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    • 专利摘要:
    METHOD AND SYSTEM FOR ACCURATE AND REAL ESTIMATE ESTIMATION OF CRS (RESPIRATORY SYSTEM COMPLIANCE), RRS (PATIENT AIRWAY RESISTANCE), AND / OR PPLT INSPIRATORY PRESSURE PLATE. The present invention relates to a system and method of calculating an accurate estimate of a patient's pulmonary mechanics, including, but not limited to compliance, resistance, and pressure plateau, without changing the ventilation flow pattern. The precise estimate of pulmonary mechanics is derived from the pressure of the air lives and flow sensors (5) fixed to the patient using mathematical models. These estimated numbers for pulmonary mechanics (compliance and resistance of the respiratory system) are important to monitor the effectiveness of the patient's treatment during mechanical ventilation and to ensure that the alveoli are not excessively distended to avoid barometric and / or volumetric trauma, especially in patients with restricted lung diseases. The method of calculating these accurately estimated numbers for pulmonary mechanics is based on linear or non-linear calculations using multiple parameters derived from the sensors (5) mentioned above.
    公开号:BR112012016102B1
    申请号:R112012016102-4
    申请日:2010-12-28
    公开日:2020-11-03
    发明作者:Nawar Nazar Al-Rawas；Andrea Gabrielli；Neil Russell Euliano；Michael Joseph Banner
    申请人:Convergent Engineering, Inc.；University Of Florida Research Foundation, Inc；
    IPC主号:
    专利说明:

    DESCRIPTION FIELD OF THE INVENTION
    [0001] The present invention relates generally to the field of respiratory therapy and physiology, including ventilation and respiratory monitoring technology, and, more particularly, to a method for calculating compliance (CRS) resistance (RRS) and plateau inspiratory pressure (Ppit) of the respiratory system, without the need to modify or interrupt the patient's ventilation pattern or airflow. BACKGROUND
    [0002] Mechanical ventilatory support is widely accepted as an effective means to assist or mechanically replace spontaneous breathing. Mechanical ventilation can be non-invasive, involving various types of nasal masks or devices, or invasive, involving endotracheal tube (ETT) or tracheal intubation. The selection and use of adequate ventilation techniques requires an understanding of pulmonary mechanics.
    [0003] Normal spontaneous inhalation generates negative intrapleural pressure, which creates a pressure gradient between the atmosphere and the alveoli, resulting in an influx of air. During mechanical ventilation, the inspiratory pressure gradient is usually the result of increased (positive) pressure from the air source, or increased by it. For patients requiring ventilatory support, it is necessary to monitor both CRS and RRS to properly assess and treat the patient's lung dysfunction or respiratory failure. Ppit monitoring is a common practice to ensure that the lung is not damaged via hyperdistension or overpressure during mechanical ventilation.
    [0004] The RRS is the amount of pressure necessary to force a given gas flow through the combined resistances in series of the breathing circuit, the ETT resistance, and the physiological airways of a mechanically ventilated patient. CRS is a measure of lung distensibility, meaning the elastic recoil of the lungs and chest wall for a given volume of gas emitted. Thus, for any given volume, the elastic pressure is increased by the stiffness of the lung (as in pulmonary fibrosis) or restricted excursion of the chest wall or diaphragm (ie, tense ascites, massive obesity). Typically, CRS and RRS are calculated using a final inspiratory pause (EIP) during a constant inspiratory flow rate. CRS is estimated by dividing the tidal volume delivered by the inspiratory Pplt, where Pplt is the steady-state pressure measured during an IPE.
    [0005] RRS is estimated by dividing the difference between the peak inflation pressure (PIP) and Pplt by the inspiratory flow rate. Some ventilators have an inspiratory flow rate setting configured such that the clinician can read the emitted flow rate while others give an inspiratory time setting where the clinician needs to divide the tidal volume by the inspiratory time to determine the inspiratory flow rate .
    [0006] Thus, Ppit is essential to calculate CRS and RRS. In addition, monitoring the Pplt is also essential to avoid excessive distention of the alveoli, thus avoiding barometric and / or volumetric trauma, especially in patients with restrictive lung diseases (ARDS network protocol (July 2008); http: // www. ardsnet.org/ node / 77791). In Pplt determination, current practice requires that an IPE be performed. For patients with respiratory failure, this can be accomplished by applying an IPE immediately after a tidal volume during controlled mechanical ventilation (CMV) or intermittent mandatory ventilation (IMV).
    [0007] Unfortunately, there are many drawbacks to having to perform an IPE. On the one hand, the duration of IPE should be predefined by an experienced physician and applied during mandatory breaths only while being monitored. Temporary interruption of inhalation and prevention of expiration through the application of an IPE can be uncomfortable for some patients, causing the patient to involuntarily or voluntarily be able to make active contractions of the inspiratory or expiratory muscles at the time of IPE, which can affect the accuracy of the Ppit measure. If an inaccurate Pplt measurement is obtained, the resulting CRS and RRS estimates would also be inaccurate. Because, as noted above, the patient's respiratory therapy and treatment are based on CRS and RRS values, erroneous calculations for CRS and RRS due to inaccurate Ppit measurements could subsequently affect the effectiveness of the treatment delivered to the patient and recovery of the patient, perhaps even to the detriment of the patient's health.
    [0008] In addition, because performing an IPE can be uncomfortable for the patient, it cannot be applied continuously. Without continuous, accurate Ppit information, the clinician is unable to fully monitor patient safety and treatment effectiveness.
    [0009] Temporary interruption of inhalation by the application of an IPE can also predispose patient-ventilator dyssynchrony. This can lead to increased breathing work, and the possibility of compromising the exchange of blood-arterial gas.
    [00010] Finally, an IPE may not be applied (or may be inaccurate) during pressure support ventilation (PSV), continuous airway procedure (CPAP), or other ventilation modes that do not employ a constant inspiratory flow rate during the inhalation phase. Due to the inability to apply an IPE in these situations, doctors are prevented from accurately assessing a patient's Ppit, CRS and RRs when ventilated with these modalities. Without correct assessment of the patient's Ppit, CRS and RRs, the effectiveness of therapy and / or the appropriate diagnosis of the disease or lung condition cannot be determined.
