阅读说明：本技术 监测具有植入式血泵的患者的健康状况的方法 (Method of monitoring health condition of patient having implantable blood pump ) 是由 M·C·布朗 A·B·郑 V·拉莫斯 A·特雷霍-莫拉 N·L·瓦苏德万贾拉贾 于 2020-03-06 设计创作，主要内容包括：一种预测具有植入式血泵的患者中的不良事件的方法包括：使脉动性值和与所述血泵相关联的流量波谷值相关以确定流量峰值；将确定的流量峰值除以泵电流以确定脉动性峰值；跟踪所述脉动性峰值的第一移动平均值,所述第一移动平均值定义阈值范围；跟踪所述脉动性峰值的第二移动平均值,所述第二移动平均值比所述第一移动平均值快；以及当所述第二移动平均值偏离所述阈值范围时,生成警报。(A method of predicting an adverse event in a patient having an implantable blood pump comprising: correlating the pulsatility value and a flow trough value associated with the blood pump to determine a flow peak; dividing the determined flow peak by the pump current to determine a pulsatility peak; tracking a first moving average of the pulsatility peaks, the first moving average defining a threshold range; tracking a second moving average of the pulsatility peaks, the second moving average being faster than the first moving average; and generating an alert when the second moving average deviates from the threshold range.)

1. A method of predicting an adverse event in a patient having an implantable blood pump, comprising:

correlating the pulsatility value and a flow trough value associated with the blood pump to determine a flow peak;

dividing the determined flow peak by the pump current to determine a pulsatility peak;

tracking a first moving average of the pulsatility peaks, the first moving average defining a threshold range;

tracking a second moving average of the pulsatility peaks, the second moving average being faster than the first moving average; and

generating an alert when the second moving average deviates from the threshold range.

2. The method of claim 1, further comprising recording a plurality of alarm occurrences over a period of time, and determining a risk factor associated with a predicted onset of the adverse event based on the plurality of alarm occurrences.

3. The method of claim 1, further comprising automatically classifying a physiological state of a patient in a stratification system based on a determined risk factor.

4. The method of claim 1, further comprising determining a standard deviation of the first moving average, the first moving average and the standard deviation defining the threshold range.

5. The method of any of the above claims, wherein the first moving average is a 24 hour moving average and the second moving average is about 2 hours in duration.

6. A system of predicting adverse events in a patient having an implantable blood pump, comprising:

the blood pump; and

a processor in communication with the blood pump, the processor having a processing circuit configured to:

correlating the pulsatility value and a flow trough value associated with the blood pump to determine a flow peak;

dividing the determined flow peak by the pump current to determine a pulsatility peak;

tracking a first moving average of the pulsatility peaks, the first moving average defining a threshold range;

tracking a second moving average of the pulsatility peaks, the second moving average being faster than the first moving average; and

generating an alert when the second moving average deviates from the threshold range.

7. The system of claim 6, wherein the processing circuit is configured to: a plurality of alarm occurrences over a period of time is recorded, and based on the plurality of alarm occurrences, a risk factor associated with the predicted onset of the adverse event is determined.

8. A method of predicting an adverse event in a patient having an implantable blood pump, comprising:

tracking a mean pulsatility value associated with the blood pump;

tracking a plurality of parameters associated with the blood pump, the plurality of parameters including a mean flow trough value, a mean flow value, and a standard flow trough deviation value, the standard flow trough deviation value measured relative to the mean flow trough value;

correlating the average pulsatility value with the plurality of parameters;

determining an adverse event index value using correlated average pulsatility values with respect to the plurality of parameters;

comparing the adverse event index value to a predetermined threshold range; and

generating an alert when the compared adverse event index value deviates from the predetermined threshold range.

9. The method of claim 8, further comprising correlating the average pulsatility value with a scaling factor.

10. The method of claim 8, further comprising correlating the standard flow trough deviation values with offset values.

11. The method of claim 8, further comprising determining a plurality of adverse event index values during a plurality of time periods, comparing the plurality of adverse event index values to one another, and classifying a physiological state of a patient in a classification system based on the compared plurality of adverse event index values.

