The systems and methods described herein determine metrics of cardiac or vascular performance, such as cardiac output, and can use the metrics to determine appropriate levels of mechanical circulatory support to be provided to the patient. The systems and methods described determine cardiac performance by determining aortic pressure measurements (or other physiologic measurements) within a single heartbeat or across multiple heartbeats and using such measurements in conjunction with flow estimations or flow measurements made during the single heartbeat or multiple heartbeats to determine the cardiac performance, including determining the cardiac output. By utilizing a mechanical circulatory support system placed within the vasculature, the need to place a separate measurement device within a patient is reduced or eliminated. The system and methods described herein may characterize cardiac performance without altering the operation of the heart pump (e.g., without increasing or decreasing pump speed).

The systems and methods described herein determine metrics of cardiac or vascular performance, such as cardiac output, and can use the metrics to determine appropriate levels of mechanical circulatory support to be provided to the patient. The systems and methods described determine cardiac performance by determining aortic pressure measurements (or other physiologic measurements) within a single heartbeat or across multiple heartbeats and using such measurements in conjunction with flow estimations or flow measurements made during the single heartbeat or multiple heartbeats to determine the cardiac performance, including determining the cardiac output. By utilizing a mechanical circulatory support system placed within the vasculature, the need to place a separate measurement device within a patient is reduced or eliminated. The system and methods described herein may characterize cardiac performance without altering the operation of the heart pump (e.g., without increasing or decreasing pump speed).

A processor of a medical device configured to communicate with a remote server can be programmed to protect the medical device from exposure to unauthorized or malicious software. A system or method to implement this form of protection can include, for example, at least one processor on the medical device, a control software module that controls the operation of the medical device and is executable on the processor, a data management module that manages data flow to and from the control software module from sources external to the medical device, and an agent module that has access to a limited number of designated memory locations in the medical device. In addition, a hemodialysis apparatus can be configured to operate in conjunction with an apparatus for providing purified water from a source such as a municipal water supply or a well. A system for controlling delivery of purified water to the hemodialysis apparatus can comprise a therapy controller of the hemodialysis apparatus configured to communicate with a controller of a water purification device, and a user interface controller of the hemodialysis apparatus configured to communicate with the therapy controller, and to send data to and receive data from a user interface.

A method for synchronizing operation of a heart assist pump device to a patient's cardiac cycle includes obtaining a signal from a motor of a heart assist pump device and filtering the signal to remove noise. The method also includes determining a speed synchronization start point at which time the motor of the heart assist pump device will begin a change in speed of operation based on the filtered signal. The method further includes modulating a speed of the motor of the heart assist pump device to a target speed at the speed synchronization start point, thereby synchronizing the change in speed of operation with a patient's cardiac cycle.

A method for synchronizing operation of a heart assist pump device to a patient's cardiac cycle includes obtaining a signal from a motor of a heart assist pump device and filtering the signal to remove noise. The method also includes determining a speed synchronization start point at which time the motor of the heart assist pump device will begin a change in speed of operation based on the filtered signal. The method further includes modulating a speed of the motor of the heart assist pump device to a target speed at the speed synchronization start point, thereby synchronizing the change in speed of operation with a patient's cardiac cycle.

The present disclosure relates to a motor for an extracorporeal blood pump, an extracorporeal blood pump, and an extracorporeal blood pump system. The motor for an extracorporeal blood pump comprises: a housing; an actuator located in the housing and used for driving an impeller in a pump head of the extracorporeal blood pump; at least one sensor located in the housing; and a motor driving-control assembly located in the housing and used to control operation of the motor. Integrating the motor driving-control assembly into the housing of the motor can significantly reduce the dependence of the motor on the control host of the extracorporeal blood pump, the risk of communication failure between the motor and the control host, and the risk of malfunction of the motor driving-control assembly, thereby greatly improving the safety and reliability of the extracorporeal blood pump.

The present disclosure relates to a motor for an extracorporeal blood pump, an extracorporeal blood pump, and an extracorporeal blood pump system. The motor for an extracorporeal blood pump comprises: a housing; an actuator located in the housing and used for driving an impeller in a pump head of the extracorporeal blood pump; at least one sensor located in the housing; and a motor driving-control assembly located in the housing and used to control operation of the motor. Integrating the motor driving-control assembly into the housing of the motor can significantly reduce the dependence of the motor on the control host of the extracorporeal blood pump, the risk of communication failure between the motor and the control host, and the risk of malfunction of the motor driving-control assembly, thereby greatly improving the safety and reliability of the extracorporeal blood pump.

An axial flux motor includes a housing; a drive shaft disposed within the housing; at least one rotor; and at least one stator. The at least one rotor includes a diametrically-magnetized single pole pair magnetic ring having a rotor aperture defined through the center of the magnetic ring, where the drive shaft extends through the rotor aperture and where the at least one rotor is fixed to the drive shaft. The at least one stator includes a number of conductive windings and a stator aperture, where the drive shaft extends through the stator aperture and where the drive shaft is rotatable within the aperture. The at least one stator is configured to generate an axial magnetic field that causes the at least one rotor to rotate, thereby rotating the drive shaft.

An axial flux motor includes a housing; a drive shaft disposed within the housing; at least one rotor; and at least one stator. The at least one rotor includes a diametrically-magnetized single pole pair magnetic ring having a rotor aperture defined through the center of the magnetic ring, where the drive shaft extends through the rotor aperture and where the at least one rotor is fixed to the drive shaft. The at least one stator includes a number of conductive windings and a stator aperture, where the drive shaft extends through the stator aperture and where the drive shaft is rotatable within the aperture. The at least one stator is configured to generate an axial magnetic field that causes the at least one rotor to rotate, thereby rotating the drive shaft.

Methods and systems are disclosed for creating and using a neural network model to estimate a cardiac parameter of a patient, and using the estimated parameter in providing blood pump support to improve patient cardiac performance and heart health. Particular adaptations include adjusting blood pump parameters and determining whether and how to increase or decrease support, or wean the patient from the blood pump altogether. The model is created based on neural network processing of data from a first patient set and includes measured hemodynamic and pump parameters compared to a cardiac parameter measured in situ, for example the left ventricular volume measured by millar (in animals) or inca (in human) catheter. After development of a model based on the first set of patients, the model is applied to a patient in a second set to estimate the cardiac parameter without use of an additional catheter or direct measurement.