Patent Application
20200327993
The present invention relates to a mechanical ventilation system for respiration of an associated patient with decision support for lung ventilator settings. In particular, the present invention relates to a decision support system aiding decisions related to lung ventilator settings, which may adapt to the patient's changing physiology upon changing ventilator settings. The invention also relates to a corresponding method for operating a mechanical ventilation system, and corresponding computer programme product for operating a mechanical ventilation system when executed on a suitable computer.
Patients residing at the intensive care unit typically receive mechanical support for their ventilation. Selecting the appropriate level of mechanical ventilation is not trivial, and it has been shown that appropriate settings can reduce mortality [1] (cf. reference list at the end of the description).
A challenge with the settings of a mechanical ventilator is that each setting may be beneficial for one physiological parameter of the patient but negative for another physiological parameter. Currently the clinician may get help by ventilator screens, extra devices monitoring physiological parameters (capnograph, pulse oximeter, monitor, etc.) and alarm settings if something is wrong. Decision support systems have been developed for minimizing failure in ventilator settings [2-5], with one system also developed for using models of patient physiology and clinical preferences. However, previous decision support systems are based on the assumption that patient physiological parameters remains constant when ventilator settings are changed. This is often the case for small changes in ventilator settings such as inspired oxygen and respiratory rate but not the case when large changes in ventilator settings are performed, or when settings such as Positive End Expiratory Pressure (PEEP) is modified. For example, PEEP affects blood flow, as well as the mechanical and gas exchange properties of the alveoli, the small units of the lungs where gas exchange takes place [6].
Patients may unfortunately respond very differently to changes in PEEP, and it is not possible to predict from measurement at one set of ventilator settings how a patient will respond to changes in PEEP. Patient responses to PEEP changes range from improvement in gas exchange and/or blood flow and/or lung mechanics to adverse effects on one or all of these aspects of lung physiology (see for example changes in some gas exchange and lung mechanics parameters,
WO97/20592 (to Cardiopulmonary Corp., USA) discloses a ventilator control system controlling a ventilator pneumatic system in a medical ventilator. A simulator may be provided for predicting the status of the patient's pulmonary system prior to adjusting the control. The simulator applied could predict breathing patterns, but apparently not based on any deeper insight into patient physiology.
Hence, an improved mechanical ventilation system with decision support for lung ventilator settings and/or a corresponding system for decision support for mechanical ventilation, which are able to adapt to changes in patient physiology in response to changes in mechanical ventilation would be advantageous.
It is a further object of the present invention to provide an alternative to the prior art.
In particular, it may be seen as an object of the present invention to provide a mechanical ventilation system with decision support that solves the above mentioned problems of the prior art with adjusting ventilator settings, and particularly the uncertainty regarding how a patient will respond to changes in PEEP.
Thus, the above described object and several other objects are intended to be obtained in a first aspect of the invention by providing a mechanical ventilation system with decision support for lung ventilator settings, which can particularly adapt to changes in patient physiology in response to changes in mechanical ventilator settings and adjust the decision support accordingly.
In a first aspect, the present invention relates to a mechanical ventilation system for respiration of an associated patient, the system being adapted for providing decision support for mechanical ventilation, the system comprising:
The invention is particularly, but not exclusively, advantageous for providing mathematical based models of changes in physiology in response to changes in ventilator settings allowing prediction of changes in patient physiological parameters, thereby allowing mathematical physiological models to predict changes in clinical variables for a given value of PEEP. Thereby an additional abstraction level of the decision support system is included. This may then allow optimization of all ventilator settings across different levels of PEEP ultimately allowing the system an initial advice on optimal PEEP.
