Patent Publication Number: US-2015059754-A1

Title: System and method for power of breathing real-time assessment and closed-loop controller

Description:
In the field of health care, mechanical ventilators may be any machine designed to mechanically move breatheable air into and out of the lungs, thereby providing the mechanism of breathing for a patient who is physically unable to breathe, or breathing insufficiently. Ventilators are primarily used in intensive care medicine, home care, and emergency medicine (e.g., standalone units) and in anesthesia (e.g., a component of an anesthesia machine). 
     On any given day, 35,000 patients may be on ventilators in the US, and 100,000 patients worldwide. Almost all of these patients would die if ventilators did not exist. Of these ventilated patients, roughly 7-10% will experience complications from the ventilator systems due to errors in system settings and inaccuracies in the assessment of a patient&#39;s lung functioning. Accordingly, it is generally a daunting task for the medical personnel to select the proper values of the various ventilator settings in order to provide effective artificial ventilation for a specific patient at a specific time period. The settings on the ventilator may relate to values in tidal volume, respiratory rate, pressure readings, etc. 
     An exemplary embodiment is directed to a method for retrieving a breathing reference value, assessing a breathing value of a test subject via a ventilator, identifying a difference between the breathing reference value and the breathing value of the test subject and generating a setting adjustment value to adjust a setting on the ventilator based on the identified difference. 
     A further exemplary embodiment is directed to a system having a data retrieval component for retrieving a breathing reference value and a processing component configured to assess a breathing value of a test subject via a ventilator, identify a difference between the breathing reference value and the breathing value of the test subject and generate a setting adjustment value to adjust a setting on the ventilator based on the identified difference. 
     A further exemplary embodiment is directed to a non-transitory computer readable storage medium including a set of instructions that are executable by a processor. The set of instructions are operable at least to retrieve a breathing reference value, assess a breathing value of a test subject via a ventilator, identify a difference between the breathing reference value and the breathing value of the test subject and generate a setting adjustment value for adjusting a setting on the ventilator based on the identified difference. 
    
    
     
         FIG. 1  shows an exemplary closed-loop system for assessing the breathing effort of a ventilated patient and providing appropriate setting values according to an exemplary embodiment described herein. 
         FIG. 2  shows an exemplary method for assessing the breathing effort of a ventilated patient and providing appropriate setting values according to an exemplary embodiment described herein. 
         FIGS. 3   a - 3   d  show exemplary graphs for the real-time estimations of airway resistance (R) of a tested lung and compliance (C) of the lung according to an exemplary embodiment described herein. 
         FIGS. 4   a - 4   d  show exemplary graphs for the real-time estimations of thoracic muscle pressure (P mus ) and power of breathing value (PoB) during a lung test according to an exemplary embodiment described herein. 
         FIGS. 5   a - 5   d  show exemplary graphs for a fast (e.g., under 2 seconds) real-time estimation R and C values for a tested lung according to an exemplary embodiment described herein. 
         FIG. 6  shows an exemplary graph of the real-time performance by a PoB controller according to an exemplary embodiment described herein. 
         FIG. 7  shows a schematic diagram of the system according to an exemplary embodiment 
     
    
    
     The exemplary embodiments may be further understood with reference to the following description of exemplary embodiments and the related appended drawings, wherein like elements are provided with the same reference numerals. The exemplary embodiments are related to systems and methods for assessing a ventilated patient&#39;s Power of Breathing (“PoB”). A patient&#39;s PoB may depend on any number of variables, such as, but not limited to, the quality of the lungs, the strength of the lungs, etc. Furthermore, the exemplary systems and methods provide supportive information for the ventilator system such as system settings and values. 
     Specifically, the exemplary systems and methods utilize a closed-loop feedback control system in order to automatically and non-invasively assess how much effort the ventilated patient is making. The assessment of this effort gives the user (e.g., clinician, care provider, hospital personnel, etc.) with the appropriate setting values for the ventilator system to make the decision on selecting functions of the ventilator as well as any adjustments to these functions. Alternatively, the exemplary systems and methods described herein may also automatically perform the selection and adjustment of these functions without user intervention. 
     As will be described in greater detail below, these exemplary systems and methods use optimization algorithms in conjunction with the closed-loop control system to determine lung variables of the patient, such as pressure and volume. Based on these determined variables, the systems and methods will provide adjustments to ventilator settings to achieve a desired breathing level. Furthermore, the assessments performed by the systems and methods allow for a user to easily identify candidates for ventilation weaning (e.g., the reduction of a patient&#39;s dependency on the ventilator system). 
