Noninvasive Predictive and/or Estimative Blood Pressure Monitoring

• US 20150065826A1
• Filed: 11/06/2014
• Published: 03/05/2015
• Est. Priority Date: 10/29/2008
• Status: Active Grant

• Current Assignee: Flashback Technologies, Inc., The Board of Regents of the University of Colorado (University of Colorado System)
• Original Assignee: Flashback Technologies, Inc., The Board of Regents of the University of Colorado (University of Colorado System)
• Inventors: Mulligan, Isobel Jane, Grudic, Gregory Zlatko, Moulton, Steven L.
• Granted Patent: US 11,382,571 B2
• Time in Patent Office: Days
• US Class Current: 600/323
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/02028 Determining haemodynamic parameters not otherwise provided for, e.g. cardiac contractility or left ventricular ejection fraction
• CPC Class Codes: A61B 5/02042 Determining blood loss or bleeding, e.g. during a surgical procedure
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/02108 from analysis of pulse wave characteristics
• CPC Class Codes: A61B 5/02241 of small dimensions, e.g. adapted to fingers
• CPC Class Codes: A61B 5/029 Measuring or recording blood output from the heart, e.g. minute volume A61B8/065 takes precedence
• CPC Class Codes: A61B 5/14551 for measuring blood gases
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/398 Electrooculography [EOG], e.g. detecting nystagmus; Electroretinography [ERG]
• CPC Class Codes: A61B 5/4875 Hydration status, fluid retention of the body
• CPC Class Codes: A61B 5/6826 Finger
• CPC Class Codes: A61B 5/7246 using correlation, e.g. template matching or determination of similarity
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 7/04 Electric stethoscopes microphones, acoustic transducers therefor H04R
• CPC Class Codes: A61M 1/1613 Profiling or modelling of patient or predicted treatment evolution or outcome
• CPC Class Codes: A61M 2230/30 Blood pressure A61M2230/04 takes precedence
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: G16H 50/50 : for simulation or modelling of medical disorders

Abstract

Tools and techniques for estimating and/or predicting a patient'"'"'s current and/or future blood pressure. In some cases, the tools will analyze physiological data captured from the patient against a model of blood pressure values to estimate/predict the patient'"'"'s blood pressure value. In particular cases, derived parameters, such as a patient'"'"'s compensatory reserve index (“CRI”) can be analyzed against such models, while in other cases, data captured from sensors can be directly analyzed against such models.

Claims

1. A system, comprising:

one or more sensors to obtain physiological data from a patient; and

a computer system in communication with the one or more sensors, the computer system comprising;

one or more processors; and

a computer readable medium in communication with the one or more processors, the computer readable medium having encoded thereon a set of instructions executable by the computer system to perform one or more operations, the set of instructions comprising;

instructions for receiving the physiological data from the one or more sensors;

instructions for analyzing the physiological data against a pre-existing model of blood pressure;

instructions for estimating a blood pressure value of the patient, based on analysis of the physiological data against the pre-existing model; and

instructions for displaying, on a display device, an estimate of the blood pressure value of the patient.

2. The system of claim 1, wherein the one or more sensors comprises a finger cuff comprising a fingertip photoplethysmograph and wherein the computer system comprises a wrist unit in communication with the fingertip photoplethysmograph, the wrist unit further comprising a wrist strap.

3. A method, comprising:

monitoring, with one or more sensors, physiological data of a patient;

analyzing the physiological data against a pre-existing model of blood pressure;

estimating a blood pressure value of the patient, based on analysis of the physiological data against the pre-existing model;

displaying, on a display device, an of the blood pressure value of the patient.

4. The method of claim 3, wherein the blood pressure value of the patient is a future blood pressure value of the patient.

5. The method of claim 3, wherein the blood pressure value of the patient is a numeric value.

6. The method of claim 3, wherein the blood pressure value of the patient is a qualitative value selected from the group consisting of low blood pressure, normal blood pressure, and high blood pressure.

7. The method of claim 3, wherein estimating a blood pressure of the patient comprises performing one or more operations selected from the group consisting of estimating that the patient'"'"'s blood pressure is high;

estimating that the patient'"'"'s blood pressure is normal; and

identifying when the patient'"'"'s blood pressure changes significantly over a time period.

8. The method of claim 3, wherein estimating a blood pressure value of the patient comprises performing one or more operations selected from the group consisting of:

predicting when the patient'"'"'s blood pressure will increase to a specific value;

predicting when the patient'"'"'s blood pressure will decrease to a specific value;

predicting when the patient'"'"'s blood pressure will increase by a specified amount; and

/or predicting when the patient'"'"'s blood pressure will decrease by a specified amount

9. The method of claim 3, wherein estimating the blood pressure value comprises estimating a compensatory reserve index of the patient and estimating the blood pressure value from the estimated compensatory reserve index.

10. The method of claim 9, wherein estimating the blood pressure value comprises estimating a plurality of values of the compensatory reserve index over time and estimating the blood pressure value based on changes in values of the compensatory reserve index over time.

