METHOD AND APPARATUS FOR DETECTING AND QUANTIFYING INTRINSIC POSITIVE END-EXPIRATORY PRESSURE

• US 20090293877A1
• Filed: 07/06/2009
• Published: 12/03/2009
• 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 8,544,466 B2
• Time in Patent Office: Days
• 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 detecting and quantifying intrinsic positive end-expiratory pressure (PEEPi) of a respiratory patient breathing with the assistance of a ventilator. A processing device receives respiratory airway data from one or more sensors adapted to non-invasively monitor a respiratory patient, calculates from the respiratory airway data two or more parameters that are indicative of or quantify intrinsic positive end-expiratory pressure of the patient, and generates a predicted quantitative value for intrinsic positive end-expiratory pressure based on the two or more parameters. The respiratory airway data is transformed into a predicted quantitative value for intrinsic positive end-expiratory pressure (PEEPi).

Claims

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

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

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

generating by the processing device a predicted quantitative value for intrinsic positive end-expiratory pressure based on the two or more parameters.

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, 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.

4. The method of claim 3, 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.

5. The method of claim 3, 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.

6. The method of claim 3, 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.

7. The method of claim 3, 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.

8. The method of claim 3, wherein the model of the expiratory waveform is derived using an analysis comprising least squares analysis or an exponential function analysis.

9. The method of claim 3, 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 lungs of a patient.

10. The method of claim 3, further comprising using a characteristic of the Capnograph waveform shape as a parameter indicative of a positive end-expiratory pressure condition.

11. The method of claim 10, wherein the characteristic comprises a rising slope occurring after an increased rise time in an expiratory carbon dioxide flow.

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

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

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

15. The method of claim 2, wherein the processing device uses a mathematical model to generate the predicted quantitative value for intrinsic positive end-expiratory pressure based on the two or more parameters and wherein the mathematical model is created using clinical data.

16. The method of claim 15, wherein the mathematical model comprises a neural network model, a fuzzy logic model, a mixture of experts model, or a polynomial model.

17. The method of claim 16, 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.

18. 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.

23. A computer-readable medium having computer-executable instructions for causing a computer to perform the steps recited in claim 1.

19. An apparatus for detecting and quantifying intrinsic positive end-expiatory pressure (PEEPi) 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 adapted to calculate 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 of intrinsic positive end-expiratory pressure and generate a predicted quantitative value for an intrinsic positive end-expiratory pressure based on the two or more parameters.

20. The apparatus of claim 19, wherein the respiratory airway data comprise one or more of airway pressure, airway flow, airway volume, and carbon dioxide flow,

21. The apparatus of claim 19, 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.

22. A system for detecting and quantifying intrinsic positive end-expiratory pressure (PEEPi) 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 adapted to calculate 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 adapted to model the patient'"'"'s intrinsic positive end-expiratory pressure PEEPi from the two or more parameters to derive a predicted quantitative value for PEEPi and output a variable based on the predicted quantitative value for intrinsic positive end-expiratory pressure.