       FIG. 1  shows an exemplary closed-loop system  100  for assessing the breathing effort of a ventilated patient and providing appropriate setting values according to an exemplary embodiment described herein. The architecture of the system  100  includes a controller  110 , a ventilator  120 , a patient  130 , a circuit model of the lung  150 , and an optimizer  170 . It should be noted that while  FIG. 1  depicts a “lung test machine” at  130 , this component may be either attached to the patient during medical practice or unattached to a patient for calibrating the system  100 . In other words, a lung test machine  130  may act as the lungs of the patient during performance testing and calibration of the system  100 . For the sake of simplicity, the lung test machine of  FIG. 1  may be referred to as the patient  130 . Accordingly, the system  100  allows for non-invasive assessment of the lung strength and lung quality of the patient  130  while offering corresponding support information for adjusting a ventilator  120 . 
     The exemplary controller  110  may be, for example, a proportional integral controller. However, the controller  110  may also be any controller with good stability margins for tracking and disturbance rejection. It should be noted that while the controller  110  and ventilator  120  are illustrated as separate components within the system  100 , these components may be integrated into a single component. 
     The exemplary feedback control system  100  of  FIG. 1  depicts a physician  190  setting a reference value for the desired power of breathing (POB ref ). Specifically, the PoB ref  value set by the physician  190  may be entered into the controller  110 . The controller  110  adjusts the settings of the ventilator  120 , thereby adjusting the airflow from the ventilator value (Q vent ) When the Q vent  value reaches the patient  130 , the patient  130  responds by providing an airflow in the lung (Q L ) value and a pressure at wye (P Y ) value  140 . The P Y  value  140  is then supplied to the circuit model of the lung  150 , wherein the model  150 , in turn, provides an airflow as computed by the model  150  (Q model ). 
     The circuit model  150  is a simple mathematical model, such as a hydraulic RC circuit, to emulate the lungs of the patient  130  in real-time based on airway resistance of lungs (R) and compliance of lungs (C). Specifically, the model  150  accurately emulates the patient&#39;s lungs whenever the R and C values of the model  150  correspond to those of the patient  130 . 
     In order to obtain the patient&#39;s R and C values in real-time, the exemplary system  100  utilizes optimization algorithms of the optimizer  170 . For instance, if the Q model  value and the Q L  value are not equal (e.g., the model  150  does not emulate the patient  130 ), then the error difference may be supplied to an optimizing algorithm of the optimizer  170 . Accordingly, the optimizer  170  may use this error difference as a point of an objective function  160  to be minimized. The construction of the objective function  160  in time by the optimizer  170  may be referred to as “gradient-free optimization.” It should be noted that this particular technique for optimization is merely an example of one algorithm used by the optimizer  170 . Any number of parameter estimation algorithms can also be implemented in order to provide adequate results in real-time. 
     Regardless of the specific algorithms implemented by the optimizer  170 , the output of the optimizer  170  is a set of new values for R and C. These new R and C values are then supplied to the model  150  and the model  150  is thus updated accordingly. Using the R and C values, the model  150  estimates the thoracic muscle pressure (P mus ). For instance, the model  150  can solve for P mus  based on the following equation: 
         P   mus   =Q   L   ·R+V   L   /C−P   Y . 
     Once the Pmus is estimated, the PoB is computed at  180  and provided back to the controller  110 . For instance, the PoB may be calculated using the following equation: 
         PoB =integrate( P   mus   ·Q   L   dt ). 
     At the controller  110 , the value of the PoB  180  is then compared to the reference value (PoB ref ) set by the physician  190 . Accordingly, the error determined from this comparison provides the setting information for appropriate adjustments to the ventilator  120 . The adjustments made to the ventilator  120  may be performed automatically by the controller  110  (e.g., without user intervention), or alternatively, the controller  110  may provide the user with adjustment instructions for manually selecting values on the ventilator  120 . 
     As described above, the exemplary system  100  allows for the user (e.g., physician  190 ) to work on a higher strategic level and eliminate the need to be troubled with the “pipes and knobs” of the ventilator  120 . One example of a strategic decision made by the physician  190  could be that the patient  130  needs to be breathing no harder than 10 J/min (e.g., Joules of thoracic muscle work per minute). Using this high level setting form the physician  190 , the exemplary system  100  accomplishes the task of automatically guiding the patient to breathe at 10 J/min without any need for the physician to adjust or control the ventilator  120 . As noted above, another embodiment of the system  100  allows for the physician to be “in the loop” as the controller  110  provides the physician  190  with appropriate ventilator setting instructions. Accordingly, the physician  190  may then ultimate decide whether to accept or reject the setting decisions (e.g., knob settings) provided by the controller  110 . 
