Noninvasive Monitoring for Fluid Resuscitation

• US 20150141769A1
• Filed: 11/14/2014
• Published: 05/21/2015
• 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 11,395,594 B2
• Time in Patent Office: Days
• US Class Current: 600/301
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/002 Monitoring the patient using a local or closed circuit, e.g. in a room or building A61B5/0017 takes precedence
• 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 5/031 Intracranial pressure
• 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 5/4836 Diagnosis combined with treatment in closed-loop systems or methods A61B5/0036 takes precedence
• 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 5/742 using visual displays A61B5/339 takes precedence
• 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 2005/14208 : with a programmable infusion control system, characterised by the infusion program
• CPC Class Codes: A61M 2205/3327 : Measuring
• CPC Class Codes: A61M 2205/50 : with microprocessors or computers
• CPC Class Codes: A61M 2205/52 : with memories providing a history of measured variating parameters of apparatus or patient
• CPC Class Codes: A61M 5/142 : Pressure infusion, e.g. using pumps
• CPC Class Codes: A61M 5/1723 : using feedback of body parameters, e.g. blood-sugar, pressure measurement of body parameters A61B5/00
• CPC Class Codes: G16H 20/17 : delivered via infusion or injection
• CPC Class Codes: G16H 40/63 : for local operation
• CPC Class Codes: G16H 40/67 : for remote operation
• CPC Class Codes: G16H 50/50 : for simulation or modelling of medical disorders

Abstract

Novel tools and techniques for assessing, predicting and/or estimating effectiveness of fluid resuscitation of a patient and/or an amount of fluid needed for effective resuscitation of the patient, in some cases, noninvasively.

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;

instructions for assessing effectiveness of fluid resuscitation of the patient based on analysis of the physiological data against the pre-existing model; and

instructions for displaying, on a display device, an assessment of the effectiveness of fluid resuscitation of the patient.

2. The system of claim 1, further comprising a therapeutic device, wherein the set of instructions further comprises instructions for controlling the therapeutic device based at least in part on the assessment of the effectiveness of fluid resuscitation of the patient.

3. 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.

4. A method, comprising:

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

analyzing, with a computer system, the physiological data against a pre-existing model;

assessing effectiveness of fluid resuscitation of the patient based on analysis of the physiological data; and

displaying, on a display device, an assessment of the effectiveness of fluid resuscitation of the patient.

5. The method of claim 4, wherein assessing effectiveness of fluid resuscitation of the patient comprises estimating the effectiveness of fluid resuscitation of the patient at a current time.

6. The method of claim 4, wherein assessing effectiveness of fluid resuscitation of the patient comprises predicting the effectiveness of fluid resuscitation of the patient at a future time.

7. The method of claim 4, wherein assessing effectiveness of fluid resuscitation of the patient comprises estimating an amount of fluid needed for effective fluid resuscitation of the patient.

8. The method of claim 4, wherein assessing effectiveness of fluid resuscitation of the patient comprises estimating a probability that the patient requires fluids.

9. The method of claim 4, further comprising:

controlling operation of a therapeutic device based on the assessment of the effectiveness of fluid resuscitation of the patient.

10. The method of claim 9, wherein controlling operation of a therapeutic device comprises controlling a flow rate of an intravenous pump, controlling a drip rate of an intravenous drip, or controlling a rapid infuser.

11. The method of claim 4, wherein assessing effectiveness of fluid resuscitation of the patient comprises assessing effectiveness of fluid resuscitation of the patient based at least in part on one or more compensatory reserve index (“

CRI”

) values of the patient.

12. The method of claim 11, where assessing effectiveness of fluid resuscitation of the patient comprises calculating a measure of effectiveness RE, based on the following formula:

RE=ƒ

_RE(CRI_t,FV_t)

where,

RE is a measure of fluid resuscitation effectiveness, ƒ

_RE(CRI_t, FV_t) is a relationship derived based on empirical study, CRI_t is a time history of CRI values, and FV_t is a time history of fluid volume being given to the patient.

13. The method of claim 12, wherein RE ranges in value from 0 to 1.

14. The method of claim 13, wherein when RE=1, resuscitation is proceeding effectively, when RE=0, fluid resuscitation is not producing the desired result.

