Leak-compensated flow triggering and cycling in medical ventilators

Information

  • Patent Grant
  • 8272379
  • Patent Number
    8,272,379
  • Date Filed
    Tuesday, September 30, 2008
    15 years ago
  • Date Issued
    Tuesday, September 25, 2012
    11 years ago
Abstract
This disclosure describes systems and methods for compensating for inelastic and elastic leaks in a ventilation system. The disclosure describes a model-based enhancement to conventional flow triggering and cycling methodologies that improves the timing and patient work of flow-based triggering and cycling performance. The methods and systems described herein compensate for the leak condition and minimize additional effort a patient has to exert to generate the same patient synchrony and comfort level compared to no leak condition. One method described includes calculating an elastic leakage and an inelastic leakage based on current measurements of pressure and flow in the ventilation system and then estimating a leak-compensated lung flow using the inelastic and elastic leaks and pneumatic attributes of the patient-ventilator system.
Description
BACKGROUND

Medical ventilators may determine when a patient takes a breath in order to synchronize the operation of the ventilator with the natural breathing of the patient. In some instances, detection of the onset of inhalation and or exhalation may be used to trigger one or more actions on the part of the ventilator.


The response performance of a medical ventilator to a patient-initiated triggering (transition from exhalation into inhalation) and cycling (transition from inhalation into exhalation) are important characteristics of a medical ventilator. A ventilator's triggering and cycling response impacts the patient's work of breathing and the overall patient-ventilator synchrony. In patient-initiated ventilation such as Pressure Support Ventilation (PSV), Proportional Assist Ventilation (PAV), etc., the airway pressure drops below the baseline and flow moves into the lung as a patient initiates an inspiratory effort. As the inhalation phase proceeds, the inspiratory flow decreases in accordance with the decreasing inspiratory effort by the patient. The ventilator detects the patient's inspiratory or expiratory effort and uses this information to provide ventilation therapy to the patient as set by the operator.


The triggering or cycling response performance of a ventilator is a function of a patient's respiratory behavior (which includes breathing effort magnitude and timing characteristics) as well as the ventilator's gas delivery dynamics and flow control parameters (actuator response, deadbands, etc.). In conventional flow triggering and cycling modality, the patient effort is detected based on the magnitude of expiratory and inspiratory flow changes generated by the patient respiratory effort. In conventional flow cycling modality, the cycling point (transition from inhalation into exhalation) is determined using the descending inspiratory flow and comparing it against peak inspiratory flow and a set percent threshold. Inspiratory flow is sensed by the computation of the ventilator net flow (estimated lung flow) and compared against a set trigger sensitivity value for triggering or a set percentage of peak inspiratory flow for cycling.


SUMMARY

This disclosure describes systems and methods for compensating for pressure-dependent elastic as well as fixed size rigid sources of leaks in a ventilation system. The disclosure describes a model-based enhancement to conventional flow triggering and cycling methodologies that optimizes or attempts to optimize the timing and patient work of flow triggering and cycling performance. During the exhalation phase, the methods and systems may be used with known conventional flow triggering algorithms (i.e., comparing net ventilator flow against trigger sensitivity) and provide leak-compensated base flow to enable PEEP maintenance and avoid autocycling. During the inhalation phase, the methods and systems may be used with known conventional flow cycling algorithms (i.e., comparing the percent ratio of inspiratory flow over peak inspiratory flow against a cycling sensitivity). The methods and systems described herein compensate for the leak flow rates and reduce the patient's work of breathing and increase the patient's comfort (patient-ventilator breath phase transition synchrony). Without the improvements provided by the disclosed methods and systems, during the exhalation phase and in the presence of leak conditions, leak flows would appear as net flow differences to the ventilator and cause erroneous determination of transition into inhalation (false triggering or autocycling). Similarly, during the inhalation phase of a pressure-regulated spontaneous-breath and in the presence of leak conditions, leaks would make the ventilator net flow (delivered minus exhausted) appear to be larger and would prevent timely cycling into exhalation (because the set threshold for percent peak inspiratory flow may not be met) and lead to prolongation of ventilator's inhalation phase and thus cause patient's discomfort and ventilator-patient dyssynchrony.


In part, this disclosure describes a method of compensating for leakage in a ventilation system during delivery of gas from a medical ventilator to a patient. The method includes estimating instantaneous lung flow of gas inhaled or exhaled by the patient during inhalation and exhalation phases and identifying breath phase transitions (triggering from exhalation into inhalation or cycling from inhalation into exhalation) indicative of patient respiratory effort. The method further includes monitoring an instantaneous flow at a location in the ventilation system based on one or more measurements of instantaneous pressure or instantaneous flow in ventilation system and estimating leakage from the ventilation system during delivery of gas to the patient based on measurements of at least one of pressure and flow. The method models the leakage as a first leakage component through a first orifice of a fixed size and a second leakage component through a second orifice of a varying size, in which the first and second leakage components are different functions of instantaneous pressure in the ventilation system. The method generates a leak-compensated lung flow based on the monitored instantaneous pressure and flow rates, the first and second leakage components, and system tubing characteristics. The lung flow is then compared to a set flow triggering threshold (sensitivity) or percent peak flow cycling threshold (sensitivity) to identify patient-initiated transitions into inhalation or exhalation.


This disclosure also describes another method of compensating for leakage in a ventilation system during delivery of gas from a medical ventilator to a patient. The method includes identifying an inelastic leakage in the ventilation system as a first function of at least one of a pressure measurement and a flow measurement in the ventilation system and identifying an elastic leakage in the ventilation system as a second function of at least one of the pressure measurement and the flow measurement in the ventilation system. The method further includes estimating the tubing characteristics of the ventilation system. The method detects patient-initiated breath phase transitions and changes the medical ventilator between a first therapy and a second therapy (i.e., from inhalation to exhalation or from exhalation to inhalation) based on leak-compensated lung flow calculated by using the inelastic leakage, the elastic leakage, the tubing characteristics and at least one of the pressure measurement and the flow measurement in the ventilation system.


In yet another aspect, the disclosure describes a pressure support system that includes: a pressure generating system adapted to generate a flow of breathing gas; a ventilation system including a patient interface device; one or more sensors operatively coupled to the pressure generating system or the ventilation system, in which each sensor capable of generating an output indicative of a pressure of the breathing gas; a processor; an elastic leak estimation module that identifies an elastic leakage in the ventilation system; and a compensation module that generates one or more leak-compensated lung flow estimates based on the elastic leakage and at least one output indicative of a pressure of the breathing gas and the tubing characteristics and flow rates.


In yet another aspect, the disclosure describes a pressure support system that includes: a pressure generating system adapted to generate a flow of breathing gas; a ventilation system including a patient interface device; one or more sensors operatively coupled to the pressure generating system or the ventilation system, in which each sensor capable of generating an output indicative of a pressure of the breathing gas; a processor; a leak estimation module that identifies both elastic and rigid orifice sources of leakage in the ventilation system; and a compensation module that generates a leak-compensated lung flow based on the elastic and inelastic sources of leakage, the tubing characteristics and at least one output indicative of a pressure and/or flow of the breathing gas.


The disclosure also describes a controller for a medical ventilator that includes a microprocessor and a flow triggering and cycling compensation means for adjusting the ventilator net flow based on instantaneous elastic leakage and instantaneous inelastic leakage of breathing gas from a ventilation system. The flow triggering and cycling compensation means may include an inelastic leak estimation module that identifies the instantaneous inelastic leakage in the ventilation system and an elastic leak estimation module that identifies the instantaneous elastic leakage in the ventilation system. The flow trigger triggering and cycling compensation means may also include a compensation module that generates a leak-compensated lung flow for comparison against the set triggering or cycling threshold.


These and various other features as well as advantages which characterize the systems and methods described herein will be apparent from a reading of the following detailed description and a review of the associated drawings. Additional features are set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the technology. The benefits and features of the technology will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.


It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are intended to provide further explanation of the invention as claimed.





BRIEF DESCRIPTION OF THE DRAWINGS

The following drawing figures, which form a part of this application, are illustrative of described technology and are not meant to limit the scope of the invention as claimed in any manner, which scope shall be based on the claims appended hereto.



