bags 622 are shown, but any number of “n” dialysate bags can be connected to the dialysis machine 600 (e.g., 1 to 5 bags, or more), and the designation of the first and second bags is not limited to the total number of bags used in the dialysis system 601. For example, the dialysis machine may have dialysate bags 622a, ..., 622n that can be connected to the system 601. In some embodiments, connectors and tubing ports may connect the dialysate bags 622 to tubing for transferring the dialysate. Dialysiss from the dialysate bags 622 can be transferred in batches to the heater bag 624. For example, a batch of dialysate can be transferred from the dialysate bag 622 to the heater bag 624, where the dialysate is heated by the heating element 635. When the batch of dialysate has reached a predetermined temperature (e.g., approximately 98°–100°F, 37°C), the batch of dialysate can be infused into the patient. Dialysis bag 622 and heater bag 624 can be connected to cartridge 615 via dialysis bag tubing or tube 625 and heater bag tubing or tube 628, respectively. Dialysis bag tubing 625 can be used to transfer dialysate from dialysate bag 622 to cartridge during use, and heater bag tubing 628 can be used to transfer dialysate back and forth between cartridge and heater bag 624 during use. Furthermore, patient tubing 636 and drain tubing 632 can be connected to cartridge 615. Patient tubing 636 can be connected to the patient's abdomen via a catheter and can be used to transfer dialysate back and forth between cartridge and patient's peritoneal cavity via pump heads 642, 644 during use. Drain tubing 632 can be connected to a drain device or drain container and can be used to transfer dialysate from cartridge to drain device or drain container during use.

[0054] While in some embodiments, dialysate can be batch heated as described above, in other embodiments, the dialysis machine can heat dialysate through serial heating, for example, continuously flowing dialysate through a warm bag positioned between heating elements prior to delivery to the patient. For example, instead of placing a heater bag for batch heating on a heater tray, one or more heating elements can be provided inside the dialysis machine. The warm bag can be inserted into the dialysis machine via an opening. It should also be understood that the warm bag can be connected to the dialysis machine via a cartridge via a tube (e.g., tube 625) or fluid line. The tube can be connectable such that dialysate can flow from a dialysate bag, through the warm bag for heating, and to the patient.

[0055] In such serial heating embodiments, the warm bag can be configured such that dialysate can continuously flow through the warm bag (rather than being batch transferred for batch heating) to reach a predetermined temperature prior to flowing into the patient. For example, in some embodiments, dialysate can continuously flow through the warm bag at a rate of between about 100-300 mL / min. Internal heating elements (not shown) can be positioned above and / or below the opening such that when the warm bag is inserted into the opening, the one or more heating elements can affect the temperature of the dialysate flowing through the warm bag. In some embodiments, the internal warm bag can instead be a portion of a tube in the system that passes through, around, or otherwise is configured relative to the heating elements.

[0056] Touch screen 618 and control panel 620 can allow an operator to input various treatment parameters to dialysis machine 600 and otherwise control dialysis machine 600. In addition, touch screen 618 can serve as a display. Touch screen 618 can function to provide information to a patient and an operator of dialysis system 601. For example, touch screen 618 can display information related to a dialysis treatment to be applied to a patient, including information related to a prescription.

[0057] Dialysis machine 600 can include a processing module 602 that is present inside dialysis machine 600, which is configured to be in communication with touch screen 618 and control panel 620. Processing module 602 can be configured to receive data from touch screen 618, control panel 620, and sensors, such as weight, air, flow, temperature, and / or pressure sensors, and control dialysis machine 600 based on the received data. For example, processing module 602 can adjust operational parameters of dialysis machine 600.

[0058] Dialysis machine 600 can be configured to connect to network 603. The connection to network 603 can be via wired and / or wireless connection. Dialysis machine 600 may include a connection member 604 configured to facilitate connection to network 603. Connection member 604 may be a transceiver for wireless connection and / or other signal processor for processing signals transmitted and received via wired connection. Other medical devices (e.g., other dialysis machines) or components may be configured to connect to network 603 and communicate with dialysis machine 600.

[0059] The user interface portion, such as touchscreen 618 and / or control panel 620, may include one or more buttons for selecting and / or entering user information. Touchscreen 618 and / or control panel 620 may be operatively connected to a controller (not shown) and are located in machine 600 for receiving and processing input for operating dialysis machine 600.

