Postoperative pain grading assessment and analgesic regimen optimization system for thoracic surgery patients
By acquiring airway flow velocity data, analyzing the airway flow velocity distribution within the inspiratory time range, calculating the total inspiratory volume and theoretical smooth flow velocity data, and quantifying lung filling and high-frequency oscillation intensity, the problem of poor pain grading assessment and analgesia effect in the intensive care environment is solved, and accurate pain grading and optimized analgesia regimens are achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- WEST CHINA HOSPITAL SICHUAN UNIV
- Filing Date
- 2026-05-15
- Publication Date
- 2026-08-04
AI Technical Summary
In intensive care settings, without baseline data, existing technologies struggle to distinguish between mechanical constraints caused by pulmonary edema and neurogenic respiratory braking caused by pain, resulting in poor pain rating assessment and analgesic efficacy, and posing a risk of underdosing or overdosing.
By acquiring airway flow rate data, analyzing the airway flow rate distribution within the inspiratory time range, calculating the total inspiratory volume and theoretical smooth flow rate data, quantifying lung filling and high-frequency oscillation intensity, obtaining coupling block integrals, conducting pain grading assessments, and optimizing analgesia regimens.
It enables accurate pain grading assessment and analgesia optimization in the absence of baseline data, reduces the risk of atelectasis and respiratory depression, and improves the accuracy of pain assessment and analgesic efficacy.
Smart Images
Figure CN122201586B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of medical data processing technology, specifically to a system for assessing postoperative pain levels and optimizing analgesia protocols for thoracic surgery patients. Background Technology
[0002] Patients undergoing thoracic surgery often experience chest pain after surgery due to damage to the intercostal nerves caused by the surgical incision and the traction on the wound caused by the expansion of the thoracic cavity. The protective reflex triggered by the pain causes patients to subconsciously inhibit the contraction of the inspiratory muscles, forming a "breathing splint effect," which manifests as shallow and rapid breathing and decreased ventilation. Long-term hypoventilation can easily lead to atelectasis and hypoxemia, so timely assessment of pain is necessary.
[0003] In existing technologies, respiratory mechanics monitoring is performed by focusing on macroscopic parameters such as lung compliance and using preoperative pain-free baseline data as a reference for pain assessment. However, in intensive care settings, patients often lack baseline data, and simple flow rate or volume monitoring cannot distinguish between mechanical limitations caused by pulmonary edema and neurogenic respiratory braking caused by pain. This makes it difficult to accurately match analgesic administration to the patient's actual needs, resulting in the risk of atelectasis due to insufficient administration or respiratory depression due to overdose. Consequently, pain rating assessment and analgesic efficacy are poor. Summary of the Invention
[0004] To address the technical problem of poor pain grading and analgesia in patients with poor pain grading and analgesia when baseline data is lacking, making it difficult to distinguish between mechanical limitations caused by pulmonary edema and neurogenic respiratory braking caused by pain, this invention aims to provide a system for postoperative pain grading and analgesia optimization for thoracic surgery patients. The specific technical solution adopted is as follows: This invention proposes a system for assessing postoperative pain levels and optimizing analgesia protocols in thoracic surgery patients, the system comprising: The data acquisition module is used to acquire airway flow rate data at each moment, as well as the start and end times of inhalation. The feature decoupling module is used to obtain the total inspiratory volume for the inspiratory time range between adjacent inspiratory start and inspiratory end times, based on the distribution of airway flow rate data at different times, and to obtain the theoretical smoothed flow rate data and lung filling at each time within the inspiratory time range; based on the distribution characteristics of the theoretical smoothed flow rate data and airway flow rate data at different times, as well as the numerical characteristics of lung filling, it obtains the volume-related flow rate missing values and high-frequency oscillation intensity for different preset lung filling levels. The pain grading assessment module is used to obtain the coupling block integral based on the high-frequency oscillation intensity of different lung filling degrees within a preset range of preset lung filling degree and the volume-related flow rate missing value; and to obtain the painful breathing baseline limitation coefficient based on the coupling block integral and the total inspiratory volume, and to perform pain grading assessment. The analgesia protocol optimization module is used to optimize analgesia protocols based on pain levels.
[0005] Furthermore, the method for obtaining the total inhalation volume includes: The average value of airway flow rate data at adjacent time points is obtained as the local airway flow rate level; The cumulative value of the local airway flow rate level at all adjacent moments within the inspiratory time range is obtained as the overall airway flow rate level; The product of the overall airway flow rate level and the interval between adjacent time points is obtained as the total inspiratory volume.