FIG. 1 illustrates an embodiment of a ventilator connected to a human patient.



FIG. 2 schematically depicts exemplary systems and methods of ventilator control.



FIG. 3 illustrates an embodiment of a method of compensating for leakage and estimate lung flow for determination of patient-initiated flow-based triggering and cycling transitions in a ventilation system during delivery of gas firm a medical ventilator to a patient.



FIG. 4 illustrates a functional block diagram of modules and other components that may be used in an embodiment of ventilator that compensates for leaks.





DETAILED DESCRIPTION

Although the techniques introduced above and discussed in detail below may be implemented for a variety of medical devices, the present disclosure will discuss the implementation of these techniques in the context of a medical ventilator for use in providing ventilation support to a human patient. The reader will understand that the technology described in the context of a medical ventilator for human patients could be adapted for use with other systems such as ventilators for non-human patients and general gas transport systems in which leaks may cause a degradation of performance.



FIG. 1 illustrates an embodiment of a ventilator 20 connected to a human patient 24. Ventilator 20 includes a pneumatic system 22 (also referred to as a pressure generating system 22) for circulating breathing gases to and from patient 24 via the ventilation tubing system 26, which couples the patient to the pneumatic system via physical patient interface 28 and ventilator circuit 30. Ventilator circuit 30 could be a two-limb or one-limb circuit for carrying gas to and from the patient. In a two-limb embodiment as shown, a wye fitting 36 may be provided as shown to couple the patient interface 28 to the inspiratory limb 32 and the expiratory limb 34 of the circuit 30.


The present systems and methods have proved particularly advantageous in noninvasive settings, such as with facial breathing masks, as those settings typically are more susceptible to leaks. However, leaks do occur in a variety of settings, and the present description contemplates that the patient interface may be invasive or non-invasive, and of any configuration suitable for communicating a flow of breathing gas from the patient circuit to an airway of the patient. Examples of suitable patient interface devices include a nasal mask, nasal/oral mask (which is shown in FIG. 1), nasal prong, full-face mask, tracheal tube, endotracheal tube, nasal pillow, etc.


Pneumatic system 22 may be configured in a variety of ways. In the present example, system 22 includes an expiratory module 40 coupled with an expiratory limb 34 and an inspiratory module 42 coupled with an inspiratory limb 32. Compressor 44 or another source(s) of pressurized gas (e.g., air and oxygen) is coupled with inspiratory module 42 to provide a gas source for ventilatory support via inspiratory limb 32.


The pneumatic system may include a variety of other components, including sources for pressurized air and/or oxygen, mixing modules, valves, sensors, tubing, accumulators, filters, etc. Controller 50 is operatively coupled with pneumatic system 22, signal measurement and acquisition systems, and an operator interface 52 may be provided to enable an operator to interact with the ventilator (e.g., change ventilator settings, select operational modes, view monitored parameters, etc.). Controller 50 may include memory 54, one or more processors 56, storage 58, and/or other components of the type commonly found in command and control computing devices.


The memory 54 is computer-readable storage media that stores software that is executed by the processor 56 and which controls the operation of the ventilator 20. In an embodiment, the memory 54 comprises one or more solid-state storage devices such as flash memory chips. In an alternative embodiment, the memory 54 may be mass storage connected to the processor 56 through a mass storage controller (not shown) and a communications bus (not shown). Although the description of computer-readable media contained herein refers to a solid-state storage, it should be appreciated by those skilled in the art that computer-readable storage media can be any available media that can be accessed by the processor 56. Computer-readable storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. Computer-readable storage media includes, but is not limited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid state memory technology, CD-ROM, DVD, or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the computer.


As described in more detail below, controller 50 issues commands to pneumatic system 22 in order to control the breathing assistance provided to the patient by the ventilator. The specific commands may be based on inputs received from patient 24, pneumatic system 22 and sensors, operator interface 52 and/or other components of the ventilator. In the depicted example, operator interface includes a display 59 that is touch-sensitive, enabling the display to serve both as an input and output device.



FIG. 2 schematically depicts exemplary systems and methods of ventilator control. As shown, controller 50 issues control commands 60 to drive pneumatic system 22 and thereby circulate breathing gas to and from patient 24. The depicted schematic interaction between pneumatic system 22 and patient 24 may be viewed in terms of pressure and/or flow “signals.” For example, signal 62 may be an increased pressure which is applied to the patient via inspiratory limb 32. Control commands 60 are based upon inputs received at controller 50 which may include, among other things, inputs from operator interface 52, and feedback from pneumatic system 22 (e.g., from pressure/flow sensors) and/or sensed from patient 24.


In many cases, it may be desirable to establish a baseline pressure and/or flow trajectory for a given respiratory therapy session. The volume of breathing gas delivered to the patient's lung (L1) and the volume of the gas exhaled by the patient (L2) are measured or determined, and the measured or predicted/estimated leaks are accounted for to ensure accurate delivery and data reporting and monitoring. Accordingly, the more accurate the leak estimation, the better the baseline calculation of delivered and exhaled volume as well as event detection (triggering and cycling phase transitions).


When modeling the delivery of gas to and from a patient 24 via a closed-circuit ventilator, one simple assumption is that compliance of the ventilator circuit 30 is fixed and that all gas injected into the ventilator circuit 30 that does not exit the circuit 30 via the expiratory limb 34 fills the circuit as well as the patient's lungs and causes an increase in pressure. As gas is injected (L1), the lung responds to the increased gas pressure in the circuit 30 by expanding. The amount the lung expands is proportional to the lung compliance and is defined as a function of gas pressure differential (Compliance=volume delivered/pressure difference).


The term circuit compliance is used to refer to the amount the pressure in the ventilator circuit 30 (or ventilator circuit 30 and attached patient interface 28, depending on how the compliance is determined) changes based on changes in volume delivered into the circuit. In an embodiment, the circuit compliance may be estimated by pressurizing the ventilator circuit 30 (or circuit 30 and interface 28 combination) when flow to the patient is blocked and measuring the volume of additional gas introduced to cause the pressure change (compliance=volume delivered/pressure different).


The term circuit resistance is used to refer to the amount the pressure changes between two sites upstream and downstream the ventilator circuit as a function of volumetric flow rate through that circuit. Tubing resistance may be modeled as a two-parameter function of flow:

Pressure difference=K1*Flow+K2*Flow2=Flow*(K1+K2*Flow)

In which K1 and K2 are empirically derived constants. For example, in an embodiment, the circuit resistance may be estimated by passing several fixed flow rates through the circuit and measuring the pressure difference between certain upstream and downstream sites and finding the best curve fit to the collected data.


Such a method of determining compliance and resistance may be executed by the operator prior to attaching the patient to the ventilator as part of the set tip of the ventilator 20 to provide therapy. Other methods of determining compliance and/or resistance are also possible and could be adapted for use with the disclosed leak-compensation systems and methods described herein.


Errors may be introduced due to leaks in the ventilation tubing system 26. The term ventilation tubing system 26 is used herein to describe the ventilator circuit 30, any equipment attached to or used in the ventilator circuit 30 such as water traps, monitors, drug delivery devices, etc. (not shown), and the patient interface 28. Depending on the embodiment, this may include some equipment contained in the inspiration module 42 and/or the expiration module 40. When referring to leaks in or from the ventilation tubing system 26, such leaks include leaks within the tubing system 26 and leaks where the tubing system 26 connects to the pressure generator 22 or the patient 24. Thus, leaks from the ventilation tubing system 26 include leaks from the ventilator circuit 30, leaks from the patient interface 28 (e.g., masks are often provided with holes or other pressure relief devices through which some leakage may occur), leaks from the point of connection of the patient interface 28 to the patient 24 (e.g., leaks around the edges of a mask due to a poor fit or patient movement), and leaks from the point of connection of the patient interface 28 to the circuit 30 (e.g., due to a poor connection between the patient interface 28 and the circuit 30).


