Patent Application
20190254566
The present invention finds application in patient ventilation systems and methods. However, it will be appreciated that the described techniques may also find application in other patient care systems, other patient parameter estimation techniques, and the like.
Estimating respiratory muscle pressure (Pmus(t)) is of paramount importance in support modalities of mechanical ventilation, such as Pressure Support Ventilation (PSV), where patient and ventilator share the mechanical work performed on the respiratory system. Quantitative assessment of Pmus(t) can be used to select the appropriate level of ventilation support in order to prevent both atrophy and fatigue of the respiratory muscles. One clinical parameter commonly used to assess the effort made by the patient per breath is known as Work of Breathing (WOB) and can be computed once the estimate of Pmus(t) is available for the breath (e.g., WOB can be obtained from Pmus(t) by integration of the latter over the inhaled volume). One conventional approach for Pmus(t) and WOB estimation relates to measuring the esophageal pressure (Pes) via insertion of a balloon-tipped catheter in the patient's esophagus. The measured Pes(t) is assumed to be a good proxy for the pleural pressure (Ppl) and can be used, in conjunction with an estimate of chest wall compliance, to compute the WoB via the so-called Campbell diagram or, equivalently, via explicit computation of Pmus(t) and then of WOB.
Estimates of R and C are important per se, as they provide quantitative information to the physician about the mechanical properties of the patient's respiratory system and they can be used to diagnose respiratory diseases and better select the appropriate ventilation modalities and therapeutic paths. Moreover, R and C can also be used to estimate Pmus(t) as a non-invasive alternative to the use of the esophageal catheter. Assuming R and C are known, it is indeed possible to estimate Pmus(t) via the following equation (known as the Equation of Motion of the lungs):
P
aw(t)=R·{dot over (V)}(t)+E·V(t)+Pmus(t)+P0 (1)
where Paw(t) is the pressure measured at the airway opening, {dot over (V)}(t) is the flow of air into and out of the patient's respiratory system (measured again at the airway opening), V(t) is the net volume of air delivered to the patient (measured by integrating the flow signal over time), E is the elastance (inverse of the compliance C) and P0 is a constant term to account for the pressure at the end of expiration (needed to balance the equation but not interesting per se).
Previous attempts to use equation (1) for a non-invasive estimation of Pmus(t) relied on a two-step approach, where R and C are estimated first and then equation (1) is applied to compute Pmus(t) using the estimated values of R and C. Estimation of R and C was performed either by applying the End-Inspiratory Occlusion (EIP) maneuver or via Least-Squares (LS) fitting of equation (1) to flow and pressure measurements under specific conditions, where the term Pmus(t) was assumed to be zero. These conditions included:
The quantitative assessment of the mechanical properties of the respiratory system and of the inspiratory efforts in patients under mechanical ventilation offers invaluable information for the clinician to tailor ventilation strategy and settings. The current state of the art for the assessment of respiratory mechanics consists of computing two parameters, namely resistance (R) and compliance (C), via the EIP technique. This technique, however, not only interferes with the normal operation of the ventilator, but it requires the respiratory muscles to be fully relaxed in order to provide accurate R and C estimates. Hence, because of the presence of respiratory activity from the patient, the EIP often leads to biased results. Assessment of inspiratory patient efforts, on the other hand, is traditionally obtained by inferring the pressure generated by the respiratory muscles (Pmus) from the pressure measured in the esophagus (Pes). Quantitative assessment of patient effort is then obtained on a breath-by-breath basis by computing Work of Breathing (WOB) from the Pmus waveform. The main limitation of such approach is that measurement of Pes requires the insertion of an esophageal catheter, with consequent discomfort for the patient in addition to the need for special instrumentation and skilled personnel.
Other methods have been developed to allow simultaneous estimation of R, C and Pmus(t) from measured airway pressure and flow waveforms during regular ventilation without requiring esophageal pressure measurements. These methods are based on the use of the traditional first-order single-compartment model of respiratory mechanics and its associated Equation of Motion in (1). They all face the fundamental difficulty of the simultaneous estimation approach related to the underdetermined nature of the mathematical problem (more unknowns than available equations). In these methods, the use of constrains based on physiological assumptions have been advocated to render the mathematical problem solvable. However, these methods have been shown to work only under specific conditions. Particularly, when the ventilator cycles off before the patient has completely released his respiratory muscles (i.e., Pmus has returned to zero baseline value), these conventional methods are not reliable. This may limit their applicability to all clinical scenarios.
