Abstract: Methods and apparatus for an auxiliary monitor for a patient monitoring system including a first patient monitoring device and a second patient monitoring device are described. The auxiliary monitor comprises a communication interface configured to receive first data from the first patient monitoring device and second data from the second patient monitoring device, and at least one computer processor. The at least one computer processor is programmed to display in a first portion of a user interface, an indication of the first data, and display in a second portion of the user interface, an indication of the second data.

Abstract: Aspects of the present disclosure describe systems and methods for predicting an intra-aortic pressure of a patient receiving hemodynamic support from a transvalvular micro-axial heart pump. In some implementations, an intra-aortic pressure time series is derived from measurements of a pressure sensor of the transvalvular micro-axial heart pump and a motor speed time series is derived from a measured back electromotive force of a motor of the transvalvular micro-axial heart pump. Furthermore, in some implementations, machine learning algorithms, such as deep learning, are applied to the intra-aortic pressure and motor speed time series to accurately predict an intra-aortic pressure of the patient. In some implementations, the prediction is short-term (e.g., approximately 5 minutes in advance).

Abstract: A method for providing a treatment recommendation to a physician for treating a patient is disclosed. The method comprises determining, from a processor in communication with a patient data repository, a first treatment recommendation based on a combination of selected patient demographics from the patient data repository applicable to the patient, and operational parameters of a plurality of ventricular assist devices (VADs) suitable for treating the patient, the first treatment recommendation having a first survival rate and comprising the use of a first VAD. The method then obtains a first signal from using the first VAD on the patient. The method then determines a second treatment recommendation based on the first signal and the first treatment recommendation, the second treatment recommendation having a second survival rate. The method then provides the second treatment recommendation to the physician if the second survival rate is higher than the first survival rate.

Abstract: A heart pump assembly includes an impeller blade coupled to a rotor or a drive shaft, a cannula, a distal projection coupled to a distal end of the cannula and a blood inlet having a plurality of apertures. The plurality of apertures is radially oriented and disposed about a circumference of the cannula. The plurality of apertures includes at least a first ring of apertures which are proximal to the distal projection and a second ring of apertures which are proximal of the first ring of apertures. The plurality of apertures can allow the heart pump assembly to continue to function if anatomical structures or tissue become suctioned to a portion of the heart pump.

Abstract: Systems and methods are provided herein for treating a patient in cardiogenic shock. An intravascular heart pump system is inserted into vasculature of the patient. The heart pump system has a cannula, pump outlet, pump inlet, and rotor. The heart pump system is positioned within the patient such that the cannula extends across the patient's aortic valve, the pump inlet is located within the patient's left ventricle, and the pump outlet is located within the patient's aorta. Data related to time-varying parameters of the heart pump system is acquired from the heart pump system. A plurality of features are extracted from the data. A probability of survival of the patient is determined based on the plurality of features and using a prediction model. The heart pump system is operated to treat the patient.

Abstract: Systems and methods are provided herein for estimating a position of a heart pump system in a patient. The system receives first data indicative of a time-varying motor current during a first time period. The motor current corresponds to an amount of current delivered to a motor, while the heart pump system is operating in the patient. The system receives second data indicative of a time-varying differential pressure during the first time period. The differential pressure is indicative of a position of the heart pump system relative to patient's heart. The system receives third data indicative of time-varying motor current during a second time period, and determines an estimate of differential pressure during the second period of time from the third data and a relationship between the first data and the second data. The estimate is usable to predict the position of the heart pump system in the patient.

Abstract: 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.

Abstract: Systems and methods are provided herein for treating a patient in cardiogenic shock. An intravascular heart pump system is inserted into vasculature of the patient. The heart pump system has a cannula, pump outlet, pump inlet, and rotor. The heart pump system is positioned within the patient such that the cannula extends across the patient's aortic valve, the pump inlet is located within the patient's left ventricle, and the pump outlet is located within the patient's aorta. Data related to time-varying parameters of the heart pump system is acquired from the heart pump system. A plurality of features are extracted from the data. A probability of survival of the patient is determined based on the plurality of features and using a prediction model. The heart pump system is operated to treat the patient.

Abstract: Methods and apparatus for determining flow through a circulatory support device are provided. The method comprises receiving a motor current signal from a motor of the circulatory support device, determining within a time window of the motor current signal, a measured current value at which flow through the circulatory support device is maximum, determining an offset value based, at least in part, on the measured current value, determining based, at least in part, on the received motor current signal, whether an abnormal condition has occurred, adjusting the motor current signal based, at least in part, on the offset value and the determination of whether an abnormal condition has occurred to produce an adjusted motor current signal, and determining the flow through the circulatory support device based, at least in part, on the adjusted motor current signal.