For the purpose of estimating how a leak flow rate changes based on changes in pressure in the ventilation tubing system 26, the instantaneous leak may be modeled as a leak through a single rigid orifice or opening of a fixed size in which that size is determined based on comparing the total flow into the inspiratory limb 32 and out of the expiratory limb 34. However, this leak model does not take into account any elastic component of leak source(s) in the system 26, that is how much of the area of any of the holes or openings in the ventilation tubing system 26 through which leakage occurs may change due to an increase or decrease in pressure.


It has been determined that not accounting for elastic leakage from the ventilation tubing system 26 can cause many problems. First, if only the inelastic/fixed orifice model is used to estimate leak, the subsequent errors caused by ignoring the elastic effects of any actual leaks end up generating inaccurate estimates of flow rates into the lung. This can cause the ventilator 20 to estimate gas volume delivered into the lung inaccurately when, in fact, the elastic leaks in the system 26 have let more gas escape than estimated. Second, if the elasticity of the leak source is ignored, any other calculation, estimate, or action that the ventilator 20 may perform which is affected by the leak estimate will be less accurate.


In the systems and methods described herein, the flow triggering and cycling in ventilation therapy (e.g., the initiation of an inhalation cycle an exhalation cycle or any other action performed by the ventilator based on where in the respiration cycle a patient is) is made more accurate by compensating the lung flow to account for both fixed (rigid) and elastic components of the system leakage. This results in a more accurate and timely triggering or cycling of breath phases by the ventilator 20 and is beneficial to the patient 24 as it reduces the patient 24 work of breathing necessary to initiate the appropriate transition. While the systems and methods are presented in the context of flow-based triggering and cycling mechanisms deployed during an exhalation or inhalation cycle of the patient-ventilator system, the technology described herein could be used to compensate for leakage when using any type of flow-based mechanisms derived from patient respiratory effort to initiate an event in a ventilator.


As described in greater detail below, in an embodiment in every sample period (e.g., 5 ms) during normal ventilation the following tasks are undertaken in conjunction with leak-compensated flow-based triggering and cycling: (1) flow and pressure measurements are acquired from sensors located at specific sites in the ventilation pathway; (2) parameters of the leak model are recalculated and/or checked for validity as applicable; (3) instantaneous total leak flow rate is calculated; (4) instantaneous lung flow is estimated; (5) (a) if in exhalation, estimated lung flow is compared against triggering threshold to determine if the condition(s) for a patient-initiated transition into inhalation has (have) been satisfied, or (b) if in inhalation, peak inspiratory lung flow is updated and the percent ratio of current estimated lung flow over peak inspiratory lung flow is compared against percent flow cycling threshold to determine if the condition(s) for a patient-initiated transition into exhalation has (have) been satisfied; (6) if a phase transition is detected, the ventilator executes a phase change and operates in accordance with specifications for the new phase. If no phase transition was detected, the operations would resume from step #1 in the next sample period. In preparation for performing the tasks described above, tubing characteristics (e.g., compliance and resistance) may be determined by running calibration procedures before a ventilator is connected to a patient and parameters of the leak model to be used may be quantified (e.g., based on data taken during a prior breath).



FIG. 3 illustrates an embodiment of a method of compensating for leakage in a ventilation system during delivery of gas from a medical ventilator to a patient. In the method 300 shown, a medical ventilator such as that described above with reference to FIGS. 1 and 2 is used to provide ventilation to a patient.


In the method 300 illustrated, the ventilator circuit compliance and resistance may be estimated in a compliance/resistance estimation operation 302. In an embodiment, this is usually performed prior to connecting the ventilator to the patient (as previously described).


In the method 300 illustrated, the ventilator is connected to the patient and operated for some period of time, e.g., for a breath, and data is obtained in a data collection operation 304. While the data collection operation 304 may be performed prior to connection to the patient, it is also anticipated that the operation should be performed while the patient is receiving therapy from the ventilator so that leaks from the connection of the ventilation system to the patient may be characterized as well as the other leaks in the system. In an embodiment the data collection operation 304 includes operating the ventilator for a period of time and collecting measurements of pressure and/or flow in ventilation system.


The data obtained in the data collection operation 304 is then used to quantify the parameters of the leak model in the parameter quantification operation 306. In an embodiment, the parameter quantification operation 306 uses the data obtained in the data collection operation 304, e.g., the total leak volume during one breath cycle (inhalation and exhalation) and some or all of the pressure and flow data collected during the data collection operation 304. The data may be applied to a preselected mathematical formula in order to solve for parameters in the formula. In an embodiment, the mathematical formula is a leakage model that separates the leak into the sum of two leak components, inelastic leak and elastic leak, in which each component represents a different relationship between the quantity of leakage from the ventilation system and the measured current/instantaneous pressure and/or flow of gas in the ventilation system. As discussed above, the inelastic leak may be modeled as the flow through a rigid orifice of a fixed size while the elastic leak may be modeled as the flow through a different orifice of a size that changes based on the pressure (or flow) of the gas in the ventilation system.


An example of a method and system for modeling leak in a ventilation system as a combination of an elastic leak component and an inelastic leak component can be found in commonly-assigned U.S. Provisional Patent Application Ser. No. 61/041,070, filed Mar. 31, 2008, titled VENTILATOR LEAK COMPENSATION, which application is hereby incorporated by reference herein. The VENTILATOR LEAK COMPENSATION represents one way of characterizing the leak from a ventilation system as a combination of elastic and inelastic components. Other methods and models are also possible and may be adapted for use with this technology.


In an embodiment, the result of the parameter quantification operation 306 is a quantitative mathematical model, equation or set of equations of leak that, from one or more measurements of pressure and/or flow in the ventilation system, can be used to calculate an estimate of the resulting leak from the ventilation system. Thus, given the mathematical model, pressure and/or flow data can be monitored during therapy and the instantaneous and total leak from the ventilation system can be calculated. The resulting leak value will take into account both elastic leak and the inelastic leak.


In an embodiment, the data collection operation 304 and the parameter quantification operation 306 may be performed as a single operation or as separate operations. In an embodiment, the operations 304 and 306 may be repeated periodically, based on time or breathing cycles, in order to obtain accurate estimates. Furthermore, the accuracy of the parameters of the leakage model may be periodically checked.


For example, in an embodiment the data collection operation 304 may calculate or estimate the total leak volume for a particular breath of a patient. This estimate may then be used in the parameter quantification operation 306 to generate the leak model for use in calculating the instantaneous leak in a subsequent breath. In an embodiment, the data collection operation 304 and the parameter quantification operation 306 are repeated at the end of each breath so that the instantaneous leak is estimated using a model derived from data taken during the immediately previous breath.


In the embodiment shown, the ventilator begins monitoring for patient-initiated phase transition as illustrated by operation 308. When monitoring for patient-initiated phase transitions, the system obtains current measurements from sensors located at specific sites in the ventilation pathway in an obtain measurements operation 310. The obtain measurements operation 310 may include obtaining pressure and/or flow measurements from one or more locations within the ventilation system. Depending upon how a particular leak model is defined, the operation 310 may also include making one or more calculations using the measurements. For example, the model may require a flow measurement as observed at the patient interface even though the ventilation system may not have a flow sensor at that location in the ventilation system. Thus, a measurement from a sensor or sensors located elsewhere in the system (or data from a different type of sensor at the location) may be mathematically manipulated in order to obtain an estimate of the flow observed at the patient interface in order to calculate the leak using the model.


In the method 300, after the current measurements have been obtained in operation 310, the current leak for the sampling period is calculated in an instantaneous leak calculation operation 312. The operation 312 uses the mathematical model of the leak including the parameters generated in the parameter quantification operation 306 and the current pressure and/or flow measurements to generate an instantaneous leakage value from the ventilation system. As the model characterizes both the elastic and inelastic leak components, the calculated leak represents the total leak due to the combination of both leak components.


The term instantaneous is used herein to describe a determination made for any particular instant or sampling period based on the measured data for that instant. For example, if a pressure measurement is taken every 5 milliseconds (sample period), the pressure measurement and the leak model can be used to determine an instantaneous leak flow rate based on the pressure measurement. With knowledge of the length of the sample period, the instantaneous flow rate may then be used to determine an instantaneous volume of gas flowing through a part of the circuit during that sample period. For longer periods covering multiple sampling periods the instantaneous values for each sampling period may be integrated to obtain a total volume. If a measurement is also the most recent measurement taken, then the instantaneous value may also be referred to as the current value.