The disadvantage of the conventional invasive procedure of esophageal pressure measurement is apparent, since the insertion of an esophageal balloon requires experienced personnel and implies discomfort and risk for the patient.
The two-step estimation technique, where the EIP maneuver is first performed to get R and C and equation (1) is then used to compute Pmus(t), has the following main drawbacks:
Finally, the above mentioned two-step techniques that apply LS fitting under specific conditions or to portions of the breath, where Pmus(t) is theoretically negligible, present limitations. In particular:
The present application provides new and improved systems and methods that facilitate noninvasively estimating of R, C, and Pmus using an airway occlusion pressure maneuver (P0.1) having a predetermined duration, thereby overcoming the above-referenced problems and others.
Still further advantages of the subject innovation will be appreciated by those of ordinary skill in the art upon reading and understand the following detailed description.
According to one embodiment, a method for estimating respiratory muscle pressure and respiratory mechanics using a P0.1 maneuver comprises detecting patient inspiration onset for a patient connected to a ventilator, occluding the airway of the patient for a first predetermined time period, and estimating a first respiratory muscle pressure (Pmus) profile during the airway occlusion. The method further comprises estimating resistance (R) and compliance (C) values and a second Pmus profile generated during a second predetermined time period, estimating a third Pmus profile during a third predetermined time period that extends from the end of the second predetermined time period until the end of the breath, and estimating Pmus(t) over an entire breath by concatenating the first, second and third Pmus profiles. The estimated R and C values and the estimated Pmus profiles are output on a display.
According to another embodiment, a system that facilitates estimating respiratory muscle pressure and respiratory mechanics using a P0.1 maneuver comprises a ventilator to which a patient is connected, and one or more processors configured to detect patient inspiration onset for a patient connected to a ventilator and occlude the airway of the patient for a first predetermined time period. The one or more processors are further configured to estimate a first respiratory muscle pressure (Pmus) profile during the airway occlusion, estimate resistance (R) and compliance (C) values and a second Pmus profile generated during a second predetermined time period, and estimate a third Pmus profile during a third predetermined time period that extends from the end of the second predetermined time period until the end of the breath. Additionally, the one or more processors are configured to estimate Pmus(t) over an entire breath by concatenating the first, second and third Pmus profiles, and output the estimated R and C values and the estimated Pmus profiles on a display.
According to another embodiment, a processor is configured to execute computer-executable instructions for estimating respiratory muscle pressure and respiratory mechanics using a P0.1 maneuver. The instructions comprise detecting processor patient inspiration onset for a patient connected to a ventilator, occluding the airway of the patient for a first predetermined time period, and estimating a first respiratory muscle pressure (Pmus) profile during the airway occlusion. The instructions further comprise estimating resistance (R) and compliance (C) values and a second Pmus profile generated during a second predetermined time period, and estimating a third Pmus profile during a third predetermined time period that extends from the end of the second predetermined time period until the end of the breath. Additionally, the instructions comprise estimating Pmus(t) over an entire breath by concatenating the first, second and third Pmus profiles, and outputting the estimated R and C values and the estimated Pmus profiles on a display.
The drawings are only for purposes of illustrating various aspects and are not to be construed as limiting.
