Method and apparatus for non-invasive prediction of intrinsic positive end-expiratory pressure (PEEPi) in patients receiving ventilator support

• US 7,562,657 B2
• Filed: 06/23/2005
• Issued: 07/21/2009
• Est. Priority Date: 06/24/2004
• Status: Active Grant

• Current Assignee: Convergent Engineering, Inc.
• Original Assignee: Convergent Engineering, Inc.
• Inventors: Meka, Vikas, Blanch, Paul B., Euliano, Neil R.
• Primary Examiner(s): Yu; Justine R
• Assistant Examiner(s): DIXON, ANNETTE FREDRICKA
• Application Number:
  US11/165,617
• Publication Number:
  US 20050284476A1
• Time in Patent Office: 1,489 Days
• Field of Search: 600/529, 600/532, 600/533, 600/538, 128/200.24, 128/204.18, 128/204.21, 128/204.23, 128/204.26, 700/50
• US Class Current: 128/204.23
• CPC Class Codes: A61M 16/00 Devices for influencing the respiratory system of patients by gas treatment, e.g. mouth-to-mouth respiration; Tracheal tubes stimulating the respiratory movement by mechanical, pneumatic or electrical means, iron lungs combined with gas breathing means A61H31/00
• CPC Class Codes: A61M 16/0009 with sub-atmospheric pressure, e.g. during expiration
• 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 2016/0036 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 2230/432 partial CO2 pressure (P-CO2)

Abstract

The present invention describes a method and apparatus for non-invasive prediction of the “intrinsic positive end-expiratory pressure” (PEEPi) which is secondary to a trapping of gas, over and above that which is normal in the lungs; the presence of PEEPi imposes an additional workload upon the spontaneously breathing patient. Several indicators or markers are presented to detect and quantify PEEP_i non-invasively The markers may include an expiratory air flow versus expiratory air volume trajectory, an expiratory carbon dioxide flow versus expiratory air volume trajectory, an expiratory carbon dioxide volume to expiratory air volume ratio, an expiratory air flow at onset of inhalation, a model of an expiratory waveform, a peak to mid-exhalation airflow ratio, duration of reduced exhaled airflow, and a Capnograph waveform shape.

Claims

1. A method for detecting and quantifying intrinsic positive end-expiratory pressure of a respiratory patient breathing with the assistance of a ventilator comprising:

receiving respiratory airway data from one or more sensors adapted to non-invasively monitor a respiratory patient;

calculating from the respiratory airway data two or more parameters that are indicative of or quantify intrinsic positive end-expiratory pressure of the patient;

inputting the two or more parameters that are indicative of or quantify intrinsic positive end-expiratory pressure into a mathematical model;

generating by the mathematical model a predicted quantitative value for intrinsic positive end-expiratory pressure representing an excess lung volume of the patient based on the two or more parameters; and

providing a variable to a ventilator or respiratory monitor based on the predicted quantitative value for intrinsic positive end-expiratory pressure.

2. The method of claim 1, wherein the two or more parameters that are indicative of or quantify intrinsic positive end-expiratory pressure are calculated in accordance with predetermined markers of intrinsic positive end-expiratory pressure.

3. The method of claim 2, further comprising using the predicted quantitative value for intrinsic positive end-expiratory pressure in diagnosis of the patient who may be experiencing intrinsic positive end-expiratory pressure.

4. The method of claim 3, further comprising using the predicted quantitative value for intrinsic positive end-expiratory pressure to adjust a ventilator setting.

5. The method of claim 3, further comprising using the predicted quantitative value for intrinsic positive end-expiratory pressure to generate display indicia.

6. The method of claim 3, further comprising using one of the parameters to calculate a different parameter.

7. The method of claim 2, further comprising using the predicted quantitative value for intrinsic positive end-expiratory pressure in treatment of the patient experiencing intrinsic positive end-expiratory pressure.

8. The method of claim 2, wherein the predetermined markers comprise one or more of an expiratory air flow versus expiratory air volume trajectory, an expiratory carbon dioxide flow versus expiratory air volume trajectory, an expiratory carbon dioxide volume to expiratory air volume ratio, an expiratory air flow at onset of inhalation, a model of an expiratory waveform, a peak to mid-exhalation airflow ratio, duration of reduced exhaled airflow, and a Capnograph waveform shape.

9. The method of claim 8, further comprising using a characteristic of the expiratory air flow versus expiratory air volume trajectory as a parameter indicative of a positive end-expiratory pressure condition.