After the current leak has been calculated, the method 300 further estimates the instantaneous lung flow to or from the patient in a lung flow estimation operation 314. The estimated lung flow is compensated for the leak calculated in the instantaneous leak calculation operation 312.


As part of the lung flow estimation operation 314, during the inhalation phase the ventilator may compare the instantaneous lung flow to previous values to identify a peak inspiratory lung flow. Depending on the embodiment, the peak inspiratory lung flow may be needed in order to determine if an operator set cycling threshold has been exceeded.


In the embodiment shown, the instantaneous lung flow information is used to identify patient-initiated phase transitions in a comparison operation 316. Depending on the current respiratory phase (exhalation or inhalation) the comparison operation 316 may perform one of two separate operations, each pertaining to one phase determination function, i.e., flow triggering module and percent peak flow cycling module. In an embodiment, the flow trigger threshold and/or peak inspiratory flow cycling threshold may be set by the operator, for example in the form of a fixed flow rate for triggering sensitivity or a fixed percentage of peak flow for cycling sensitivity. The inhalation flow trigger threshold is a threshold selected to determine when the patient has begun to inhale. The flow cycling threshold is a threshold selected to determine when the patient has begun to exhale. However, in the presence of leak conditions, these thresholds (sensitivities) may be derived or fixed by the ventilator to optimize therapy and patient-ventilator synchrony. The instantaneous lung flow information is used by the ventilator to compare against a threshold or calculate the percent ratio of current lung flow over peak inspiratory lung flow and determine when the patient is ready to transition into a new phase (inhalation or exhalation).


In an embodiment, a flow threshold may be characterized as an average flow rate or a total flow volume for a designated period (e.g., a breath). In the absence of any leak, as discussed in greater detail below, when the ventilator detects an occurrence of the net flow into the lung equal to or greater than the flow trigger threshold, the ventilator will treat this condition as indicating that the patient has triggered the appropriate condition (i.e., the patient has begun to inhale). Thus, an inhalation flow trigger threshold represents a leak-free flow increase into the lung caused by the patient's inspiratory effort that the ventilator actively monitors for. Note that by “exceeding” a threshold it is meant that some condition relative to the threshold has been identified. Thus, depending on the algebraic context a flow trigger threshold may be exceeded by either a net flow greater than the flow trigger threshold or a net flow less than the flow trigger threshold in absolute terms noting that net flow may be defined in different algebraic ways.


The percent peak flow cycling threshold is a threshold selected to determine when the patient has completed inspiration and is ready exhale. In the absence of any leak, when the ventilator detects an occurrence of the ratio of the current net flow into the lung (current lung flow) over the measured peak inspiratory lung flow to be equal to or less than the percent peak flow cycling threshold, the ventilator will treat this condition as indicating that the appropriate patient-initiated cycling condition is satisfied (e.g., the patient is ready to exhale). Thus, an inspiratory percent peak flow threshold represents a leak-free indicator that the ventilator actively monitors for.


The comparison operation 316 checks to determine if conditions for a patient-initiated phase transition have been satisfied. If a phase transition is confirmed as shown by the determination operation 318, the ventilator proceeds into the new phase (illustrated in by operation 320) and operates accordingly. If the conditions for a phase change are not met, as shown by the determination operation 318 the ventilator repeats the phase transition detection process in the next sample period (as illustrated by the waiting operation 322).


Under conventional flow triggering modality there may be a base flow delivered by the ventilator during exhalation. Base flow under flow triggering condition is usually defined to be the sum of flow triggering sensitivity (threshold) and a nominal flow constant (e.g., base flow=flow trigger sensitivity+1.5, in liters per minute units). Base flow under flow triggering is intended to provide a source of gas flow to draw from when the patient initiates an inspiration. It also helps in maintaining the desired set PEEP during exhalation. Therefore, in the absence of any leaks, when the patient initiates an inspiratory effort by generating a flow into the lung, the ventilator net flow estimated and/or measured at the patient-wye is close to the lung flow and may serve as a good indicator of the magnitude of the force exerted by the inspiratory muscles. In the presence of leaks, in the first place there is a need for adequate amount of base flow to be delivered by the ventilator to maintain a set PEEP level and prevent false triggering during the exhalation phase. When the patient initiates an inspiratory effort by drawing a portion of the base flow into the lung, the leak-compensated lung flow may then be compared against the set flow trigger threshold (sensitivity) to check for initiation of a new inhalation.


During the inhalation phase, the inspiratory flow normally increases to a peak and then decreases exponentially in accordance with the decreasing inspiratory effort of the patient. Under conventional percent peak flow cycling modality, the percent peak flow cycling threshold is a sensitivity selected to determine when the patient has completed inspiration and is ready exhale. In the absence of any leak, when the ventilator detects an occurrence of the ratio of the current net flow into the lung over the measured peak inspiratory lung flow to be equal to or less than the percent peak flow cycling threshold, the ventilator will treat this condition as indicating that the appropriate patient-initiated cycling condition is satisfied (e.g., the patient is ready to exhale). In the presence of leak conditions, the leak-compensated lung flow may be used to calculate percent ratio of current lung flow over peak measured inspiratory lung flow and compare it against the set cycling threshold (sensitivity) to check for initiation of a new exhalation phase.


In the embodiment shown, the ventilator compares the leak-compensated lung flow and triggering and cycling thresholds in a comparison operation 316 to determine if the transition condition is met (e.g., from exhalation into inhalation). Based on the results of the comparison, a determination operation 318 is performed. If it is determined that the leak-compensated transition condition has not been satisfied, the method 300 waits until the next sampling period (illustrated by the waiting operation 322) and then returns to the obtain measurements operation 310 so that the analysis may be repeated until a phase transition is detected.


If the determination operation 318 determines that a threshold has been exceeded, then the ventilator considers that the corresponding condition (e.g., exhalation, inhalation, etc.) to have begun by the patient as indicated by the phase transition operation 320. Depending on the settings of the ventilator, the phase transition operation 320 may include the cycling into an exhalation cycle or triggering into an inhalation cycle in which pressure and/or flow is changed to follow a desired trajectory during that cycle, e.g., an operator-set positive end-expiratory pressure (PEEP) or an inhalation assistance pressure with pre-set waveform characteristics. Other actions may also be performed upon detection of exhalation or inhalation such as the logging of different or additional data, displaying advisory patient data, and the opening and/or closing of valves in the ventilation system to modify the flow path of gas.


Although not shown, in an embodiment after inhalation or exhalation has been deemed detected by the method 300, some or all of the method 300 may be repeated for the next breathing cycle. For example, upon further detection of the end of the inhalation cycle of the patient (i.e., detection of a patient's readiness to proceed into exhalation) the parameter quantification operation 306 may be repeated to calculate a revised leak model or update model parameters based on the most recent data. This revised model may then be used to identify the next phase transition initiated by the patient.


In another embodiment, the lung flow estimation operation 314 may be performed for a certain interval or performed continuously during therapy. For example, there may be restricted periods at the beginning of both inhalation as well as exhalation phases during which no transitions are allowed (to ensure a minimum period of inhalation or exhalation). During the restricted periods, the phase detection algorithms for triggering or cycling may or may not be executed. Lung flow may be estimated based on characteristics derived from a single breath or based on a representative parameter derived from an ensemble of several breaths.


A flow threshold may be checked after each new estimation of the lung flow until the start of a new phase is established. In yet another embodiment, the operator may be allowed to disable leak compensation of the flow triggering and/or cycling mechanism. In yet another embodiment, different flow-based criteria may be defined to check for patient-initiated breath phase transitions during leak-compensated ventilation. Examples of such criteria include but are not limited to leak-compensated inspired volume for triggering. The sensitivities (thresholds) for such phase detection mechanisms may be set by the operator, fixed, or appropriately determined during ventilation as feasible.