12. The method of any of claims 8-11, wherein the average pulsatility value and the plurality of parameters associated with the blood pump are represented as waveforms and the adverse event index values exceeding the predetermined threshold range are represented as abnormal features of the waveforms.

13. A system of predicting adverse events in a patient having an implantable blood pump, comprising:

the blood pump; and

a processor in communication with the blood pump, the processor having a processing circuit configured to:

tracking a mean pulsatility value associated with the blood pump;

tracking a plurality of parameters associated with the blood pump, the plurality of parameters including a mean flow trough value, a mean flow value, and a standard flow trough deviation value, the standard flow trough deviation value measured relative to the mean flow value;

correlating the average pulsatility value with the plurality of parameters;

determining an adverse event index value using correlated average pulsatility values with respect to the plurality of parameters;

comparing the adverse event index value to a predetermined threshold range; and

generating an alert when the compared adverse event index value deviates from the predetermined threshold range.

14. A method of predicting an adverse event in a patient having an implantable blood pump, comprising:

identifying a flow trough associated with the blood pump during use;

comparing the flow trough value to a standard deviation flow value and a mean flow value;

determining a flow trough index value using the compared flow trough value and the standard deviation flow value and the mean flow value; and

generating an alert when the flow trough index value deviates from a predetermined threshold range.

15. The method of claim 14, further comprising: quantifying a suction rate associated with the blood pump based on the determined flow trough index value.

Technical Field

The present technology relates generally to implantable blood pumps.

Background

Mechanical circulatory support devices (e.g., implantable blood pumps) are used to assist the pumping action of a failing heart. Such blood pumps may include a housing having an inlet, an outlet, and a rotor mounted within the housing. The inlet may be connected to a chamber of the patient's heart, such as the left ventricle, using an inflow cannula. The outlet may be connected to an artery, such as the aorta. Rotation of the rotor drives blood from the inlet toward the outlet, thereby assisting blood flow from the ventricle into the artery.

Known blood pumps are prone to adverse events, which may lead to expensive hospitalization and medical intervention by the patient. For example, adverse events, whether systemic or cardiopulmonary in nature, may affect ventricular volume and pressure, which are reflected in pump parameters such as power, flow, current, speed, and/or derivatives of pump parameters such as the patient's circadian rhythm, heart rate, aortic valve status, and aspiration burden. Pump parameters obtained in real time may be indicative of an adverse event, but do not provide for analysis of changes in pump parameters over time, which may be used to identify changes in patient health.

Disclosure of Invention

The technology of the present disclosure generally relates to analyzing the health condition of a patient having an implantable blood pump and providing alerts associated with negative health conditions.

In one aspect, the present disclosure provides a method of predicting adverse events in a patient having an implantable blood pump, including correlating pulsatility values and flow trough values associated with the blood pump to determine flow peaks; dividing the determined flow peak by a pump current to determine a pulsatility peak; tracking a first moving average of the pulsatility peaks, the first moving average defining a threshold range; tracking a second moving average of the pulsatility peaks, the second moving average being faster than the first moving average; and generating an alert when the second moving average deviates from the threshold range.

In another aspect, the present disclosure provides for recording a plurality of alarm occurrences over a period of time, and determining a risk factor associated with a predicted onset of the adverse event based on the plurality of alarm occurrences.

In another aspect, the present disclosure provides for automatically classifying a physiological state of a patient in a stratification system based on the determined risk factor.

In another aspect, the present disclosure provides for determining a standard deviation of the first moving average, the first moving average and the standard deviation defining the threshold range.

In another aspect, the present disclosure provides that the first moving average is a 24 hour moving average and the second moving average is about 2 hours in duration.

In one aspect, the present disclosure provides a system for predicting adverse events in a patient having an implantable blood pump, the system comprising the blood pump and a processor in communication with the blood pump, the processor having processing circuitry configured to correlate pulsatility values and flow trough values associated with the blood pump to determine flow peaks; dividing the determined flow peak by a pump current to determine a pulsatility peak; tracking a first moving average of the pulsatility peaks, the first moving average defining a threshold range; tracking a second moving average of the pulsatility peaks, the second moving average being faster than the first moving average; and generating an alert when the second moving average deviates from the threshold range.

