MODEL-PREDICTIVE ONLINE IDENTIFICATION OF PATIENT RESPIRATORY EFFORT DYNAMICS IN MEDICAL VENTILATORS

• US 20100071696A1
• Filed: 09/25/2008
• Published: 03/25/2010
• Est. Priority Date: 09/25/2008
• Status: Abandoned Application

• Current Assignee: Covidien LP (Medtronic Plc)
• Original Assignee: Nellcor Puritan Bennett LLC (Medtronic Plc)
• Inventors: Jafari, Mehdi M.
• Application Number:
  US12/238,248
• Publication Number:
  US 20100071696A1
• Time in Patent Office: Days
• US Class Current: 128/204.230
• 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 16/0063 Compressors
• 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 16/0833 T- or Y-type connectors, e.g. Y-piece
• CPC Class Codes: A61M 2016/0021 with a proportional output signal, e.g. from a thermistor
• CPC Class Codes: A61M 2016/0027 pressure meter
• CPC Class Codes: A61M 2016/0036 in the breathing tube and used in both inspiratory and expiratory phase
• CPC Class Codes: A61M 2016/0039 in the inspiratory circuit
• CPC Class Codes: A61M 2016/0042 in the expiratory circuit
• CPC Class Codes: A61M 2205/15 Detection of leaks
• CPC Class Codes: A61M 2205/3553 remote, e.g. between patient's home and doctor's office
• CPC Class Codes: A61M 2205/3584 using modem, internet or bluetooth
• CPC Class Codes: A61M 2205/502 User interfaces, e.g. screens or keyboards
• CPC Class Codes: A61M 2205/52 with memories providing a history of measured variating parameters of apparatus or patient
• CPC Class Codes: A61M 2230/40 Respiratory characteristics

Abstract

Systems and methods for efficient computation of patient respiratory muscle effort are provided. According to one embodiment, patient-ventilator characteristics are received, estimated and/or measured representing values of parameters of interest associated with properties or attributes of a ventilated patient system. Online quantification of respiratory muscle effort of the patient is continuously performed by (i) establishing a respiratory predictive model of the ventilated patient system based on an equation of motion and functions that approximate clinically-observed, patient-generated muscle pressures, (ii) determining an instantaneous leak flow value for the ventilated patient system, and (iii) based on the patient-ventilator characteristics and the instantaneous leak flow value, solving the respiratory predictive model to extract an estimated physiologic respiratory muscle effort value. Then, based on the respiratory muscle effort value or other parameters derived therefrom, the ventilation system is configured and operated for monitoring or breath delivery purposes.

Claims

1. A method comprising:

receiving, measuring, or estimating one or more patient-ventilator characteristics representing values of parameters of interest associated with static or dynamic properties or attributes of a ventilated patient system, the ventilated patient system including a respiratory subsystem of a patient and a ventilation system, which delivers a flow of gas to the patient;

performing quantification of respiratory muscle effort of the patient by (i) establishing a respiratory predictive model of the ventilated patient system based on an equation of motion and one or more functions that approximate clinically-observed, patient-generated muscle pressures, (ii) determining an instantaneous leak flow value for the ventilated patient system, and (iii) based on the one or more patient-ventilator characteristics and the instantaneous leak flow value, solving the respiratory predictive model to extract an estimated physiologic respiratory muscle effort value; and

configuring and operating the ventilation system based on the estimated physiologic respiratory muscle effort value or other parameters derived therefrom for monitoring or breath delivery purposes.

2. The method of claim 1, wherein the one or more functions comprise periodic or semi-periodic functions.

3. The method of claim 2, wherein the periodic or semi-periodic functions have constant amplitudes.

4. The method of claim 2, wherein the one or more periodic or semi-periodic functions have time-varying amplitudes.

5. The method of claim 1, wherein the one or more functions that approximate clinically-observed, patient-generated muscle pressures include a periodic inspiratory function for an inspiratory phase of respiration that approximates clinically-observed, inspiratory muscle pressures and the estimated physiologic respiratory muscle effort value comprises an estimate of inspiratory muscle effort generated by the patient.

6. The method of claim 5, wherein the periodic inspiratory function is generally expressed as:

$P_{{\mathrm{musi}}_i}<br><br>\left(t\right)=-P_{\max }<br><br>\left(1-\frac{t}{t_v}\right)<br><br>\sin <br><br>\left(\frac{\mathrm{\pi <br><br>}<br><br><br><br>t}{t_v}\right)$

where,

P_max represents a maximum inspiratory muscle pressure, which may be a constant or a time-varying parameter;

t_v represents duration of inspiration; and

t represents an elapsed breath time varying between 0 and a total sum of inspiration and expiration periods.