FIG. 4 illustrates a functional block diagram of modules and other components that may be used in an embodiment of ventilator that compensates for elastic and rigid orifice sources of leaks. In the embodiment shown, the ventilator 400 includes pressure sensors 404 (two are shown placed at different locations in the system), flow sensors (one is shown), and a ventilator control system 402. The ventilator control system 402 controls the operation of the ventilator and includes a plurality of modules described by their function. In the embodiment shown, the ventilator control system 402 includes a processor 408, memory 414 which may include mass storage as described above, a leak estimation module 412 incorporating a parametric leak model accounting for both elastic and rigid orifice leak sources such as that described in U.S. Provisional Application 61/041,070 previously incorporated herein, a leak-compensated flow trigger module 416, a pressure and flow control module 418, a monitoring module 422, a leak model module 420, a leak-compensated cycle detection module 424, and a leak-compensated lung flow estimation module 426. The processor 408 and memory 414 have been discussed above. Each of the other modules will be discussed in turn below.


The main functions of the ventilator such as receiving and interpreting operator inputs and changing pressure and flow of gas in the ventilator circuit are performed by the control module 418. In the context of the methods and systems described herein, the module 418 will perform one or more actions upon the determination that a patient receiving therapy is inhaling or exhaling. The determination of a patient-initiated inspiratory trigger is made by the trigger detection module 416 which passes the determination on to the control module 418 when it occurs. The determination of a patient-initiated cycle is made by the cycle detection module 424 which passes the determination on to the control module 418 when it occurs.


In an embodiment, the cycle detection module 424 uses a leak-compensated lung flow to determine when a cycling event is being initiated by the patient. The conditions of the ventilation system, such as pressure and/or flow, are monitored using the sensors 404, 406 and the conditions are processed and used by the cycle detection module 424. When it is determined that the conditions indicate that the cycling criteria have been met, e.g., by continuously or periodically comparing the percent ratio of current lung flow over peak inspiratory lung flow against the set threshold (cycling sensitivity), the associated cycling event, i.e., exhalation, may be deemed to have been initiated by the patient. Percent peak inspiratory flow may be used for cycling purposes, however, different flow-based criteria may be defined to check for patient-initiated cycling transitions during leak-compensated ventilation.


In an embodiment, a backup leak-compensated pressure triggering mechanism may be incorporated as a precautionary measure to build redundancy for safety reasons. The sensitivity of the backup pressure trigger may be fixed or set by the operator. The estimated instantaneous leak-compensated circuit-wye pressure drop may be compared against the pressure trigger sensitivity (threshold) to determine if an inspiratory event has occurred.


In an embodiment, the phase detection modules 416 and 424 may be independent modules as shown. Alternatively, the triggering and cycling detection modules may be combined into a single breath phase transition module (not shown). Other detection modules may also be included as necessary for other thresholds.


The current conditions in the ventilation system are monitored by the monitoring module 422. This module 422 collects the data generated by the sensors 404, 406 and may also perform certain calculations on the data to make the data more readily usable by other modules or may process the current data and or previously acquired data or operator input to derive auxiliary parameters or attributes of interest. In an embodiment, the monitoring module 422 receives data and provides it to each of the other modules in the ventilator control system 402 that need the current pressure or flow data for the system.


The leak-compensated flow trigger test is performed by the flow trigger leak-compensation module 416. The flow trigger leak-compensation module 416 uses the flow trigger threshold (normally set for no leak conditions) and the estimated or measured lung flow. The ventilator may use pressure and flow measurements and the tubing characteristics such as resistance and compliance to obtain an estimate of the actual flow into the lung caused by the patient's inspiratory effort. This derived leak-compensated lung flow is compared against the flow sensitivity threshold to check for a patient-initiated phase change. For lung flow estimation, the calculated total instantaneous leak (sum of elastic leak and inelastic leak provided by the leak estimation module 412) gas flows delivered by the ventilator, flow rate leaving the ventilator, and flow compression in the circuit may be used. Circuit tubing and airway properties (resistance, compliance, inductance, etc.) are either measured prior to patient setup, or given by the operator, or may be derived on an ongoing basis when deemed required.


In the embodiment shown, a compensated lung flow is calculated by the lung flow module 426. The lung flow module 426 uses a quantitative model for lung flow of the patient during both inhalation and exhalation and from this characterization and pressure and flow measurements generates an estimate for instantaneous lung flow.


The leak model parameters are generated by the leak estimation module 412 which creates one or more quantitative mathematical models, equations or correlations that uses pressure and flow observed in the ventilation system over regular periods of respiratory cycles (inhalation and exhalation) and apply physical and mathematical principles derived from mass balance and characteristic waveform settings of ventilation modalities (regulated pressure or flow trajectories) to derive the parameters of the leak model incorporating both rigid and elastic (variable pressure-dependent) orifices. In an embodiment, the mathematical model may be a model such as:

Qinelastic=R1*Pix
Qelastic=R2*Piy

wherein Qelastic is the instantaneous leak flow due to elastic leaks in the ventilation system, Qinelastic is the instantaneous leak flow due to inelastic leaks in the ventilation system, R1 is the inelastic leak constant, R2 is the elastic leak constant, Pi is the current or instantaneous pressure measurement, x is an exponent for use when determining the inelastic leak and y is an exponent different than x for use when determining the elastic leak. The group R1*Pix represents flow through an orifice of fixed size as a function of instantaneous pressure Pi and the group R2*Piy represents flow through a different orifice that varies in size based on the instantaneous pressure. The equations above presuppose that there will always be an elastic component and an inelastic component of leakage from the ventilation system. In the absence of an elastic component or a leak source of varying size, R2 would turn out be zero.


In the embodiment shown, the current or instantaneous elastic leak is calculated by the leak estimation module 412. The calculation is made using the elastic leak portion of the leak model developed by the leak estimation module 412 and the pressure data obtained by the monitoring module 422. The leak estimation module 412 may calculate a new instantaneous elastic leak flow or volume for each pressure sample taken (i.e., for each sampling period) by the monitoring module 422. The calculated elastic leak may then be provided to any other module as needed.


In the embodiment shown, the current or instantaneous inelastic leak is also calculated by the leak estimation module 412. The calculation is made using the inelastic leak portion of the leak model and the pressure data obtained by the monitoring module 422. The leak estimation module 412 may calculate a new instantaneous inelastic leak flow or volume for each pressure sample taken (i.e., for each sampling period) by the monitoring module 422. The calculated inelastic leak may then be provided to any other module as needed.


The system 400 illustrated will compensate lung flow for leaks due to elastic and inelastic leaks in the ventilation system. Furthermore, the system performs a dynamic compensation of lung flow based on the changing leak conditions of the ventilation system and the instantaneous pressure and flow measurements. By compensating for the inelastic as well as the elastic components of dynamic leaks, the medical ventilator can more accurately and precisely identify changes in the patient's respiratory cycle determined by flow-based thresholds.


It will be clear that the systems and methods described herein are well adapted to attain the ends and advantages mentioned as well as those inherent therein. Those skilled in the art will recognize that the methods and systems within this specification may be implemented in many manners and as such is not to be limited by the foregoing exemplified embodiments and examples. For example, the operations and steps of the embodiments of methods described herein may be combined or the sequence of the operations may be changed while still achieving the goals of the technology. In addition, specific functions and/or actions may also be allocated in such as a way as to be performed by a different module or method step without deviating from the overall disclosure. In other words, functional elements being performed by a single or multiple components, in various combinations of hardware and software, and individual functions can be distributed among software applications. In this regard, any number of the features of the different embodiments described herein may be combined into one single embodiment and alternate embodiments having fewer than or more than all of the features herein described are possible.


While various embodiments have been described for purposes of this disclosure, various changes and modifications may be made which are well within the scope of the present invention. For example, the systems and methods described herein could be adapted for more accurately determining the onset of coughing events or other interruptions in a natural respiration cycle. Numerous other changes may be made which will readily suggest themselves to those skilled in the art and which are encompassed in the spirit of the disclosure and as defined in the appended claims.

