Method, device, medium and equipment for determining respiratory mechanics parameters under mechanical ventilation

Abstract

The present disclosure provides a method, device, medium and equipment for determining respiratory mechanics parameters under mechanical ventilation; it relates to the field of medical device technology. The method comprises: in a volume-controlled ventilation mode, obtaining the time data and pressure data corresponding to the patient's airway during the inhalation phase; constructing an original parameter fitting model based on the nonlinear relationship between the time data, respiratory mechanics parameters and pressure data; performing linear approximation processing on the original parameter fitting model to obtain a target parameter fitting model; solving the target parameter fitting model using the matrix least squares method to obtain fitting coefficients, and determining the respiratory mechanics parameters based on the fitting coefficients. The present disclosure can efficiently determine the respiratory mechanics parameters in mechanical ventilation, and judge the patient's lung overinflation based on the respiratory mechanics parameters, so as to timely adjust the patient's ventilation support to avoid lung damage.

Description

Technical Field

[0001] The present disclosure relates to the technical field of medical devices, and in particular to a method, device, medium, and equipment for determining respiratory mechanics parameters under mechanical ventilation. Background Art

[0002] As an effective device that can replace artificial spontaneous ventilation, assisted respiratory equipment, such as ventilators, plays an important role in the treatment of various diseases that require respiratory support.

[0003] For example, when a patient is mechanically ventilated using a ventilator in volume-controlled ventilation mode, the tidal volume can be set too high, leading to over-inflation and potentially lung damage. In constant-flow volume-controlled ventilation mode, changes in airway pressure directly reflect real-time changes in lung compliance.

[0004] Currently, a nonlinear optimization algorithm is usually used to quantify changes in lung compliance using airway pressure. However, this algorithm requires complete fitting data for each iteration, which means that all data of the patient's airway during the inhalation phase needs to be stored, greatly increasing the storage capacity of the ventilator. This will affect the operating timing of the ventilator to a certain extent, thereby reducing the ventilation support effect of the ventilator.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the invention and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0006] The purpose of the embodiments of the present disclosure is to provide a method, device, medium and equipment for determining respiratory mechanics parameters under mechanical ventilation, thereby at least to a certain extent solving the problem in the related art that the ventilator stores a large amount of fitting data for quantifying changes in lung compliance, which affects the operating timing of the ventilator and thus makes the ventilation support effect of the ventilator poor.

[0007] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by practice of the present disclosure.

[0008] According to a first aspect of an embodiment of the present disclosure, a method for determining respiratory mechanics parameters during mechanical ventilation is provided, comprising:

[0009] In volume-controlled ventilation mode, obtain the time and pressure data corresponding to the patient's airway during the inspiratory phase;

[0010] constructing an original parameter fitting model based on the nonlinear relationship between the time data, the respiratory mechanics parameters and the pressure data;

[0011] Performing linear approximation processing on the original parameter fitting model to obtain a target parameter fitting model;

[0012] The target parameter fitting model is solved using a matrix least squares method to obtain fitting coefficients, and the respiratory mechanics parameters are determined based on the fitting coefficients.

[0013] In an exemplary embodiment of the present disclosure, the fitting coefficient is a combination operation of a fitting matrix and a fitting vector;

[0014] The method of solving the target parameter fitting model by using the matrix least squares method to obtain the fitting coefficients includes:

[0015] Determine the corresponding independent variable matrix and target variable matrix according to the relationship between the discrete time signal and the discrete pressure signal in the target parameter fitting model;

[0016] Determine the fitting matrix according to the independent variable matrix;

[0017] Determine the fitting vector according to the independent variable matrix and the target variable matrix;

[0018] The fitting coefficient is calculated according to the fitting matrix and the fitting vector.

[0019] In an exemplary embodiment of the present disclosure, the corresponding independent variable matrix is ​​determined according to the relationship between the discrete time signal and the discrete pressure signal in the target parameter fitting model:

[0020]

[0021] The corresponding target variable matrix is:

[0022]

[0023] Among them, S i For [0, t i ]Integral of pressure data over time within the interval, P aw (t) represents the pressure signal collected by the real-time sensor at the tth time point, and the integration interval of t is [0, t i ], t i represents the i-th time point.

[0024] In an exemplary embodiment of the present disclosure, the fitting matrix determined according to the independent variable matrix is:

[0025]

[0026] Among them, X TX is the fitting matrix, X is the independent variable matrix, i is the time point value included in the inspiration phase, n is the total number of time points included in the inspiration phase, P aw (t i ) represents the pressure signal collected by the real-time sensor at time point t, t i represents the i-th time point, T represents the matrix transpose;

[0027] The fitting vector determined according to the independent variable matrix and the target variable matrix is:

[0028]

[0029] Among them, X T Y is the fitting vector, X is the independent variable matrix, Y is the target variable matrix, i is the time point value included in the inspiration phase, n is the total number of time points included in the inspiration phase, P aw (t i ) represents the real-time sensor at the t i The pressure signal collected at a time point, t i represents the i-th time point, S i For [0, t i ] is the integration of pressure data over time within the interval, and T is the matrix transpose.

[0030] In an exemplary embodiment of the present disclosure, the step of obtaining the time data and pressure data corresponding to the patient's airway during the inhalation phase includes:

[0031] Determining a fitting interval of the respiratory mechanics parameter during the inspiratory phase;

[0032] The time data and pressure data corresponding to the preset time points of the patient's airway within the fitting interval are obtained.