Because it is not possible to predict the optimal level of PEEP from one measurement point in time, PEEP is normally selected in a stepwise manner where PEEP is changed in small steps and the patient's response is monitored. Two overall strategies are applied in the clinical setting; lung recruitment (high airway pressure for a short time period) followed by stepwise PEEP reduction until the optimal balance is found, or stepwise increase in PEEP until optimal balance is found. As the two strategies depend on the inspiratory and expiratory lung mechanics, respectively, the resulting optimal PEEP may be different. The present invention, as a further novelty, therefore includes an algorithm for selection of appropriate physiological models for calculating optimal PEEP, such that the decision support system can provide advice for either of the two overall stepwise PEEP strategies (see
The advised level of PEEP and other ventilator settings may result in a different change in patient physiological parameters than expected, and initially described by the models. For example, a PEEP increase may recruit some lung regions (shunt improves, compliance improves), some of these regions may develop lower lobe atelectasis (low V/Q deterioration, worse oxygenation) and the increase in pressure may cause some lung regions to hyperinflate constricting blood vessels and consequently creating alveolar dead space where gas exchange is impaired (high V/Q deterioration, CO2 increase, pH decrease, compliance decrease). Alternatively, a PEEP increase can result in no recruitment with constant shunt, a decline in compliance and with significant increase in alveolar dead space representing a deleterious selection of PEEP. To overcome this problem, the present invention includes algorithms for learning the response of the patient and adapting the models of changes in physiological parameters to this response and update the decision support accordingly.
Some mathematical physiological models require experimental procedures for tuning the physiological parameters to the current patient status. This would be problematic during a stepwise strategy for finding optimal ventilator settings, as the repetitive performance of experimental procedures at each step would prolong finding the optimal ventilator settings. Therefore, as a further embodiment, the present invention may include a Bayesian or similar learning methods where model parameters used for calculating decision support are stepwise learned where values at previous steps are taken into account when learning the current value. Bayesian methods have previously been used for an initial estimation of model parameters [7] but not as a response to changing physiology.
It should be mentioned that the present invention may be implemented in connection with patients that are in fully supported ventilation mode, or assisted modes of ventilation. Alternatively, the present invention may be implemented in connection with patients that are in so-called mixed modes of ventilation i.e. combinations of fully controlled and supported modes of ventilation.
In the present context the term “decision support” should be understood in the broadest sense of the term, decision support covers any means for assisting the user, here the clinician, in making the decision. This includes organization, integration and presentation of data as well as providing suggestions for rational decisions. Conventionally, decision support systems (DSS) may be either passive, active or cooperative in the interaction with the user of the DSS. The present invention is generally not limited to any of these kind of interactions.
In the present context the term “physiological model” should be understood as the broadest sense of applied physiological models, the term physiological model here covering any means for linking clinically measurable values mathematically and for the individual patient, that is, that any parameters that needs tuning for the model to describe the individual patient's physiology should be possible to estimate from clinical measurements. In its simplest form such a physiological model can be a linear model of the equation y=a*x with a being the parameter of the model and y and x being clinical measurable values such as the link between pressure (x) and volume (y) with lung compliance (a) linking the two (Volume=Pressure*Compliance). The more complex end of the range include a model encompassing all chemical reaction equations of the buffer systems of human blood with parameters such as rate constants which may be assumed equal for all individuals but with for example Base Excess that should be tuned to the individual patient [8]. The invention requires at least physiological models of lung mechanics and/or gas exchange but may benefit greatly from models of blood acid-base status, respiratory drive and/or blood circulation etc.
A physiological model of lung mechanics links flows, volumes and or pressures which may be measured at the mouth, in the oesophagus, at the ventilator, in the ventilator tube, in the airways etc. Most importantly for the setting of PEEP is the pressure-volume relationship of the lungs which primarily depends on the compliance or elastance (=1/compliance) of the respiratory system. More complex physiological models may describe the resistances of the respiratory system and the individual elastic characteristics of the alveoli, the lung tissue, the diaphragm, the surfactant, the chest wall, the airways, etc. In control modes, identification of the overall characteristics of the respiratory system such as compliance is straightforward and is routinely measured by the ventilator. In support mode, the volumes inspired and expired by the patient not only depend on lung mechanics and ventilator settings, but also patient respiratory work. Simple models of lung mechanics in support ventilation may therefore describe an effective compliance, which is the combined pressure volume relationship of ventilator and patient work.