    [00011] Thus, accurate repeated measurements of Ppit, and therefore CRS and RRS, are difficult to obtain using EIP. If Ppit could be determined without the need to apply an IPE, then more accurate estimates of CRS and RRs can be performed, even in real time, eliminating the need to interrupt the inhalation phase. Such an approach would be simpler and preferred, both for the physician and the patient and, therefore, necessary in clinical practice.
    [00012] In addition, knowledge of Ppit during PSV would provide continuous monitoring of CRSθ RRS, θ excludes the need to change ventilation modes.
    [00013] Therefore, there is a need in the art for a system and method to non-invasively, in real time, accurately calculate Ppit, CRS, and RRS without the need to modify the inspiratory flow waveform pattern of the ventilator that can cause adverse effects, such as patient-ventilator dyssynchrony. A continuous and accurate understanding, in real time, of the effects of mechanical ventilation and other therapeutic interventions (for example, bronchial dilators and airway aspiration) on pulmonary mechanics (ie, CRS and RRS) is necessary to promote patient synchrony -fan and arterial blood-gas exchange. The present invention is designed to meet this need. SUMMARY OF THE INVENTION
    [00014] The present invention provides a method and apparatus for non-invasively accurately calculating, in real time, Ppit, CRS, and RRS without the need to interrupt or modify breathing in any way. The precise calculation of these respiratory parameters provides the ability to accurately determine other information that is valuable in the treatment of a ventilated patient. In one embodiment, the precise and useful values for Ppit, CRS, and RRS are estimated in real time through a processing system.
    [00015] The present invention is particularly advantageous in that it can use commonly measured respiratory parameters (ie, airway pressure and flow rate over time during the inspiratory phase of mechanical ventilation) to generate accurate, mechanical estimates pulmonary in real time, including but not limited to Ppit, CRS and RRS. The resulting pulmonary mechanics estimates are particularly useful in real-time monitoring of the patient's reaction to changes in mechanical ventilation mode, the effects of various interventions (ie drugs) on pulmonary mechanics and physiology, the risks of pulmonary hyperdistension, and the adequacy of pulmonary protection strategies. Accurate real-time estimates of Ppit, CRS, and RRS are also useful during pressure-regulated ventilation, most commonly used for weaning to assist spontaneous breathing.
    [00016] In one aspect of the invention, the method comprises creating a mathematical model of the patient's expiration time constant (ET) of the respiratory system using predetermined parameters that are collected non-invasively, such as those collected with respiratory monitors standard. Such parameters include, but are not limited to, expiration volume, air flow rate and pressure.
    [00017] Respiratory monitors and ventilators typically contain airway pressure and airway flow sensors that measure the flow in and out of the lungs, and they often also contain a carbon dioxide sensor and pulse oximeter. From these time waveforms, a variety of parameters are selectively derived that are used to characterize different aspects of the patient's breathing and / or the patient's interaction with the ventilator. These parameters contain information that is extracted to accurately estimate the patient's inspiratory and expiratory flow and pressure waveform data. With the patient's inspiratory waveform data, accurate and continuous real-time calculations of the patient's inspiratory Ppit estimates, plus the patient's CSR and RRs, and those derived from pulmonary mechanics are performed. All of these estimates are useful for determining appropriate therapy, including ventilator settings.
    [00018] In one embodiment, the patient's Ppit and CSR and RRS in real time, and derivatives of pulmonary mechanics, are continuously estimated accurately using the passive deflation TE of the lungs during all breathing modes. More preferably, the patient's Ppit and CSR and RRS in real time, and derived from pulmonary mechanics, are continuously accurately estimated using the passive deflation TE of the lungs during regulated pressure breathing.
    [00019] The methods described here may use a linear combination of parameters or a linear combination of parameters, including, but not limited to, a neural network, fuzzy logic, expert mix, or the polynomial model. In addition, several different models can be used to estimate the pulmonary mechanics of different subsets of patients. These subsets can be determined by various means, including, but not limited to, the patient's condition (pathophysiology), the patient's physiological parameters (ie, inspiratory flow rate, airway resistance, tidal volume, etc.) , or other parameters, such as ventilator parameters (i.e., positive end expiratory pressure or PEEP, inflation pressure of the patient's airways, etc.).
    [00020] In a preferred aspect of the invention, the method for calculating pulmonary mechanics involves applying a set of equations derived univocally based on the standard equation of the patient's airway during inhalation and expiration, combined with the equations for calculating of the expiration time constant. A fundamental aspect of the present methodology is the calculation of a time constant from the expiration part of the waveform (for example, pressure, flow volume, etc.), followed by the use of this expiratory time constant and data from of one of one or more cases of time in the inspiratory time waveforms (for example, airway pressure, flow and volume at a defined t time) to calculate Ppit, RRs and CSR. In a preferred embodiment, a single instance of time from the inspiratory time waveform is a low effort time of the patient, typically found in the early or late part of the inspiratory waveform. Since the patient's effort is unknown and typically not modeled, locating the patient's lowest effort point will increase the accuracy of the parameter estimate.
    [00021] In another aspect of the invention, the method for calculating pulmonary mechanics in a patient comprises the use of a neural network, in which the neural network provides pulmonary mechanics information to the patient based on input data, where the input data includes at least one of the following parameters: airway pressure, flow, airway volume, expiratory carbon dioxide flow waveform, and oximeter plethysmogram waveforms pulse normally collected by a respiratory monitor, including but not limited to tidal volume, breath rate (f), PIP, inspiratory time, Po.i, inspiratory trigger time, the trigger depth, in which accurate and useful estimates for TE , Ppit, RRS and CSR are provided as an output variable.
    [00022] In the aforementioned method, the neural network is trained by clinical testing of a patient testing population to obtain teaching data, teaching data that includes the aforementioned input information. Teaching data is provided for the neural network, whereby the neural network is trained to provide an output variable corresponding to CRS, and RRS.
    [00023] The invention can be implemented in several ways, including as a system (including a computer processing or database system), a method (including a computerized method of collecting and processing input data and a method for evaluating such data to provide an output (s), an apparatus, a computer-readable medium, a computer program product, or a tangible data structure fixed in a computer-readable memory. Various embodiments of the invention are discussed below.