Abstract: Aspects of the present disclosure describe systems and methods for predicting an intra-aortic pressure of a patient receiving hemodynamic support from a transvalvular micro-axial heart pump. In some implementations, an intra-aortic pressure time series is derived from measurements of a pressure sensor of the transvalvular micro-axial heart pump and a motor speed time series is derived from a measured back electromotive force of a motor of the transvalvular micro-axial heart pump. Furthermore, in some implementations, machine learning algorithms, such as deep learning, are applied to the intra-aortic pressure and motor speed time series to accurately predict an intra-aortic pressure of the patient. In some implementations, the prediction is short-term (e.g., approximately 5 minutes in advance).

Abstract: 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).

Abstract: 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.

Abstract: A method for automatically classifying clinical descriptions of patients by a computer processor using natural language processing is provided. The clinical descriptions relate to the use of a ventricular assist device to treat the patient. The method comprises receiving at least one clinical description comprising text. The method also then comprises determining the position of a target word within the text. Further the method comprises determining the existence of at least one negation word within an active region, the active region comprising a predetermined number of words within the text occurring immediately before and immediately after the target word, including the target word. The method then comprises determining the existence of at least one body-part word within the active region. Lastly, the method comprises determining that the clinical description is to be disregarded if the active region contains either a negation word or a body-part word.

Abstract: A blood pump system includes a catheter, a pump housing disposed distal of a distal end of the catheter, a rotor positioned at least partially in the pump housing, a controller, and an electrode coupled a distal region of the blood pump. The electrode can be used to sense electrocardiogram (EKG) signals and transmit the signals to a controller of the blood pump. The operation of the blood pump can be adjusted based on the EKG signal and on cardiac parameters derived from the EKG signal. Further, the controller can determine a need for defibrillation or pacing of the patient's heart based on the signal and can administer treatment with electrical shocks to the heart via the electrode coupled to the blood pump. The use of an electrode with a blood pump already in place in the heart allows for more efficient and safer treatment of serious cardiac conditions.

Abstract: The systems, devices, and methods presented herein use a blood pump to obtain measurements of cardiac function. The system can quantify the functioning of the native heart by measuring certain parameters/signals such as aortic pressure or motor current, then calculate and display one or more cardiac parameters and heart function parameters, such as left ventricular pressure, left ventricular end diastolic pressure, or cardiac power output. These parameters provide valuable information to a user regarding current cardiac function, as well as positioning and function of the blood pump. In some embodiments, the system can act as a diagnostic and therapeutic tool.

Abstract: Systems and methods for detecting suction events in blood pumps by monitoring pump motor current. A pump suction event is detected based on a comparison of a pulsatility index that is calculated based on a filtered pump motor current signal with a first predetermined threshold or based on a comparison of a calculated index associated with a normalized band-pass filtered pump motor current signal with a second predetermined threshold. A suction event is also detected based on comparisons of both the calculated pulsatility index and the calculated index associated with the normalized band-pass filtered pump motor current signal with respective first and second predetermined thresholds.

Abstract: Aspects of the present disclosure describe systems and methods for predicting an intra-aortic pressure of a patient receiving hemodynamic support from a transvalvular micro-axial heart pump. In some implementations, an intra-aortic pressure time series is derived from measurements of a pressure sensor of the transvalvular micro-axial heart pump and a motor speed time series is derived from a measured back electromotive force of a motor of the transvalvular micro-axial heart pump. Furthermore, in some implementations, machine learning algorithms, such as deep learning, are applied to the intra-aortic pressure and motor speed time series to accurately predict an intra-aortic pressure of the patient. In some implementations, the prediction is short-term (e.g., approximately 5 minutes in advance).

Abstract: 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).

Abstract: A method for providing a treatment recommendation to a physician for treating a patient is disclosed. The method comprises determining, from a processor in communication with a patient data repository, a first treatment recommendation based on a combination of selected patient demographics from the patient data repository applicable to the patient, and operational parameters of a plurality of ventricular assist devices (VADs) suitable for treating the patient, the first treatment recommendation having a first survival rate and comprising the use of a first VAD. The method then obtains a first signal from using the first VAD on the patient. The method then determines a second treatment recommendation based on the first signal and the first treatment recommendation, the second treatment recommendation having a second survival rate. The method then provides the second treatment recommendation to the physician if the second survival rate is higher than the first survival rate.

Abstract: An electronic interface facilitates automatically transferring data from a heart pump controller database to an EMR system, thereby reducing or eliminating the need for caregivers to manually transcribe heart pump operational data from a heart pump controller screen to the EMR system. Some embodiments reduce or eliminate the need to periodically visit a patient to record the operational data. Instead, once programmed, the interface can automatically periodically send the operational data to the EMR system.