In another aspect, the present disclosure provides a system comprising processing circuitry configured to: a plurality of alarm occurrences over a period of time is recorded, and based on the plurality of alarm occurrences, a risk factor associated with the predicted onset of the adverse event is determined.

In one aspect, the present disclosure provides a method of predicting adverse events in a patient having an implantable blood pump, comprising tracking a mean pulsatility value associated with the blood pump; tracking a plurality of parameters associated with the blood pump, the plurality of parameters including a mean flow trough value, a mean flow value, and a standard flow trough deviation value, the standard flow trough deviation value measured relative to the mean flow trough value; correlating the average pulsatility value with the plurality of parameters; determining an adverse event index value using the correlated average pulsatility values with respect to the plurality of parameters; comparing the adverse event index value to a predetermined threshold range; and generating an alert when the compared adverse event index value deviates from the predetermined threshold range.

In another aspect, the present disclosure provides correlating the average pulsatility value with a scaling factor.

In another aspect, the present disclosure provides for correlating standard flow trough deviation values with offset values.

In another aspect, the present disclosure provides determining a plurality of adverse event index values during a plurality of time periods, comparing the plurality of adverse event index values to one another, and classifying a physiological state of a patient in a classification system based on the compared plurality of adverse event index values.

In another aspect, the present disclosure provides that the average pulsatility value and a plurality of parameters associated with the blood pump are represented as waveforms, and the value of the adverse event index exceeding a predetermined threshold range is represented as an abnormal characteristic of the waveforms.

In one aspect, the present disclosure provides a system for predicting adverse events in a patient having an implantable blood pump, the system comprising the blood pump; and a processor in communication with the blood pump, the processor having processing circuitry configured to track a mean pulsatility value associated with the blood pump; tracking a plurality of parameters associated with the blood pump, the plurality of parameters including a mean flow trough value, a mean flow value, and a standard flow trough deviation value, the standard flow trough deviation value measured relative to the mean flow trough value; correlating the average pulsatility value with the plurality of parameters; determining an adverse event index value using the correlated average pulsatility values with respect to the plurality of parameters; comparing the adverse event index value to a predetermined threshold range; and generating an alert when the compared adverse event index value deviates from the predetermined threshold range.

In one aspect, the present disclosure provides a method of predicting adverse events in a patient having an implantable blood pump, comprising identifying a flow trough associated with the blood pump during use; comparing the flow trough value to a standard deviation flow value and a mean flow value; determining a flow valley index value using the compared flow valley value and the standard deviation flow value and the mean flow value; and generating an alert when the flow trough index value deviates from a predetermined threshold range.

In another aspect, the present disclosure provides quantifying a suction rate associated with the blood pump based on the determined flow trough index value.

In another aspect, the present disclosure provides for determining a plurality of flow trough index values, and quantifying a suction rate associated with the blood pump based on the determined plurality of flow trough index values.

In another aspect, the present disclosure provides for classifying a physiological state of a patient in a classification system based on a rate of suction.

In another aspect, the present disclosure provides for determining that a negative flow trough value exists relative to the flow scale, correlating the flow trough value to a constant if the negative flow trough value exists, and determining the flow trough index value after the correlated flow trough value and constant.

In another aspect, the present disclosure provides multiplying the flow trough index value by a correction factor.

In another aspect, the present disclosure provides a division of the standard deviation flow value and the mean flow value.

In one aspect, the present disclosure provides a system for predicting adverse events in a patient having an implantable blood pump, the system comprising the blood pump; and a processor in communication with the blood pump, the processor having a processing circuit configured to identify a flow trough associated with the blood pump during use; comparing the flow trough value to a standard deviation flow value and a mean flow value; determining a flow valley index value using the compared flow valley value and the standard deviation flow value and the mean flow value; and generating an alert when the flow trough index value deviates from a predetermined threshold range.

The details of one or more aspects of the disclosure are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the techniques described in the disclosure will be apparent from the description and drawings, and from the claims.

