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

• US 20050284476A1
• Filed: 06/23/2005
• Published: 12/29/2005
• Est. Priority Date: 06/24/2004
• Status: Active Grant

• Current Assignee: Convergent Engineering, Inc.
• Original Assignee: Convergent Engineering, Inc.
• Inventors: Euliano, Neil R., Meka, Vikas, Blanch, Paul B.
• Granted Patent: US 7,562,657 B2
• Time in Patent Office: Days
• US Class Current: 128/204.21
• 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 estimating intrinsic positive end-expiratory pressure of a respiratory patient comprising:

non-invasively monitoring respiratory airway parameters of a patient spontaneously breathing with assistance by a ventilator; and

calculating an intrinsic positive end-expiratory pressure based on the monitored respiratory airway parameters.

2. The method of claim 1, wherein calculating an intrinsic positive end-expiratory pressure comprises identifying a marker of the respiratory parameters indicative of the intrinsic positive end-expiratory pressure of the patient.

3. The method of claim 2, further comprising measuring the marker to generate an input parameter for use in diagnosis of the patient who may be experiencing intrinsic positive end-expiratory pressure.

4. The method of claim 2, further comprising measuring a plurality of different markers to generate respective input parameters for use in treatment of the patient experiencing intrinsic positive end-expiratory pressure.

5. The method of claim 2, wherein the marker 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.

6. The method of claim 5, further comprising identifying a characteristic of the expiratory air flow versus expiratory air volume trajectory indicative of a positive end-expiratory pressure condition.

7. The method of claim 6, wherein identifying the characteristic comprises predicting an un-exhaled gas volume.

8. The method of claim 5, further comprising identifying a characteristic of the expiratory carbon dioxide flow versus air volume trajectory indicative of a positive end-expiratory pressure condition.

9. The method of claim 5, 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.

10. The method of claim 5, 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 patent'"'"'s lungs being tested.

11. The method of claim 5, wherein the model of the expiratory waveform is derived using a least squares analysis.

12. The method of claim 5, wherein the model of the expiratory waveform is derived using an exponential function analysis.

13. The method of claim 5, wherein the peak to mid-exhalation airflow ratio is derived using a peak exhalation air flow and an expiratory airflow calculated when about 20% to 30% of a tidal air volume remains in the patient'"'"'s lungs.

14. The method of claim 5, further comprising identifying a characteristic of the Capnograph waveform shape indicative of a positive end-expiratory pressure condition.

15. The method of claim 14, wherein the characteristic comprises a rising slope occurring after an increased rise time in an expiratory carbon dioxide flow.

16. The method of claim 3, further comprising using the input parameter to adjust a ventilator setting.

17. The method of claim 3, further comprising using the input parameter to generate display indicia.

18. The method of claim 3, further comprising using the input parameter to calculate a different parameter.

19. The method of claim 2, further comprising:

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

providing at least one output variable from the mathematical model corresponding to intrinsic positive end-expiratory pressure.

20. The method of claim 19, 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.

21. The method of claim 19, 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 airway data of respective patients as clinical data input to the neural network.

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

23. A method for estimating intrinsic positive end-expiratory pressure of a respiratory patient, comprising:

receiving respiratory parameters of a patient being non-invasively monitored and spontaneously breathing with assistance of a ventilator, wherein the respiratory parameters comprise one or more of airway pressure, airway flow, airway volume, and carbon dioxide flow; and

identifying a marker of the respiratory parameters indicative of an intrinsic positive end-expiratory pressure of the patient, wherein the marker 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.

24. The method of claim 23, further comprising measuring the marker to generate an input parameter for diagnosing the patient who may be experiencing intrinsic positive end-expiratory pressure.

25. The method of claim 23, further comprising:

inputting the marker into a mathematical model configured from clinical data for predicting intrinsic positive end-expiratory pressure; and

providing at least one output variable from the mathematical model corresponding to intrinsic positive end-expiratory pressure.

26. An apparatus for estimating intrinsic positive end-expiatory pressure of a respiratory patient comprising:

a sensor for non-invasively monitoring respiratory airway parameters of a patient spontaneously breathing with assistance by a ventilator; and

a processing device comprising a first computer code for deriving an intrinsic positive end-expiratory pressure based on the monitored respiratory airway parameters.

27. The apparatus of claim 26, the processing device further comprising a second computer code for measuring a marker of the respiratory airway parameters indicative of an intrinsic positive end-expiratory pressure of the patient, wherein the respiratory parameters comprise one or more of airway pressure, airway flow, airway volume, and carbon dioxide flow, and wherein the marker 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 further comprising:

a mathematical modeling device created using clinical data to receive the marker and predict PEEPi; and

an output signal that provides at least one output variable from the mathematical model corresponding to PEEPi.

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 airway parameters of respective patients as clinical data input to the neural network.

30. A system for estimating intrinsic positive end-expiratory pressure of a respiratory patient, comprising:

means for non-invasively measuring respiratory parameters of the patient spontaneously breathing with assistance of a ventilator, wherein the respiratory parameters comprise one or more of airway pressure, airway flow, airway volume, and carbon dioxide flow; and

means for deriving an intrinsic positive end-expiratory pressure based on the monitored respiratory airway parameters.

31. The system of claim 30, further comprising means for identifying a marker of the respiratory parameters indicative of an intrinsic positive end-expiratory pressure of the patient, wherein the marker 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.

32. The system of claim 30, further comprising:

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

means for providing at least one output variable from the mathematical model corresponding to PEEPi.

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 airway parameters at the mouth of respective patients as clinical data input to the neural network.

34. A computer readable medium for estimating intrinsic positive end-expiratory pressure of a respiratory patient, comprising:

code devices for receiving non-invasively measured respiratory parameters of the patient spontaneously breathing with assistance of a ventilator; and

code devices for deriving an intrinsic positive end-expiratory pressure based on the received respiratory airway parameters.

35. The computer readable medium of claim 34, further comprising code devices for measuring a marker of the respiratory parameters indicative of an at least one 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 computer readable medium of claim 34, further comprising:

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

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

37. The 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 airway flows at the mouth of respective patients as clinical data input to the neural network.

38. A system for estimating 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 deriving markers indicative of intrinsic positive end-expiratory pressure from the data.

39. The system of claim 38, further comprising an adaptive processor for modeling the patient'"'"'s PEEPi from the desired parameters and providing a control variable responsive to at least one input indicative of the patient'"'"'s present PEEPi.

40. The system of claim 39, further comprising a controller for providing the control variable to a ventilator assisting of the patient.