11. The method of claim 10, wherein estimating the blood pressure value based on changes in values of the compensatory reserve index over time comprises estimating the blood pressure value based on a slope of a plot of compensatory reserve index values over a time segment.

12. The method of claim 9, wherein estimating a compensatory reserve index of the patient comprises estimating a compensatory reserve index by comparing the physiological data to a model constructed using the following formula:

CRI(t)=1−

BLV(t)/BLV_HDD

where CRI(t) is the compensatory reserve at time t, BLV(t) is an intravascular volume loss of a test subject at time t, and BLV_HDD is an intravascular volume loss at a point of hemodynamic decompensation of the test subject.

13. The method of claim 9, wherein the physiological data comprises waveform data and wherein estimating a compensatory reserve index of the patient comprises comparing the waveform data with one or more sample waveforms generated by exposing one or more test subjects to state of hemodynamic decompensation or near hemodynamic decompensation, or a series of states progressing towards hemodynamic decompensation, and monitoring physiological data of the test subjects.

14. The method of claim 3, wherein the physiological data comprises waveform data, and wherein estimating the blood pressure value comprises:

comparing the waveform data with a plurality of sample waveforms, each of the sample waveforms corresponding to a different blood pressure value, to produce a similarity coefficient expressing a similarity between the waveform data and each of the sample waveforms;

normalizing the similarity coefficients for each of the sample waveforms; and

summing the normalized similarity coefficients to produce an estimated blood pressure value for the patient.

15. The method of claim 3, further comprising:

predicting, with the computer system, the blood pressure value of the patient at one or more time points in the future, based on analysis of the physiological data; and

displaying, with the display device, a predicted blood pressure value of the patient at one or more points in the future.

16. The method of claim 3, wherein the estimate of the blood pressure value of the patient is based on a fixed time history of monitoring the physiological data of the patient.

17. The method of claim 3, wherein the estimate of the blood pressure value of the patient is based on a dynamic time history of monitoring the physiological data of the patient.

18. The method of claim 3, wherein blood pressure value is a systolic value of the patient'"'"'s blood pressure.

19. The method of claim 3, wherein blood pressure value is a diastolic value of the patient'"'"'s blood pressure.

20. The method of claim 3, wherein blood pressure value is a mean arterial pressure value of the patient'"'"'s blood pressure

21. The method of claim 3, wherein at least one of the one or more sensors is selected from the group consisting of a blood pressure sensor, an intracranial pressure monitor, a central venous pressure monitoring catheter, an arterial catheter, an electroencephalograph, a cardiac monitor, a transcranial Doppler sensor, a transthoracic impedance plethysmograph, a pulse oximeter, a near infrared spectrometer, a ventilator, an accelerometer, an electrooculogram, a transcutaneous glucometer, an electrolyte sensor, and an electronic stethoscope.

22. The method of claim 3, wherein the physiological data comprises blood pressure waveform data.

23. The method of claim 3, wherein the physiological data comprises plethysmograph waveform data.

24. The method of claim 3, wherein the physiological data comprises photoplethysmograph (PPG) waveform data.

25. The method of claim 3, further comprising:

generating the pre-existing model.

26. The method of claim 25, wherein generating the pre-existing model comprises:

receiving data pertaining to one or more physiological parameters of a test subject to obtain a plurality of physiological data sets;

directly measuring one or more physiological states of the test subject with a reference sensor to obtain a plurality of physiological state measurements; and

correlating the received data with the physiological state measurements of the test subject.

27. The method of claim 26, wherein the one or more physiological states comprises one or more states selected from the group consisting of reduced circulatory system volume, dehydration, cardiovascular collapse or near-cardiovascular collapse, euvolemia, hypervolemia, high blood pressure, normal blood pressure, low blood pressure, and blood pressure at a specific numeric value.

28. The method of claim 26, wherein correlating the received data with the physiological state measurements of the test subject comprises:

identifying a most predictive set of signals S_k out of a set of signals s₁, s₂, . . . , s_D for each of one or more outcomes o_k, wherein the most-predictive set of signals S_k corresponds to a first data set representing a first physiological parameter, and wherein each of the one or more outcomes o_k represents a physiological state measurement;

autonomously learning a set of probabilistic predictive models ô

_k=M_k(S_k), where ô

_k is a prediction of outcome o_k derived from a model M_k that uses as inputs values obtained from the set of signals S_k; and

repeating the operation of autonomously learning incrementally from data that contains examples of values of signals s₁, s₂, . . . , s_D and corresponding outcomes o₁, o₂, . . . , o_K.

29. The method of claim 3, further comprising controlling a therapeutic device in response to an estimated blood pressure value of the patient.

30. An apparatus, comprising:

a computer readable medium having encoded thereon a set of instructions executable by one or more computers to perform one or more operations, the set of instructions comprising;

instructions for receiving physiological data from one or more sensors;

instructions for analyzing the physiological data against a pre-existing model of blood pressure;

instructions for estimating a blood pressure value of the patient, based on analysis of the physiological data against the pre-existing model; and

instructions for displaying, on a display device, an estimate of the blood pressure value of the patient.