[0033] In an exemplary embodiment of the present disclosure, determining the fitting interval of the respiratory mechanics parameter in the inhalation phase includes:

[0034] determining a patient respiratory flow rate recognition threshold based on a target tidal volume during the inspiratory phase;

[0035] Performing time differentiation processing on the patient's respiratory flow rate obtained in real time to obtain the derivative of the patient's respiratory flow rate;

[0036] When the derivative of the patient's respiratory flow rate reaches the patient's respiratory flow rate identification threshold, determining that the patient's respiratory flow rate is in an ascending state, and determining an extreme value of the derivative of the patient's respiratory flow rate during the ascending process;

[0037] The starting time when the ventilator is in the constant flow stage is determined according to the derivative extreme value, and the fitting interval is determined according to the starting time and a preset delay time period.

[0038] In an exemplary embodiment of the present disclosure, after determining the respiratory mechanics parameter according to the fitting coefficient, the method further includes:

[0039] determining a current ventilation state of the patient's alveoli according to the respiratory mechanics parameters;

[0040] Adjust the ventilator setting parameters corresponding to the current ventilation state to adjust the respiratory mechanics parameter to a target respiratory mechanics parameter value.

[0041] According to a second aspect of an embodiment of the present disclosure, there is provided a device for determining respiratory mechanics parameters under mechanical ventilation, comprising:

[0042] A data acquisition module is used to obtain the time data and pressure data corresponding to the patient's airway during the inspiratory phase in the volume-controlled ventilation mode;

[0043] A model building module, configured to build an original parameter fitting model based on a nonlinear relationship between the time data, the respiratory mechanics parameters and the pressure data;

[0044] A model conversion module is used to perform linear approximation processing on the original parameter fitting model to obtain a target parameter fitting model;

[0045] The parameter determination module is used to solve the target parameter fitting model using the matrix least squares method to obtain fitting coefficients, and determine the respiratory mechanics parameters according to the fitting coefficients.

[0046] According to a third aspect of an embodiment of the present disclosure, there is provided an electronic device comprising: a processor; and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the method for determining respiratory mechanics parameters under mechanical ventilation in the first aspect is implemented.

[0047] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method for determining respiratory mechanics parameters under mechanical ventilation in the first aspect is implemented.

[0048] The technical solutions provided by the embodiments of the present disclosure may have the following beneficial effects:

[0049] In an exemplary embodiment of the present disclosure, a method for determining respiratory mechanical parameters during mechanical ventilation, under volume-controlled ventilation mode, obtains time and pressure data corresponding to the patient's airway during the inspiratory phase and constructs an original parameter fitting model based on the nonlinear relationship between the time data, respiratory mechanical parameters, and pressure data. The original parameter fitting model is linearized and approximated to obtain a target parameter fitting model. The target parameter fitting model is then solved using matrix least squares to obtain fitting coefficients, and respiratory mechanical parameters are determined based on the fitting coefficients. On the one hand, the linearization and approximation process converts the nonlinear model into a linear model, reducing the complexity of the problem, significantly reducing the computational effort involved in the fitting process, and effectively lowering the operational burden of the ventilator. This allows for efficient determination of respiratory mechanical parameters during mechanical ventilation, and allows for the patient's lung hyperinflation to be determined based on these parameters, enabling timely adjustment of ventilation support to prevent lung damage. On the other hand, solving the target parameter fitting model using matrix least squares yields the best-fit relationship between airway pressure and time series, thereby accurately estimating respiratory mechanical parameters, facilitating assessment of patient lung health and optimization of mechanical ventilation strategies.

[0050] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification, are used to explain the principles of the present disclosure. Obviously, the drawings described below are only some embodiments of the present disclosure, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0052] Figure 1 A system architecture diagram is shown to which the method for determining respiratory mechanics parameters under mechanical ventilation in an embodiment of the present disclosure can be applied.

[0053] Figure 2 A flow chart of a method for determining respiratory mechanics parameters under mechanical ventilation in an embodiment of the present disclosure is shown.

[0054] Figure 3 A flow chart of another method for determining respiratory mechanics parameters under mechanical ventilation in an embodiment of the present disclosure is shown.

[0055] Figure 4 A block diagram of a device for determining respiratory mechanics parameters under mechanical ventilation in an embodiment of the present disclosure is shown.

[0056] Figure 5 A schematic structural diagram of an electronic device suitable for implementing the embodiments of the present disclosure is shown.

[0057] In the drawings, the same or corresponding reference numerals denote the same or corresponding parts. DETAILED DESCRIPTION

[0058] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0059] In addition, the accompanying drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0060] Figure 1 A schematic diagram of the architecture of a method for determining respiratory mechanics parameters under mechanical ventilation to which the embodiments of this specification can be applied is shown.

[0061] like Figure 1 As shown, the system architecture 100 may include one or more terminal devices such as a smartphone 101, a portable computer 102, a desktop computer 103, a network 104, and a server 105. The network 104 is used to provide a medium for a communication link between the terminal device and the server 105. The network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables, etc. The terminal device may be any electronic device with a data processing function, which has a display screen for displaying the determined respiratory mechanics parameters to the user, etc. The electronic device includes but is not limited to the above-mentioned desktop computers, portable computers, smartphones, tablet computers, etc. It can be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as needed. For example, the server 105 may be a server cluster consisting of multiple servers.