The need for estimation of the respiratory system parameters (resistance R and compliance C) and patient inspiratory efforts (respiratory muscle pressure Pmus(t)) is well-known in the medical community. In order to overcome the above-described problems in the art, the herein-described systems and methods relate to an alternative approach for the noninvasive estimation of R, C, and Pmus that makes use of an airway occlusion pressure maneuver (P0.1) having a predetermined duration (e.g., less than 150 ms or the like) to circumvent the inherent difficulty of the simultaneous estimation approach. The described method involves, inter alia, the following steps: 1) In the first step, the patient's airway is occluded at end of exhalation as soon as zero flow condition is detected; occlusion is maintained for a first predetermined time period, (e.g., 100 ms) and the airway pressure waveform during these 100 ms is used to estimate the coefficients of a polynomial model of Pmus(t); 2) once the occlusion has been released, the estimated Pmus(t) profile is extended (in time) for a second predetermined time period (e.g., for an additional 100 ms) and airway pressure and flow waveforms are used together with the extended Pmus profile to estimate R and C using the equation of motion via a standard Least-Square method; 3) the estimated R and C are used in conjunction with airway pressure and flow waveforms to reconstruct a Pmus profile for a third predetermined time period (e.g., throughout the remaining portion of the breath) based on the standard equation of motion. The P0.1 maneuver can be intermittently repeated at a variable or fixed rate (e.g., every X number of breaths) while the values of R and C estimated during the previous maneuver can still be used to compute an estimate of Pmus between each consecutive P0.1 maneuver. This also allows computation of WOB (or power of breathing (POB)) from the estimated Pmus profile on a breath-by-breath basis. In one embodiment, the claimed systems and methods are employed in hospital and home ventilators for real-time patient monitoring, ventilation optimization and closed-loop control.
The herein-described systems and methods overcome the aforementioned limitations of conventional approaches by; not requiring an esophageal balloon; explicitly accounting for the presence of Pmus; and not requiring a change ventilation mode during the maneuver so that the resulting R and C estimates are still related to the current ventilation operating conditions. Moreover, unlike the EIP, the P0.1 maneuver does not modify the patient's natural breathing pattern. Unlike other conventional approaches, the P0.1 is still reliable even when the ventilator cycles off before Pmus has returned to zero baseline value.
The described systems and methods facilitate performing noninvasive estimation of R, C and Pmus in patients receiving mechanical ventilation and able to breathe spontaneously. The R, C and Pmus estimates can be used for real-time patient monitoring, ventilation optimization and closed-loop control. The described systems and methods can be implemented as part of software or firmware running on a ventilator, anesthesia machines, or patient monitoring products (including remote patient monitors, e.g. eICU). The described systems and methods improve ventilator function by improving the accuracy of estimated R, C and Pmus values.
When performing estimation of the initial inspiratory Pmus profile during airway occlusion at 14, the patient's airway is occluded (at 12) at end of exhalation, as soon as the patient's inspiratory effort is detected (at 10). The occlusion is then maintained for, e.g., 100 ms, during which the patient is essentially trying to inhale against a closed airway. In one embodiment, the P0.1 maneuver is software-automated. During the occlusion, since there is no airflow between the point at which the airway pressure is measured and the patient's lungs, the negative deflection in pressure is measured at the airways (Paw) and is essentially a reflection of the Pmus developed by the patient's respiratory muscles (gas decompression can be neglected) such that:
P
aw(t)=Pmus(t) for 0≤t≤100 ms
The small duration of the occlusion (e.g., less than 150 ms) ensures that the patient's natural respiratory Pmus output is not affected by the application of the occlusion. Hence, it is possible to fit a polynomial model of Pmus to the airway pressure measurements during the 100 ms occlusion and estimate the initial inspiratory Pmus profile via standard Least-Square (LS) technique. For instance, a 2nd order polynomial Pmus model could be assumed and its unknown coefficients could then be estimated as shown below:
P
mus(t)=+a2t+a3·t2 for 0≤t≤100 ms
where θ is the vector of unknown parameters [a1 a2 a3] (i.e., the coefficients of the polynomial Pmus model), Y is the vector containing the airway pressure measurements, k is the total number of samples collected during the 100 ms occlusion and t1, t2 tk are the time at which the airway pressure signal is sampled (i.e., t1=0, t2=T, t3=2T, . . . tk=(k−1)T with T being the sample period).