7. The method of claim 1, wherein the one or more functions that approximate clinically-observed, patient-generated muscle pressures include a periodic expiratory function for an expiratory phase of respiration that approximates clinically-observed, expiratory muscle pressures and the estimated physiologic respiratory muscle effort value comprises an estimate of expiratory muscle effort generated by the patient.

8. The method of claim 7, wherein the periodic expiratory function is generally expressed as:

$P_{{\mathrm{mus}}_e}<br><br>\left(t\right)=P_{\max }<br><br>\left(\frac{t}{t_v}\right)<br><br>\sin <br><br>\left(\frac{\mathrm{\pi <br><br>}<br><br>\left(t-t_v\right)}{t_{\mathrm{tot}}-t_v}\right)$

where,

P_max represents a maximum expiratory muscle pressure, which may be a constant or a time-varying parameter;

t_v represents duration of expiration;

t_tot represents a total sum of inspiration and expiration periods; and

t represents an elapsed breath time varying between 0 and t_tot.

9. The method of claim 6, wherein the respiratory predictive model is assumed to be valid for a plurality of breath cycles of the patient and the method further comprises periodically reestablishing, updating or optimizing the respiratory predictive model at predetermined temporal windows during breath cycles of the patient.

10. The method of claim 9, wherein said solving the respiratory predictive model to extract an estimated physiologic respiratory muscle effort value comprises solving the respiratory predictive model during a breath cycle of the plurality of breath cycles subsequent to establishment of the respiratory predictive model and compensating the estimated physiologic respiratory muscle effort value for time delays introduced by a measurement system and indirect indication of muscular activity by surrogate phenomena.

11. The method of claim 10, wherein said compensating the estimated physiologic respiratory muscle effort value for time delays involves application of a single-pole dynamic generally expressed as:

$P_{\mathrm{mus},\mathrm{deliver}<br><br>\left(s\right)}=\frac{{\mathrm{We}}^{-s<br><br><br><br>\mathrm{\tau <br><br>}}}{s+z}<br><br>P_{\mathrm{mus}<br><br>\left(s\right)}$

where,

W represents a scaling factor incorporating a magnitude ratio of actual to delivered muscle pressure;

τ

represents a delay time constant; and

z represents the single pole; and

$P_{\mathrm{mus}}<br><br>\left(s\right)=\left(\mathrm{\pi <br><br>}\right)<br><br>\frac{\frac{P_{\max }}{t_v}<br><br>{\left(s-\frac{\mathrm{\pi <br><br>}}{t_v}\right)}^2}{{\left[s^2+{\left(\frac{\mathrm{\pi <br><br>}}{t_v}\right)}^2\right]}^2};<br><br>\mathrm{for}<br><br><br><br>\mathrm{inspiration}$ $\mathrm{and},\text{<br><br>}<br><br>P_{\mathrm{mus}}<br><br>\left(s\right)=\left(\mathrm{\pi <br><br>}<br><br>\frac{P_{\max }}{t_v<br><br>\left(t_{\mathrm{tot}}-t_v\right)}\right)<br><br>\frac{t_v<br><br>\left[s^2+{\left(\frac{\mathrm{\pi <br><br>}}{t_{\mathrm{tot}}-t_v}\right)}^2\right]+2<br><br><br><br>s}{{\left[s^2+{\left(\frac{\mathrm{\pi <br><br>}}{t_{\mathrm{tot}}-t_v}\right)}^2\right]}^2}<br><br><br><br>\mathrm{for}<br><br><br><br>\mathrm{exhalation}.$

12. The method of claim 1, wherein said solving the respiratory predictive model to extract a respiratory muscle effort value includes optimizing derived parameters of the equation of motion on an ongoing basis to tune to dynamics of the ventilated patient system.

13. The method of claim 12, wherein the dynamics include breathing behavior of the patient.

14. A ventilator system comprising:

a ventilator-patient interface through which a flow of gas is delivered to a patient;

a patient model estimator operable to receive measurements or estimates of one or more patient-ventilator characteristics of a ventilated patient system, the ventilated patient system including a respiratory subsystem of the patient and inspiratory and expiratory accessories, the patient model estimator adapted to perform quantification of respiratory muscle effort of the patient by

(i) establishing a respiratory predictive model of the ventilated patient system based on an equation of motion and one or more periodic or semi-periodic functions that approximate clinically-observed, patient-generated muscle pressures, and

(ii) based on the received one or more measured or estimated characteristics, solving the respiratory predictive model to extract a respiratory muscle effort value; and

a controller operable to control various aspects of delivery of the flow of gas to the patient based on the respiratory muscle effort value or one or more other respiratory parameters derived based on the respiratory muscle effort value.

15. The ventilator system of claim 14, wherein the one or more periodic or semi-periodic functions that approximate clinically-observed, patient-generated muscle pressures include a periodic or semi-periodic function that approximates clinically-observed, inspiratory muscle pressures and the respiratory muscle effort value comprises an estimate of inspiratory muscle effort generated by the patient.