15. The method of claim 11, where assessing effectiveness of fluid resuscitation of the patient comprises calculating a measure of effectiveness RE, based on the following formula:

RE=ƒ

_RE(CRI_t,FV_t,S_t)

where,

RE is a measure of fluid resuscitation effectiveness, ƒ

_RE(CRI_t,FV_t) is a relationship derived based on empirical study, CRI_t is a time history of CRI values, FV_t is a time history of fluid volume being given to the patient, and S_t is a time history of physiological data received from the one or more sensors.

16. The method of claim 11, wherein estimating an amount of fluid needed for effective resuscitation of the patient comprises calculating a volume V needed for effective resuscitation of the patient, based on the following formula:

V=ƒ

_V(CRI_t,FV_t)

where V is an estimated volume of fluid needed by a patient need to prevent over or under resuscitation, ƒ

_V(CRI_t,FV_t) is a relationship derived based on empirical study, CRI_t is a time history of CRI values, and FV_t is a time history of fluid volume being given to the patient.

17. The method of claim 11, wherein estimating an amount of fluid needed for effective resuscitation of the patient comprises calculating a volume V needed for effective resuscitation of the patient, based on the following formula:

V=ƒ

_V(CRI_t,FV_t,S_t)

where V is an estimated volume of fluid needed by a patient need to prevent over or under resuscitation, ƒ

_V(CRI_t, FV_t, S_t) is a relationship derived based on empirical study, CRI_t is a time history of CRI values, FV_t is a time history of fluid volume being given to the patient, and S_t is a time history of physiological data received from the one or more sensors.

18. The method of claim 11, wherein estimating effectiveness of fluid resuscitation of the patient comprises estimating a probability P_f that the patient requires fluids, based on the following formula:

P_f=ƒ

_Pf(CRI_t,S_t)

where P_f is the estimated probability that the patient requires fluid, ƒ

_Pf(CRI_t,S_t) is a relationship derived based on empirical study, CRI_t is a time history of CRI values, and S_t is a time history of physiological data received from the one or more sensors.

19. The method of claim 11, wherein assessing effectiveness of fluid resuscitation of a patient comprises estimating the one or more CRI values.

20. The method of claim 19, wherein assessing effectiveness of fluid resuscitation of a patient comprises estimating a plurality of values of CRI over time and assessing effectiveness of fluid resuscitation of a patient based on changes in values of CRI over time.

21. The method of claim 19, wherein estimating one or more CRI values of the patient comprises estimating one or more CRI values by comparing the physiological data to the pre-existing model, which is constructed using the following formula:

22. The method of claim 19, wherein the physiological data comprises waveform data and wherein estimating each of the one or more CRI values 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.

23. The method of claim 19, wherein the physiological data comprises waveform data, and wherein estimating each of the one or more CRI values comprises:

comparing the waveform data with a plurality of sample waveforms, each of the sample waveforms corresponding to a different value of the compensatory reserve index 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 CRI value for the patient.

24. The method of claim 4, wherein assessing effectiveness of fluid resuscitation of a patient comprises assessing effectiveness of fluid resuscitation of a patient based on a fixed time history of monitoring the physiological data of the patient.

25. The method of claim 4, wherein assessing effectiveness of fluid resuscitation of a patient comprises assessing effectiveness of fluid resuscitation of a patient based on a dynamic time history of monitoring the physiological data of the patient.

26. The method of claim 4, 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.

27. The method of claim 4, wherein the physiological data comprises blood pressure waveform data.

28. The method of claim 4, wherein the physiological data comprises plethysmograph waveform data.

29. The method of claim 4, wherein the physiological data comprises photoplethysmograph (PPG) waveform data.

30. The method of claim 4, further comprising:

generating the pre-existing model.

31. The method of claim 30, 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.

32. The method of claim 31, wherein the one or more physiological states comprises one or more states selected from the group consisting of reduced circulatory system volume, blood loss, added fluids to blood volume, dehydration, cardiovascular collapse or near-cardiovascular collapse, euvolemia, hypervolemia.

33. The method of claim 31, 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.

34. 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 the physiological data from the one or more sensors;

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

instructions for assessing effectiveness of fluid resuscitation of the patient based on analysis of the physiological data against the pre-existing model; and

instructions for displaying, on a display device, an assessment of the effectiveness of fluid resuscitation of the patient.