       FIG. 2  shows an exemplary method  200  for assessing the breathing effort of a ventilated patient  130  and providing appropriate setting values according to an exemplary embodiment described herein. It should be noted that method  200  will be discussed with reference to system  100  and related components of the system  100  illustrated in  FIG. 1 . 
     As detailed above, the system  100  allows for users (e.g., clinicians, hospital personnel, etc.) to assess the PoB of the patient  130  and suggest adjustments to the operation of the ventilator  120 . 
     According to one of the exemplary embodiments, the method  200  may be performed by an add-on embedded component to existing services, such as an anesthesia machine or monitor (e.g., the Philips ALPS platform or Philips NM3 platform). Alternatively, the method  200  may be performed by a stand-alone ventilator within a hospital (e.g., in an intensive care unit (“ICU”), emergency room (“ER”), operating room (“OR”), etc.). 
     In step  205 , the system  100  receives a PoB reference value from the user (e.g., physician  190 ). While the exemplary system  100  describes a physician  190  as the source of the PoB reference value, this information may be retrieved from any source, either manually (e.g., via other personnel) or automatically (e.g., via a Clinical Decision Support (“CDS”) system). 
     In step  210 , the system  100  determines the lung output values of a test subject. These output values include a pressure value P Y    140  and an airflow value Q L  from the test subject. As noted above, the test subject may either be the patient  190  under medical care or lung test machine used to calibrate the system  100 . 
     In step  215 , the system  100  emulates the lungs of the test subject with the model  150 . Specifically, the model  150  receives the pressure value P Y    140  of the test subject as an input and emulates the lungs based on this value. As described above, the model  150  may be a mathematical hydraulic RC circuit used to emulate the lungs in real-time. 
     In step  220 , the system  100  determines the model output values from the model  150  as it emulates the lungs. These output values include an airflow value Q model  from the model  150 . 
     In step  225 , the system  100  compares the airflow value Q L  of the test subject to the airflow value Q model  of the model  150 . If the values match, the method  200  may advance to step  235 . However, if the values do not match, the method  200  advances to step  230  for optimization. 
     In step  230 , the system  100  the optimizer  170  receives the difference between the airflow value Q L  of the test subject to the airflow value Q model  of the model  150 , and uses this difference as a point of an objective function  160  to be minimized. Using optimization algorithms, the optimizer  170  sets new values for airway resistance R of the model  150  and lung compliance C of the model  150 . These new values are used to update the model  150 , and the method  200  returns to step  215  to emulate the test subject. 
     In step  235 , the system  100  calculates the thoracic muscle pressure (P mus ) of the test subject based on the matching model output values. As detailed above, the system  100  may utilize the P mus  model equation to solve for P mus  using the R and C values. It should be noted that any of the variables in these equations would change over time. 
     In step  240 , the system  100  estimates the PoB of the test subject based on the calculated P mus  of step  235 . As detailed above, the system  100  may utilize the PoB equation  180  to solve for the PoB of the test subject using the P mus  and Q L  values. 
     In step  245 , the system  100  compares the PoB of the test subject to the reference PoB. If the PoB values match, then the system  100  has achieved the breathing pressure and functions desired by the physician  190 . However, if the PoB values do not match, the method  200  advances to step  250  for optimization. 
     In step  250 , the system  100  determines adjustments to the settings of the ventilator  120 . These adjustments may include changing settings such as tidal volume, respiratory rate, pressure readings, airflow, etc. Furthermore, any adjustments to the settings may include changes to an operating mode of the ventilator  120 . One skilled in the art would understand that these various modes may come in any number of delivery concepts, such as, but not limited to, volume controlled continuous mandatory ventilation, volume controlled intermittent mandatory ventilation, pressure controlled continuous mandatory ventilation, pressure controlled intermittent mandatory ventilation, continuous spontaneous ventilation, high frequency ventilation systems, etc. 
     In step  255 , the system  100  adjusts the settings of the ventilator  120  according to the determined adjustments of step  250 . As detailed above, the adjustment performed on the operation of the ventilator  120  may be either automatically performed by the system  100 , or alternatively, performed by the user as directed by the system  100 . Once the settings of the ventilator  120  have been adjusted (automatically or manually), the system  100  has achieved the breathing pressure and functions desired by the physician  190 . 