[0006] Furthermore, the method for obtaining the theoretical smoothed flow velocity data includes: Based on the distribution of airway flow rate data at different times within the inspiratory time range, interpolated airway flow rate data for each time moment is obtained. Obtain the cumulative value of the airway flow rate interpolation data at all times, calculate the product of the cumulative value and the interval between adjacent times, and use it as the integration area; The ratio of total inspiratory volume to integral area is obtained, and the product of the ratio result and the interpolated airway flow rate data at each moment is calculated as the theoretical smooth flow rate data at each moment.
[0007] Furthermore, the method for obtaining the airway flow rate interpolation data includes: The moment when the airway flow rate data is at its maximum within the inspiratory time range is taken as the peak moment. Using time as the horizontal axis and airway flow rate data as the vertical axis, interpolation is performed based on the airway flow rate data corresponding to the start time, peak time, and end time of inspiration to obtain interpolated airway flow rate data for each time moment.
[0008] Furthermore, the method for obtaining lung filling includes: For the inspiratory time range, the cumulative value of the local airway flow rate level of all adjacent time points within the historical range of each time point is obtained as the instantaneous airway flow rate level at each time point; the product of the instantaneous airway flow rate level and the interval between adjacent time points is obtained as the instantaneous cumulative volume at each time point. Obtain the percentage of instantaneous cumulative volume to total inspiratory volume at each moment, and round up to obtain the lung filling degree at each moment.
[0009] Furthermore, the method for obtaining the missing volume-related flow rate values includes: For any preset lung filling degree, if there exists a time when the lung filling degree is equal to the preset lung filling degree, the corresponding time is taken as the target time; the mean of the difference between the theoretical smoothed flow rate data and the airway flow rate data at all target times is obtained as the volume-related flow rate missing value for the corresponding preset lung filling degree. Iterate through all preset lung filling values. If there is no time when the lung filling value is equal to the preset lung filling value, perform linear interpolation based on the missing volume-related flow rate values of other preset lung filling values to obtain the missing volume-related flow rate values of the preset lung filling value.
[0010] Furthermore, the method for obtaining the high-frequency oscillation intensity includes: For airway flow rate data or theoretical smoothed flow rate data, the difference between the data at each time step and the previous time step is obtained as the rate of change of the data at each time step; the difference between the rate of change of the data at each time step and the previous time step is obtained as the data acceleration at each time step. The average difference in acceleration between theoretical smooth flow velocity data and airway flow velocity data at all target times is obtained as the high-frequency oscillation intensity corresponding to the preset lung filling degree. Iterate through all preset lung filling values. If there is no time when the lung filling value is equal to the preset lung filling value, perform linear interpolation based on the high-frequency oscillation intensity of other preset lung filling values to obtain the high-frequency oscillation intensity of the preset lung filling value.
[0011] Furthermore, the method for obtaining the coupling hindrance integral includes: The maximum value of the high-frequency oscillation intensity among all preset lung filling values is selected, and the ratio of the high-frequency oscillation intensity of each preset lung filling value to the corresponding maximum value is obtained as the oscillation weighting coefficient. Gain adjustment is performed on the volume-related flow velocity missing value based on the oscillation weighting coefficient of each preset lung filling degree to obtain the degree of local coupling blockage of each preset lung filling degree. The cumulative value of the local coupling blockage degree of all preset lung filling degrees within the preset range is obtained as the coupling blockage integral.
[0012] Furthermore, the method for obtaining the baseline respiratory limitation coefficient for painful breathing includes: The product of the coupling blockade integral and the interval between adjacent time points is obtained as the blockade volume; the percentage of the blockade volume to the total inspiratory volume is obtained as the painful respiratory limitation coefficient. Select other painful respiratory limitation coefficients (excluding the maximum and minimum values) within the neighborhood of the latest painful respiratory limitation coefficient, and calculate the mean of all other painful respiratory limitation coefficients as the baseline painful respiratory limitation coefficient.
[0013] Furthermore, the pain grading assessment includes: If the baseline respiratory restriction coefficient for pain is less than a preset first threshold, the patient is judged to be in the first level of pain. If the pain-related respiratory baseline limitation coefficient is greater than or equal to the preset first threshold and less than the preset second threshold, it is determined to be in the second pain level. If the pain-related respiratory baseline restriction coefficient is greater than or equal to a preset second threshold, the patient is judged to be in the third pain level; the preset first threshold is less than the preset second threshold.
