VENTILATION TRIGGERING

• US 20160114115A1
• Filed: 10/27/2014
• Published: 04/28/2016
• Est. Priority Date: 10/27/2014
• Status: Active Grant

• Current Assignee: Covidien LP (Medtronic Plc)
• Original Assignee: Covidien LP (Medtronic Plc)
• Inventors: Glenn, Gregory J., Grant, Matthew Tyson
• Granted Patent: US 9,950,129 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 5/087 Measuring breath flow
• CPC Class Codes: A61M 16/0003 Accessories therefor, e.g. sensors, vibrators, negative pressure
• CPC Class Codes: A61M 16/0069 the speed thereof being controlled by respiratory parameters, e.g. by inhalation
• 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 16/04 Tracheal tubes catheters in general A61M25/00
• CPC Class Codes: A61M 16/06 Respiratory or anaesthetic masks
• CPC Class Codes: A61M 16/20 Valves specially adapted to medical respiratory devices
• CPC Class Codes: A61M 2016/0015 inhalation detectors
• CPC Class Codes: A61M 2016/0027 pressure meter
• CPC Class Codes: A61M 2205/3331 Pressure; Flow
• CPC Class Codes: A61M 2205/52 with memories providing a history of measured variating parameters of apparatus or patient
• CPC Class Codes: A61M 2230/005 Parameter used as control input for the apparatus
• CPC Class Codes: G16H 20/40 relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment

Abstract

The systems and methods provide for novel a triggering mode that allows the patient to trigger or initiate the delivery of a breath during ventilation on a ventilator. Further, the systems and methods provide for triggering ventilation utilizing a statistical trigger mode. Additionally, the systems and methods provide for analyzing and/or displaying information related to a potential change in a triggering threshold for a currently utilized breath type.

Claims

1. A method for ventilating a patient with a ventilator, comprising:

monitoring a parameter signal during exhalation;

determining a stable portion of exhalation based at least on the parameter signal for a current computational cycle;

setting an initial probability for each of a null hypothesis and a trigger hypothesis based on a current exhalation time;

calculating a mean of the parameter signal based on a predetermined set of parameter signals for a most recent set of computational cycles;

updating a noise estimate based at least on the mean of the parameter signal;

calculating a residual for the null hypothesis and the trigger hypothesis based at least on the parameter signal for the current computational cycle;

calculating a first probability for the null hypothesis and calculating a second probability for the trigger hypothesis for the parameter signal for the current computational cycle based on the initial probability, the noise estimate, and the residual;

comparing the first probability and the second probability to a threshold; and

controlling ventilation delivered to the patient by the ventilator based on the comparison.

2. The method of claim 1, wherein the controlling ventilation further comprising:

determining that the first probability meets the threshold; and

continuing to deliver the exhalation based on the determining that the first probability meets the threshold.

3. The method of claim 1, wherein the controlling ventilation further comprising:

determining that the second probability meets the threshold; and

delivering inspiration based on the determining that the second probability meets the threshold.

4. The method of claim 1, wherein setting the initial probability for each of the null hypothesis and the trigger hypothesis comprises:

adding a hypothesis transition probability to a probability of a previous state for each of the null hypothesis and the trigger hypothesis to identify the initial probability for each of the null hypothesis and the trigger hypothesis.

5. The method of claim 4, wherein the hypothesis transition probability is calculated based on a respiration rate and a sampling frequency.

6. The method of claim 1, wherein the stable portion of exhalation is detected when at least one of the following conditions occur:

when a slope of patient exhalation flow is about zero; and

when (Max(P_e)−

Min(P_e))<

1.5 cm H₂₀) and (Max(Q_e)−

Min(Q_e))<

1.5 LPM).

7. The method of claim 1, wherein the noise estimate is determined by calculating a standard deviation for all parameter signals for each measured computational cycle during the exhalation.

8. The method of claim 1, wherein calculating the residual for each of the null hypothesis and the trigger hypothesis comprises:

subtracting the mean for the null hypothesis from the parameter signal for the current computational cycle with; and

subtracting a predicted mean value for the trigger hypothesis from the parameter signal for the current computational cycle.

9. The method of claim 1, wherein calculating the first probability for the null hypothesis and calculating the second probability for the trigger hypothesis for the parameter signal for the current computational cycle based on the initial probability, the residual, and the noise estimate further comprises:

calculating a probability density function for each of the null hypothesis and the trigger hypothesis;

identifying the first probability for the parameter signal for the current computational cycle by multiplying the probability density function with the initial probability for the null hypothesis and normalizing; and

identifying the second probability for the parameter signal for the current computational cycle by multiplying the probability density function with the initial probability for the trigger hypothesis and normalizing.