    [00024] As a system, an embodiment of the invention includes a processing unit having input and output devices. The processing unit operates to receive input parameters, process the input and provide an output corresponding to the pulmonary mechanics information. This output can then be used to control external devices, such as a fan. Data processing can be performed by various means, such as microcontrollers, neural networks, parallel processing systems, distributed neuromorphic systems, or the like.
    [00025] As a method of accurately calculating the patient's Ppit, CRS, RRS and TE in real time, the present invention includes the processing of predetermined input variables (parameters) using the formulas described here, preferably using use of a computer-readable media program containing program instructions, a processing system, or a neural network.
    [00026] As a computer-readable medium containing program instructions, one embodiment of the invention includes: computer-readable code devices for receiving input variables, processing input, and providing an indicative output of CRS and RRS. In a preferred embodiment, the processing comprises using a neural network. The method may also include controlling a fan in response to the output obtained.
    [00027] The methods of the present invention can be implemented as a computer program product with a computer readable medium of optical reading having a code on it. The program product includes a program and a signal support medium supporting the program.
    [00028] As an apparatus, the present invention can include at least one processor, a memory coupled to the processor, and a memory resident program that implements the methods of the present invention.
    [00029] Other aspects and advantages of the invention will be evident from the following detailed description taken in conjunction with the accompanying drawings, illustrating, by way of example, the principles of the invention.
    [00030] All patents, patent applications, provisional applications, and publications referred to above or cited herein, or from which a priority benefit application has been made, are hereby incorporated by reference in their entirety, insofar as they are not incompatible with the explicit teachings of this specification. BRIEF DESCRIPTION OF THE DRAWINGS
    [00031] Figure 1 is an illustration of a patient whose pulmonary mechanics is estimated according to the present invention.
    [00032] Figure 2 is a histogram showing the relative frequency of Ppit measurement in the group described in Example 1.
    [00033] Figure 3 is a graphic illustration of the Pplt bands measured for each subject in Example 1.
    [00034] The figures. 4A - 4C are graphic illustrations of TE (blue diamonds) from three random individuals (A, B and C), with three different inhalation flows (0.5, 0.75 and 1 L / S) and compared with TEPpit obtained by final inhalation pause (red squares) and the difference between TEPpit and TE (green triangles), depending on the interval between PIF and PEF (X axis). According to the object invention, the peak inspiratory flow (PIF) - peak expiratory flow (PEF), then, becomes the correction factor for TEPpit = RRS * CRS.
    [00035] Figure 5 is a graphic illustration of the effect of flow difference (PIF-PEF) on RRS inhalation and expiration (RRSPPU - RRSTE), where the equation y = 5.2487x - 0.8393 was used as a correction factor for the RRS flow difference, where y is the corrected factor for RRSTE, ex is PIF-PEF.
    [00036] Figure 6A is the regression analysis of calculated Ppit (TE) versus measured Ppit, note that r2 = 0.99 (p <0.001).
    [00037] Figure 6B is a Bland-AItman graph showing the difference between calculated Ppit (TE) and measured Ppit, deviation is essentially zero and the measurement accuracy is excellent.
    [00038] Figure 6C is a graph of linear adjustment between calculated Ppit (TE) and measured Ppit, note that r2 = 0.99.
    [00039] Figure 7A is the regression analysis of TE CRS purchased from Ppit CRS, noting that r2 = 0.97 (p <0.001).
    [00040] Figure 7B is a Bland-AItman graph showing the difference between CRS Ppit and
    [00041] CRS TE, deviation is essentially zero and the measurement accuracy is excellent.
    [00042] Figure 7C is a graph of linear adjustment between and CRS TEθ CRS Ppit.
    [00043] Figure 8A is the regression analysis of RRS of TE compared to RRS of Ppit, note that r2 = 0.92 (p <0.001).
    [00044] Figure 8B is a Bland-AItman graph showing the difference between RRS Ppit and RRS of TE. The deviation is essentially zero and the measurement accuracy is excellent.
    [00045] Figure 8C is a graph of linear adjustment between RRS PPit θ RRS TE.
    [00046] Figure 9 represents a neural network showing hidden layers.
    [00047] Figure 10 represents inputs and outputs of an adaptive system with regressive propagation.
    [00048] Figure 11 is a graph of linear adjustment between PSV Ppit and IMV Ppit.
    [00049] Figure 12 is a graph of linear adjustment between PSV and CRS and IMV CRS.
    [00050] Figure 13 is a graphic illustration of the patient's variable effort while under support ventilation pressure.
    [00051] Figure 14 provides graphical illustrations of flow and volume curves for the same breath illustrated in figure 13.
    [00052] Figure 15 is a Ppit curve calculated at each inhalation point.
    [00053] Figure 16 is an airway pressure curve showing the valid points for the compliance estimate.
    [00054] Figure 17 is a CRS curve, indicating the points that can be used for the accurate estimation of pulmonary parameters.
    [00055] Figure 18 is an RRS (resistance) curve of the inhalation part of the breath illustrated in figure 16.
    [00056] Figure 19A is a graphic illustration of airway pressure (Paw; dark line curve) and Pes (esophageal pressure; light line curve).
    [00057] Figure 19B is a diagram that illustrates flow (dark line curve) and volume (light line curve) DETAILED DESCRIPTION
    [00058] Current standard estimates of Ppit, CRS, and RRS are obtained in patients during positive pressure inflation and by measuring lung inflation pressure during an EIP, ie a pause for at least 0.5 seconds) . Unfortunately, there are several disadvantages to performing an IPE, including patient discomfort, requirement for clinical entry and careful monitoring, inaccurate measures due to patient interference, ventilator - patient dyssynchrony, inability to be applied continuously, and inability to perform EIP with certain forms of ventilation. To address these deficiencies, the object of the invention provides systems and methods to accurately calculate estimated values of Ppit, CRS, θ RRS using a modified estimate of TE from passive lung deflation, preventing the need for an IPE. The result is a continuous, real-time estimate of pulmonary mechanics that can monitor breathing for pulmonary breathing function and the effect of therapeutic interventions.
