Method and Apparatus for Predicting Work of Breathing

• US 20120215081A1
• Filed: 02/20/2012
• Published: 08/23/2012
• Est. Priority Date: 08/30/2002
• Status: Active Grant

• Current Assignee: University of Florida Research Foundation, Incorporated (State University System of Florida)
• Original Assignee: Michael J. Banner, Neil R. Euliano, Paul B. Blanch, Victor L. Brennan
• Inventors: Euliano, Neil R., Brennan, Victor L., Banner, Michael J., Blanch, Paul B.
• Granted Patent: US 8,672,858 B2
• Time in Patent Office: Days
• US Class Current: 600/323
• CPC Class Codes: A61B 5/037 Measuring oesophageal pressure
• CPC Class Codes: A61B 5/0836 Measuring rate of CO2 production
• CPC Class Codes: A61B 5/085 Measuring impedance of respiratory organs or lung elasticity
• CPC Class Codes: A61B 5/087 Measuring breath flow
• CPC Class Codes: A61M 16/026 specially adapted for predicting, e.g. for determining an information representative of a flow limitation during a ventilation cycle by using a root square technique or a regression analysis
• CPC Class Codes: A61M 2016/0021 with a proportional output signal, e.g. from a thermistor
• CPC Class Codes: A61M 2016/0027 pressure meter
• CPC Class Codes: A61M 2016/0036 in the breathing tube and used in both inspiratory and expiratory phase
• CPC Class Codes: A61M 2016/103 the CO2 concentration
• CPC Class Codes: A61M 2205/15 Detection of leaks
• CPC Class Codes: A61M 2230/205 partial oxygen pressure (P-O2)
• CPC Class Codes: A61M 2230/432 partial CO2 pressure (P-CO2)
• CPC Class Codes: A61M 2230/46 Resistance or compliance of the lungs

Abstract

A method of creating a noninvasive predictor of both physiologic and imposed patient effort of breathing from airway pressure and flow sensors attached to the patient using an adaptive mathematical model. The patient effort is commonly measured via work of breathing, power of breathing, or pressure-time product of esophageal pressure and is important for properly adjusting ventilatory support for spontaneously breathing patients. The method of calculating this noninvasive predictor is based on linear or non-linear calculations using multiple parameters derived from the above-mentioned sensors.

Claims

1. A method for estimating effort of breathing of a patient, comprising:

collecting noninvasive respiratory parameters of the patient receiving mechanical ventilation;

calculating respiratory data from the respiratory parameters;

inputting the respiratory data into a mathematical model created using clinical data; and

providing at least one output variable from the mathematical model corresponding to effort of breathing.

2. The method of claim 1 wherein effort of breathing represents a physiologic work of breathing and an imposed work of breathing.

3. The method of claim 1 wherein the respiratory parameters comprise one or more of airway pressure, airway flow, airway volume, carbon dioxide flow, leak flow, and pulse oximeter plethysmogram.

4. The method of claim 3, wherein compensation for leak flow is performed before the respiratory data are calculated.

5. The method of claim 4, wherein the compensation for leak flow is calculated from a step selected from the group consisting of:

(a) equally redistributing a measured lost volume over a breath and subtracting a bias flow from a measured flow waveform, (b) calculating a parabolic conductance and using the parabolic conductance to correct the measured flow waveform, (c) windowing the calculation of parabolic conductance for real-time correction of the measured flow waveform, and (d) calculating the parabolic conductance using a breath detector that functions to detect a start and end of each breath to generate the parabolic conductance that is applied to the measured flow waveform.

6. The method of claim 1, wherein the mathematical model is selected from the group consisting of a neural network model, a fuzzy logic model, a mixture of experts model, or a polynomial model.

7. The method of claim 1, wherein the respiratory data comprises one or more of tidal volume, breathing frequency, peak inspiratory pressure, inspiratory time, occlusion pressure at 0.1 seconds after breath initiation trigger time, trigger depth, respiratory resistance, respiratory compliance, end-tidal carbon dioxide, variations in the pulse oximeter plethysmogram, and concavity/convexity of a pressure waveform.

8. The method of claim 1, wherein the mechanical ventilation is delivered via an invasive or noninvasive ventilator.

9. The method of claim 8, wherein the noninvasive ventilator comprises a patient interface assembly selected from the group consisting of:

a tube, a nasal mask, a nasal/oral mask, a total face mask, a nasal cannula, and nasal prongs/pillows.

10. The method of claim 1, further comprising providing said at least one output variable from the mathematical model to a ventilator to adjust a ventilator setting.

11. The method of claim 1, further comprising providing the output variable from the mathematical model corresponding to effort of breathing to a display.

12. The method of claim 1, wherein the output variable comprises one or more of a physiologic work of breathing variable, an imposed work of breathing variable, a power of breathing variable, and a pressure time product variable, each representing the effort exerted by the patient to breathe.

13. The method of claim 1, wherein the mathematical model is a neural network trained to provide said at least one output variable, wherein the training of the neural network comprises clinical testing of a test population of patients using esophageal pressure as clinical data input to the neural network.

14. The method of claim 13, wherein a drop in esophageal pressure is plotted on a pressure-volume plot and a loop is created and integrated with chest wall compliance line to calculate inspiratory work of breathing as one of said output variables.

15. The method of claim 14, wherein the approximation of 0.1 L/cm H2O is, used for chest wall compliance.

16. The method of claim 14, further comprising calculating power of breathing as a per-minute average of work of breathing as one of said output variables.

17. The method of claim 14, further comprising calculating Pressure Time Product (PTP) as one of said output variables.

18. The method of claim 1, further comprising:

classifying the patient; and

selecting a mathematical model based on a classification of the patient.