Drawings

A more complete understanding of the present invention and the attendant advantages and features thereof will be more readily understood by reference to the following detailed description when considered in conjunction with the accompanying drawings wherein:

fig. 1 is a block diagram illustrating a system including a processor and an implantable blood pump;

FIG. 2 is a flow diagram illustrating a method of predicting adverse events in a patient having the implantable blood pump of FIG. 1;

FIG. 3 is a block diagram of the method of FIG. 2;

FIG. 4 is a graph showing daily and cyclical variations in parameters associated with the blood pump of FIG. 1 during use without the onset of adverse events, the parameters including flow values, pulsatility values, current values, and pump speed;

FIG. 5 is a graph illustrating the variation of the parameters of FIG. 4 during an adverse event;

FIG. 6 is four graphs showing the variation of the parameters of FIG. 4;

FIG. 7 is four graphs showing an adverse event as right heart failure;

FIG. 8 is a flow chart illustrating a method of predicting adverse events in a patient having the blood pump of FIG. 1, which method differs from the method of FIG. 2;

FIG. 9 is an equation illustrating the method of FIG. 8;

FIG. 10 is a block diagram illustrating the method of FIG. 8;

FIG. 11 is three graphs showing the absence of an adverse event;

FIG. 12 is three graphs showing the information categories of FIG. 11 and the presence of adverse events;

FIG. 13 is a flow chart illustrating a method of predicting adverse events in a patient having a blood pump, which method differs from the methods of FIGS. 2 and 8;

FIG. 14 is a block diagram illustrating the method of FIG. 13 for determining a flow trough index value;

FIG. 15 is a graph showing the presence of a suction condition; and

FIG. 16 is two graphs illustrating the presence of a puff condition and a determined flow trough index value exceeding a predetermined threshold range.

Detailed Description

Before describing in detail exemplary embodiments, it should be observed that the embodiments reside primarily in combinations of system components and processing steps associated with implantable blood pumps. Accordingly, the system and process components have been represented where appropriate by conventional symbols in the drawings, showing only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the disclosure with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein.

Referring now to the drawings, in which like numerals represent like elements, there is shown an exemplary system constructed in accordance with the principles of the present disclosure and designated generally as "10". Systems and corresponding methods provide a retrospective analysis of one or more blood pump parameters obtained during operation of a blood pump. The information obtained from the analysis may be used to determine the health condition of the patient, including whether the patient's condition has deteriorated over time. The system 10 may generate an alert when the patient's condition deviates from a predetermined threshold (which may indicate one or more deteriorating conditions).

Fig. 1 is a block diagram of a system 10 including an implantable blood pump 12 in communication with a controller 14. The blood pump 12 may be a pumpA pump, or another mechanical circulatory support device implanted wholly or partially within the patient's body, and having a movable element, such as a rotor, configured to pump blood from the heart to other parts of the body. The controller 14 includes a control circuit 16 for monitoring and controlling the activation and subsequent operation of a motor 18 implanted within the blood pump 12. The controller 14 may also include a processor 20, a memory 22, and an interface 24. The memory 22 is configured to store information accessible by the processor 20, including instructions 26 executable by the processor 20 and/or data 28 retrievable, manipulated and/or stored by the processor 20. In particular, the processor 20 includes circuitry configured to perform the steps discussed herein with respect to the method. As such, references to the system 10 performing method steps are intended to include the processor 20.

In one example, the information stored by the processor 20 includes blood pump parameters determined by the system 10, such as estimated blood flow through the blood pump 12, flow trough values, and flow pulsatility values. Blood flow through the blood pump 12 is calculated from pump speed, patient hematocrit, and pump current in liters per minute or another unit of measurement. For example, when the blood pump 12 is operating, the parameters are captured and stored as a log file during a selected time frame (e.g., a sliding two second window) of the estimated flow waveform. The minimum flow value and the maximum flow value are observed during the two second window. The flow trough value is the minimum flow value and the flow peak value is the maximum flow value. The flow pulsatility value (i.e., pulsatility value) is the difference between the minimum flow value and the maximum flow value. Flow pulsatility may be affected by patient conditions such as left ventricular contractility, right heart function, and left ventricular afterload. The time frame may vary and an exemplary time frame of two seconds is provided for capturing at least one complete cardiac cycle while accounting for patient heart rates as low as 30 BPM. The same process can be used to determine parameters using real-time waveforms rather than log files.