[0062] The method for determining respiratory mechanics parameters during mechanical ventilation provided in the embodiments of the present disclosure can be executed by a terminal device, and accordingly, the apparatus for determining respiratory mechanics parameters during mechanical ventilation can be disposed in the terminal device. However, it will be readily understood by those skilled in the art that the method for determining respiratory mechanics parameters during mechanical ventilation provided in the embodiments of the present disclosure can also be executed by the server 105, and accordingly, the apparatus for determining respiratory mechanics parameters during mechanical ventilation can also be disposed in the server 105, and this is not particularly limited in this exemplary embodiment.

[0063] This exemplary embodiment provides a method for determining respiratory mechanics parameters under mechanical ventilation. Figure 2 FIG. 2 shows a flow chart of a method for determining respiratory mechanics parameters under mechanical ventilation. The method may include the following steps S210 to S240:

[0064] Step S210, in volume-controlled ventilation mode, obtaining time data and pressure data corresponding to the patient's airway during the inhalation phase;

[0065] Step S220, constructing an original parameter fitting model based on the nonlinear relationship between the time data, the respiratory mechanics parameters and the pressure data;

[0066] Step S230, performing linear approximation processing on the original parameter fitting model to obtain a target parameter fitting model;

[0067] Step S240 , solving the target parameter fitting model using matrix least squares method to obtain fitting coefficients, and determining the respiratory mechanics parameters according to the fitting coefficients.

[0068] The method for determining respiratory mechanics parameters under mechanical ventilation in the example embodiments of the present disclosure, on the one hand, converts a nonlinear model into a linear model through linear approximation processing, thereby reducing the complexity of the problem, greatly reducing the amount of calculation in the fitting process, and effectively reducing the operating pressure of the ventilator. Therefore, the respiratory mechanics parameters in mechanical ventilation can be efficiently determined, and the patient's lung overexpansion can be judged based on the respiratory mechanics parameters, so that the patient's ventilation support can be adjusted in time to avoid lung damage; on the other hand, the matrix least squares method is used to solve the target parameter fitting model, and the best fitting relationship between airway pressure and time series can be obtained, thereby accurately estimating the respiratory mechanics parameters, which is convenient for evaluating the patient's lung health and optimizing the mechanical ventilation strategy.

[0069] The above steps of this exemplary embodiment are described in more detail below.

[0070] In step S210, in the volume-controlled ventilation mode, time data and pressure data corresponding to the patient's airway during the inhalation phase are obtained.

[0071] In the exemplary embodiments of the present disclosure, the lungs (including the airway) and the ventilator ventilation circuit in the patient's body can be considered as a single airway system. Since some ventilator models cannot sample the airway pressure at the patient end, the ventilator's expiratory pressure can usually be used to represent the airway pressure at the patient end.

[0072] In constant flow capacity controlled ventilation mode, the ventilator works by ensuring that a preset constant tidal volume is delivered to the patient's lungs during each respiratory cycle. Each gas delivery process is controlled by adjusting the inspiratory flow rate so that the volume of gas provided to the patient is fixed regardless of changes in lung compliance or airway pressure.

[0073] Accordingly, there is a respiratory mechanics formula as shown in formula (1):

[0074]

[0075] Among them, P aw (t) is the airway pressure at time t, R is the resistance term in the airway system, Flow(t) is the patient's respiratory flow rate at time t, V(t) is the real-time volume of the lungs at time t, C is the lung compliance, and PEEP (Positive End Expiratory Pressure) is the positive end-expiratory pressure.

[0076] It can be seen from formula (1) that for constant flow ventilation, the patient's respiratory flow rate Flow(t) does not change with time, the resistance term R value is fixed, the real-time volume V(t) is linear, and the positive end-expiratory pressure PEEP is a preset value. Therefore, the airway pressure P aw Changes in (t) can directly reflect real-time changes in lung compliance C. For example, if lung compliance C remains unchanged, the airway pressure waveform during the constant flow ventilation phase is a straight line; if lung compliance C decreases, the airway pressure waveform during the constant flow ventilation phase becomes a concave curve, indicating hyperventilation; if lung compliance C increases, the airway pressure waveform during the constant flow ventilation phase becomes a convex curve, indicating lung recruitment.

[0077] Respiratory mechanics parameters that can monitor changes in lung compliance include the stretch index and the lung overdistension coefficient. The presently disclosed embodiments use the stretch index as an example for illustration. The stretch index refers to the relationship between the shape of the pressure-time curve during mechanical ventilation in a constant-flow, volume-controlled ventilation mode and changes in lung compliance. Therefore, using the stretch index to quantify changes in lung compliance requires time and pressure data corresponding to the patient's airway during the inspiratory phase.

[0078] In one example implementation, it is necessary to first determine the fitting interval of the respiratory mechanics parameter, i.e., the stretch index, during the inhalation phase, and then obtain the time data and pressure data corresponding to the preset time points of the patient's airway within the fitting interval, so as to solve the stretch index using the time data and pressure data within the fitting interval. For example, the time data x (t1, t2, ...) and pressure data Y (P aw (t1), P aw (t2),…).

[0079] It should be noted that the stretch index fitting interval is in the constant flow phase of inspiration in volume-controlled ventilation mode. Therefore, after the start of inspiration, the constant flow phase can be identified first to accurately determine the stretch index fitting interval in the inspiration phase.