Claims
  • 1. A method of compensating for leakage in a ventilation system during delivery of gas from a medical ventilator to a patient comprising: monitoring an instantaneous flow at a location in the ventilation system based on one or more measurements of pressure and flow in the ventilation system;modeling leakage as a first leakage component through a first orifice of a fixed size and a second leakage component through a second orifice of a varying size, wherein the first and second leakage components are different functions of instantaneous pressure in the ventilation system;estimating a leak-compensated instantaneous lung flow of gas inhaled or exhaled by the patient based on the one or more measurements, the first leakage component and second leakage component; andusing the leak-compensated instantaneous lung flow and a threshold to identify patient-initiated transitions into inhalation or exhalation.
  • 2. The method of claim 1 wherein the threshold is a leak-free cycle threshold indicative of a patient-initiated transition into exhalation and using comprises: cycling into an exhalation phase of the medical ventilator based on the leak-free cycle threshold and the leak-compensated instantaneous lung flow.
  • 3. The method of claim 1 wherein the threshold is a leak-free trigger threshold indicative of a patient-initiated transition into inhalation and using comprises: triggering an inhalation phase of the medical ventilator based on the leak-free trigger threshold and the leak-compensated instantaneous lung flow.
  • 4. The method of claim 1 wherein estimating a leak-compensated instantaneous lung flow further comprises: generating an estimate of instantaneous lung flow at a patient interface based on one or more measurements taken at different locations in the ventilation system.
  • 5. The method of claim 1 further comprising: receiving the threshold from an operator.
  • 6. The method of claim 1 further comprising: estimating current leakage from the ventilation system during delivery of gas to the patient based on the one or more measurements and the first leakage component and second leakage component.
  • 7. The method of claim 1 further comprising: measuring leakage from the ventilation system during delivery of gas to the patient based on measurements of at least one of pressure and flow taken from a first breath;generating a leakage equation based on the measurements of at least one of pressure and flow taken from a first breath, the leakage equation including the first leakage component and the second leakage component; andestimating current leakage from the ventilation system in a second breath immediately subsequent to the first breath based on the leakage equation and measurements of at least one of pressure and flow taken from the second breath.
  • 8. The method of claim 7 wherein the threshold is a trigger threshold indicative of patient inhalation and using comprises: estimating the leak-compensated instantaneous lung flow of gas inhaled by the patient based on the estimated current leakage in the second breath; andtriggering an inhalation phase of the medical ventilator during the second breath based on the leak-compensated lung flow.
  • 9. The method of claim 1 further comprising; estimating leakage from the ventilation system during delivery of gas to the patient based on measurements of at least one of pressure and flow taken over a plurality of previous breaths.
  • 10. The method of claim 1 further comprising; estimating one or more tubing characteristics indicative of compliance and resistance of the ventilation system; andgenerating the leak-compensated instantaneous lung flow based on the estimated tubing characteristics, the one or more measurements of pressure and flow, the first leakage component and second leakage component.
  • 11. A method of compensating for leakage in a ventilation tubing system during delivery of gas from a medical ventilator to a patient comprising: identifying an inelastic leakage in the ventilation system as a first function of at least one of a pressure measurement and a flow measurement in the ventilation system;identifying an elastic leakage in the ventilation system as a second function of at least one of the pressure measurement and the flow measurement in the ventilation system;estimating compliance and resistance of the ventilation tubing system; andtransitioning the medical ventilator between inhalation and exhalation based on the inelastic leakage, the elastic leakage, the compliance and resistance and the at least one of the pressure measurement and the flow measurement in the ventilation system.
  • 12. The method of claim 11 transitioning the medical ventilator between inhalation and exhalation further comprises: generating a leak-compensated lung flow based on the elastic leakage and at least one of the pressure measurement and the flow measurement in the ventilation system;comparing the leak-compensated lung flow to a flow trigger threshold indicative of the patient's breathing effort when there is no leakage in the ventilation system; andtriggering an inhalation phase of the medical ventilator based on results of the comparing operation.
  • 13. The method of claim 11 wherein transitioning the medical ventilator between inhalation and exhalation comprises: generating a leak-compensated lung flow based on the elastic leakage and at least one of the pressure measurement and the flow measurement in the ventilation system;comparing the leak-compensated lung flow to a flow cycle threshold indicative of the patient's breathing effort when there is no leakage in the ventilation system; andcycling into an exhalation phase of the medical ventilator based on results of the comparing operation.
  • 14. A pressure support system comprising: a pressure generating system adapted to generate a flow of breathing gas;a ventilation system including a patient interface device;one or more sensors operatively coupled to the pressure generating system or the ventilation system, each sensor capable of generating an output indicative of a pressure of the breathing gas;a processor;an elastic leak estimation module that identifies an elastic leakage in the ventilation system; anda compensation module that generates a leak-compensated lung flow based on the elastic leakage and at least one output indicative of a pressure of the breathing gas.
  • 15. The system of claim 14 further comprising: an exhalation detection module that determines when a patient has begun to exhale based on a cycle threshold, the leak-compensated lung flow and an output of at least one sensor.
  • 16. The system of claim 14 further comprising: an inhalation detection module that determines when a patient has begun to inhale based on a trigger threshold, the leak-compensated lung flow and an output of at least one sensor.
  • 17. The system of claim 14 further comprising: an inelastic leak estimation module that identifies an inelastic leakage in the ventilation system; andwherein the compensation module further generates the leak-compensated lung flow based on the inelastic leakage.
  • 18. The system of claim 17 further comprising: a pressure monitoring module that monitors at least one output of the one or more sensors and provides data indicative of the pressure of the breathing gas to the elastic leak estimation module.
  • 19. A controller for a medical ventilator comprising: a microprocessor; anda lung flow estimation means that adjusts a ventilator net flow threshold based on: identifying an instantaneous elastic leakage in the ventilation system as a first function of at least one of a pressure measurement and a flow measurement in the ventilation system; andidentifying an instantaneous inelastic leakage in the ventilation system as a second function of at least one of a pressure measurement and a flow measurement in the ventilation system.
  • 20. The controller of claim 19 wherein the lung flow estimation means comprises: an inelastic leak estimation module that identifies the instantaneous inelastic leakage in the ventilation system; andan elastic leak estimation module that identifies the instantaneous elastic leakage in the ventilation system.
  • 21. The controller of claim 19 wherein the lung flow estimation means comprises: a compensation module that generates a leak-compensated lung flow based on the instantaneous elastic leakage and at least one sensor output indicative of the pressure measurement in the ventilation system.
RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No. 61/041,070, filed Mar. 31, 2008, titled “Ventilator Leak Compensation,” which application is hereby incorporated by reference.