    [00059] The Inspiratory Pit is an important parameter to calculate a patient's CRS and RRs during mechanical ventilation. Ppit monitoring is also essential to avoid excessive distention of the alveoli, thus avoiding barometric and / or volumetric trauma, especially in patients with restrictive lung diseases (ARDS network protocol (July 2008); http: //www.ardsnet. org / node / 77791).
    [00060] According to the present invention, the method for estimating, accurately and in real time, the pulmonary mechanics of patients receiving mechanical ventilation, or any device that interfaces with the patient's pulmonary system, involves the following steps: (a) receiving a patient's respiratory parameters; (b) calculate, with a processor, a modified TE from the respiratory parameters, (c) introduce the modified TE into a mathematical model, and (d) provide at least one output variable from the mathematical model that corresponds to Ppit , CRS and / or RRS, or other parameters of pulmonary mechanics.
    [00061] In one embodiment, the mathematical model is a neural network trained to provide estimated pulmonary mechanics. The neural network can be formed to include the clinical test of a population of individuals, using monitored pressure and ventilator flow as input of clinical data to the neural network.
    [00062] The passive pulmonary expiration TE is a parameterization of the time required to complete the expiration based on an exponential decay and contains information about the mechanical properties of the respiratory system (Guttmann, J. et al., "Time constant / volume relationship of passive expiration in mechanically ventilated ARDS patients, "Eur Respir J., 8: 114-120 (1995); and Lourens, MS et al.," Expiratory time constants in mechanically ventilated patients with and without COPD, "Intensive Care Med, 26 (11): 1612-1618 (2000). TE is defined as the product of CRS and RRS (Brunner, JX et al. "Simple method to measure total expiratory time constant based on the passive expiratory flow volume curve," Crit Care Med, 23: 1117-1122 (1995)) TE can be estimated in real time simply by dividing the expiration volume (V (t)) by the expiration flow (Vexpiration (t)) ie (TE (t) = V (t) / Vexpiration (t)) (Brunner, JX et al ,. and Guttmann, J. et al., "Simple method to measure total expiratory tim and constant based on the passive expiratory flow volume curve, "Crit Care Med, 23: 1117-1122 (1995); and Guttmann, J. et al., "Time constant / volume relationship of passive expiration in mechanically ventilated ARDS patients," Eur Respir J., 8: 114-120 (1995)). This method gives an estimate of TE for each point during expiration.
    [00063] Unfortunately, current methods for estimating TE are somewhat inaccurate, especially with patients connected to mechanical ventilators, due to possible interference from the ventilator's expiration valve during the initial opening. In addition, current TE estimates include part of the end of expiration, which (1) can cause observable late (slow) kinetic behavior of ET that can be attributed to the viscoelastic properties of the respiratory system, which may be inaccurate, due to inequalities in alveolar emptying within the lung ("pendelluft" effect) (Guerin, C. et al., "Effect of PEEP on work of breathing in mechanically ventilated COPD patients," Intensive Care Med, 26 (9): 1207- 1214 (2000)); and (2) creates less stable values of V (t) ZVexpiration (t), due to the reduced flow at the end of expiration and the division by numbers close to zero. In addition, because the resistance of the ventilator's exhalation valve becomes more significant at the end of exhalation, it can also affect whether an accurate TE estimate is determined using current methods.
    [00064] Thus, in one embodiment of the invention, a more accurate, modified estimate of TE is achieved using the expiratory waveform only during the middle and most reliable part of expiration. For example, averaging the slope of the exhalation waveform from 0.1 to 0.5 seconds after the start of exhalation (for example, the mean function) is a method for obtaining a more reliable estimate of TE. The first part of the exhalation (between 0 and 0.1 sec.) Is excluded to reduce the possible interference of the ventilator exhalation valve during the initial opening, as well as the patient's residual effort. The end of expiration (in addition to 0.5 sec.) Is excluded to resolve problems attributable to the end of expiration as described above.
    [00065] Figures 13 and 14 are graphical illustrations of various respiratory parameters useful in calculating Ppit, RRS and CRS. Figure 13 shows variable patient effort breathing while in pressure support ventilation (PSV) mode. The label shows the last inhalation point on the airway pressure curve (Paw), which represents the patient's least effort and is used to estimate Ppit, RRS and CRS.
    [00066] Figure 14 shows the flow and volume curves for the same breath in figure 13. The marked points are the points where the values associated with pulmonary mechanics (Ppit, RRS and CRS) are calculated. These points correspond to the last inhalation point on the Paw curve. As shown in figure 14 the calculations for Ppit, RRS and CRS based on the estimated TE are usually confined to a flow greater than 0.1 L / sec. Another approach to a more accurate estimate of ET involves a median function. For example, a more accurate estimate of TE can be derived by taking the mean or median of multiple estimates of TE during expiration. Advantageously, limiting the locations where these time constant estimates are made provides better time constant values. High flow and low flow expiration regions are more likely to produce erroneous estimates and are therefore excluded.
    [00067] Figure 15 shows a Ppit curve, where Ppit was calculated from TE estimates at each point of inhalation (and expiration, which should be excluded). The label on the Ppit curve is in the last valid inhalation part, which corresponds to the end of the inhalation illustrated in figure 14.
    [00068] In certain embodiments, the median of TE estimates using the expiration part where the flow is less than 80% of the peak inspiratory flow, but greater than 0.1 LPS provides a more accurate estimate of TE. In another embodiment, the filtered average or median of various estimates of the time constant for multiple breaths or to provide a better TE estimate for a region of breaths can be calculated.
    [00069] In alternative modalities, the exhalation part can be defined by the percentage of the volume of exhaled air. In certain modalities, the median of ET estimates from different combinations of volume percentages and / or peak expiratory flow provides a more accurate estimate of ET. In one embodiment, the percentage of peak flow is between 95% and 20% of peak flow. In another embodiment, the percentage of peak flow is between 95% and 70% of peak flow. In yet another modality, the part of expiration between 80% of the exhaled volume and 20% of the exhaled volume is used.