[0080] For example, a patient respiratory flow rate identification threshold can be first determined based on the target tidal volume during the inspiratory phase. The patient respiratory flow rate identification threshold can be represented by a patient respiratory flow rate derivative threshold. For example, the patient respiratory flow rate derivative threshold corresponding to a target tidal volume less than 50 mL is 0.07 L / min / s, the patient respiratory flow rate derivative threshold corresponding to a target tidal volume between 50 mL and 100 mL is 0.1 L / min / s, and the patient respiratory flow rate derivative threshold corresponding to a target tidal volume greater than 100 mL is 0.18 L / min / s.

[0081] Then, the patient's respiratory flow rate obtained in real time is differentiated in time to obtain the derivative of the patient's respiratory flow rate, which is recorded as Flow'(t). When the derivative of the patient's respiratory flow rate reaches the patient's respiratory flow rate recognition threshold, it is determined that the patient's respiratory flow rate is in an increasing state, and the extreme value of the derivative of the patient's respiratory flow rate during the increasing process is determined, which is recorded as Flow' max , in order to find the time starting point when the ventilator is in the constant flow stage according to the extreme value, and determine the fitting interval according to the time starting point and the preset delay time period.

[0082] For example, when the conditions shown in formula (2) are met, the ventilator can be considered to be in the constant flow stage:

[0083] |Flow′(t)| <k·Flow′ max (2)

[0084] Wherein, k is a preset proportional coefficient, which can be set to 0.1. At this time, the time t corresponding to Flow′(t) is the starting point of the constant current stage.

[0085] In order to accurately perform data fitting, a preset delay period needs to be introduced in the exemplary embodiment of the present disclosure, and data is accumulated before processing. For example, after locating the time starting point of the constant current phase, the time starting point of the constant current phase is added with a preset delay period, such as 50ms, to obtain the first interval value T a , subtract the preset delay time period, such as 50ms, from the end point of the constant current phase to obtain the second interval value T b , and get the final fitting interval [T a , T b ].

[0086] In step S220, an original parameter fitting model is constructed based on the nonlinear relationship between the time data, the respiratory mechanics parameters and the pressure data.

[0087] For example, for the respiratory mechanics parameter stretch index, an original parameter fitting model can be constructed based on the nonlinear relationship between time data, stretch index and pressure data, as follows:

[0088] P aw (t) = at c +e (3)

[0089] Among them, P aw (t) represents pressure data, t represents time data, a and e are constants, c represents the stretch index, when c is equal to 1, the corresponding airway pressure waveform is a straight line, indicating that the compliance remains unchanged, when c is less than 1, the corresponding P aw (t) is a concave function, indicating that the compliance becomes smaller. When c is greater than 1, the corresponding P aw (t) is a convex function, indicating that the compliance increases.

[0090] The stretch index can accurately quantify changes in lung compliance and potential lung injury risks, making it easier to adjust ventilation parameters such as the tidal volume and positive end-expiratory pressure (PEEP) of the ventilator to ensure adequate oxygenation while minimizing damage to lung tissue.

[0091] In step S230, the original parameter fitting model is subjected to linear approximation processing to obtain a target parameter fitting model.

[0092] In order to solve the stretch index on a ventilator with limited computing power and memory, it is necessary to convert the nonlinear fitting problem into a linear fitting problem at the cost of fitting some parameters. The linear fitting problem has a known solution in the form of the least squares method. Therefore, after converting the nonlinear fitting problem into a linear fitting problem, there is no need to use a nonlinear optimization algorithm to iteratively solve the fitting coefficients. Instead, the stretch index can be calculated directly based on the expression of the known solution. The entire calculation process does not require the storage of complete fitting data, which greatly saves the computing power and memory of the ventilator.

[0093] Specifically, in the exemplary embodiments of the present disclosure, only the exponential factor c in formula (3) needs to be determined, and no parameter a, etc., needs to be determined. Therefore, the original parameter fitting model shown in formula (3) can be subjected to linear approximation processing, and after the nonlinear fitting problem is converted into a linear fitting problem, the converted target parameter fitting model can be solved by combining the real-time iterative matrix least squares method. The linear approximation processing includes time integration processing and time differentiation processing of the original parameter fitting model, which is not limited in the present disclosure.

[0094] Through linear approximation processing, the nonlinear model is converted into a linear model, which reduces the complexity of the problem, greatly reduces the amount of calculation in the fitting process, and effectively reduces the operating pressure of the ventilator, making it easier to subsequently determine the respiratory mechanics parameters in mechanical ventilation. The patient's lung overinflation can be judged based on the respiratory mechanics parameters, and the ventilation support for the patient can be adjusted in time to avoid lung damage.

[0095] In step S240, the target parameter fitting model is solved using matrix least squares method to obtain fitting coefficients, and the respiratory mechanics parameters are determined based on the fitting coefficients.

[0096] In an exemplary implementation, a method of performing time integration processing on an original parameter fitting model to obtain a target parameter fitting model and solving the target parameter fitting model is used as an example for description.

[0097] Specifically, integrate both sides of the equal sign in formula (3) over time to obtain the target parameter fitting model:

[0098]

[0099] Among them, P aw (t) represents pressure data, t represents time data, e is a constant, and c represents the stretch index. Furthermore, the integral term of pressure over time and the product term of pressure and time in formula (4) can be calculated in real time. In this case, the equation shown in formula (4) represents a real-time linear fitting problem. It can be seen that the constant a in formula (3) disappears through the integration process, but the desired stretch index c can still be obtained by fitting.