10. The method of claim 1, wherein the threshold is a probability of greater than 99% received from user input.

11. A method for ventilating a patient with a ventilator, comprising:

monitoring a parameter signal during an exhalation;

determining a stable portion of exhalation based at least on the parameter signal for a current computational cycle;

calculating an initial predicted parameter signal of a next computational cycle and an initial covariance for the initial predicted parameter signal for the parameter signal for the current computational cycle;

calculating a post predicted parameter signal of the next computational cycle and a post covariance for the post predicted parameter signal for a first and second derivative of the parameter signal of the parameter signal for the current computational cycle;

determining that a run threshold has been met based on at least one of a current exhalation time, the initial covariance, and the post covariance;

updating a noise estimate based at least on a single value of the covariance utilized in calculating at least one of the initial covariance and the post covariance;

calculating a residual for each of a null hypothesis and a trigger hypothesis based at least on the parameter signal for the current computational cycle;

calculating a first probability for the null hypothesis and calculating a second probability for the trigger hypothesis for the parameter signal for the current computational cycle based at least on a predicted parameter signal, the noise estimate, and the residual;

comparing the first probability and the second probability to a trigger threshold; and

delivering inspiration to the patient when the second probability meets the trigger threshold.

12. The method of claim 11, wherein Kalman Tracking Filter equations are utilized to calculate the initial predicted parameter signal, the initial covariance, the post predicted parameter signal, and the post covariance.

13. The method of claim 11, wherein calculating the residual for each of the null hypothesis and the trigger hypothesis comprises:

subtracting the initial predicted parameter signal for the null hypothesis from the parameter signal for the current computational cycle; and

subtracting the post predicted parameter signal for the trigger hypothesis from the parameter signal for the current computational cycle.

14. The method of claim 11, wherein calculating the first probability for the null hypothesis and calculating the second probability for the trigger hypothesis for the parameter signal for the current computational cycle based on the predicted parameter signal, the residual, and the noise estimate comprises:

calculating a probability density function for each of the null hypothesis and the trigger hypothesis;

identifying the first probability for the parameter signal for the current computational cycle by multiplying the probability density function with the initial predicted parameter signal and normalizing; and

identifying the second probability for the parameter signal for the current computational cycle by multiplying the probability density function with the post predicted parameter signal and normalizing.

15. A ventilator system comprising:

a pressure generating system configured to generate a flow of breathing gas,

a ventilation tubing system including a patient interface for connecting the pressure generating system to a patient;

at least one sensor operatively coupled to at least one of the pressure generating system, the patient, and the ventilation tubing system; and

a trigger module utilizing change-point detection algorithms to determine a first probability for a null hypothesis and a second probability for a trigger hypothesis based on a monitored parameter signal,

wherein the trigger module triggers inspiration when the second probability meets a trigger threshold.

16. A method implemented by a ventilator for analyzing potential trigger sensitivities, the method comprising:

receiving an active trigger setting for a set breath type;

receiving a potential trigger setting for the set breath type;

monitoring respiratory data with at least one sensor;

detecting an active patient trigger based on the active trigger setting and the respiratory data;

detecting a potential patient trigger based on the potential trigger setting and the respiratory data; and

displaying information about the active patient trigger and the potential patient trigger.

17. The method of claim 16, wherein the displaying information comprises:

displaying an active indicator on a patient waveform where each active patient trigger is detected;

displaying a potential indicator on the patient waveform where each potential patient trigger is detected.

18. The method of claim 16, further comprising:

comparing the active patient trigger to the potential patient trigger; and

determining difference statistics based on the comparing,

wherein the displaying information includes displaying the difference statistics.

19. The method of claim 16, wherein the set breath type utilizes a flow trigger, wherein the active trigger setting is a first flow trigger threshold and the potential trigger setting is a second flow trigger threshold.

20. The method of claim 16, further comprising:

storing past respiratory data monitored with the at least one sensor for at least one previous computation cycle;

storing a past active patient trigger detected for at least one previous computation cycle;

wherein the potential trigger setting is received with activation of a potential trigger application by an operator;

retrieving the stored respiratory data upon activation of the potential trigger application;

determining a past potential patient trigger based on the stored respiratory data; and

wherein the displaying information includes displaying information about the past active patient trigger and the past potential patient trigger.