[0060] Figure 7 An embodiment of an exemplary computing architecture 700 suitable for implementing the various embodiments described above is illustrated. In various embodiments, the computing architecture 700 may include an electronic device or be implemented as part of an electronic device. In some embodiments, the computing architecture 700 may represent, for example, a computing device 702 and / or components thereof. The embodiments are not limited to this context.

[0061] As used in this application, the terms "system," "component," and "module" refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution, examples of which are provided by the exemplary computing architecture 700. For example, a component can be, but is not limited to, a process running on a processor, a processor, a hard disk drive, multiple storage drives (of optical and / or magnetic storage media), an object, an executable file, an execution thread, a program, and / or a computer. As an illustration, an application running on a server and a server can both be components. One or more components can exist within a process and / or execution thread, and components can reside on a single computer and / or be distributed across two or more computers. Furthermore, components can communicatively couple with each other to coordinate operation via various types of communication media. Coordination can involve one-way or two-way exchange of information. For example, a component can convey information in the form of signals transmitted via a communication medium. This information can be implemented as signals assigned to various signal lines. In such an assignment, each message is a signal. However, other embodiments may alternatively use data messages. Such data messages can be sent via various connections. Exemplary connections include parallel interfaces, serial interfaces, and bus interfaces.

[0062] Computing architecture 700 includes various common computing elements, such as one or more processors, multi-core processors, co-processors, memory units, chipsets, controllers, peripherals, interfaces, oscillators, timing devices, video cards, audio cards, multimedia input / output (I / O) components, power supplies, and so forth. Embodiments, however, are not limited to implementations by computing architecture 700.

[0063] As shown Figure 7 , computing architecture 700 includes a processing unit 704, a system memory 706, and a system bus 708. The processing unit 704 can be any of various commercially available processors, including without limitation: and processors; application, embedded, and special-purpose processors; and and processors; IBM and processors; Core (2) and processors; and like processors. Dual microprocessors, multi-core processors, and other multi-processor architectures can also be employed as the processing unit 704.

[0064] The system bus 708 provides an interface for system components including, but not limited to, the system memory 706 to the processing unit 704. The system bus 708 can be any of several types of bus structures including, but not limited to, a memory bus with memory controller, a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. Interface adapters can connect to the system bus 708 via slots, as in a conventional bus-based computer architecture. Example slot architectures can include, without limitation, accelerated graphics port (AGP), card bus, (extended) industry standard architecture (E)ISA, micro-channel architecture (MCA), NuBus, peripheral component interconnect (extended) (PCI(X)), PCI Express, personal computer memory card international association (PCMCIA), and the like.

[0065] The system memory 706 can include various types of computer- readable storage media in the form of one or more higher speed memory units, such as read-only memory (ROM), random-access memory (RAM), dynamic RAM (DRAM), Double-Data Rate DRAM (DDRAM), synchronous DRAM (SDRAM), static RAM (SRAM), programmable ROM (PROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, polymer memory such as ferroelectric polymer memory, ovonic memory, phase change or ferroelectric memory, silicon-oxide-nitride-oxide-silicon (SONOS) memory, magnetic or optical cards, array-based Figure 7 In the illustrated embodiment, the system memory 706 can include non-volatile memory 710 and / or volatile memory 712. A basic input / output system (BIOS) can be stored in the non-volatile memory 710.

[0066] The computer 702 can include various types of computer-readable storage media in the form of one or more lower speed memory units, including an internal (or external) hard disk drive (HDD) 714, a magnetic floppy disk drive (FDD) 716 to read or write a removable magnetic disk 718, and an optical disk drive 720 to read or write a removable optical disk 722 (such as a CD-ROM or DVD). The HDD 714, FDD 716 and optical disk drive 720 can be connected to the system bus 708 by a HDD interface 724, an FDD interface 726 and an optical drive interface 728, respectively. The HDD interface 724 for external drive implementations can include at least one or both of Universal Serial Bus (USB) and IEEE 1384 interface technologies.

[0067] The drives and associated computer-readable media provide volatile and / or nonvolatile storage of data, data structures, computer-executable instructions, and so on. For example, a number of program modules can be stored in the drives and memory units 710, 712, including an operating system 730, one or more application programs 732, other program modules 734, and program data 736. In one embodiment, the one or more application programs 732, other program modules 734, and program data 736 can include, for example, the various applications and / or components of the computing device 110.