16. The ventilator system of claim 15, wherein the periodic function for inspiration is generally expressed as:

$P_{{\mathrm{musi}}_i}<br><br>\left(t\right)=-P_{\max }<br><br>\left(1-\frac{t}{t_v}\right)<br><br>\sin <br><br>\left(\frac{\mathrm{\pi <br><br>}<br><br><br><br>t}{t_v}\right)$

where,

P_max represents a maximum inspiratory muscle pressure;

t_v represents duration of inspiration; and

t represents an elapsed breath time varying between 0 and a total sum of inspiration and expiration periods.

17. The ventilator system of claim 14, wherein the one or more periodic or semi-periodic functions that approximate clinically-observed, patient-generated muscle pressures include a periodic or semi-periodic function that approximates clinically-observed, expiratory muscle pressures and the respiratory muscle effort value comprises an estimate of expiratory muscle effort generated by the patient.

18. The ventilator system of claim 17, wherein a periodic function for an expiratory phase of respiration is generally expressed as:

$P_{{\mathrm{mus}}_e}<br><br>\left(t\right)=P_{\max }<br><br>\left(\frac{t}{t_v}\right)<br><br>\sin <br><br>\left(\frac{\mathrm{\pi <br><br>}<br><br>\left(t-t_v\right)}{t_{\mathrm{tot}}-t_v}\right)$

where,

P_max represents a maximum expiratory muscle pressure;

t_v represents duration of expiration;

t_tot represents a total sum of inspiration and expiration periods;

t represents an elapsed breath time varying between 0 and t_tot.

19. The ventilator system of claim 16, wherein the respiratory predictive model is assumed to be valid for a plurality of breath cycles of the patient and the method further comprises periodically reestablishing, updating or optimizing the respiratory predictive model at predetermined temporal windows during breath cycles of the patient.

20. The ventilator system of claim 19, wherein said solving the respiratory predictive model to extract a respiratory muscle effort value comprises solving the respiratory predictive model during a breath cycle of the plurality of breath cycles subsequent to establishment of the respiratory predictive model and correcting the respiratory muscle effort value to account for time delays introduced by measurement and indirect indication of muscular activity by surrogate phenomena.

21. The ventilator system of claim 20, wherein said correcting the respiratory muscle effort value to account for time delays involves application of a single-pole dynamic generally expressed as:

$P_{\mathrm{mus},\mathrm{deliver}<br><br>\left(s\right)}=\frac{{\mathrm{We}}^{-s<br><br><br><br>\mathrm{\tau <br><br>}}}{s+z}<br><br>P_{\mathrm{mus}<br><br>\left(s\right)}$

where,

W represents a scaling factor incorporating a magnitude ratio of actual to delivered muscle pressure;

τ

represents a delay time constant; and

z represents the single pole; and

$P_{\mathrm{mus}}<br><br>\left(s\right)=\left(\mathrm{\pi <br><br>}\right)<br><br>\frac{\frac{P_{\max }}{t_v}<br><br>{\left(s-\frac{\mathrm{\pi <br><br>}}{t_v}\right)}^2}{{\left[s^2+{\left(\frac{\mathrm{\pi <br><br>}}{t_v}\right)}^2\right]}^2}<br><br><br><br>\mathrm{for}<br><br><br><br>\mathrm{inspiration}$ $\mathrm{and},\text{<br><br>}<br><br>P_{\mathrm{mus}}<br><br>\left(s\right)=\left(\mathrm{\pi <br><br>}<br><br>\frac{P_{\max }}{t_v<br><br>\left(t_{\mathrm{tot}}-t_v\right)}\right)<br><br>\frac{t_v<br><br>\left[s^2+{\left(\frac{\mathrm{\pi <br><br>}}{t_{\mathrm{tot}}-t_v}\right)}^2\right]+2<br><br><br><br>s}{{\left[s^2+{\left(\frac{\mathrm{\pi <br><br>}}{t_{\mathrm{tot}}-t_v}\right)}^2\right]}^2}<br><br><br><br>\mathrm{for}<br><br><br><br>\mathrm{exhalation}.$

22. The ventilator system of claim 14, wherein said solving the respiratory predictive model to extract a respiratory muscle effort value includes optimizing derived parameters of the equation of motion.

23. The ventilator system of claim 14, wherein the patient model estimator is further adapted to determine an instantaneous leak flow value for the ventilated patient system, and wherein solving the respiratory predictive model is further based on the instantaneous leak flow value.

24. The ventilator system of claim 23 wherein the instantaneous leak flow value comprises an elastic leak orifice component and an inelastic leak orifice component.

25. The ventilator system of claim 14, wherein the patient model estimator is further adapted to perform continuous online quantification of respiratory muscle effort of the patient.