US Referenced Citations (332)
Number Name Date Kind
3805780 Cramer et al. Apr 1974 A
3941124 Rodewald et al. Mar 1976 A
4056098 Michel et al. Nov 1977 A
4305388 Brisson Dec 1981 A
4340044 Levy et al. Jul 1982 A
4766894 Legrand et al. Aug 1988 A
4939647 Clough et al. Jul 1990 A
4971052 Edwards Nov 1990 A
4972842 Korten et al. Nov 1990 A
4986268 Tehrani Jan 1991 A
5072728 Pasternack Dec 1991 A
5094235 Westenskow et al. Mar 1992 A
5148802 Sanders et al. Sep 1992 A
5239995 Estes et al. Aug 1993 A
5259373 Gruenke et al. Nov 1993 A
5313937 Zdrojkowski et al. May 1994 A
5315989 Tobia May 1994 A
5316009 Yamada May 1994 A
5365922 Raemer Nov 1994 A
5388575 Taube Feb 1995 A
5398682 Lynn Mar 1995 A
5429123 Shaffer et al. Jul 1995 A
5433193 Sanders et al. Jul 1995 A
5492113 Estes et al. Feb 1996 A
5503146 Froehlich et al. Apr 1996 A
5503147 Bertheau Apr 1996 A
5535738 Estes et al. Jul 1996 A
5540220 Gropper et al. Jul 1996 A
5551418 Estes et al. Sep 1996 A
5551419 Froehlich et al. Sep 1996 A
5555880 Winter et al. Sep 1996 A
5598838 Servidio et al. Feb 1997 A
5605151 Lynn Feb 1997 A
5623923 Bertheau et al. Apr 1997 A
5632269 Zdrojkowski May 1997 A
5645053 Remmers et al. Jul 1997 A
5664562 Bourdon Sep 1997 A
5685296 Zdrojkowski et al. Nov 1997 A
5687715 Landis et al. Nov 1997 A
5692497 Schnitzer et al. Dec 1997 A
5752509 Lachmann et al. May 1998 A
5794615 Estes Aug 1998 A
5803065 Zdrojkowski et al. Sep 1998 A
5823187 Estes et al. Oct 1998 A
5884622 Younes Mar 1999 A
5891023 Lynn Apr 1999 A
5901704 Estes et al. May 1999 A
5904141 Estes et al. May 1999 A
5921920 Marshall et al. Jul 1999 A
5927274 Servidio et al. Jul 1999 A
5970975 Estes et al. Oct 1999 A
6029664 Zdrojkowski et al. Feb 2000 A
6055981 Laswick et al. May 2000 A
6105575 Estes et al. Aug 2000 A
6123074 Hete et al. Sep 2000 A
6148814 Clemmer et al. Nov 2000 A
6152129 Berthon-Jones Nov 2000 A
6158432 Biondi et al. Dec 2000 A
6223064 Lynn et al. Apr 2001 B1
6253765 Högnelid et al. Jul 2001 B1
6257234 Sun Jul 2001 B1
6279569 Berthon-Jones Aug 2001 B1
6286508 Remmers et al. Sep 2001 B1
6305372 Servidio Oct 2001 B1
6305374 Zdrojkowski et al. Oct 2001 B1
6321748 O'Mahoney Nov 2001 B1
6342039 Lynn et al. Jan 2002 B1
6360741 Truschel Mar 2002 B2
6371114 Schmidt et al. Apr 2002 B1
6390091 Banner et al. May 2002 B1
6425395 Brewer et al. Jul 2002 B1
6427689 Estes et al. Aug 2002 B1
6484719 Berthon-Jones Nov 2002 B1
6512938 Claure et al. Jan 2003 B2
6532957 Berthon-Jones Mar 2003 B2
6532958 Buan et al. Mar 2003 B1
6532959 Berthon-Jones Mar 2003 B1
6532960 Yurko Mar 2003 B1
6536429 Pavlov et al. Mar 2003 B1
6536432 Truschel Mar 2003 B2
6539940 Zdrojkowski et al. Apr 2003 B2
6543449 Woodring et al. Apr 2003 B1
6550478 Remmers et al. Apr 2003 B2
6553992 Berthon-Jones et al. Apr 2003 B1
6561187 Schmidt et al. May 2003 B2
6575163 Berthon-Jones Jun 2003 B1
6578575 Jonson Jun 2003 B1
6609016 Lynn Aug 2003 B1
6609517 Estes et al. Aug 2003 B1
6615834 Gradon et al. Sep 2003 B2
6626175 Jafari et al. Sep 2003 B2
6629527 Estes et al. Oct 2003 B1
6640806 Yurko Nov 2003 B2
6644312 Berthon-Jones et al. Nov 2003 B2
6644316 Bowman et al. Nov 2003 B2
6659101 Berthon-Jones Dec 2003 B2
6671529 Claure et al. Dec 2003 B2
6688307 Berthon-Jones Feb 2004 B2
6701926 Olsen et al. Mar 2004 B2
6722365 Nilsson et al. Apr 2004 B2
6723055 Hoffman Apr 2004 B2
6723132 Salehpoor Apr 2004 B2
6748252 Lynn et al. Jun 2004 B2
6752150 Remmers et al. Jun 2004 B1
6752151 Hill Jun 2004 B2
6755193 Berthon-Jones et al. Jun 2004 B2
6758216 Berthon-Jones et al. Jul 2004 B1
6760608 Lynn Jul 2004 B2
6761165 Strickland, Jr. Jul 2004 B2
6761167 Nadjafizadeh et al. Jul 2004 B1
6761168 Nadjafizadeh et al. Jul 2004 B1
6789541 Olsen et al. Sep 2004 B2
6796305 Banner et al. Sep 2004 B1
6810876 Berthon-Jones Nov 2004 B2
6814074 Nadjafizadeh et al. Nov 2004 B1
6820618 Banner et al. Nov 2004 B2
6823866 Jafari et al. Nov 2004 B2
6837242 Younes Jan 2005 B2
6843250 Efrati Jan 2005 B2
6868346 Larson et al. Mar 2005 B2
6874503 Rydgren Apr 2005 B2
6910480 Berthon-Jones Jun 2005 B1
6910481 Kimmel et al. Jun 2005 B2
6920875 Hill et al. Jul 2005 B1
6920877 Remmers et al. Jul 2005 B2
6932084 Estes et al. Aug 2005 B2
6945248 Berthon-Jones Sep 2005 B2
6948497 Zdrojkowski et al. Sep 2005 B2
6962155 Sinderby Nov 2005 B1
6986347 Hickle Jan 2006 B2
7000612 Jafari et al. Feb 2006 B2
7008380 Rees et al. Mar 2006 B1
7013892 Estes et al. Mar 2006 B2
7017576 Olsen et al. Mar 2006 B2
7040320 Fjeld et al. May 2006 B2
7055522 Berthon-Jones Jun 2006 B2
7066173 Banner et al. Jun 2006 B2
7073501 Remmers et al. Jul 2006 B2
7081095 Lynn et al. Jul 2006 B2
7089936 Madaus et al. Aug 2006 B2
7092757 Larson et al. Aug 2006 B2
7100607 Zdrojkowski et al. Sep 2006 B2
7100608 Brewer et al. Sep 2006 B2
7100609 Berthon-Jones et al. Sep 2006 B2
7107991 Kolobow Sep 2006 B2
7137389 Berthon-Jones Nov 2006 B2
7152598 Morris et al. Dec 2006 B2
7168429 Matthews et al. Jan 2007 B2
7195028 Basset et al. Mar 2007 B2
7210478 Banner et May 2007 B2
7229430 Hickle et al. Jun 2007 B2
7267122 Hill Sep 2007 B2
7275540 Bolam et al. Oct 2007 B2
7296573 Estes et al. Nov 2007 B2
7297119 Westbrook et al. Nov 2007 B2
7331343 Schmidt et al. Feb 2008 B2
7353824 Forsyth et al. Apr 2008 B1
7367337 Berthon-Jones et al. May 2008 B2
7370650 Nadjafizadeh et al. May 2008 B2
7398115 Lynn Jul 2008 B2
7406870 Seto Aug 2008 B2
7448381 Sasaki et al. Nov 2008 B2
7455583 Taya et al. Nov 2008 B2
7475685 Dietz et al. Jan 2009 B2
7509957 Duquette et al. Mar 2009 B2
7527056 Turiello May 2009 B2
7533671 Gonzalez et al. May 2009 B2
7621269 Turiello Nov 2009 B2
7644713 Berthon-Jones Jan 2010 B2
7661428 Berthon-Jones Feb 2010 B2
7673629 Turiello Mar 2010 B2
7677247 Turiello Mar 2010 B2
7694678 Turiello Apr 2010 B2
7717112 Sun et al. May 2010 B2
7770578 Estes et al. Aug 2010 B2
7810496 Estes et al. Oct 2010 B2
7810497 Pittman et al. Oct 2010 B2
7814906 Moretti Oct 2010 B2
7827988 Matthews et al. Nov 2010 B2
7856979 Doshi et al. Dec 2010 B2
7882835 Eger et al. Feb 2011 B2
7886739 Soliman et al. Feb 2011 B2