When estimating R and C after the occlusion based on a 100 ms extended Pmus profile at 16, after the 100 ms occlusion period, the airways are released and air flows to the lungs under the pressure gradient established by both the patient's own Pmus drive and the contribution of the ventilator. In such conditions, it may be difficult to estimate R, C and Pmus simultaneously from flow and pressure measurements based on the simple equation of motion because the underlying LS problem is underdetermined. However, it is reasonable to assume that for a very short period of time (e.g., 100 ms), the profile of Pmus remains unchanged compared to the profile estimated during the previous 100 ms occlusion period. Hence, it is possible to extend the Pmus profile based on the previously estimated polynomial coefficients and attain an estimate of Pmus during this additional 100 ms post-occlusion period such that:
{circumflex over (P)}
mus(t)=a1+a2·t+a3·t2 for 100 ms≤t≤200 ms
The extended Pmus(t) profile can be used together with the airway pressure and flow waveforms to estimate R and C using the equation of motion via a LS method such that:
where Paw(t) is the pressure measured at the airway opening, {dot over (V)}(t) is the flow of air into and out of the patient's respiratory system (measured again at the airway opening), V(t) is the net volume of air delivered to the patient (measured by integrating the flow signal over time), E is the elastance (inverse of the compliance C), P0 is a constant term to account for the pressure at the end of expiration (needed to balance the equation but not interesting per se),
When estimating Pmus throughout the remaining portion of the breath at 18, the values of R and C computed during the previous step are used in conjunction with airway pressure and flow waveforms to compute an estimate of Pmus throughout the remaining portion of the breath based on the standard equation of motion such that:
{circumflex over (P)}
mus(t)=Paw(t)−{circumflex over (R)}·{dot over (V)}(t)−Ê·V(t)−P0 for 200 ms≤t≤tend (2)
where tend is the last available time sample (time at the end of the breath).
While the systems and methods discussed herein have been described with respect to certain embodiments, it is to be understood that said systems and methods are not limited to the disclosed embodiments and examples. To the contrary, described systems and methods are intended to cover various modifications and equivalent arrangements included within the spirit and scope of the described algorithm. For instance: The degree of the polynomial Pmus model used in step 14 of
In another embodiment, the duration of step 16 (post-occlusion period) is not limited to being 100 ms. A short duration is useful in order for the assumption of unaltered Pmus profile from step 14 to step 16 to be as valid as possible. In fact, the initiation of pressurization, provided by the ventilator after release of the airways occlusion, can induce changes in the patient's own Pmus drive via mechanical reflexes (e.g. Hering-Breuer reflex). However, the activation of such reflexes and the manifestation of their effects on Pmus may occur on a time scale that is larger than 100 ms. On the other hand, a too short duration of step 16 may induce noise in the measurements that could compromise the LS procedure and lead to biased R and C estimates.
The final estimated Pmus profile does not necessarily need to be constructed by concatenating the three Pmus profiles obtained during steps 14, 16, and 18, respectively. According to one embodiment, the values of R and E, which is essentially the inverse of C, from step 16 are used to compute the estimated Pmus profile over the entire breath according to:
{circumflex over (P)}
mus(t)=Paw(t)−{circumflex over (R)}·{dot over (V)}(t)−Ê·V(t)−P0 for 0≤t≤tend (3)
The small error between the Pmus, R and E estimates and the corresponding gold standard values can be in part ascribed to the existence of a non-zero flow during the occlusion (see dashed arrow in
P
aw(t)≠Pmus(t) for 0≤t≤100 ms
Hence, when a polynomial model of Pmus is fit to the airway pressure measurements during the occlusion period, error may be introduced as shown in the graph 50 of
The estimation algorithm 66 illustrated in the embodiment of
The system further comprises a processor 76 that executes, and a memory 78 that stores, computer executable instructions for carrying out the various functions and/or methods described herein. The memory 78 may be a computer-readable medium on which a control program is stored, such as a disk, hard drive, or the like. Common forms of computer-readable media include, for example, floppy disks, flexible disks, hard disks, magnetic tape, or any other magnetic storage medium, CD-ROM, DVD, or any other optical medium, RAM, ROM, PROM, EPROM, FLASH-EPROM, variants thereof, other memory chip or cartridge, or any other tangible medium from which the processor 76 can read and execute. In this context, the described systems may be implemented on or as one or more general purpose computers, special purpose computer(s), a programmed microprocessor or microcontroller and peripheral integrated circuit elements, an ASIC or other integrated circuit, a digital signal processor, a hardwired electronic or logic circuit such as a discrete element circuit, a programmable logic device such as a PLD, PLA, FPGA, Graphics processing unit (GPU), or PAL, or the like.
The innovation has been described with reference to several embodiments. Modifications and alterations may occur to others upon reading and understanding the preceding detailed description. It is intended that the innovation be construed as including all such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/IB2017/056562 | 10/23/2017 | WO | 00 |
| Number | Date | Country | |
|---|---|---|---|
| 62412927 | Oct 2016 | US |