Fig. 2 is a flow chart of a method 30 implemented by the system 10 of predicting adverse events in a patient having an implanted blood pump, such as the blood pump 12. The methods provided herein may include additional steps, omit one or more steps, and/or may be provided in a different order than that shown. Furthermore, the method may be applied to log file data, i.e. trends and/or real-time waveforms of patient parameters. Method 30 determines the flow pulsatility of a patient and provides a retrospective review of patient information that may reveal meaningful and relatively drastic changes in flow pulsatility associated with worsening of the patient's physiological state, in addition to daily and cyclical changes. Typically, the peak of the flow signal (i.e., the flow peak) is used to track changes in the patient's flow signal despite events such as aspirations that are otherwise known to interfere with flow troughs and pulsatility values. In addition, the pulsatility peak is determined using the flow peak divided by the pump current to normalize the flow signal to the operating speed and power condition of the patient.

In one configuration, the method begins at step 32, where the system 10 repeatedly or continuously determines pulsatility values and flow trough values. In step 34, method 30 includes system 10 correlating pulsatility values with flow trough values to determine flow peaks. For example, pulsatility values are added to the flow trough values. In step 36, the determined flow peak is divided by the pump current to determine the pulsatility peak. To help quantify the pulsatility peaks, the system 10 continues to track a first moving average of the pulsatility peaks and corresponding standard deviations, which define a threshold range for detecting the onset of adverse events, in step 38.

In step 40, the system 10 continues to track a second moving average of the pulsatility peaks, the second moving average being faster than the first moving average. In one configuration, the first moving average is a 24 hour moving average and the second moving average is about 2 hours in duration; however, other durations are within the scope of the method 30. In step 42, the method 30 includes generating an alert by the system 10 when the second moving average deviates from the threshold range. A second moving average and corresponding alarm that deviates from the threshold range indicates a significant change in patient pulsatility from a previous time period.

The alarm may be audible, visual, vibratory, etc., and may be transmitted to the controller 14 or remote location in real time for review by the clinician and/or provided in a report. One or more instances of an alarm occurring over a period of time may be recorded, and based on the alarm occurring, the system 10 may determine a risk factor associated with the predicted onset of the adverse event. For example, the risk factor may be a scale of one to ten, where the likelihood of an adverse event occurring increases from one to ten. The risk factors may be used to classify changes in the patient's physiological state in a classification system, such as a scale of one to ten, where ten is a relatively drastic change in the patient's physiological state relative to a previous time period, indicating a need for immediate medical intervention.

Fig. 3 shows method 30 as waveform "FMA" as being performed as an algorithm including a first moving average. Fig. 4 is a graph depicting daily and cyclical variations in flow values, pulsatility values, current values, and pump speeds associated with the blood pump 12 during use without the onset of adverse events. The graph is displayed as a patient log file. Fig. 5 is a graph depicting the variation of the parameters of fig. 4 during an adverse event. Changes in pulsatility values are highlighted and analyzed by retrospective examination to determine if the patient's condition has deteriorated over time.

Fig. 6 depicts four graphs showing continuous or repeated operation of the system 10 to track pulsatility peaks, e.g., by an algorithm. The graph "G1" depicts the changes in flow value, pulsatility value, current value, and pump speed associated with the blood pump 12. Graph "G2" depicts the flow peak "FP" plotted against the flow trough value "FT", while graph "G3" depicts the current value. Graph "G4" depicts a threshold range "T" defined by the first moving average and the associated standard deviation. A second moving average "SMA" is plotted against the threshold range, and deviations from the threshold range indicate significant changes that alert. The deviation may be above or below a threshold range. In other words, when the second moving average crosses the threshold range, either above or below the threshold range, a significant change is indicated. The amount and/or frequency of deviation can be used to quantify changes in patient homeostasis and/or hemodynamics, which can be used as a risk factor or indicator of the onset of an adverse event. For example, fig. 7 depicts 4 graphs including the parameters of fig. 6, showing an adverse event as right heart failure. The deviation of the second moving average from the threshold range is designated as "AE".