7886740 Thomas et al. Feb 2011 B2
7905231 Chalvignac Mar 2011 B2
7918222 Chen Apr 2011 B2
7918223 Soliman et al. Apr 2011 B2
7920067 Durtschi et al. Apr 2011 B2
7928852 Durtschi et al. Apr 2011 B2
7934499 Berthon-Jones May 2011 B2
7938114 Matthews et al. May 2011 B2
7963283 Sinderby Jun 2011 B2
7984712 Soliman et al. Jul 2011 B2
8002154 Fontela et al. Aug 2011 B2
8021309 Zilberg Sep 2011 B2
8033280 Heinonen Oct 2011 B2
20020014240 Truschel Feb 2002 A1
20020053345 Jafari et al. May 2002 A1
20020185126 Krebs Dec 2002 A1
20030158466 Lynn et al. Aug 2003 A1
20030221689 Berthon-Jones Dec 2003 A1
20040074492 Berthon-Jones Apr 2004 A1
20040089561 Herman May 2004 A1
20040163648 Burton Aug 2004 A1
20040187870 Matthews et al. Sep 2004 A1
20050109340 Tehrani May 2005 A1
20050172965 Thulin Aug 2005 A1
20050188991 Sun et al. Sep 2005 A1
20050241639 Zilberg Nov 2005 A1
20060000475 Matthews et al. Jan 2006 A1
20060011200 Remmers et al. Jan 2006 A1
20060086357 Soliman et al. Apr 2006 A1
20060102180 Berthon-Jones May 2006 A1
20060112959 Mechlenburg et al. Jun 2006 A1
20060118112 Cattano et al. Jun 2006 A1
20060144144 Seto Jul 2006 A1
20060150974 Berthon-Jones Jul 2006 A1
20060155206 Lynn Jul 2006 A1
20060155207 Lynn et al. Jul 2006 A1
20060161071 Lynn et al. Jul 2006 A1
20060174883 Aylsworth et al. Aug 2006 A1
20060189880 Lynn et al. Aug 2006 A1
20060195041 Lynn et al. Aug 2006 A1
20060201505 Remmers et al. Sep 2006 A1
20060217633 Glocker et al. Sep 2006 A1
20060235324 Lynn Oct 2006 A1
20060241708 Boute Oct 2006 A1
20060247508 Fennell Nov 2006 A1
20060249150 Dietz et al. Nov 2006 A1
20060249156 Moretti Nov 2006 A1
20060254588 Brewer et al. Nov 2006 A1
20060264762 Starr Nov 2006 A1
20060272642 Chalvignac Dec 2006 A1
20060278218 Hoffman Dec 2006 A1
20070000494 Banner et al. Jan 2007 A1
20070027375 Melker et al. Feb 2007 A1
20070028921 Banner et al. Feb 2007 A1
20070044796 Zdrojkowski et al. Mar 2007 A1
20070068530 Pacey Mar 2007 A1
20070072541 Daniels, II et al. Mar 2007 A1
20070077200 Baker Apr 2007 A1
20070089738 Soliman et al. Apr 2007 A1
20070093721 Lynn et al. Apr 2007 A1
20070101992 Soliman et al. May 2007 A1
20070129647 Lynn Jun 2007 A1
20070135736 Addington et al. Jun 2007 A1
20070144522 Eger et al. Jun 2007 A1
20070149860 Lynn et al. Jun 2007 A1
20070157931 Parker et al. Jul 2007 A1
20070163579 Li et al. Jul 2007 A1
20070191688 Lynn Aug 2007 A1
20070191697 Lynn et al. Aug 2007 A1
20070215154 Borrello Sep 2007 A1
20070221224 Pittman et al. Sep 2007 A1
20070251532 Friedberg Nov 2007 A1
20070272241 Sanborn et al. Nov 2007 A1
20070277823 Al-Ali et al. Dec 2007 A1
20070283958 Naghavi Dec 2007 A1
20080000478 Matthiessen et al. Jan 2008 A1
20080000479 Elaz et al. Jan 2008 A1
20080041382 Matthews et al. Feb 2008 A1
20080041383 Matthews et al. Feb 2008 A1
20080051674 Davenport et al. Feb 2008 A1
20080053442 Estes et al. Mar 2008 A1
20080053443 Estes et al. Mar 2008 A1
20080053444 Estes et al. Mar 2008 A1
20080066752 Baker et al. Mar 2008 A1
20080066753 Martin et al. Mar 2008 A1
20080081974 Pav Apr 2008 A1
20080097234 Nicolazzi et al. Apr 2008 A1
20080168988 Lu Jul 2008 A1
20080178880 Christopher et al. Jul 2008 A1
20080178882 Christopher et al. Jul 2008 A1
20080185002 Berthon-Jones et al. Aug 2008 A1
20080200775 Lynn Aug 2008 A1
20080200819 Lynn et al. Aug 2008 A1
20080221469 Shevchuk Sep 2008 A1
20080251079 Richey Oct 2008 A1
20080295837 McCormick et al. Dec 2008 A1
20080302359 Loomas et al. Dec 2008 A1
20090014007 Brambilla et al. Jan 2009 A1
20090050153 Brunner Feb 2009 A1
20090082653 Rohde Mar 2009 A1
20090088613 Marttila et al. Apr 2009 A1
20090093697 Mir et al. Apr 2009 A1
20090137927 Miller May 2009 A1
20090149730 McCrary Jun 2009 A1
20090171226 Campbell et al. Jul 2009 A1
20090178675 Turiello Jul 2009 A1
20090178676 Villax et al. Jul 2009 A1
20090194100 Minagi Aug 2009 A1
20090229605 Efrati et al. Sep 2009 A1
20090241951 Jafari et al. Oct 2009 A1
20090241962 Jafari et al. Oct 2009 A1
20090250061 Marasigan Oct 2009 A1
20090281481 Harding Nov 2009 A1
20090308398 Ferdinand et al. Dec 2009 A1
20090314294 Chalvignac Dec 2009 A1
20090318851 Schenck Dec 2009 A1
20100018529 Chalvignac Jan 2010 A1
20100024819 Tiedje Feb 2010 A1
20100065057 Berthon-Jones Mar 2010 A1
20100071696 Jafari Mar 2010 A1
20100078018 Heinonen Apr 2010 A1
20100081958 She Apr 2010 A1
20100101574 Bassin Apr 2010 A1
20100101576 Berthon-Jones Apr 2010 A1
20100116276 Bayasi May 2010 A1
20100137737 Addington et al. Jun 2010 A1
20100147303 Jafari et al. Jun 2010 A1
20100186741 Aylsworth et al. Jul 2010 A1
20100218767 Jafari et al. Sep 2010 A1
20100234758 de Menezes Sep 2010 A1
20100236553 Jafari et al. Sep 2010 A1
20100236555 Jafari et al. Sep 2010 A1
20100252048 Young et al. Oct 2010 A1
20100258123 Somaiya et al. Oct 2010 A1
20100262038 Tan et al. Oct 2010 A1
20100331768 Hedmann et al. Dec 2010 A1
20110034863 Hoffa Feb 2011 A1
20110061648 Durtschi et al. Mar 2011 A1
20110071367 Court et al. Mar 2011 A1
20110077549 Kitai et al. Mar 2011 A1
20110100373 Efrati et al. May 2011 A1
20110125052 Davenport et al. May 2011 A1
20110132363 Chalvignac Jun 2011 A1
20110178427 Tan et al. Jul 2011 A1
20110196251 Jourdain et al. Aug 2011 A1
20110201956 Alferness et al. Aug 2011 A1
20110209702 Vuong et al. Sep 2011 A1
20110220112 Connor Sep 2011 A1
20110226250 LaBollita et al. Sep 2011 A1
20110259330 Jafari et al. Oct 2011 A1
Foreign Referenced Citations (16)
Number Date Country
19808543 Nov 1998 DE
0425092 May 1991 EP
1270036 Jan 2003 EP
WO 9423780 Oct 1994 WO
WO 9806449 Feb 1998 WO
WO 0010634 Mar 2000 WO
WO 0045880 Aug 2000 WO
WO 0174430 Oct 2001 WO
WO 0228460 Apr 2002 WO
WO 03055552 Jul 2003 WO
WO 04000114 Dec 2003 WO
WO 2004084980 Oct 2004 WO
WO 2005105189 Nov 2005 WO
WO 2006137784 Dec 2006 WO
WO 2007145948 Dec 2007 WO
WO 2009123981 Oct 2009 WO
Related Publications (1)
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20090241955 A1 Oct 2009 US
Provisional Applications (1)
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61041070 Mar 2008 US