[0100] Furthermore, the integral term of pressure over time in formula (4) is defined as:

[0101]

[0102] It can be understood that the integration interval [0, t] in formula (5) is the same as the fitting interval [T a , T b ]correspond.

[0103] The target parameter fitting model shown in formula (4) can be described as Y = A1x1 + A2x2. When the matrix least squares method is used to solve the model, the discrete time signal t and the discrete pressure signal P in the target parameter fitting model can be obtained. aw (t) determine the corresponding independent variable matrix and target variable matrix, which are:

[0104] Independent variable matrix:

[0105]

[0106] Target variable matrix:

[0107]

[0108] Among them, S i For [0, t i ]Integral of pressure data over time within the interval, P aw (t) represents the pressure signal collected by the real-time sensor at the tth time point, and the integration interval of t is [0, t i ], t i represents the i-th time point.

[0109] The corresponding solution vector is:

[0110]

[0111] The fitting matrix determined according to the independent variable matrix X is:

[0112]

[0113] Among them, X T X is the fitting matrix, X is the independent variable matrix, i is the time point value included in the inspiration phase, n is the total number of time points included in the inspiration phase, P aw (t i ) represents the pressure signal collected by the real-time sensor at time point t, t i represents the i-th time point, and T represents the matrix transpose.

[0114] The fitting vector determined by the independent variable matrix X and the target variable matrix Y is:

[0115]

[0116] Among them, X T Y is the fitting vector, X is the independent variable matrix, Y is the target variable matrix, i is the time point value included in the inspiration phase, n is the total number of time points included in the inspiration phase, P aw (t i ) represents the real-time sensor at the ti The pressure signal collected at a time point, t i represents the i-th time point, S i For [0, t i ] is the integration of pressure data over time within the interval, and T is the matrix transpose.

[0117] According to the fitting matrix X T X and the fitting vector X T The fitting coefficient A is calculated by Y and recorded as:

[0118] A=(X T X) -1 X T Y(11)

[0119] Where X is the independent variable matrix, Y is the target variable matrix, T and -1 are matrix transpose and matrix inversion respectively.

[0120] A=(X T X) -1 X T Y is rewritten as:

[0121] (X T X)A=X T Y(12)

[0122] Among them, the fitting matrix X T X is a 2x2 matrix, the fitting coefficient A and the fitting vector X T Y is a column vector containing 2 elements, such as fitting matrix X T X and the fitting vector X T The matrix elements in Y are represented by a and b:

[0123]

[0124] You can get:

[0125]

[0126] The stretch index c can be calculated using formula (14):

[0127]

[0128] Finally, the calculated stretch index c can be subjected to mean filtering during the respiratory cycle to obtain a more stable fitting result.

[0129] The respiratory mechanics parameter determination method in the example embodiment of the present disclosure can accurately quantify the individual differences of each patient, thereby achieving personalized ventilator settings and management, helping to reduce ventilator-related lung injury, and improving the success rate of treatment and patient comfort.

[0130] In another exemplary embodiment, the nonlinear fitting problem can be transformed into a linear fitting problem by using a derivative method, and then solved by combining it with a real-time iterative matrix least squares method. For example, the derivative of both sides of the equal sign in formula (3) with respect to time yields:

[0131]

[0132] Among them, P aw (t) represents pressure data, t represents time data, e is a constant, and c represents the stretch index. Furthermore, the differential term of pressure with respect to time in formula (16) can be calculated in real time. In this case, the slope of the linear equation shown in formula (16) is the stretch index c. It should be noted that a noise-resistant derivation method can be used to avoid distortion in the fitting results.

[0133] In an example embodiment of the present disclosure, after determining the respiratory mechanics parameters based on the fitting coefficients, the current ventilation state of the patient's alveoli can also be determined based on the respiratory mechanics parameters, and the respiratory mechanics parameters can be adjusted to the target respiratory mechanics parameter values ​​by adjusting the corresponding ventilator setting parameters under the current ventilation state.

[0134] Ventilator settings include parameters such as positive end-expiratory pressure (PEEP) and tidal volume. For example, the target stretch index parameter value is any value between [0.9 and 1.1]. After determining the stretch index, determine whether it falls within this range. If it does, the pressure waveform rises linearly, indicating that the alveoli are open during inspiration and not over-inflated during inspiration. In this case, the PEEP or tidal volume setting is relatively appropriate. When the stretch index is less than 0.9, the pressure waveform is concave, indicating that the alveoli are not open at the beginning of inspiration, meaning that the PEEP setting may be too low. When the stretch index is greater than 1.1, the pressure waveform is convex, indicating that the alveoli are over-inflated during inspiration, which is usually caused by excessive PEEP or tidal volume settings. By adjusting the PEEP or tidal volume settings, observe whether the stretch index can be maintained between 0.9 and 1.1. For example, when the alveoli are over-expanded during inspiration, the stretch index can be maintained between 0.9 and 1.1 by reducing the positive end-expiratory pressure (PEEP) or the tidal volume setting.