[0068] A user can enter commands and information into the computer 702 through one or more wire / wireless input devices, e.g., a keyboard 738 and a pointing device, such as a mouse 740. Other input devices can include a microphone, an infrared (IR) remote control, a radio-frequency (RF) remote control, a game pad, a stylus, a card reader, a dongle, a fingerprint reader, a glove, a graphic input pen, a joystick, a keyboard, a retina reader, a touch screen (e.g., capacitive, resistive, etc.), a trackball, a trackpad, a sensor, a stylus, etc. These and other input devices are often connected to the processing unit 704 through an input device interface 742 that is coupled to the system bus 708, but can be connected by other interfaces such as a parallel port, IEEE 994 serial port, a game port, a USB port, an IR interface, etc.

[0069] A monitor 744 or other type of display device is also connected to the system bus 708 via an interface, such as a video adaptor 746. The monitor 744 can be internal or external to the computer 702. In addition to the monitor 744, a computer typically includes other peripheral output devices, such as speakers, printers, etc.

[0070] The computer 702 can operate in a networked environment using logical connections to one or more remote computers, such as a remote computer 748. The remote computer 748 can be a workstation, a server computer, a router, a personal computer, portable computer, microprocessor-based entertainment appliance, a peer device or other common network node, and typically includes many or all of the elements described relative to the computer 702, although, for purposes of brevity, only a memory / storage device 750 is illustrated. The logical connections depicted include wire / wireless connectivity to a local area network (LAN) 752 and / or larger networks, for example, a wide area network (WAN) 754. Such LAN and WAN networking environments are commonplace in offices and companies, and facilitate enterprise-wide computer networks, such as intranets, all of which can connect to a global communications network, for example, the Internet.

[0071] When used in a LAN networking environment, the computer 702 is connected to the LAN 752 through a wire / wireless communication network interface or adaptor 756. The adaptor 756 can facilitate wire / wireless communications to the LAN 752, which can also include a wireless access point disposed therein for communicating with the wireless functionality of the adaptor 756.

[0072] When used in a WAN networking environment, the computer 702 can include a modem 758 or a network interface 750, or other means for establishing communications over the WAN 754, such as by way of the Internet. The modem 759, which can be internal or external and a wired and / or wireless device, is connected to the system bus 708 via the input device interface 742. In a networked environment, program modules depicted relative to the computer 702, or portions thereof, can be stored in the remote memory / storage device 750. It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers can be used.

[0073] The computer 702 is operable to communicate with wired and wireless devices or entities using the IEEE 802 family of standards, for example IEEE 802.16 over-the-air modulation techniques. This includes Wi-Fi (or Wireless Fidelity), WiMax, and Bluetooth®, which are wireless technologies similar to that used in a cellular telephone handset, but which are used near the earth's surface. Thus, the communication can be a TM wireless communication operable to employ a radio-frequency spectrum, and the radio- frequency spectrum can be a non-licensed spectrum. Thus, the communication can be a pre-defined structure as with a regular network, or simply an ad hoc communication between at least two devices. A Wi-Fi network is but one implementation of IEEE 802.11x (where x can be a, b, g, n, etc.) radio technology that provides secure, reliable, fast wireless connectivity. A Wi-Fi network can be used to connect computers to each other, to the Internet, and to wired networks (which use IEEE 802.3-related media and functions).

[0074] Many specific details have been set forth herein to provide a thorough understanding of the embodiments. It will be appreciated, however, that the embodiments can be practiced without these specific details. In other instances, well-known operations, components and circuits have not been described in detail in order to avoid obscuring the embodiments. It will be appreciated that the specific structural and functional details disclosed herein are representative and do not necessarily limit the scope of the embodiments.

[0075] Some embodiments can be described using the expression "coupled" and "connected" along with their derivatives. These terms are not intended as synonyms for each other. For example, some embodiments can be described using the terms "connected" and / or "coupled" to indicate that two or more elements are in direct physical or electrical contact with each other. The term "coupled," however, can also mean that two or more elements are not in direct contact with each other, but yet still co-operate or interact with each other.

[0076] Unless specifically stated otherwise, it can be appreciated that terms such as "processing," "computing," "calculating," "determining," or the like, refer to the action and / or processes of a computer or computing system, or similar electronic computing device, that manipulates and / or transforms data represented as physical quantities (e.g., electronic) within the computing system's registers and / or memories into other data similarly represented as physical quantities within the computing system's memories, registers or other such information storage, transmission or display devices. Embodiments are not limited in this context.

[0077] It should be noted that the methods described herein do not have to be performed in the order described or in any particular order. Additionally, various activities described with respect to the methods identified herein can be performed in serial or parallel.