A physiological model of gas exchange links measurements of contents of inspired and expired gases to measured contents of these gases in blood (arterial, venous, mixed venous or capillary blood). Often this is mathematically implemented as a set of equations describing the exchange of one, or more, respiratory gases (oxygen, carbon dioxide, nitrogen) or inert gases added to the inspired gas (such as xenon, krypton, nitric oxide, methane etc.) across the alveolar membrane to capillary blood in one or more compartments and the mixing of the blood leaving these compartments with shunted venous blood to constitute the arterial blood, the latter which is often sampled and analysed in the clinical settings. Venous or mixed venous blood may also be sampled in the clinical setting and can also be described by a model by including equations with patient metabolism and blood flow in the model. Estimation of model parameters for gas exchange models, in particular for models with one or more of the aforementioned compartments, requires either invasive measurements such as a mixed venous blood sample from the pulmonary artery or that the gas contents of inspired gas is modified in one or more steps with the patient response to these changes measured continuously and/or at steady state this making it possible to separate the contributions of one or more mechanisms for gas exchange problems these in the model represented by parameters such as intrapulmonary shunt, ventilation/perfusion ratio etc. Gas exchange models often encompass a model of blood acid base status and the binding and transport of respiratory gases in blood.
A model of blood acid-base status and/or oxygen and carbon dioxide transport describes either empirically or by reaction equations some or all of the chemical reactions occurring in blood for chemical buffering and binding of gases to haemoglobin. The models may be in so-called steady state describing the reaction equations at equilibrium or dynamical describing the reactions over time. As such, the models link values of blood acid base that can be measured routinely at the bedside (such as oxygen and carbon dioxide pressures and pH) to values not easily obtained (such as base excess, concentrations of buffers, concentrations of respiratory gases etc.).
A physiological model of respiratory drive links levels of oxygen, carbon dioxide and or hydrogen in blood and/or cerebrospinal fluid to the respiratory drive of the patient is the patient's ventilation. The patient's ventilation depends on the respiratory control center of the patient but also the condition of the respiratory muscles as well as any anaesthetics and analgesics given to the patient. The measured ventilation may therefore be different from the drive signal form the respiratory control center. A respiratory drive model may therefore also include in empirical or other form a representation of muscle strength and/or fatigue and any effects of anaesthetics and/or analgesics.
A physiological model of circulation relevant for PEEP links the effect of changes in mechanical ventilator settings and/or patient posture and/or patient fluid intake and status to the circulation of the patient as can be measured as cardiac output, pulse rate, stroke volume, cardiac resistance etc. or as can be assessed through changes in non circulatory values such as changes in gas exchange and lung volumes.
A physiological parameter should in this context be understood as a value describing the characteristics of a physiological system and can be assumed constant for changes in some clinical variables and/or ventilator settings. For example, pulmonary shunt describes the fraction of blood flow to the lungs not reaching ventilated alveoli representing the most important gas exchange problem in mechanically ventilated patients. Pulmonary shunt is known to remain constant for variation in a number of clinical variables and ventilator settings such as respiratory frequency and tidal volume.
Below are listed, in an illustrative, non-limiting and non-exhaustively manner, some physiological parameters suitable for application in the present invention:
Lung mechanics parameters include lung compliance, respiratory resistances, respiratory system elastance, effective shunt, compliances and resistances of individual components of the respiratory system (alveoli, airways, chest wall, diaphragm, surfactant).
Gas exchange parameters include but is not limited to pulmonary shunt, venous admixture, effective shunt, Ventilation/perfusion (V/Q) ratio, degree of low V/Q, degree of high V/Q, ΔPO2, ΔPCO2, Arterial-end tidal O2 difference, Arterial-end tidal CO2 difference, anatomical dead space, alveolar dead space, physiological dead space, end-expiratory lung volume, functional residual capacity etc.
Blood acid-base parameters include but is not limited to base excess (BE), strong iron difference (SID), haemoglobin concentration, blood volume, rate constants for chemical reactions etc.
Respiratory drive parameters include but is not limited to central chemoreceptor threshold for oxygen and carbon dioxide, peripheral chemoreceptor threshold for oxygen and carbon dioxide, central chemoreceptor sensitivity for oxygen and carbon dioxide, peripheral chemoreceptor sensitivity for oxygen and carbon dioxide, strong ion difference for cerebrospinal fluid, muscle response factor, muscle fatigue, muscle strength etc.
Circulation parameters include but is not limited to cardiac output, vascular resistances, stroke volume, blood volume, venous return etc.
A clinical outcome variable should in this context be understood in the broadest sense of the term describing any clinical measurable value, which can be associated with a clinical outcome and preference.
Clinical outcome variables can be categorised according to the relating aspect of respiratory physiology and can include all, but not exclusively or exhaustively, of the following lists:
Including but not limited to Peak pressure, Plateau pressure, peak inspiratory pressure, end expiratory pressure, peak flow, tidal volume, tidal volume per ml of predicted body weight, stress, strain, transpulmonary pressure, oesophageal pressure, abdominal pressure etc.
