# US 2024/0197973 A1 · Dialysis system having motor driver pressure estimation of fluid within a patient line > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2024/0197973 A1 - Document ID: US20240197973A1 - Status: Published application - Kind: A1 - Application number: 18/539,694 - Filed: 2023-12-14 - Published: 2024-06-20 - Earliest claimed: 2022-12-14 - Assignees: Baxter International Inc.; Baxter Healthcare S.A. - Applicants: Baxter International Inc.; Baxter Healthcare SA - Inventors: Michael Pettersson; Andreas Winquist; Per Andersson - Web page: https://patentgrep.com/patent/US20240197973A1 ## CPC - A61M 1/282: {Operational modes} - A61M 1/1613: {Profiling or modelling of patient or predicted treatment evolution or outcome} - A61M 1/281: {Instillation other than by gravity} - A61M 2205/3334: Measuring or controlling the flow rate - A61M 2205/3341: stabilising pressure or flow to avoid excessive variation - A61M 2205/502: User interfaces, e.g. screens or keyboards - A61M 2205/52: with memories providing a history of measured variating parameters of apparatus or patient - A61M: DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY (introducing media into or onto the bodies of animals A61D7/00; means for inserting tampons A61F13/26; devices for administering food or medicines orally A61J; containers for collecting, storing or administering blood or medical fluids A61J1/05); DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY (surgery A61B; chemical aspects of surgical articles A61L); DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR {(Electrotherapy, e.g. producing anaesthesia by the use of alternating or intermittent currents A61N1/36021)} ## Abstract A dialysis system having motor driven pressure estimation of fluid within a patient line is disclosed. A peritoneal dialysis (“PD”) system includes a fluid pump and a patient line that fluidly couples the fluid pump to an indwelling catheter leading into a patient's peritoneal cavity. The PD system also includes a motor driver that controls a motor of the fluid pump and transmits an output signal that is indicative of a load on the motor. The PD system further includes a machine learning algorithm that associates data related to output signals from motor drivers with known fluid pressures within patient lines. A processor of the PD system transmits an input signal to activate the motor driver, receives the output signal from the motor driver, and estimates a fluid pressure within the patient line by applying the data from the received output signal to the machine learning algorithm. ## Claims ### Claim 1 A peritoneal dialysis (“PD”) system comprising: a housing; a PD fluid pump housed by the housing, the PD fluid pump including an actuator that is actuated by a motor; a patient line fluidly coupling the PD fluid pump to a transfer set that is connected to an indwelling catheter leading into a peritoneal cavity of a patient; a motor driver configured to control the motor and transmit an output signal that is indicative of a load on the motor; a memory storing a machine learning algorithm that associates data related to output signals from motor drivers with known PD fluid pressures within patient lines; a processor electrically coupled to the motor driver and the memory, the processor configured to: transmit an input signal to activate the motor driver, receive the output signal from the motor driver, estimate a PD fluid pressure within the patient line by applying data from the received output signal to the machine learning algorithm, and cause the motor driver to stop when the estimated PD fluid pressure is above a threshold value. ### Claim 2 (depends on 1) The PD system of claim 1, wherein the processor is further configured to: receive a plurality of output signals from the motor driver; sample the plurality of output signals at a specified rate using a moving window filter; and apply the machine learning algorithm to the data from the sampled plurality of output signals that are within the moving window filter. ### Claim 3 (depends on 2) The PD system of claim 2, wherein the specified rate is between 50 Hz and 1000 Hz for sampling between 1 and 100 output signals within the moving window filter. ### Claim 4 (depends on 2) The PD system of claim 2, wherein the processor is further configured to determine at least one of the following input variable values as the data from the sampled plurality of output signals: (i) a minimum output signal value, (ii) a maximum output signal value, (iii) an average output signal value, (iv) a median output signal value, or (v) a difference of the maximum output signal value and the minimum output signal value; and use the at least one of the (i) to (v) for applying the machine learning algorithm to the sampled plurality of output signals that are within the moving window filter. ### Claim 5 (depends on 4) The PD system of claim 4, wherein the processor is further configured to: determine or receive an indication of a flow rate of the PD fluid; and use the flow rate of the PD fluid in conjunction with the at least one of the (i) to (v) for applying the machine learning algorithm to the sampled plurality of output signals that are within the moving window filter. ### Claim 6 (depends on 5) The PD system of claim 5, wherein the processor is further configured to: normalize flow rate of the PD fluid by dividing the flow rate of the PD fluid by a maximum flow rate; and use the normalized flow rate of the PD fluid in conjunction with the at least one of the (i) to (v) for applying the machine learning algorithm to the sampled plurality of output signals that are within the moving window filter. ### Claim 7 (depends on 5) The PD system of claim 5, wherein the processor is configured to receive the flow rate of the PD fluid from a flow rate sensor that is fluidly coupled to the patient line or determine the flow rate of the PD fluid from a programmed PD fluid flow rate. ### Claim 8 (depends on 5) The PD system of claim 