    [00070] Because resistance is a function of flow and TE is a function of resistance, TE values may vary with flow rate. In another modality, better TE estimates can be achieved by selecting exhalation areas to estimate TE based on inspiratory flow rates. For example, better TE estimates can be achieved in those ventilation modes where inspiratory flow rates are constant, such as IMV, VC + (Control Volume +), or Auxiliary Control. During expiration, the part of TE resistance calculated in a mechanically ventilated patient is the sum of the three resistances in series, that is, total resistance (RTOT), which is the sum of resistance of the physiological airways (Raw), imposed resistance endotracheal tube (RETT), θ resistance of the ventilator expiration valve (Rvent). RTOT = Raw + RETT + Rvent
    [00071] In accordance with the present invention, the resistance applied by the ventilator exhalation valve can be excluded from the TE estimate as derived above for improved modified accurate estimates of TE. Thus, Rvent can be calculated by: Rvent (t) = (Paw (t) - PEEP) / Vexpiration where, Paw is the airway pressure, PEEP is a final positive expiratory pressure (if applied, otherwise it is zero) , and Vexpiration (t) is the rate of exhaled flow from the airways.
    [00072] To calculate the most accurate, modified TE of the invention, the total TE (t) is calculated for the first time as described above. Then, the following equations derive an equation for the patient's ET, which excludes ventilator resistance: Cest = (VT + total TE * Vexpiration (t)) / (PEEP paw), where Cest is the estimated total TE compliance, then total TE (t) = (RRS (t) + Rvent (t)) * Cest total TE (t) = (RRS (t) * Cest) + (Rvent (t) * Cest)
    [00073] Using Cest as an estimate for Crs yields the following most accurate estimate of the time constant: TE (t) = total TE (t) - (Rvent (t) Cest)
    [00074] Once TE is estimated and corrected as taught above, inspiratory waveform data is used to estimate Ppit, RRS, CRS and other pulmonary mechanics parameters. CRS θ RRS, ©, thus, Ppit, can be estimated accurately according to the invention as follows:
    [00075] The CRS calculation is obtained as follows: Paw - PEEP - VT / CRS + RRS * Vnation: CRS airway equation (Paw - PEEP) - VT + RRS * CRS * Vinaiation: multiply both sides by CRS to derive the following equation for CRS.

    [00076] Where Paw is airway inflation pressure, PEEP is the positive end-expiratory pressure, VT is the tidal volume and Vinaiação is the inspiratory flow rate.
    [00077] Calculating RRS involves the following equations: Paw - PEEP = VT / CRS + RRS * Vination: Paw airway equation - PEEP = VT * RRS / CRS + RRS * Vination: multiply VT / CRS by RRS / RRS Paw - PEEP = RRS (VT / TE + Vinaiação: simplify the right side to obtain the following equation for RRS.

    [00078] Calculating Ppit involves the following equations: Pplt = (VT / CRS) PEEP Or, alternatively,

    [00079] As a further improvement of the above methodology, it should be noted that TE varies with the flow as the resistance varies a lot with the flow. As such, an error in the TE estimate can be predicted to vary (Peak inspiratory flow (PIF) - Peak expiratory flow (PEF)). To correct this error, a correction factor can be applied to the RRS component of TE. This correction factor will depend on the ventilation mode, the amount of patient effort, PIF and PEF. However, a reasonable correction factor for the patients described in Figure 5 was found to be y = 5.2487 * (PIF-PEF) -0.8393. According to the present invention, the peak inspiratory flow (PIF) - peak expiratory flow (PEF), then, becomes the correction factor for TE.
    [00080] In the above equations, it is important to realize that the values of flow, pressure and volume used are not waveforms, but are individual measurements. In the preferred embodiment, these measurements occur simultaneously so that the values of volume, flow, and pressure are associated with a single point in time during inhalation. Since the airway equation does not include a term for inspiratory effort generated by the patient's inspiratory muscles, the ideal place to measure these values is when the inspiratory effort is minimal (to avoid errors caused by the inspiratory effort in the airway equation). As such, the preferred point where these measurements are made is the point during inhalation with minimal effort. Locating the patient's minimum effort point, however, can be difficult because the patient's instant effort can only be accurately calculated using invasive methods, such as esophageal pressure catheters. In typical breathing, however, the patient's low effort often occurs near the end (or sometimes the beginning) of inspiration.
    [00081] With the expiratory time constant, individual estimates of pressure plateau, resistance, compliance and related respiratory parameters can be estimated at each point of the inspiratory wave. Estimating these parameters during inhalation provides useful information, including flow-dependent resistance estimates and compliance, pressure plateau, and other parameters varying over breath. The data from these estimates over the course of the estimate can be valuable for better estimating other derived parameters, including, but not limited to, patient effort, average resistance and more accurate compliance, and accurate triggering information.
    [00082] In a modality, a minimum effort point for the accurate parameter estimation is determined by calculating Ppitao curves over inhalation and using the point where the Ppit was at its maximum. In another modality, a low point of effort of the patient is located, finding the position during inspiration when complacency is at its minimum value. Both of these modalities are based on the fact that the patient's effort will tend to increase the estimate of compliance and decrease the pressure plateau.
    [00083] For example, points A, B, and C in figure 16 provide examples of compliance points, which can be used to identify the values of volume, flow, and pressure in the calculation of TE, Ppit, RRS and CSR. Figure 17 shows the calculated compliance during breathing and two minimum points (as labeled x = 82 and X = 186) that can be used to accurately estimate lung parameters.
    [00084] Figure 18 shows the resistance curve of the inhalation part of this breath. Resistance is flow dependent, so when the flow decreases, the resistance will decrease as well.
    [00085] The figures. 19A and B illustrate patient data, with high inspiratory effort during PSV increased by the low IMV rate. The first breath (left) is a control breath (IMV), which has the same tidal volume as the PSV breath. Figure 19A is a diagram illustrating airway pressure (Paw; dark line curve) and Pes (esophageal pressure; light line curve). Figure 19B is a diagram illustrating flow (dark line curve) and volume (light line curve).
    [00086] In a real-time environment, some breaths are contaminated by coughing, the patient struggling with the ventilator, poor triggering by the ventilator, sensor noise, or errors, and other problems. As such, it is advantageous to reject breaths that are contaminated and lead to estimates that are outside the normal range. Calculating the mean or using median values from a group of uncontaminated breathing results provides a better overall estimate of respiratory parameters. In one mode, breaths that had a compliance value outside the normal range were eliminated. In addition, common respiratory parameters are computed and compared to normal values. Breathing parameters include peak inspiratory pressure, tidal volume, inspiratory time, expiratory time, mean airway pressure, and the like.