10. The method of claim 8, further comprising using a characteristic of the expiratory carbon dioxide flow versus air volume trajectory as a parameter indicative of a positive end-expiratory pressure condition.

11. The method of claim 8, wherein the expiratory carbon dioxide to expiratory air volume ratio is derived from an elevated end tidal carbon dioxide flow and expiratory air volume of a breath of a patient.

12. The method of claim 8, wherein the expiratory air flow at onset of inhalation is derived from an airflow at onset of inhalation of the patient and a resistance to airflow produced by the airways of the lungs of a patient being tested.

13. The method of claim 8,wherein the model of the expiratory waveform is derived using a least squares analysis.

14. The method of claim 8 wherein the model of the expiratory waveform is derived using an exponential function analysis.

15. The method of claim 8, wherein the peak to mid-exhalation airflow ratio is derived using a peak exhalation air flow and an expiratory air flow calculated when about 20% to 30% of a tidal air volume remains in the lungs of a patient.

16. The method of claim 8, further comprising using a characteristic of the Capnograph waveform shape as a parameter indicative of a positive end-expiratory pressure condition.

17. The method of claim 16, wherein the characteristic comprises a rising slope occurring after an increased rise time in an expiratory carbon dioxide flow.

18. The method of claim 2,

wherein the mathematical model is created using clinical data.

19. The method of claim 18, 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.

20. 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 two or more parameters calculated from respiratory airway data of respective patients as clinical data input to the neural network.

21. The method of claim 1, wherein the respiratory airway data comprise one or more of airway pressure, airway flow, airway volume, and end-tidal carbon dioxide flow.

22. A system for detecting and quantifying intrinsic positive end-expiratory pressure of a respiratory patient comprising:

a signal processor for non-invasively collecting data corresponding to a patient'"'"'s expiratory effort while spontaneously breathing with assistance of a ventilator; and

a parameter extraction module for calculating two or more parameters that are indicative of or quantify intrinsic positive end-expiratory pressure from the data used in estimating intrinsic positive end-expiratory pressure;

an adaptive processor for modeling the patient'"'"'s intrinsic positive end-expiratory pressure PEEPi from the two or more parameters to derive a predicted quantitative value for PEEPi representing an excess lung volume of the patient and providing a variable based on the predicted quantitative value for intrinsic positive end-expiratory pressure; and

a controller for providing the variable to a ventilator or respiratory monitor.

23. A method for detecting and quantifying intrinsic positive end-expiratory pressure of a respiratory patient breathing with the assistance of a ventilator, comprising:

receiving respiratory airway data from one or more sensors adapted to non-invasively monitor a patient, wherein the respiratory data comprise one or more of airway pressure, airway flow, airway volume, and carbon dioxide flow;

calculating from the respiratory airway data two or more parameters that are indicative of or quantify intrinsic positive end-expiratory pressure of the patient based on predetermined markers, wherein said markers comprise one or more of an expiratory air flow versus expiratory air volume trajectory, an expiratory carbon dioxide flow versus expiratory air volume trajectory, an expiratory carbon dioxide volume to expiratory air volume ratio, an expiratory air flow at onset of inhalation, a model of an expiratory waveform, a peak to mid-exhalation airflow ratio, duration of reduced exhaled airflow, and a Capnograph waveform shape;

inputting the two or more parameters that are indicative of or quantify intrinsic positive end-expiratory pressure into a mathematical model;

generating by the mathematical model a predicted quantitative value for intrinsic positive end-expiratory pressure representing an excess lung volume of the patient based on the two or more parameters; and

providing a variable to a ventilator or respiratory monitor based on the predicted quantitative value for intrinsic positive end-expiratory pressure.

24. The method of claim 23, further comprising using the predicted quantitative value for intrinsic positive end-expiratory pressure for diagnosing the patient who may be experiencing intrinsic positive end-expiratory pressure.

25. The method of claim 23,

wherein the mathematical model is configured from clinical data for predicting intrinsic positive end-expiratory pressure.

26. An apparatus for detecting and quantifying intrinsic positive end-expiatory pressure of a respiratory patient breathing with the assistance of a ventilator comprising:

a sensor for non-invasively monitoring respiratory airway data of a patient; and

a processing device for calculating from the respiratory airway data two or more parameters that are indicative of or quantify intrinsic positive end-expiratory pressure of the patient based on predetermined markers;

inputting the two or more parameters that are indicative of or quantify intrinsic positive end-expiratory pressure into a mathematical modeling device;

deriving by the mathematical modeling device a predicted quantitative value for an intrinsic positive end-expiratory pressure representing an excess lung volume of the patient based on the two or more parameters; and

providing a variable to a ventilator or respiratory monitor based on the predicted quantitative value for intrinsic positive end-expiratory pressure.