5, wherein the machine learning algorithm includes the following input variables as inputs: at least one of the flow rate of the PD fluid or a normalized fluid flow rate of the PD fluid; and at least one of (i) a minimum output signal value, (ii) a maximum output signal value, (iii) an average output signal value, (iv) a median output signal value, or (v) a difference of the maximum output signal value and the minimum output signal value. ### Claim 9 (depends on 8) The PD system of claim 8, wherein the machine learning algorithm additionally includes an upstream PD fluid pressure as an input variable. ### Claim 10 (depends on 8) The PD system of claim 8, wherein the machine learning algorithm is trained using a data set that associates at least one of the flow rate of the PD fluid or the normalized fluid flow rate of the PD fluid and the at least one of (i) to (v) with a PD fluid pressure within the patient line that is measured by a pressure sensor. ### Claim 11 (depends on 4) The PD system of claim 4, wherein the processor is further configured to: receive an indication of an upstream PD fluid pressure from a pressure sensor that is located upstream from the PD fluid pump; and use the upstream PD fluid pressure in conjunction with the at least one of the (i) to (v) for applying the machine learning algorithm to the sampled plurality of output signals that are within the moving window filter. ### Claim 12 (depends on 11) The PD system of claim 11, wherein the upstream PD fluid pressure corresponds to a head height pressure. ### Claim 13 (depends on 1) The PD system of claim 1, further comprising a filter set including a hydrophilic filter membrane fluidly coupled between the patient line and the transfer set, wherein the patient line is a dual lumen patient line including a fresh PD fluid lumen and a used PD fluid lumen, and wherein the processor is configured to estimate the fluid pressure within the fresh PD fluid lumen. ### Claim 14 (depends on 1) The PD system of claim 1, wherein the motor is a stepper motor and the output signal represents a load angle of the stepper motor. ### Claim 15 (depends on 1) The PD system of claim 1, wherein the processor is further configured to apply a smoothing function to a sequence or stream of the estimated PD fluid pressures. ### Claim 16 (depends on 1) The PD system of claim 1, wherein the actuator includes a piston actuated by the motor. ### Claim 17 A peritoneal dialysis (“PD”) method to estimate PD fluid pressure, the method comprising: storing in a memory of a PD machine, a machine learning algorithm that associates data related to output signals from motor drivers with known PD fluid pressures within patient lines; transmitting from a processor of the PD machine to a motor driver of a PD fluid pump, an input signal to activate the motor driver, which causes a motor to actuate an actuator for pumping PD fluid at a specified rate from the PD fluid pump to a patient line that is fluidly coupled to a transfer set that is connected to an indwelling catheter leading into a patient's peritoneal cavity; receiving, in the processor, output signals from the motor driver that are indicative of a load estimation output of the motor; estimating, via the processor using the machine learning algorithm, a PD fluid pressure within the patient line based on data related to the received output signals; and causing, via the processor, a user interface of the PD machine to display the estimated PD fluid pressure. ### Claim 18 (depends on 17) The method of claim 17, further comprising causing, via the processor, the motor driver to stop when the estimated PD fluid pressure is above a threshold value out outside of a specified range. ### Claim 19 (depends on 17) The method of claim 17, wherein the machine learning algorithm includes at least one of a Gaussian process regression, a linear regression, a logic regression, a decision tree, a gradient boosting algorithm, a random forest algorithm, a k-nearest neighbor algorithm, a k-means algorithm, a support-vector machine, a Naïve Bayes algorithm or combinations thereof. ### Claim 20 (depends on 17) The method of claim 17, further comprising: sampling, via the processor, the output signals at a specified rate using a moving window filter; and applying, via the processor, the machine learning algorithm to data from the sampled plurality of output signals that are within the moving window filter. ### Claim 21 (depends on 20) The method of claim 20, wherein the specified rate is between 50 Hz and 1000 Hz for sampling between 1 and 100 output signals within the moving window filter. ### Claim 22 (depends on 20) The method of claim 20, further comprising: determining, via the processor, at least one of the following input variable values as the data from the sampled output signals: (i) a minimum output signal value, (ii) a maximum output signal value, (iii) an average output signal value, (iv) a median output signal value, or (v) a difference of the maximum output signal value and the minimum output signal value; and using, via the processor, the at least one of the (i) to (v) for applying the machine learning algorithm to the sampled plurality of output signals that are within the moving window filter. ### Claim 23 (depends on 22) The method of claim 22, further comprising: determining or receiving, via the processor, an indication of a flow rate of the PD fluid; and using, via the processor, the flow rate of the PD fluid in conjunction with the at least one of the (i) to (v) for applying the machine learning algorithm to the sampled plurality of output signals that are within the moving window filter. ### Claim 24 (depends on 22) The method of claim 22, further comprising training the machine learning algorithm using a data set that associates at least one of the flow rate of the PD fluid or the normalized fluid flow rate of the PD fluid and the at least one of (i) to (v) with a PD fluid pressure within the patient line that is measured by a pressure sensor.