Fig. 8 is a flow chart of another method 44 implemented by the system 10 of predicting adverse events in a patient having a blood pump 12. Method 44 provides a retrospective review of patient information that may reveal that the relatively high pulsatility and duration of low flow troughs relative to the patient's standard pulsatility and flow measurements correspond to a worsening of the patient's condition and the onset of adverse events. As such, method 44 is configured to target durations of relatively high pulsatility and low flow trough values in individual patients.

In one configuration, the method 44 begins at step 46 and proceeds to step 48, including the system 10 tracking average pulsatility values associated with the blood pump 12 during operation. The system 10 may operate according to an algorithm in which blood pump parameters are tracked over a duration of window size represented as a day, week or month. The average pulsatility value may be related to a scaling factor. In one example, the average pulsatility value is multiplied by a scaling factor of 100.

In step 50, the system 10 continues to track one or more parameters associated with the blood pump 12, including mean flow trough values, mean flow values, and standard flow trough deviation values. The standard flow trough deviation value is measured relative to the average flow trough value and is related to the offset value. The offset value is an additional constant configured to prevent misidentification of periods of high pulsatility and low traffic troughs that might otherwise be affected by a negative traffic condition without offset.

In step 52, method 44 includes correlating the average pulsatility value with a parameter. For example, fig. 9 depicts an equation including a mean pulsatility value expressed as numerator and a parameter that is a mean flow trough value that is added to an offset value and multiplied by a mean flow value and a standard flow trough deviation value. In step 54, the method 44 includes determining an adverse event index value using the associated average pulsatility value with respect to the parameter. In other words, the equation is used to determine an adverse event index value that may be referred to as a pulsatility trough index.

Proceeding to step 56, the method 44 compares the adverse event index value to a predetermined threshold range. In step 58, the system 10 generates an alert when the compared adverse event index value deviates from a predetermined threshold range. The alarm includes the features described above with respect to method 30. An adverse event index value that deviates from a predetermined threshold range indicates the presence of an adverse event, e.g., periods of high pulsatility and low flow troughs. Fig. 10 is a block diagram depicting a method 44 for determining an adverse event index value.

FIG. 11 is three graphs of sample data in the form of log file data. The graph "G1" depicts the average pulsatility value and the parameters associated with the blood pump, represented as waveforms. Graph "G2" depicts an adverse event index value represented as waveform "W" and predetermined threshold "PT" within a 12-hour window. The predetermined threshold "PT" is depicted as being below a waveform indicating the absence of a tracked condition (i.e., an adverse event or a period of high pulsatility and low flow trough). The graph "G3" depicts a Boolean (Boolean) output "T" indicating whether the index is greater than a predetermined threshold.

Fig. 12 is three graphs of sample data in the form of log file data, which displays the information categories shown in the graph of fig. 11, and is designated as "G4", "G5", and "G6". Graph G4 provides log file data representing parameters represented as waveforms, while graph G5 depicts waveforms within a 12-hour window during which the waveforms deviate from a predetermined threshold "PT" at regions labeled "D1" and "D2", which is indicative of a tracked condition. In particular, FIG. 12 depicts waveforms crossing a predetermined threshold as indicating the presence of a tracked condition. In other words, an adverse event index value exceeding a predetermined threshold range is represented as an abnormal characteristic of the waveform. The graph "G6" depicts a boolean output "T" indicating whether the index is greater than a predetermined threshold.

The system 10 may be configured to determine one or more of the adverse event index values during one or more time periods, compare the adverse event index values to one another, and classify the physiological state of the patient in a classification system based on the compared adverse event index values. The rating system may be of various types, such as the one to ten scale discussed above.

Fig. 13 is a flow chart of another method 60 implemented by the system 10 of predicting adverse events in a patient having a blood pump 12. An adverse event may be a suction event characterized by a relatively sharp negative deflection in the estimated flow and power through the blood pump 12 relative to the patient's normal state. The method 60 provides a retrospective review of patient information that may reveal meaningful and relatively drastic changes in patient flow (e.g., flow troughs) corresponding to a pumping event. This change can be quantified to assess conditions surrounding low flow and aspiration burden. Generally, the method 60 includes determining a flow trough index value for more than one log file data point by taking the ratio of the standard deviation of the trough value to the average of the trough value.