    [00087] The estimated values of Ppit, CRS, and RRS determined according to the methodologies described here are particularly useful for any device that interfaces with the patient's pulmonary system. The devices contemplated include, but are not limited to, ventilators, respiratory monitors, lung function machines, sleep apnea systems, hyperbaric devices, and the like. As understood by the specialist physician, such devices include various sensors and / or processing systems to provide data related to patients' respiratory parameters, such as airway pressure, flow, airway volume, tidal volume, f, PIP, inspiratory time , P0.1, inspiratory trigger time, trigger depth, expiration period, as well as airway, endotracheal tube, and ventilator expiration valve resistors. Contemplated fans include those that perform one or more of the following ventilation modes: ventilated volume; auxiliary control ventilation (A / C); synchronized intermittent mandatory ventilation (SIMV), cycled pressure ventilation; pressure support ventilation (PSV); pressure control ventilation (PCV), positive pressure non-invasive ventilation (VNIPP) and continuous positive pressure (CPAP) or positive airway pressure at two levels (BIPAP).
    [00088] In one embodiment of the invention, continuous real-time estimates of Ppit, CRS, and RRS are determined in order to diagnose the pulmonary condition or disease (including detection and treatment of obstructive sleep apnea apnea, as well as the detection of COPD and ARDS) and / or to assess the effectiveness of the intervention. For example, the patient's accurate continuous knowledge of CRS and RRS is particularly useful in establishing more accurate ventilator configurations for the patient and in pharmaceutical applications (such as bronchial dilators). Continuous and accurate knowledge of the patient's pulmonary mechanics during the application of pharmaceutical products is particularly useful in assessing therapeutic efficacy and in determining the appropriate dosage. In addition, the real-time data of this invention can be used to determine obstructions or obstacles to affect patient ventilation. For example, the invention can be used to determine when the breathing tube requires aspiration to remove mucus or other obstructions, or it can determine when the tube can be bent.
    [00089] In another modality, real-time estimates of Ppit, CRS, and RRS are used to estimate or improve patient effort estimates by applying the airway equation (for example, calculating Pmus as the difference between the expected airway pressure and actual airway pressure). This is also useful for determining and optimizing the patient's synchrony, allowing for accurate measurement of the start and displacement of the patient's effort. The optimization of the triggering and triggering of the fan can be implemented manually or automatically.
    [00090] In another mode, real-time estimates are used to track the patient's health and response to treatment. Compliance tracking during PEEP changes indicates when the lung is being optimally ventilated. Changes in resistance indicate that drugs to relax the patient's airway are working as expected. Using physiological parameters allows the titration and optimization of treatments, both through the ventilator and pharmaceutically.
    [00091] In one embodiment, the model, such as a neural network, is previously trained with clinical data and the input parameters can be collected non-invasively, with a standard respiratory monitor. The neural network is trained to predict physiological and imposed pulmonary mechanics using the non-invasively acquired parameters described above (although invasive parameters can be added to the system, if desired.) Once a model has been reached and verified to a desired degree of predictability, the network output, such as a real pulmonary mechanics variable, can be used as an accurate indicator of the patient's pulmonary mechanics. Description of Neural Networks
    [00092] Artificial neural networks loosely model the functioning of a biological neural network, such as the human brain. Thus, neural networks are typically implemented as computer simulations of a system of interconnected neurons. In particular, neural networks are hierarchical collections of interconnected processing elements (PEs). These elements are typically layered, where the input layer receives the input data, the hidden layers transform the data, and the output layer produces the desired output. Other modalities of a neural network can also be used.
    [00093] Each processing element in the neural network receives multiple input signals, or data values, which are processed to compute a single output. The inputs are received from the outputs of the PEs in the previous layer or from the input data. The output value of a PE is calculated using a mathematical equation, known in the art as an activation function or a transfer function that specifies the relationship between the input data values. As is known in the art, the activation function can include a threshold, or a deflection element. The outputs of the elements at lower levels of the network are provided as inputs for the elements at higher levels. The element or elements of the highest level, produces (in) an output, or outputs, of the final system.
    [00094] In the context of the present invention, the neural network is a computer simulation that is used to produce a non-invasive estimate of the quantified patient effort described above. The neural network of the present invention can be constructed by specifying the number, arrangement, and connection of the processing elements that make up the network. A simple modality of a neural network consists of a fully connected network of processing elements. As shown in figure 9, the processing elements of the neural network are grouped into the following layers: an input layer 30 where the parameters collected and / or derived from airway pressure and flow sensors are introduced into the network; a hidden layer or layers 32 of processing elements; and an output layer 34, where prediction resulting from patient effort 36 is produced. The number of connections and, consequently, the number of connection weights, is fixed by the number of elements in each layer 30, 32, 34.
    [00095] The most common training methodology for neural networks is based on the iterative improvement of the system parameters (usually called weights), minimizing the mean square difference between the desired output and the network output (mean square error, MSE ). The input is applied to the neural network, the neural network passes data through its hierarchical structure, and an output is created. This network output is compared with the desired output corresponding to that input and an error is calculated. This error is then used to adjust the system weights so that the next time the particular input is applied to the system the network output will be closer to the desired output. There are many possible methodologies for adjusting weights, the so-called training algorithm. As shown in figure 10, the most common is called inverse propagation which involves calculating each weight's responsibility for the error, and calculating a local gradient from this error, in the sense of using a descending gradient of learning rule for each weight .
    [00096] Based on the previous specification, the invention can be implemented using computer programming or engineering techniques including computer software, firmware, hardware or any combination or a subset thereof. Any of the resulting programs, having computer-readable code means, can be incorporated or provided within one or more computer-readable means, thus making a computer program product, that is, an article of manufacture, according to the invention. Computer-readable media can be, for example, a fixed (hard disk) drive, floppy disk, optical disc, magnetic tape, semiconductor memory, such as read-only memory (ROM), etc., or any transmission medium / reception such as the Internet or other network or communication connection. The article of manufacture containing the computer code can be made and / or used to directly execute the code of a medium, by copying the code from one medium to another medium, or by transmitting the code over a network.