Including but not limited to arterial, mixed venous, venous and capillary values of oxygen saturation, oxygen partial pressure, oxygen concentration, carbon dioxide partial pressure and carbon dioxide concentration.
Including but not limited to arterial, mixed venous, venous, capillary values of pH, hydrogen ion concentration, carbon dioxide partial pressure and concentration, bicarbonate concentration.
Including but not limited to oxygen consumption, carbon dioxide production, changes in ventilation, respiratory rate, respiratory rate to tidal volume ratio, respiratory rate to tidal volume per kg predicted body weight ratio, work of breathing according to different formulae as that of Otis [9] or as assessed from changes in pressure-volume curves, electrical activity of respiratory muscles, electrical activity of respiratory muscles related to tidal volume or ventilation.
Including but not limited to blood pressure, pulse rate, cardiac output, stroke volume, circulatory resistances, changes in lung volumes and/or gas exchange parameters and outcome variables with PEEP.
Below are given some embodiments of the present invention:
Advantageously, the control means may be arranged for simulating the effect on one, or more, model parameters (MOD_P) of the physiological models for a plurality of values (PEEP; 1, . . . , n) of the positive end expiratory pressure setting for the ventilation means, and thereby provide decision support in relation to said plurality of PEEP values. This is particular advantageous because a clinician may thereby be given an improved overview of the possible PEEP values suitable. Preferably, the control means may be further arranged for suggesting an optimum value between the plurality of values of the positive end expiratory pressure setting for the ventilation means (PEEP; 1, . . . , n) in order to guide the clinician.
In some embodiments, the control means may be further arranged for simulating the effect on one, or more, parameters (MOD_P) of the physiological models for one, or more, values in the positive end expiratory pressure setting for the ventilation means (PEEP) performed by a simulation based on at least two previous values of the PEEP setting for the ventilation means, optionally at least three, four or five values of PEEP setting, in order to improve the reliability and/or precision and/or the accuracy of simulation. In other variants, the control means may be further arranged for simulating the effect on one, or more, parameters (MOD_P) of the physiological models for one, or more, values in the positive end expiratory pressure setting for the ventilation means (PEEP) performed by a simulation based on at least two simulated values of the PEEP setting for the ventilation means, optionally at least three, four or five values of simulated PEEP setting, to improve the decision support available.
In preferred embodiments, the control means may comprise one, or more, positive end expiratory pressure (PEEP) models, each PEEP model comprising a model parameter (MOD_P) of a physiological model as a function of the PEEP settings for the ventilation means. These PEEP models then in turn allow use of physiological models to simulate the effect of PEEP on the effect of changes in other mechanical ventilator settings by input to models of for example gas exchange, lung mechanics, blood acid-base, circulation and metabolism. Beneficially, one, or more, of the PEEP models may be adapted to the patient, and/or the clinical condition of the patient, before initiating changes of the PEEP settings, and/or while changing the PEEP settings. Alternatively or additionally, the one, or more, PEEP models may be adapted to the patient by a learning module, the learning module having a set of a priori settings of PEEP model parameters (MOD_P_PEEP) based on the currently measured model parameter (MOD_P) value of the corresponding physiological models (MOD), preferably based on a Bayesian distribution based on patient type and/or clinical condition as it will be explained in more detail below.
In other preferred embodiments, one, or more, of the physiological models (MOD) may be adapted to the patient, and/or the clinical condition of the patient, before initiating changes of the PEEP settings, and/or while changing the PEEP settings. This may for instance improve the PEEP setting faster and/or in more reliable manner.
Advantageously, one, or more, additional physiological models (MOD) may be further descriptive of the metabolism of the patient, the blood circulation of the patient, acid-base status of the patient, oxygen and carbon dioxide transport for the patient, and/or the respiratory drive of the patient in order to provide a more complete model of the patient being mechanically ventilated. Preferably, at least two, three, four, or five, physiological models are integrated by having one, or more, variables in common. Thus, (outcome) variables such as saturation of arterial oxygen (SaO2) or pH in arterial blood (pHa) may be used in several physiological models of the present invention enabling a close integration of the physiological models.