In one configuration, the method 60 begins at step 62 and proceeds to step 64, including the system 10 identifying a flow trough associated with the blood pump 12 during use. The flow trough value may be a minimum flow value relative to other flow values obtained during a selected duration or window of blood flow through the blood pump 12 during use. In step 66, the method 60 includes comparing the flow trough values to the standard deviation flow values and mean flow values also determined during the time duration. In step 68, the system 10 determines a flow valley index value using the compared flow valley value and the standard deviation flow value and the mean flow value. Specifically, the standard deviation flow value is divided by the average flow value to determine a flow trough index value. In step 70, the system 10 generates an alarm when the flow trough index value deviates from a predetermined threshold range indicating the presence of a pumping condition. The alert may include the features provided above with respect to the method 30. The predetermined threshold may be customized by the clinician based on how aggressively the clinician intends to track and assess adverse events such as aspiration conditions.

FIG. 14 is a block diagram depicting a method 60 for determining a flow trough index value. As shown, prior to determining the flow trough, the system 10 is configured to determine that a negative flow trough exists relative to the flow scale. The flow scale may be a selected flow threshold for a particular patient. When there are negative flow troughs, the flow troughs are related to a constant (which is an offset value equal to the magnitude of the lowest trough). In other words, the flow trough values are offset by a constant. The shifted flow trough values are correlated with the standard deviation flow values and the mean flow values to determine a flow trough index value. Thereafter, the flow trough index value is multiplied by a constant, such as an offset value or a correction factor.

The flow trough index value may be used to quantify a suction rate associated with the blood pump 12. For example, the system 10 may be configured to determine more than one flow trough index value, and quantify a suction rate associated with the blood pump based on the flow trough index values that have been determined. The aspiration rate is the predicted frequency or likelihood that the patient experiences an aspiration condition and may be used to classify the physiological state of the patient in a grading system. As described above, the rating system may indicate a deterioration in the patient's condition.

Fig. 15 is a graph depicting the variation of flow values, pulsatility values, current values, and pump speeds associated with the blood pump 12 during a suction condition at region "S" determined by method 66. Fig. 16 is a graph "G1" including exemplary log file data for flow values, pulsatility values, current values, and pump speeds associated with the blood pump 12. The flow value and pulsatility value deviate from the predetermined threshold at region "S". Plot "G2" corresponds to plot G1 and depicts exemplary trough index values determined using method 66 and shown as output exceeding a predetermined threshold range "PT" at region S.

It should be understood that the various aspects disclosed herein may be combined in different combinations than those specifically presented in the description and drawings. It will also be understood that certain acts or events of any of the processes or methods described herein can be performed in a different sequence, may be added, merged, or omitted entirely, depending on the embodiment (e.g., all described acts or events may not be necessary to perform the techniques). Additionally, for clarity, while certain aspects of the disclosure are described as being performed by a single module or unit, it should be understood that the techniques of the disclosure may be performed by a combination of units or modules associated with, for example, a medical device.

In one or more embodiments, the described techniques may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored as one or more instructions or code on a computer-readable medium and executed by a hardware-based processing unit. The computer-readable medium may include a non-transitory computer-readable medium corresponding to a tangible medium such as a data storage medium (e.g., RAM, ROM, EEPROM, flash memory, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer).

The instructions may be executed by one or more processors, such as one or more Digital Signal Processors (DSPs), general purpose microprocessors, an Application Specific Integrated Circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Thus, the term "processor" as used herein may refer to any of the foregoing structure or any other physical structure suitable for the techniques described by the solid lines. Furthermore, the present techniques may be fully implemented in one or more circuits or logic elements.

It will be appreciated by persons skilled in the art that the present invention is not limited to what has been particularly shown and described hereinabove. In addition, unless mention was made above to the contrary, it should be noted that all of the accompanying drawings are not to scale. Many modifications and variations are possible in light of the above teaching without departing from the scope and spirit of the invention, which is limited only by the following claims.