    [00097] A person skilled in the art of computer science will easily be able to combine the software created as described with appropriate general-purpose or special-purpose computer hardware, to create a computer system or computer subsystem, which accomplishes the method of the invention. An apparatus for making, using or selling the invention may be one or more processing systems, including, but not limited to, a central processing unit (CPU), memory, storage devices, connections and communication devices, servers, I / O devices, or any subcomponents of one or more processing systems, including software, hardware, firmware or any combination or subset thereof, which incorporate the invention. User input can be received from the keyboard, mouse, pen, voice, touchscreen, or any other means by which a human can enter data into a computer, including through other programs, such as application programs . EXAMPLE 1
    [00098] The subject systems and methods for accurately estimating pulmonary mechanics in real time based on monitored ventilation parameters have been validated using a heterogeneous population of thirty (30) adult patients in respiratory failure requiring mechanical ventilation, that is, patients that received positive pressure ventilation.
    [00099] For each patient, the Ppit was recorded in the mode of intermittent mandatory ventilation (IMV) with an inspiratory flow waveform and a final inspiratory pause of 0.5 seconds. The inspiratory flow rate detected varied between 0.5 and 1.0 L / s).
    [000100] As illustrated in figure 1, data from a combined pressure / flow sensor 5 (NICO, Respironics) positioned between the patient's endotracheal tube 10 and the Y 15 piece of the ventilator's breathing circuit, were directed to a portable computer 20 with the software for executing the methods described here (Convergent Engineering) for the measurement and recording of pressure, flow, and volume data. As illustrated in Table 1 below and in the figures. 2 and 3, Ppit data ranged from 10 to 44 centimeters of H2O in the studied patient population.



    [000101] TE was obtained from three random patients (A, B and C) with three different inhalation flows (0.5, 0.75 and 1 L / S) and compared with TE Ppit obtained by a final inhalation pause (PEI). As illustrated in the figures. 4A- 4C, TE Ppit differs from TE, depending on the interval between PIF and PEF (X axis). TE is represented in the figures. 4A-4C as blue diamonds and TE Ppit as red squares, where the difference from TE Ppit to TE is represented as green triangles. PIF-PEF, then, becomes the correction factor for TE (Guerin, C. et al., "Effect of PEEP on work of breathing in mechanically ventilated COPD patients," Intensive Care Med, 26 (9): 1207- 1214 (2000).
    [000102] To derive the effect of the difference in flow between peak inspiratory flow and peak expiratory flow (PIF-PEF) on inspiration and expiration, RRS (RRS Ppit - RRS TE), the equation y = 5.2487 * (PIF -PEF) - 0.8393 was used as a correction factor for the RRS flow difference RRS. Figure 5 shows the effect of the flow difference on inhalation, where PIF = peak inspiratory flow rate, PEF = peak expiratory flow rate, RRS PPit = patient RRs from Ppit, RRS TE = patient RRs from TE. Analysis of validation data:
    [000103] A regression analysis between the calculated and actual Ppit (final inspiratory pause), which includes the correction calculated above, is illustrated in the table in figure 6A. A Bland-AItman graph showing the difference between correlation r2 is 0.99, Deviation = 0.00125, 95% agreement limit = -1.347 to 1.376 is illustrated in figure 6B. Figure 6C illustrates the linear adjustment diagram showing the proportional value = 1.006. CRS calculation analysis:
    [000104] Figure 7A is a graphic illustration of a regression analysis between CRS calculated from TE compared with the standard CRS calculated from Ppit, where R2 = 0.97. Figure 7B illustrates a Bland-AItman graph of the difference between CRsPpit and CRSTE, where Deviation = 0.0000199, 95% agreement limit = {-0.00634 to 0.00638}. Figure 7C is a graph of linear adjustment between CRSTE and CRsPpit, where Proportional = 0.948. RRS calculation analysis:
    [000105] Figure 8A is a graphical illustration of a regression analysis between RRs calculated from TE compared to the standard RRs calculated from Ppit, where R2 = 0.918. Figure 8B illustrates a Bland-AItman graph of the difference between RRsPpit and RRSTE, where Polarization = 0.00000008, 95% agreement limit = {-2.15 to 2.15}. Figure 8C is a graph of linear adjustment between RRsPpit θ RRSTE where Proportional = 0.923. EXAMPLE 2
    [000106] Continuous and accurate estimates of Ppit and CSR using 0 TE of passive lung deflation during PSV, without the need for IMV with IPE, have been validated using a patient population of twenty-four (24) adults in respiratory failure, needing mechanical ventilation and receiving PSV.
    [000107] The 24 adults consisted of 10 men and 14 women with age ranges 56.1 ± 16.6 yrs and weight 79.9 ± 28.8 kg. They had heterogeneous causes of respiratory failure and were breathing spontaneously with 0 PSV. PSV varied between 5 and 20 cm H2O. Applying the same tidal volume, PSV and IMV with IPE were compared in the same patients. During PSV, Pplt and CRS were obtained by integrating TE with expiratory volume and flow waveforms. During IMV and EIP, Pplt was obtained from the pressure plateau visualizing the airway pressure wave in EIP. The data were analyzed using regression and Bland-AItman; alpha was set at 0.05.
    [000108] During PSV, Pplt and CRS of the TE method were 19.65 ± 6.6 cm H2O and 0.051 ± 0.0124 ml / cm H2O, respectively. During IMV with IPE, Pplt and CRS were 20.84 ± 7.17 cm H2O and 0.046 ± 0.011 ml / cm H2O, respectively (there are no significant differences in all measurements). Comparing both measurement methods, the relationships between Pplt and SRC were r2 = 0.98 (figure 11) and r2 = 0.92 (figure 12), respectively (p <0.05)). Bland-AItman for Pplt and CRS showed deviation of 1.17 and - 0.0035, respectively, and precision of ± 1 and ± 0.0031, respectively.
    [000109] It should be understood that the example and modalities described herein are for illustrative purposes only and that various modifications or alterations in light of them will be suggested to persons skilled in the art and are to be included within the spirit and scope of this application and the scope the same.