27. The apparatus of claim 26, wherein the respiratory airway data comprise one or more of airway pressure, airway flow, airway volume, and carbon dioxide flow, and wherein the predetermined markers comprises one or more of an expiratory air flow versus expiratory air volume trajectory, an expiratory carbon dioxide flow versus expiratory air volume trajectory, an expiratory carbon dioxide volume to expiratory air volume ratio, an expiratory air flow at onset of inhalation, a model of an expiratory waveform, a peak to mid-exhalation airflow ratio, duration of reduced exhaled airflow, and a Capnograph waveform shape.

28. The apparatus of claim 26

wherein the mathematical modeling device is created using clinical data.

29. The apparatus of claim 28, 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 using two or more parameters that are indicative of or quantify intrinsic positive end-expiratory pressure calculated from the respiratory airway data of respective patients as clinical data input to the neural network.

30. A system for detecting and quantifying intrinsic positive end-expiratory pressure of a respiratory patient breathing with the assistance of a ventilator, comprising:

means for non-invasively measuring respiratory data of the patient, wherein the respiratory data comprise one or more of airway pressure, airway flow, airway volume, and carbon dioxide flow;

means for calculating two or more parameters that are indicative of or quantify intrinsic positive end-expiratory pressure from the respiratory data;

means for inputting the two or more parameters that are indicative of or quantify intrinsic positive end-expiratory pressure into a mathematical model;

means for deriving by the mathematical model a predicted quantitative value for an intrinsic positive end-expiratory pressure representing an excess lung volume of the patient based on the two or more parameters; and

means for providing a variable to a ventilator or respiratory monitor based on the predicted quantitative value for intrinsic positive end-expiratory pressure.

31. The system of claim 30, wherein the two or more parameters that are indicative of or quantify intrinsic positive end-expiratory pressure of the patient are based on predetermined markers comprising two or more of an expiratory air flow versus expiratory air volume trajectory, an expiratory carbon dioxide flow versus expiratory air volume trajectory, an expiratory carbon dioxide volume to expiratory air volume ratio, an expiratory air flow at onset of inhalation, a model of an expiratory waveform, a peak to mid-exhalation airflow ratio, duration of reduced exhaled airflow, and a Capnograph waveform shape.

32. The system of claim 30, further comprising:

means for predicting effort of breathing using the mathematical model created using clinical data.

33. The system of claim 32, 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 two or more parameters that are indicative of or quantify intrinsic positive end-expiratory pressure calculated from respiratory data at the mouth of respective patients as clinical data input to the neural network.

34. A tangible computer readable medium for detecting and quantifying intrinsic positive end-expiratory pressure of a respiratory patient breathing with assistance of a ventilator, comprising:

code devices for receiving non-invasively measured respiratory data of the patient;

code devices for calculating two or more parameters that are indicative of or quantify intrinsic positive end-expiratory pressure from the respiratory data;

code devices for simultaneously inputting the two or more parameters that are indicative of or quantify intrinsic positive end-expiratory pressure into a mathematical model comprising a linear model or a non-linear model;

code devices for deriving by the mathematical model a predicted quantitative value for an intrinsic positive end-expiratory pressure representing an excess lung volume of the patient based on the two or more parameters; and

code devices for providing a variable to a ventilator or respiratory monitor based on the predicted quantitative value for intrinsic positive end-expiratory pressure.

35. The tangible computer readable medium of claim 34, wherein the two or more parameters that are indicative of or quantify intrinsic positive end-expiratory pressure are based on predetermined markers comprising at least two of expiratory air flow versus expiratory air volume trajectory, an expiratory carbon dioxide flow versus expiratory air volume trajectory, an expiratory carbon dioxide volume to expiratory air volume ratio, an expiratory air flow at onset of inhalation, a model of an expiratory waveform, a peak to mid-exhalation airflow ratio, duration of reduced exhaled airflow, and a Capnograph waveform shape.

36. The tangible computer readable medium of claim 34, further comprising:

code devices for predicting effort of breathing using the mathematical model created using clinical data.

37. The tangible computer readable medium of claim 36, 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 two or more parameters calculated from respiratory data of airway flows at the mouth of respective patients as clinical data input to the neural network.