[0135] In another exemplary embodiment, it is assumed that the ventilator is operating in a constant flow volume controlled ventilation mode. Figure 3 FIG. 4 shows another method for determining respiratory mechanics parameters under mechanical ventilation, which includes steps S301 to S308:

[0136] Step S301, start inhalation and initialize fitting data: initialize the stretch index mean filter, for example, define the number of mean filters as 3, set the remaining elements to zero, initialize the stretch index least squares fitter, for example, define the number of fitting parameters as 3, set the remaining parameters to zero, set the fitting matrix elements to zero, and set all positioning data of the stretch index constant current interval to zero;

[0137] Step S302, locating the constant flow phase: identifying the constant flow phase to locate the fitting interval of the stretch index. For example, a patient respiratory flow rate identification threshold can be determined based on the target tidal volume in the inspiratory phase, and the patient respiratory flow rate obtained in real time is subjected to time differentiation processing to obtain the derivative of the patient respiratory flow rate; when the derivative of the patient respiratory flow rate reaches the patient respiratory flow rate identification threshold, it is determined that the patient respiratory flow rate is in an ascending state, and the extreme value of the derivative of the patient respiratory flow rate during the ascending process is determined; the starting point of the ventilator in the constant flow phase is determined based on the extreme value of the derivative, and the fitting interval is determined based on the starting point of the time and a preset delay time period. The specific process is described in step S210 and will not be repeated here.

[0138] Step S303, determining whether positioning is successful: if positioning is successful, executing step S304, otherwise, the stretch index is not calculated in the current respiratory cycle. If the respiratory cycle is extremely short and the stretch index fitting interval cannot be selected, the stretch index is not calculated in the current respiratory cycle, and step S302 is executed again.

[0139] Step S304: Delaying the integration after a preset time period: The reason for using integration delay is that the result of any function integration is close to 0 at the beginning, which has no fitting value. It takes a certain amount of integration time before the characteristics of the integrand are revealed and the fitting result is accurate. The preset delay time period can be 35ms or longer, which is not limited in the present embodiment.

[0140] Step S305, determine whether the integral delay is reached, if so, execute step S306, otherwise execute step S304 to step S304 again;

[0141] Step S306, fitting the integral formula: using the real-time pressure data and time data to perform a least squares fit of the matrix until a preset time period before the set inhalation time arrives, e.g., stopping the least squares fit 50 ms before the completion of inhalation. The specific fitting process is described in step S240 and will not be repeated here.

[0142] Step S307, determine whether the constant flow stage is over: the end of the constant flow stage can be identified by the ventilator control state. For example, the state of the ventilator inhalation valve is obtained. If the inhalation valve stops constant flow control, it means that the constant flow stage is over. In other examples, after the constant flow stage ends, the airway pressure will reach a preset peak value. If it is monitored that the airway pressure no longer rises and reaches a peak value, it can be considered that the constant flow stage is over; or, in the ventilator graphic data display, the inspiratory flow rate curve appears as a flat straight line in the constant flow stage. This is because the flow rate remains constant. When the flow rate begins to decrease, it means that the constant flow stage is over and is about to enter the end of inspiration or plateau period; or, the inhalation time can be preset. When the preset inhalation time is reached, even if the airway pressure has not reached the maximum value, the constant flow stage will end and enter the exhalation stage. If it is determined that the constant flow stage is over, step S308 is executed, otherwise step S306 is continued to be fitted;

[0143] At the end of inspiration, step S308 solves the fitting equation and calculates the stretch index. The calculated stretch index can also be filtered by mean over the respiratory cycle to obtain a more stable fitting result. At the beginning of the inspiration phase of the next respiratory cycle, steps S302 through S308 are repeated to monitor the stretch index over multiple respiratory cycles.

[0144] The method for determining respiratory mechanics parameters under mechanical ventilation in the example embodiments of the present disclosure, on the one hand, converts a nonlinear model into a linear model through linear approximation processing, thereby reducing the complexity of the problem, greatly reducing the amount of calculation in the fitting process, and effectively reducing the operating pressure of the ventilator. Therefore, the respiratory mechanics parameters in mechanical ventilation can be efficiently determined, and the patient's lung overexpansion can be judged based on the respiratory mechanics parameters, so that the patient's ventilation support can be adjusted in time to avoid lung damage; on the other hand, the matrix least squares method is used to solve the target parameter fitting model, and the best fitting relationship between airway pressure and time series can be obtained, thereby accurately estimating the respiratory mechanics parameters, which is convenient for evaluating the patient's lung health and optimizing the mechanical ventilation strategy.

[0145] Furthermore, in this exemplary embodiment, a device for determining respiratory mechanics parameters under mechanical ventilation is also provided. Figure 4 As shown, the respiratory mechanics parameter determination device 400 under mechanical ventilation may include a data acquisition module 410, a model construction module 420, a model conversion module 430 and a parameter determination module 440, wherein:

[0146] The data acquisition module 410 is used to obtain the time data and pressure data corresponding to the patient's airway during the inspiration phase in the volume control ventilation mode;

[0147] A model building module 420 is configured to build an original parameter fitting model based on the nonlinear relationship between the time data, the respiratory mechanics parameters, and the pressure data;

[0148] The model conversion module 430 is used to perform linear approximation processing on the original parameter fitting model to obtain a target parameter fitting model;

[0149] The parameter determination module 440 is used to solve the target parameter fitting model using the matrix least square method to obtain fitting coefficients, and determine the respiratory mechanics parameters according to the fitting coefficients.

[0150] In an optional embodiment, the fitting coefficient is a combined operation of a fitting matrix and a fitting vector; the parameter determination module 440 is configured to determine the corresponding independent variable matrix and target variable matrix based on the relationship between the discrete time signal and the discrete pressure signal in the target parameter fitting model; determine the fitting matrix based on the independent variable matrix; determine the fitting vector based on the independent variable matrix and the target variable matrix; and calculate the fitting coefficient based on the fitting matrix and the fitting vector.