[0014] The present invention has the following beneficial effects: This invention obtains the total inspiratory volume based on the distribution of airway flow rate data at different times, and obtains the theoretical smoothed flow rate data and lung filling at each moment within the inspiratory time range, which helps to accurately simulate the smoothness characteristics of muscle contraction in living organisms. Based on the distribution characteristics of the theoretical smoothed flow rate data and airway flow rate data at different times, as well as the numerical characteristics of lung filling, it obtains the volume-related flow rate missing values and high-frequency oscillation intensity for different preset lung filling levels, quantifying the degree of inhibition of inspiratory effort by pain and the degree of muscle tension caused by pain. Based on the high-frequency oscillation intensity and volume-related flow rate missing values for different lung filling levels within a preset range of preset lung filling, it obtains the coupling block integral, comprehensively reflecting these two types of respiratory mechanics abnormalities caused by pain. Based on the coupling block integral and the total inspiratory volume, it obtains the pain-related respiratory baseline limitation coefficient, reflecting the filtered stable respiratory limitation level, for pain grading assessment and analgesia optimization. This invention uses the accurate acquisition of the pain-related respiratory baseline limitation coefficient for pain grading assessment and optimization of analgesia protocols. Attached Figure Description
[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0016] Figure 1 This is a structural block diagram of a system for assessing postoperative pain levels and optimizing analgesia protocols for thoracic surgery patients, provided in one embodiment of the present invention. Figure 2 This is a flowchart of a method for obtaining theoretical smoothed flow velocity data according to an embodiment of the present invention; Figure 3 This is a flowchart illustrating a method for obtaining a coupling-delay integral according to an embodiment of the present invention. Detailed Implementation
[0017] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a postoperative pain grading assessment and analgesia program optimization system for thoracic surgery patients proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.
[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.
[0019] The following description, in conjunction with the accompanying drawings, details the specific scheme of the postoperative pain grading assessment and analgesia optimization system for thoracic surgery patients provided by this invention.
[0020] Please see Figure 1 The diagram illustrates a structural block diagram of a postoperative pain grading assessment and analgesia protocol optimization system for thoracic surgery patients according to an embodiment of the present invention. The system specifically includes: a data acquisition module 101, a feature decoupling module 102, a pain grading assessment module 103, and an analgesia protocol optimization module 104. The data acquisition module 101 is used to acquire airway flow rate data at each moment, as well as the start and end times of inhalation.
[0021] In embodiments of the present invention, in order to eliminate jitter interference in the real-time data stream and ensure the integrity of the analysis object, the airway flow rate data of the airway flow rate sensor at each moment is acquired through a digital interface at a set sampling frequency. It should be noted that the system executes zero-crossing detection logic. When the airway flow rate data at the detected moment changes from zero or negative to positive, the corresponding moment is taken as the inhalation start moment; when the airway flow rate data at the detected moment changes from positive to zero or negative, the corresponding moment is taken as the inhalation end moment.
[0022] It should be noted that, in one embodiment of the present invention, the sampling frequency is set to 50Hz to acquire airway flow rate data, that is, the time interval is 0.02S; in other embodiments of the present invention, the size of the time interval can be set according to the specific situation, and will not be limited or described in detail here.
[0023] It should be noted that, considering that the original signal may contain high-frequency spikes caused by airflow turbulence or sensor noise, which may cause non-physiological fluctuations to interfere with subsequent morphological analysis, the system uses a fourth-order Butterworth low-pass filter to filter the time series of airway flow rate data at all times. The filter has a cutoff frequency of 20Hz, which helps to preserve the original morphological characteristics of the respiratory waveform to the greatest extent without introducing phase distortion. The specific means are well known to those skilled in the art and will not be described in detail here.
[0024] The feature decoupling module 102 is used to obtain the total inspiratory volume for the inspiratory time range between adjacent inspiratory start and inspiratory end times, based on the distribution of airway flow rate data at different times, and to obtain the theoretical smooth flow rate data and lung filling degree at each time within the inspiratory time range; based on the distribution characteristics of the theoretical smooth flow rate data and airway flow rate data at different times, and the numerical characteristics of lung filling degree, to obtain the volume-related flow rate missing value and high-frequency oscillation intensity for different preset lung filling degrees.
[0025] Within a defined inhalation time range, airway flow rate data reflects the rate of change of gas volume, quantifying the cumulative effect over the overall inhalation time range; for the inhalation time range between adjacent inhalation start and end times, the total inhalation volume is obtained based on the distribution of airway flow rate data at different times.
[0026] Preferably, in one embodiment of the present invention, the method for obtaining the total inhalation volume includes: The average value of airway flow rate data at adjacent time points is obtained as the local airway flow rate level; The cumulative value of the local airway flow rate level at all adjacent moments within the inspiratory time range is obtained as the overall airway flow rate level; The product of the overall airway flow rate level and the interval between adjacent time points is obtained as the total inspiratory volume.
[0027] It should be noted that the interval between adjacent moments represents the difference between the maximum and minimum moments in the calculation. The larger the interval, the larger the inspiratory volume between adjacent moments. The total inspiratory volume represents the total energy value of a single breath.