    [000110] All patents, patent applications, provisional applications, and publications referred to above or cited herein are incorporated by reference in their entirety, including all figures and tables, insofar as they are not incompatible with the explicit teachings of this specification.
    权利要求:
    Claims (18)
    [0001]
    1. Method for real-time estimation of at least one selected from the group consisting of CRS (respiratory system compliance), RRS (patient's airway resistance) and Ppit inspiratory plateau pressure, characterized by the fact of understanding: (a ) receiving respiratory parameters from a patient from a device that interfaces with the patient's pulmonary system and measures respiratory parameters; (b) calculate, with a processing unit, the patient's ET (expiratory time constant) using at least one respiratory parameter from step (a), and (c) calculate with the processing unit at least one selected estimate from the group consisting of in CRS, RRS and / or Ppit, using at least one respiratory parameter from step (a) and the patient's TE from step (b), where the at least one respiratory parameter used to calculate the patient's TE is from expiration and in that the at least one respiratory parameter used to calculate at least one real-time estimate selected from the group consisting of CRS, RRS and Ppit is inspirational.
    [0002]
    2. Method, according to claim 1, characterized by the fact that the respiratory parameters include one or more of the group consisting of: inspiratory airway pressure, expiratory airway pressure, inspiratory flow rate, expiratory flow rate , airway volume, airway resistance, expiratory waveform of carbon dioxide flow, pulse oximeter plethysmogram waveform, tidal volume, respiratory rate (f), inspiratory pressure peak (PIP), inspiratory time , Poi, inspiratory trigger time and trigger depth.
    [0003]
    3. Method, according to claim 1, characterized by the fact that the respiratory parameter used to calculate at least one real-time estimate selected from the group consisting of CRS, RRS, and / or Ppit, is a single point in an inspirational time waveform.
    [0004]
    4. Method, according to claim 3, characterized by the fact that the single point is taken during the patient's minimum effort.
    [0005]
    5. Method, according to claim 3, characterized by the fact that the single point is taken at or near the end of the breath.
    [0006]
    6. Method, according to claim 1, characterized by the fact that the respiratory parameter used to calculate the patient's ET is an expiratory waveform.
    [0007]
    7. Method, according to claim 6, characterized by the fact that the respiratory parameter is in the middle of the expiratory waveform.
    [0008]
    8. Method according to claim 1, characterized by the fact that the patient's ET is calculated from a median or average of multiple estimates of ET calculated during expiration.
    [0009]
    9. Method, according to claim 8, characterized by the fact that the multiple estimates of ET calculated are during expiration, in which the flow rate is between 95% and 20% of the peak expiratory flow rate.
    [0010]
    10. Method according to claim 8, characterized by the fact that the calculated multiple TE estimates are during expiration in which the flow rate is between 95% and 70% of the peak expiratory flow rate.
    [0011]
    11. Method according to claim 8, characterized by the fact that the multiple TE estimates calculated are during expiration between 80% of the expired volume and 20% of the expired volume.
    [0012]
    12. Method, according to claim 1, characterized by the fact that it also comprises the step of applying a correction factor to the patient's ET that is calculated from expiration, in which the correction factor is derived from any one or more of the following items: peak inspiratory flow, peak expiratory flow, and the equation TE (t) = TEtotal (t) - (Rvent (t) * Cest).
    [0013]
    13. Method according to claim 1, characterized by the fact that the calculated estimates of Ppit, CRS or RRS are used in any one or more of the following functions: estimate the patient's effort to breathe, estimate the patient's resistance, estimate patient compliance, diagnose lung condition or disease, evaluate the effectiveness of ventilatory intervention, establish ventilator settings for treating the patient, evaluate the efficacy of pharmaceutical therapy, evaluate the patient's pulmonary mechanics during medication administration, identify obstructions or obstacles that affect the patient's ventilation, determine and / or optimize the patient's synchrony, optimize the activation of the ventilator and trigger, evaluate the patient's general health and evaluate the patient's general response to treatment.
    [0014]
    14. Method, according to claim 1, characterized by the fact that 0 to 0.1 seconds of expiration are excluded from at least one respiratory parameter used to calculate the patient's ET.
    [0015]
    15. Method, according to claim 1, characterized by the fact that exhalation beyond 0.5 seconds of exhalation is excluded from at least one respiratory parameter used to calculate the patient's ET.
    [0016]
    16. Method, according to claim 3, characterized by the fact that the single point in the inspiratory time waveform used to calculate at least one selected estimate from the group consisting of CRS, RRSθ Ppit is the last inhalation point at which the patient is exerting less effort.
    [0017]
    17. Method, according to claim 8, characterized by the fact that the median of multiple TE estimates is calculated during expiration, in which the flow is less than 80% of the peak inspiratory flow, but more than 0.1 liters per second (LPS).
    [0018]
    18. Method, according to claim 7, characterized by the fact that the respiratory parameter is in the middle of the expiratory waveform from 0.1 seconds to 0.5 seconds after the beginning of expiration.
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    法律状态:
    2018-03-20| B25A| Requested transfer of rights approved|Owner name: CONVERGENT ENGINEERING, INC. (US) , UNIVERSITY OF Owner name: CONVERGENT ENGINEERING, INC. (US) , UNIVERSITY OF GLORIDA RESEARCH FOUNDATION, INC. (US) |
    2019-01-08| B06F| Objections, documents and/or translations needed after an examination request according art. 34 industrial property law|
    2019-08-06| B06U| Preliminary requirement: requests with searches performed by other patent offices: suspension of the patent application procedure|
    2020-04-14| B09A| Decision: intention to grant|
    2020-11-03| B16A| Patent or certificate of addition of invention granted|Free format text: PRAZO DE VALIDADE: 20 (VINTE) ANOS CONTADOS A PARTIR DE 28/12/2010, OBSERVADAS AS CONDICOES LEGAIS. |
    优先权:
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    US29039209P| true| 2009-12-28|2009-12-28|
    US61/290,392|2009-12-28|
    US30997910P| true| 2010-03-03|2010-03-03|
    US61/309,979|2010-03-03|
    PCT/US2010/062232|WO2011090716A2|2009-12-28|2010-12-28|System and method for assessing real time pulmonary mechanics|
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