[0151] In an optional embodiment, the parameter determination module 440 determines the corresponding independent variable matrix according to the relationship between the discrete time signal and the discrete pressure signal in the target parameter fitting model as follows:

[0152]

[0153] The corresponding target variable matrix is:

[0154]

[0155] Among them, S i For [0, t i ]Integral of pressure data over time within the interval, P aw (t) represents the pressure signal collected by the real-time sensor at the tth time point, and the integration interval of t is [0, t i ], t i represents the i-th time point.

[0156] In an optional implementation, the fitting matrix determined by the parameter determination module 440 according to the independent variable matrix is:

[0157]

[0158] Among them, X T X is the fitting matrix, X is the independent variable matrix, i is the time point value included in the inspiration phase, n is the total number of time points included in the inspiration phase, P aw (ti ) represents the pressure signal collected by the real-time sensor at time point t, t i represents the i-th time point, T represents the matrix transpose;

[0159] The fitting vector determined according to the independent variable matrix and the target variable matrix is:

[0160]

[0161] Among them, X T Y is the fitting vector, X is the independent variable matrix, Y is the target variable matrix, i is the time point value included in the inspiration phase, n is the total number of time points included in the inspiration phase, P aw (t i ) represents the real-time sensor at the t i The pressure signal collected at a time point, t i represents the i-th time point, S i For [0, t i ] is the integration of pressure data over time within the interval, and T is the matrix transpose.

[0162] In an optional embodiment, the data acquisition module 410 is configured to determine the fitting interval of the respiratory mechanics parameters in the inhalation phase; and obtain time data and pressure data corresponding to the preset time points of the patient's airway within the fitting interval.

[0163] In an optional embodiment, the data acquisition module 410 is configured to determine a patient respiratory flow rate identification threshold based on a target tidal volume during an inhalation phase; perform time differentiation processing on the patient respiratory flow rate acquired in real time to obtain a derivative of the patient respiratory flow rate; when the derivative of the patient respiratory flow rate reaches the patient respiratory flow rate identification threshold, determine that the patient respiratory flow rate is in an ascending state, and determine the extreme value of the derivative of the patient respiratory flow rate during the ascending process; determine the starting time point when the ventilator is in a constant flow phase based on the extreme value of the derivative, and determine the fitting interval based on the starting time point and a preset delay time period.

[0164] In an optional embodiment, the respiratory mechanics parameter determination device 400 under mechanical ventilation may also include a parameter adjustment module, which is configured to determine the current ventilation state of the patient's alveoli based on the respiratory mechanics parameters; and adjust the corresponding ventilator setting parameters under the current ventilation state to adjust the respiratory mechanics parameters to the target respiratory mechanics parameter values.

[0165] The specific details of each module in the above-mentioned device for determining respiratory mechanics parameters under mechanical ventilation have been described in detail in the corresponding method for determining respiratory mechanics parameters under mechanical ventilation, and therefore will not be repeated here.

[0166] The exemplary embodiments of the present disclosure also provide a computer-readable storage medium on which a program product capable of implementing the above-mentioned method of this specification is stored. In some possible embodiments, various aspects of the present disclosure can also be implemented in the form of a program product, which includes a program code, and when the program product is run on an electronic device, the program code is used to cause the electronic device to execute the steps according to the various exemplary embodiments of the present disclosure described in the above-mentioned "Exemplary Method" section of this specification. The program product can adopt a portable compact disc read-only memory (CD-ROM) and include program code, and can be run on an electronic device, such as a personal computer. However, the program product of the present disclosure is not limited to this, and in this document, a readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, an apparatus or a device.

[0167] The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0168] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0169] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0170] Program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C#, C++, and the like, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a standalone software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0171] The exemplary embodiment of the present disclosure also provides an electronic device capable of implementing the above method. Figure 5 5 to describe the electronic device 500 according to this exemplary embodiment of the present disclosure. Figure 5 The electronic device 500 shown is merely an example and should not limit the functionality and scope of use of the embodiments of the present disclosure.

[0172] like Figure 5 As shown, electronic device 500 may be implemented as a general-purpose computing device. Components of electronic device 500 may include, but are not limited to, at least one processing unit 510, at least one storage unit 520, a bus 530 connecting various system components (including storage unit 520 and processing unit 510), and a display unit 540.

[0173] The storage unit 520 stores program codes that can be executed by the processing unit 510, so that the processing unit 510 performs the steps described in the "Exemplary Method" section above according to various exemplary embodiments of the present disclosure. For example, the processing unit 510 may execute Figure 2 The method steps in .

[0174] The storage unit 520 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 521 and / or a cache memory unit (Cache) 522 , and may further include a read-only memory unit (ROM) 523 .

[0175] The storage unit 520 may also include a program / utility 525 having a set (at least one) of program modules 524, such program modules 524 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0176] Bus 530 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0177] The electronic device 500 can also communicate with one or more external devices 600 (e.g., a keyboard, a pointing device, a Bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 500, and / or any device that enables the electronic device 500 to communicate with one or more other computing devices (e.g., a router, a modem, etc.). Such communication can occur via an input / output (I / O) interface 550. Furthermore, the electronic device 500 can communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet) via a network adapter 560. As shown, the network adapter 560 communicates with other modules of the electronic device 500 via a bus 530. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device 500, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0178] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the exemplary embodiment of the present disclosure.

