Method and apparatus for predicting work of breathing

• US 7,588,543 B2
• Filed: 06/05/2007
• Issued: 09/15/2009
• Est. Priority Date: 08/30/2002
• Status: Expired due to Term

• 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: Brennan, Victor L., Euliano, Neil R., Banner, Michael J., Blanch, Paul B.
• Primary Examiner(s): Nasser; Robert L
• Application Number:
  US11/758,159
• Publication Number:
  US 20070232951A1
• Time in Patent Office: 833 Days
• Field of Search: None
• 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, with a processor, 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;

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.

2. The method of claim 1, 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.

3. The method of claim 2, wherein the approximation of 0.1 L/cm H20 is used for chest wall compliance.

4. The method of claim 2, further comprising calculating power of breathing as a per-minute average of work of breathing as one of said output variables.

5. The method of claim 2 further comprising calculating Pressure Time Product (PTP) as one of said output variables.

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

receiving respiratory parameters of the patient;

calculating, with a processor, respiratory data from the respiratory parameters;

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;

classifying the patient; and

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

7. The method of claim 6, wherein the patient is classified according to pathophysiology and physiologic parameters related to the patient.

8. The method of claim 7, wherein the physiologic parameters comprise lung resistance and compliance.

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

receiving respiratory parameters of a patient, wherein the respiratory parameters comprise one or more of airway pressure, airway flow, airway volume, carbon dioxide flow, and pulse oximeter plethysmogram;

calculating, with a processor, 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;

inputting the respiratory data into a mathematical model configured from clinical data to predict effort of breathing; and

providing at least one output variable from 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, 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.

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

processing device for calculating respiratory data from respiratory parameters of the patient, wherein the respiratory parameters comprise one or more of airway pressure, airway flow, airway volume, carbon dioxide 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 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 least one output variable from the mathematical model corresponding to effort of breathing;

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 esophageal pressure as clinical data input to the neural network.

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

means for measuring respiratory parameters of the patient, wherein the respiratory parameters comprise one or more of airway pressure, airway flow, airway volume, carbon dioxide 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;

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.

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

code devices for receiving measured respirator parameters of the patient, wherein the respiratory parameters comprise one or more of airway pressure, airway flow, 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 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;

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.