     The exemplary method  200  described above is merely an example of any number of steps performable by the system  100  and related components of the system  100 . Accordingly, the system  100  is not limited to steps recited in exemplary method  200 , and may perform additional steps or less steps than steps  210 - 255  and any sub-steps, and in any order. 
       FIGS. 3   a - 3   d  show exemplary graphs  300  for the real-time estimations of airway resistance (R) of a tested lung and compliance (C) of the lung according to an exemplary embodiment described herein.  FIG. 3   a  demonstrates how, over time, the airflow as computed by the model  150  (Q model ) comes to approximate the airflow in the patient&#39;s lung (Q L ) very well.  FIG. 3   b  illustrates the error difference between the two signals of the upper portion. Furthermore,  FIGS. 3   c  and  3   d  represent the R and C values, respectively. Both the R and C values may converge to the correct values as a priori has set them via a lung test machine. Accordingly, the optimization algorithms do not require these two set values. 
       FIGS. 4   a - 4   d  show exemplary graphs  400  for the real-time estimations of thoracic muscle pressure (Pmus) and power of breathing value (PoB) during a lung test according to an exemplary embodiment described herein.  FIG. 4   a  illustrates pressure at wye as a function of time, P Y (t).  FIG. 4   b  illustrates the estimated airflow output (solid line) and the real output (dashed line), where the approximation in real-time is acceptable.  FIGS. 4   c  and  4   d  illustrate the real-time non-invasive estimation of P mus  and PoB, respectively. 
       FIGS. 5   a - 5   d  show exemplary graphs  500  for a fast (e.g., under 2 seconds) real-time estimation R and C values for a tested lung according to an exemplary embodiment described herein. While  FIG. 3  depicted the real-time estimation over a longer time period (e.g., 500 seconds),  FIGS. 5   a - 5   d  accomplishes the same task in under 2 seconds. The values of R and C quickly converge to the correct values, as detailed in  FIGS. 5   a  and  5   b , respectively.  FIGS. 5   c  and  5   d  illustrate the convergence of the Q model  and Q L  both without parameter estimation and with parameter estimation, respectively. 
       FIG. 6  shows an exemplary graph  600  of the real-time performance by the PoB controller  110  according to an exemplary embodiment described herein. As described above, the PoB ref  may be set by the physician  190 . According to  FIG. 6 , the PoB ref  is set to −10 J/min, wherein pulling or pushing of air provides the change in sign on this value. Within 25 breaths (by either the patient  130  or a lung machine), the desired PoB ref  is achieved. 
       FIG. 7  shows a schematic diagram of the system  100  according to an exemplary embodiment including a processing component (e.g., processor  702 ), an input/output component  704 , a display  706  and a non-transitory computer readable storage medium (e.g., memory  708 ). The processor  702  that is capable of processing data entered via the input/output component  704 , such as data received from a user interface  705  and a data retrieval component  707 . The data may include a breathing reference value for identifying any error difference between the breathing value of an exemplary circuit model and the breathing value of a test subject. The display  706  may be used to display model information, various measurements and reading from the patient, machine setting values, setting adjustment valve, operating instructions to the user, etc. For instance, the displayed modeling information may be loaded from the memory  708 , which includes a database storing the computerized representations of industry-accepted circuit models, guidelines, protocols and/or workflows. The memory  708  also stores information that has been updated with patient-specific information. The user interface  704  may include a mouse to point and click on items on the display  706 , a touch display and/or a keyboard. The memory  708  may be any known type of computer-readable storage medium. It will be understood by those of skill in the art that the system  100  is, for example, a personal computer, a server, or any other processing arrangement. 
     Those skilled in the art will understand that the above-described exemplary embodiments may be implemented in any number of manners, including, as a separate software module, as a combination of hardware and software, etc. For example, the system  100  and related components may be a program containing lines of code stored on a non-transitory computer readable storage medium that, when compiled, may be executed on a processor. It should also be apparent from the above description that the exemplary embodiments allow the processing device to operate more efficiently when a user implements the system  100 , e.g., by improving patient breathing assessment for health care professionals, by automatically suggesting one or more ventilator settings based on the assessed effort, by contributing in the identification of candidates for ventilation weaning, by assisting the health care professionals with the weaning process, etc. 
     It is noted that the claims may include reference signs/numerals in accordance with PCT Rule 6.2(b). However, the present claims should not be considered to be limited to the exemplary embodiments corresponding to the reference signs/numerals. 
     It will be apparent to those skilled in the art that various modifications may be made in the present invention, without departing from the spirit or the scope of the invention. Thus, it is intended that the present invention cover modifications and variations of this invention provided they come within the scope of the appended claims and their equivalents.