In some embodiments, the control means may comprise one, or more, modules for choosing a PEEP changing strategy for the patient. In one variant, one choice of PEEP changing strategy may be based on an assumption of lung recruitment wherein a relatively high airway pressure is applied for a relatively short time followed by stepwise PEEP reduction until the optimal balance is reached. In another variant, one choice of PEEP changing strategy may be based on a stepwise increase of PEEP until an optimal balance is reached.
In a particular advantageous embodiment, the control means may further comprise a plurality of clinical preference functions (CPFs) relating settings of positive end expiratory pressure (PEEP) for the ventilation means to a corresponding set of clinical outcome variables to connect the decision support with clinical practise and experience. Additionally or alternatively, clinical preference functions (CPFs) may—in the context of the present invention—be understood as a means for relating settings on the mechanical ventilator to a corresponding set of clinical outcome variables by associating said different values of said outcome variables with a level of preference. This may be implemented by a mathematical description of the clinically preferred values of said clinical outcome variables. For example, a level of oxygen saturation in the arterial blood of 95% is preferable to 94%, and 95% is therefore in the CPF associated with greater preference and this may be related to changing the inspired oxygen on the mechanical ventilator. As such, a CPF may allow the outcome to connect decision support with clinical practice and experience. Clinical preference functions (CPFs) may be formulated in many ways including in a non-exhaustive list: mathematical function (linear, exponential or other form), combination of several mathematical formulations, logical ruleset, mathematical weighing of outcome variables in calculation of decision support, and any combination of the aforementioned forms.
For example, the plurality of clinical preference functions (CPFs) may be chosen from the group consisting of: CPFs inserted by a clinician, CPFs chosen from a database of possible CFPs, CFPs a priori adapted to a specific patient based on general clinical input from a clinician, and CPFs a priori adapted to a specific patient according to patient needs, and any combinations and/or equivalents thereof.
A particular advantage of the present invention is that the plurality of clinical preference functions (CPFs) may be applied for providing decision support related to an overall optimisation of the PEEP setting of the mechanical ventilation for the patient.
In an another particular advantageous embodiment, the positive end expiratory pressure (PEEP) setting may be further optimized with respect to other mechanical ventilation settings, preferably inspired oxygen (FI02), tidal volume (VT), and/or pressure above PEEP, which is quite difficult to perform with previous methods for PEEP adjustment in clinical conditions.
In a second aspect, the present invention relates to a decision support system for providing decision support to an associated mechanical ventilation system for respiration aid of a patient, the mechanical ventilation system comprising:
This aspect of the invention is particularly, but not exclusively, advantageous in that the present invention may be implemented by operating the decision support system together with an existing mechanical ventilation system, the mechanical ventilation system possibly requiring little or no modification to cooperate with the decision support system according to the present invention.
In a third aspect, the present invention relates to method for operating an mechanical ventilation system for respiration of an associated patient, the system being adapted for providing decision support for mechanical ventilation, the method comprising:
In a fourth aspect, the invention relates to a computer program product being adapted to enable a computer system comprising at least one computer having data storage means in connection therewith to control an mechanical ventilation system as described in the third aspect.
This aspect of the invention is particularly, but not exclusively, advantageous in that the present invention may be accomplished by a computer program product enabling a computer system to carry out the operations of the mechanical ventilation system or the decision support system of the first and second aspect of the invention, respectively, when down- or uploaded into the computer system. Such a computer program product may be provided on any kind of computer readable medium, or through a network.
The individual aspects of the present invention may each be combined with any of the other aspects. These and other aspects of the invention will be apparent from the following description with reference to the described embodiments.
The invention will now be described in more detail with regard to the accompanying figures. The figures show one way of implementing the present invention and is not to be construed as being limiting to other possible embodiments falling within the scope of the attached claim set.
In a preferred embodiment, for the mechanical ventilation system with decision support for PEEP setting, the invention can be implemented as illustrated in
Additionally, control means 12 are provided, the ventilator means being controllable by said control means by operational connection thereto. The control means may be integrated on a computer system operationally connected to the ventilation means 11 and the measurements 11a, 11b, and/or 11c.