[0078] While particular embodiments have been shown and described, it will be obvious to those skilled in the art that, based upon the teachings herein, changes and modifications can be made in the apparatus described without departing from the spirit of the present disclosure. The present disclosure is to be considered in all respects as illustrative and not restrictive. It will be apparent to those skilled in the art that various modifications and variations can be made in the present embodiments without departing from the spirit or scope of the application. Thus, it is intended that the present application cover the modifications and variations of this application provided they come within the scope of the appended claims and their equivalents.

[0079] While the subject matter has been described above in terms of specific embodiments, it is to be understood that the subject matter defined herein is not necessarily limited to the specific embodiments described, but rather is intended to cover any and all modifications and equivalents within the scope of the claims. It is to be understood that the above description is meant to be illustrative only and not limiting. Combinations of the above embodiments and other embodiments not specifically described herein will be apparent to those of skill in the art upon reviewing the above description. Accordingly, the scope of various embodiments includes any and all combinations of the above-described elements, structures, and methods.

[0080] As used herein, an element or operation recited in the singular and preceded with the word "a" or "an" should be understood as not excluding plural elements or operations, unless explicitly stated otherwise. Furthermore, references to "one embodiment" of the present disclosure are not intended to be interpreted as excluding the existence of additional embodiments that also incorporate those features.

[0081] The present disclosure is not limited by the specific embodiments described herein. Indeed, various embodiments of the present disclosure and modifications thereof, in addition to those described herein, will be apparent to those of ordinary skill in the art from the foregoing description and accompanying drawings. Such other embodiments and modifications are intended to fall within the scope of the present disclosure. Further, although the present disclosure has been described herein in the context of particular implementations in a particular environment for a particular purpose, those of ordinary skill in the art will appreciate that its usefulness is not limited thereto and that it can be beneficially implemented in any number of environments for any number of purposes. Accordingly, the claims set forth below shall be construed in view of the full scope and spirit of the present disclosure as described herein.

Claims

1. A method of determining a transport status of a dialysis patient, the method comprising: obtaining a volume of peritoneal dialysis (PD) effluent of a dialysis patient; generating patient information via mass analysis of the volume of PD effluent; and determining patient profile information based on matching the patient information with a profile library of mass analysis information of patients having known patient profiles, the patient profile information comprising a peritoneal transport status classification.

2. The method of claim 1, wherein, the mass analysis comprises one of liquid chromatography-mass spectrometry (LC-MS) or mass spectrometry (MS).

3. The method of claim 1 or 2, wherein, the volume is obtained during routine dialysis of the patient.

4. The method of claim 1 or 2, wherein, the volume comprises less than or equal to 1 milliliter (ml).

5. The method of claim 4, wherein, the volume comprises about 0.005 ml.

6. The method of any one of claims 1, 2, 5, wherein, the peritoneal transport status classification comprises a classification of high, high average, low average, or low transporter based on solute transport characteristics.

7. The method of any one of claims 1, 2, 5, wherein, the method further comprises determining a dialysis prescription based on the peritoneal transport status classification.

8. The method of any of claims 1, 2, 5, the profile library comprising at least one transport fingerprint of a molecule.

9. The method of any of claims 1, 2, 5, the mass analysis comprising non-targeted mass analysis.

10. The method of any of claims 1, 2, 5, the profile library comprising at least one unknown molecule.

11. An apparatus comprising: at least one memory; and a logic unit coupled to the at least one memory, the logic unit: receiving patient information generated via mass analysis of a volume of peritoneal dialysis (PD) effluent of a patient; and determining patient profile information based on matching the patient information with a profile library of mass analysis information of patients having known patient profiles, the patient profile information comprising a peritoneal transport status classification.

12. The apparatus of claim 11, the mass analysis comprises one of liquid chromatography-mass spectrometry (LC-MS) or mass spectrometry (MS).

13. The apparatus of claim 11 or 12, the volume is obtained during routine dialysis of the patient.

14. The apparatus of claim 11 or 12, the volume comprises less than or equal to 1 milliliter (ml).

15. The apparatus of claim 14, the volume comprises about 0.005 ml.

16. The apparatus of any of claims 11, 12, 15, the peritoneal transport status classification comprises a classification of high, high average, low average, or low transporter based on solute transport characteristics.

17. The apparatus of any of claims 11, 12, 15, the profile library comprises at least one transport fingerprint of a molecule.

18. The apparatus of any of claims 11, 12, 15, the mass analysis comprises non-targeted mass analysis.

19. The apparatus of any of claims 11, 12, 15, the profile library comprises at least one unknown molecule.

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