[0179] Furthermore, the figures above are merely illustrative of the processes included in the methods according to exemplary embodiments of the present disclosure and are not intended to be limiting. It is readily understood that the processes illustrated in the figures above do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0180] It should be noted that although several modules or units of the device for action execution are mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be concretized in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0181] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow from the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the claims.

[0182] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method for determining respiratory mechanics parameters under mechanical ventilation, characterized in that: include: In volume-controlled ventilation mode, obtain the time and pressure data corresponding to the patient's airway during the inspiratory phase; constructing an original parameter fitting model based on the nonlinear relationship between the time data, the respiratory mechanics parameters and the pressure data; Performing linear approximation processing on the original parameter fitting model to obtain a target parameter fitting model; The target parameter fitting model is solved using a matrix least squares method to obtain fitting coefficients, and the respiratory mechanics parameters are determined based on the fitting coefficients.

2. The method for determining respiratory mechanics parameters under mechanical ventilation according to claim 1, wherein: The fitting coefficient is a combination of a fitting matrix and a fitting vector; The method of solving the target parameter fitting model by using the matrix least squares method to obtain the fitting coefficients includes: Determine the corresponding independent variable matrix and target variable matrix according to the relationship between the discrete time signal and the discrete pressure signal in the target parameter fitting model; Determine the fitting matrix according to the independent variable matrix; Determine the fitting vector according to the independent variable matrix and the target variable matrix; The fitting coefficient is calculated according to the fitting matrix and the fitting vector.

3. The method for determining respiratory mechanics parameters under mechanical ventilation according to claim 2, wherein: According to the relationship between the discrete time signal and the discrete pressure signal in the target parameter fitting model, the corresponding independent variable matrix is ​​determined as follows: The corresponding target variable matrix is: Among them, S i For [0, t i ]Integral of pressure data over time within the interval, P aw (t) represents the pressure signal collected by the real-time sensor at the tth time point, and the integration interval of t is [0, t i ], t i represents the i-th time point.

4. The method for determining respiratory mechanics parameters under mechanical ventilation according to claim 3, wherein: The fitting matrix determined according to the independent variable matrix is: Among them, X T X is the fitting matrix, X is the independent variable matrix, i is the time point value included in the inspiration phase, n is the total number of time points included in the inspiration phase, P aw (t i ) represents the pressure signal collected by the real-time sensor at time point t, t i represents the i-th time point, T represents the matrix transpose; The fitting vector determined according to the independent variable matrix and the target variable matrix is: Among them, X T Y is the fitting vector, X is the independent variable matrix, Y is the target variable matrix, i is the time point value included in the inspiration phase, n is the total number of time points included in the inspiration phase, P aw (t i ) represents the real-time sensor at the t i The pressure signal collected at a time point, t i represents the i-th time point, S i For [0, t i ] is the integration of pressure data over time within the interval, and T is the matrix transpose.

5. The method for determining respiratory mechanics parameters under mechanical ventilation according to claim 1, wherein: The obtaining of the time data and pressure data corresponding to the patient's airway during the inhalation phase includes: Determining a fitting interval of the respiratory mechanics parameter during the inspiratory phase; The time data and pressure data corresponding to the preset time points of the patient's airway within the fitting interval are obtained.

6. The method for determining respiratory mechanics parameters under mechanical ventilation according to claim 5, characterized in that: Determining the fitting interval of the respiratory mechanics parameter in the inhalation phase includes: determining a patient respiratory flow rate recognition threshold based on a target tidal volume during the inspiratory phase; Performing time differentiation processing on the patient's respiratory flow rate obtained in real time to obtain the derivative of the patient's respiratory flow rate; When the derivative of the patient's respiratory flow rate reaches the patient's respiratory flow rate identification threshold, determining that the patient's respiratory flow rate is in an ascending state, and determining an extreme value of the derivative of the patient's respiratory flow rate during the ascending process; The starting time when the ventilator is in the constant flow stage is determined according to the derivative extreme value, and the fitting interval is determined according to the starting time and a preset delay time period.

7. The method for determining respiratory mechanics parameters under mechanical ventilation according to claim 1, wherein: After determining the respiratory mechanics parameters according to the fitting coefficients, the method further includes: determining a current ventilation state of the patient's alveoli according to the respiratory mechanics parameters; Adjust the ventilator setting parameters corresponding to the current ventilation state to adjust the respiratory mechanics parameter to a target respiratory mechanics parameter value.

8. A device for determining respiratory mechanics parameters under mechanical ventilation, characterized in that: include: A data acquisition module is used to obtain the time data and pressure data corresponding to the patient's airway during the inspiratory phase in the volume-controlled ventilation mode; A model building module, configured to build an original parameter fitting model based on a nonlinear relationship between the time data, the respiratory mechanics parameters and the pressure data; A model conversion module is used to perform linear approximation processing on the original parameter fitting model to obtain a target parameter fitting model; The parameter determination module is used to solve the target parameter fitting model using the matrix least squares method to obtain fitting coefficients, and determine the respiratory mechanics parameters according to the fitting coefficients.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processing unit, the method for determining respiratory mechanics parameters under mechanical ventilation according to any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: include: processing unit; as well as a storage unit, configured to store executable instructions of the processing unit; Wherein, the processing unit is configured to execute the method for determining respiratory mechanics parameters under mechanical ventilation according to any one of claims 1 to 7 by executing the executable instructions.

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