Measurement means, 11a, and 11b, are arranged for measuring the inspired gas and/or the respiratory feedback of said patient in the expired gas in response to the mechanical ventilation, the measurement means being capable of delivering first data D1 to said control means. The measurement means are shown as separate entities but could alternatively form part of the ventilator means 11 without significantly change the basic principle of the present invention. The measurement means are capable of delivering first data D1 to the control means 12 by appropriate connection, by wire, wirelessly or by other suitably data connection.
The control means 12 CON is also capable of operating the ventilation means by providing ventilator assistance so that said patient 5 is breathing spontaneously and/or breathing fully controlled by the ventilating means 11. As schematically indicated, a clinician, or other medical personnel, may survey and ultimately control the ventilation system 10. The control means is adapted for using both the first data D1, and second data D2 indicative of the respiratory feedback in the blood provided by measurement means 11c, in one or more physiological models MOD descriptive of, at least, lung mechanics, and/or gas exchange in the lungs of the patient, the physiological models comprising a number of model parameters MOD_P.
In one embodiment of the invention, second data may particularly be described as data originating from other sources than the mechanical ventilator itself (this could be sensor, blood gases, doctor input etc.).
In one variant of the invention, the second data D2 could be estimated or guessed values being indicative of the respiratory feedback in the blood of said patient, preferably based on the medical history and/or present condition of the said patient. Thus, values from previously (earlier same day or previous days) could form the basis of such estimated guess for second data D2. In other variants, the second data can be provided from measurement means 11c on a continuous basis from actual measurements.
The control means is further arranged for simulating the effect on one, or more, model parameters MOD_P of the physiological models for a suggested value of the positive end expiratory pressure (PEEP) setting for the ventilation means, and thereby provide decision support in relation to said suggested PEEP value for the clinician. The suggested value of PEEP can be inputted by the clinician and/or suggested from the decision support part of the system 10 itself.
The effect on the model parameters MOD_P may be outputted to an appropriate human-machine interface (not shown) for displaying the result, e.g. a computer with a screen therefore. Alternatively or additionally, the model parameters MOD_P output may be communicated to the decision support part of the system 10 for use in connection with mechanical ventilation of patients, optionally for diagnostic purposes.
It may be noted that one PEEP value maybe have more than one model parameter value MOD_P associated to it, and only for illustrative purposes, a one-to-one relation between these two is shown in
PEEP models can for example be of linear form with only expected changes in physiological parameters over a certain range of PEEP (see patient example with expected changes in shunt, deltaPO2, deltaPCO2, row 1,
With prediction of the effect of PEEP on physiological parameters, said parameters can then for a given value of PEEP be used in physiological models to predict changes in clinical outcome variables such that the outcome for a given set of mechanical ventilator setting may be evaluated in relation to clinical preferences. Then it is possible to obtain the optimal balance of competing clinical goals such as normalising patient acid-base status but preventing deleterious high settings of volume and pressures. As an example in data from a mechanically ventilated patient,
The decision support depend on the PEEP selection strategy, as the effect of PEEP is different when set as part of an iterative set of increments in PEEP compared to an iterative set of decrements in PEEP following a recruitment manoeuvre. This is due to the hysteresis of pressure-volume relationship of human lungs. As PEEP is the ventilator maintaining a pressure during expiration during which the alveoli may collapse (de-recruit), PEEP is best set during expiration after a large increase of pressure to maintain open recently recruited alveoli as smaller levels of pressure are necessary to keep these lung units open (hysteresis). However, as recruitment maneuvers can be detrimental in some patients and the identification of whom may benefit from a recruitment manoeuvre is difficult, this approach to setting PEEP is primarily performed by clinical experts. Whilst these experts do perform recruitment maneuvers, little evidence supports an overall successful strategy. Therefore, at most institutions, PEEP changes are performed in small steps without first performing a recruitment maneuver, with the benefit of PEEP here being represented by the recruitment of alveoli due to small increases in pressure during inspiration and the PEEP maintaining these lung units open. As a consequence of the hysteresis of the lung pressure-volume relationship the optimal PEEP found during an incremental (“crawl”) PEEP strategy is not the same as would have been found during a decremental (recruitment) strategy. The present invention therefore includes separate flows for setting PEEP depending on the PEEP selection strategy (see
Changes in patient physiological variables are measured by measurement means for assessing ventilation, flow, pressure and volumes, and/or inspired and expired contents of O2 and CO2, and/or blood gas contents (O2 and CO2) and/or changes in acid base status of blood. These measurements are collected and processed. In a preferred embodiment, the data processing evaluates the changes in measurements and delay model parameter estimation until steady state is detected, this allowing the change in PEEP to take effect and avoiding the system from providing new advice when the previous advice has not had an effect.
