Method and apparatus for predicting work of breathing

• US 7,425,201 B2
• Filed: 08/29/2003
• Issued: 09/16/2008
• Est. Priority Date: 08/30/2002
• Status: Active Grant

• Current Assignee: University of Florida Research Foundation, Incorporated (State University System of Florida)
• Original Assignee: University of Florida Research Foundation, Incorporated (State University System of Florida)
• Inventors: Euliano, Neil R., Banner, Michael J., Blanch, Paul B., Brennan, Victor L.
• Primary Examiner(s): Nasser, Jr.; Robert I.
• Application Number:
  US10/652,992
• Publication Number:
  US 20040040560A1
• Time in Patent Office: 1,845 Days
• Field of Search: 600/529, 600/533, 600/538, 600/300
• US Class Current: 600/529
• CPC Class Codes: A61B 5/037 Measuring oesophageal pressure
• CPC Class Codes: A61B 5/08 Detecting, measuring or recording devices for evaluating the respiratory organs A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/082 Evaluation by breath analysis, e.g. determination of the chemical composition of exhaled breath A61B5/083, A61B5/091 take precedence
• CPC Class Codes: A61B 5/083 Measuring rate of metabolism by using breath test, e.g. measuring rate of oxygen consumption
• 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 2016/0036 in the breathing tube and used in both inspiratory and expiratory phase

Abstract

A method of creating a non-invasive predictor of both physiologic and imposed patient effort 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 non-invasive 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:

receiving respiratory parameters of the patient;

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

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

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

providing said at least one output variable from the mathematical model to a ventilator to adjust a ventilator setting;

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.

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, and pulse oximeter plethysmogram.

4. The method of claim 1, wherein the respiratory data further comprises one or more of tidal volume, respiratory resistance, and respiratory compliance.

5. The method of claim 4, wherein the respiratory resistance is derived from an initial airway pressure rise at a beginning portion of an inspiratory phase.

6. The method of claim 5, wherein the beginning portion of the inspiratory phase is selected in the range of 0.0 seconds to 0.05 seconds from a start of the inspiratory phase.

7. 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.

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

receiving respiratory parameters of the patient;

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

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

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

providing the output variable from the mathematical model corresponding to effort of breathing to a display;

wherein the mathematical model is a neural network trained to provide said at least one output variable, and 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.

9. The method of claim 8, 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.

10. The method of claim 9 further comprising calculating Pressure Time Product (PTP) as one of said output variables.

11. The method of claim 8 wherein effort of breathing represents a physiologic work of breathing and an imposed work of breathing.

12. The method of claim 8 wherein the respiratory parameters comprise one or more of airway pressure, airway flow, airway volume, carbon dioxide flow, and pulse oximeter plethysmogram.

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

receiving respiratory parameters of the patient;

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

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

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

providing said at least one output variable from the mathematical model to a ventilator to adjust a ventilator setting;

classifying the patient; and

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

14. The method of claim 13, wherein the patient is classified according to pathophisiology and physiologic parameters related to the patient.

15. The method of claim 14, wherein the physiologic parameters comprise lung resistance and compliance.

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

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

code devices for calculating respiratory data from the respiratory parameters, wherein the respiratory data comprises one or more of breathing frequency, peak inspiratory pressure, inspiratory time, occlusion pressure at 0.1 seconds after breath initiation trigger time, trigger depth, 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 form the mathematical model corresponding to effort of breathing;

wherein the mathematical model is a neural network trained to provide said at least one output variable, and 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.

17. The computer readable medium of claim 16, 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.

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

receiving respiratory parameters of the patient;

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

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

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

providing the output variable from the mathematical model corresponding to effort of breathing to a display;

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.

19. The method of claim 18, wherein the respiratory data further comprises one or more of tidal volume, respiratory resistance, and respiratory compliance.

20. The method of claim 19, wherein the respiratory resistance is derived from an initial airway pressure rise at a beginning portion of an inspiratory phase.

21. The method of claim 20, wherein the beginning portion of the inspiratory phase is selected in the range of 0.0 seconds to 0.05 seconds from a start of the inspiratory phase.

22. The method of claim 18, 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.

23. The method of claim 18, 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.

24. The method of claim 23, 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.

25. The method of claim 24, further comprising calculating Pressure Time Product (PTP) as one of said output variables.

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

receiving respiratory parameters of the patient;

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

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

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

providing the output variable from the mathematical model corresponding to effort of breathing to a display

classifying the patient; and

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

27. The method of claim 26, wherein the patient is classified according to pathophisiology and physiologic parameters related to the patient.

28. The method of claim 27, wherein the physiologic parameters comprise lung resistance and compliance.