19. The method of claim 18, wherein the patient is classified according to pathophysiology and physiologic parameters related to the patient.

20. The method of claim 19, wherein the physiologic parameters comprise lung resistance and compliance.

21. An apparatus for estimating effort of breathing of a patient, comprising:

a processing device for calculating respiratory data derived from noninvasive respiratory parameters of a patient receiving mechanical ventilation, wherein the noninvasive respiratory parameters comprise one or more of airway pressure, airway flow, airway volume, carbon dioxide flow, leak flow and pulse oximeter plethysmogram, and wherein the respiratory data comprises one or more of tidal volume, breathing frequency, peak inspiratory pressure, inspiratory time, occlusion pressure at 0.1 seconds after breath initiation trigger time, trigger depth, respiratory resistance, respiratory compliance, end-tidal carbon dioxide, variations in the pulse oximeter plethysmogram, and concavity/convexity of a pressure waveform;

a mathematical modeling device created using clinical data to receive the respiratory data and predict effort of breathing; and

an output signal that provides at east one output variable from the mathematical model corresponding to effort of breathing.

22. The apparatus of claim 21, wherein the mathematical modeling device is a neural network trained to provide said at least one output variable, wherein the training of the neural network comprises clinical testing of a test population of patients.

23. The apparatus of claim 21, wherein the output variable comprises one or more of a physiologic work of breathing variable, an imposed work of breathing variable, a power of breathing variable, and a pressure time product variable, each representing the effort exerted by the patient to breathe.

24. The apparatus of claim 21, wherein the mechanical ventilation is delivered via a noninvasive ventilator that comprises a patient interface assembly selected from the group consisting of:

a tube, a nasal mask, a nasal/oral mask, a total face mask, a nasal cannula, and nasal prongs/pillows.

25. A system for estimating effort of breathing of a patient, comprising:

means for collecting noninvasive respiratory parameters of the patient receiving mechanical ventilation, wherein the noninvasive respiratory parameters comprise one or more of airway pressure, airway flow, airway volume, carbon dioxide flow, leak flow and pulse oximeter plethysmogram;

means for calculating respiratory data from the respiratory parameters, wherein the respiratory data comprises one or more of tidal volume, breathing frequency, peak inspiratory pressure, inspiratory time, occlusion pressure at 0.1 seconds after breath initiation trigger time, trigger depth, respiratory resistance, respiratory compliance, end-tidal carbon dioxide, variations in the pulse oximeter plethysmogram, and concavity/convexity of a pressure waveform;

means for predicting effort of breathing using a mathematical model created using clinical data that receives the respiratory data; and

means for providing at least one output variable from the mathematical model corresponding to effort of breathing.

26. The system of claim 25, wherein the mathematical model is a neural network trained to provide said at least one output variable, wherein the training of the neural network comprises clinical testing of a test population of patients.

27. The system of claim 25, wherein the output variable comprises one or more of a physiologic work of breathing variable, an imposed work of breathing variable, a power of breathing variable, and a pressure time product variable, each representing the effort exerted by the patient to breathe.

28. The system of claim 25, wherein the mechanical ventilation is delivered via a noninvasive ventilator that comprises a patient interface assembly selected from the group consisting of:

a nasal mask, a nasal/oral mask, a total face mask, a nasal cannula, and nasal prongs/pillows.

29. A computer readable medium for estimating effort of breathing of a patient, comprising:

code devices for receiving measured noninvasive respiratory parameters of the patient receiving mechanical ventilation, wherein the noninvasive respiratory parameters comprise one or more of airway pressure, airway flow, airway volume, carbon dioxide flow, leak flow and pulse oximeter plethysmogram;

code devices for calculating respiratory data from the respiratory parameters, wherein the respiratory data comprises one or more of tidal volume, breathing frequency, peak inspiratory pressure, inspiratory time, occlusion pressure at 0.1 seconds after breath initiation trigger time, trigger depth, respiratory resistance, respiratory compliance, end-tidal carbon dioxide, variations in the pulse oximeter plethysmogram, and concavity/convexity of a pressure waveform;

code devices for predicting effort of breathing using a mathematical model created using clinical data that receives the respiratory data; and

code devices for providing at least one output variable from the mathematical model corresponding to effort of breathing.

30. The computer readable medium of claim 29, wherein the mathematical model is a neural network trained to provide said at least one output variable, wherein the training of the neural network comprises clinical testing of a test population of patients.

31. The computer readable medium of claim 29, wherein the output variable comprises one or more of a physiologic work of breathing variable, an imposed work of breathing variable, a power of breathing variable, and a pressure time product variable, each representing the effort exerted by the patient to breathe.

32. A method of modeling work of breathing for a ventilator patient comprising:

monitoring a plurality of noninvasive respiratory parameters of a patient connected to a noninvasive ventilator related to a plurality of sample patients’

inspiratory efforts while connected to respective noninvasive ventilators;

collecting information related to the parameters for a desired period of time;

creating a mathematical model of patient inspiratory effort from the information collected for the plurality of patients over the desired period of time;

applying an input indicative of the ventilator patient'"'"'s current inspiratory effort to the mathematical model; and

providing an actual breathing effort variable based on the input.

33. The method of claim 32, wherein the mathematical model is a neural network.

34. The method of claim 33, further comprising:

providing the information related to the parameters as primary teaching inputs to the neural network; and

training the neural network to provide an actual breathing effort variable based on the primary teaching inputs.

35. The method of claim 32, wherein the noninvasive ventilator comprises a patient interface assembly selected from the group consisting of a nasal mask, a nasal/oral mask, a total face mask, a nasal cannula, and nasal prongs/pillows.