When the patient is in steady state or the system has in other ways evaluated that the PEEP has had an effect, the changes in patient physiological parameters can be measured. These may be available from a single point measurement, such as parameters for cardiac output, lung compliance, lung resistance, respiratory drive, anatomical dead space, alveolar deadspace, Effective shunt etc. However, some physiological parameters require the clinician to perturb the patient physiology for accurate measurement. For example, gas exchange parameters (e.g. shunt, low V/Q, high V/Q, dPO2, dPCO2, End expiratory lung volume) can be accurately measured from a stepwise variation in inspired oxygen fraction and measurement of several variables at steady state, cf. WO 2000 45702 and WO 2012069051, which are hereby incorporated by reference in their entirety, these two references describing the so-called automatic lung parameter estimator (‘ALPE’) of the present applicant, without and with carbon dioxide measurements, respectively. An example is shown in
The estimated physiological model parameters are input to the PEEP model learning component where the PEEP models are adapted to the patient response to PEEP.
In addition to gas exchange, PEEP affects lung mechanics and in support ventilation modes PEEP may also affect metabolism. Lung mechanics are affected differently whether the patient is ventilated in a controlled ventilation mode where the ventilator fully initiates and carries out the breath or in support mode ventilation where the patient initiates the breaths and the ventilator supports the work of breathing.
In support mode, PEEP may also affect patient metabolism as too high PEEP levels force the patient into a rapid shallow breathing pattern where respiratory work is significantly increased. To prevent the decision support core from advising on such PEEP levels if this response is observed, a preferred embodiment includes models of patient metabolism increasing several fold at this PEEP level.
The procedure for measurement of gas exchange parameters shown in
The invention can be implemented by means of hardware, software, firmware or any combination of these. The invention or some of the features thereof can also be implemented as software running on one or more data processors and/or digital signal processors.
The individual elements of an embodiment of the invention may be physically, functionally and logically implemented in any suitable way such as in a single unit, in a plurality of units or as part of separate functional units. The invention may be implemented in a single unit, or be both physically and functionally distributed between different units and processors.
Although the present invention has been described in connection with the specified embodiments, it should not be construed as being in any way limited to the presented examples. The scope of the present invention is to be interpreted in the light of the accompanying claim set. In the context of the claims, the terms “comprising” or “comprises” do not exclude other possible elements or steps. Also, the mentioning of references such as “a” or “an” etc. should not be construed as excluding a plurality. The use of reference signs in the claims with respect to elements indicated in the figures shall also not be construed as limiting the scope of the invention. Furthermore, individual features mentioned in different claims, may possibly be advantageously combined, and the mentioning of these features in different claims does not exclude that a combination of features is not possible and advantageous.
In short, the invention relates to a mechanical ventilation system 10 for respiration of a patient 5, the system being adapted for providing decision support for mechanical ventilation. Control means 12 is adapted for using both first data D1 and second data D2, indicative of the respiratory feedback in the blood, in physiological models MOD descriptive of, at least, lung mechanics, and/or gas exchange in the lungs of the patient, the physiological models comprising a number of model parameters MOD_P. The control means is further arranged for simulating the effect on one, or more, model parameters MOD_P of the physiological models for a suggested value of the positive end expiratory pressure (PEEP) setting for the ventilation means, and thereby provide decision support in relation to said suggested PEEP value. The invention is advantageous for providing mathematical based models of changes in physiology in response to changes in ventilator settings of the PEEP thereby allowing mathematical physiological models to predict changes in clinical variables for a given value of PEEP.
| Number | Date | Country | Kind |
|---|---|---|---|
| PA 2014 70565 | Sep 2014 | DK | national |
This application is a Continuation of Ser. No. 15/509,368 filed Mar. 7, 2017, which is the U.S. national stage of PCT/DK2015/050271 filed Sep. 11, 2015, which claims priority of DK PA 2014 70565 filed Sep. 12, 2014. The entire content of each application is incorporated herein by reference.
| Number | Date | Country | |
|---|---|---|---|
| Parent | 15509368 | Mar 2017 | US |
| Child | 16913004 | US |