[0028] In an ideal state without pain or mechanical blockage, the contraction of the human respiratory muscles follows the principle of minimizing energy consumption, that is, the change in airflow velocity should be smooth and continuous, and its velocity waveform conforms to the minimum acceleration characteristics in fluid mechanics, which helps to conduct subsequent pain analysis. Therefore, theoretical smooth velocity data at each moment within the inspiratory time range can be obtained.
[0029] Preferably, in one embodiment of the present invention, the method for obtaining theoretical smoothed flow velocity data is described in [reference needed]. Figure 2It illustrates a flowchart of a method for obtaining theoretically smoothed flow velocity data, including: Step S201: Based on the distribution of airway flow rate data at different times within the inhalation time range, obtain interpolated airway flow rate data for each time moment.
[0030] Preferably, in one embodiment of the present invention, the method for obtaining airway flow rate interpolation data includes: The moment when the airway flow rate data is at its maximum within the inspiratory time range is taken as the peak moment. Using time as the horizontal axis and airway flow rate data as the vertical axis, interpolation is performed based on the airway flow rate data corresponding to the start, peak, and end of inspiration to obtain interpolated airway flow rate data for each time moment. It should be noted that, considering that spline interpolation is prone to overshoot when dealing with rapidly changing peaks, leading to non-physiological reverse fluctuations in the generated theoretical flow rate, this embodiment of the invention employs a piecewise cubic Hermitian interpolation polynomial algorithm for interpolation. This algorithm has strict monotonicity preservation and can accurately simulate the smooth characteristics of muscle contraction in living organisms. The specific algorithm is a well-known technique to those skilled in the art and will not be elaborated here.
[0031] Step S202: Obtain the cumulative value of the airway flow rate interpolation data at all times, calculate the product of the cumulative value and the interval between adjacent times, and use it as the integration area.
[0032] The product of the accumulated value and the interval between adjacent time points reflects the numerical integration process and is used to estimate the total ventilation or total flow rate from discrete velocity data points; the integral area reflects the ideal inspiratory volume.
[0033] Step S203: Obtain the ratio of total inspiratory volume to integral area, calculate the product of the ratio result and the interpolated airway flow rate data at each moment, and use it as the theoretical smooth flow rate data at each moment.
[0034] The ratio of total inspiratory volume to integral area reflects the volume scaling factor; the larger the ratio, the larger the volume scaling factor. Considering that the airway flow rate interpolation data only guarantees the alignment of peak points and time lengths, in order to ensure that subsequent data only reflect the microscopic distortions of the waveform in terms of hydrodynamic morphology, thereby completely eliminating the influence of ventilation scale differences on the accuracy of pain assessment, the airway flow rate interpolation data is weighted based on the ratio, i.e., the volume scaling factor, to obtain theoretical smooth flow rate data. This provides the ideal respiratory trajectory that the patient should have under the current ventilation conditions when the patient is not affected by pain.
[0035] Considering that pain has a significant anatomical position dependence and frequency domain specificity in the inhibition of respiratory movement, that is, when the lungs are filled to the high-tension area, the spinal reflex triggered by nociceptors causes high-frequency antagonistic contraction of the intercostal muscles, accompanied by an involuntary decrease in macroscopic flow velocity, lung filling is quantified based on airway flow velocity data.
[0036] Preferably, in one embodiment of the present invention, the method for obtaining lung filling includes: For the inspiratory time range, the cumulative value of the local airway flow rate level of all adjacent time points within the historical range of each time point is obtained as the instantaneous airway flow rate level at each time point; the product of the instantaneous airway flow rate level and the interval between adjacent time points is obtained as the instantaneous cumulative volume at each time point. The formula is expressed as: ;in, Indicates the first The instantaneous cumulative volume at any given moment; Indicates the first Airway flow rate data at any given time; Indicates the first Airway flow rate data at any given time; Indicates the interval between adjacent moments; Indicates the first The number of moments within the historical range of a given moment.
[0037] It should be noted that the interval between adjacent moments reflects the temporal resolution of the sampling, and the product of the instantaneous airway flow rate level and the interval between adjacent moments represents the volume of gas flowing through the airway within the historical range.
[0038] It should be noted that, in one embodiment of the present invention, the historical range is the range formed by taking each moment as a reference and the historical moments preceding each moment within the inhalation time range; in other embodiments of the present invention, the size of the historical range can be specifically set according to the specific circumstances, and will not be limited or elaborated here.
[0039] Obtain the percentage of instantaneous cumulative volume to total inspiratory volume at each moment, and round up to obtain the lung filling degree at each moment.
[0040] The formula is expressed as: ,in, Indicates the first Lung fullness at any given time; Indicates the first The instantaneous cumulative volume at any given moment; Indicates the total inhalation volume; This indicates rounding up to the nearest integer.
[0041] It should be noted that the larger the instantaneous cumulative volume is relative to the total inspiratory volume, the greater the lung filling.
[0042] Pain-triggered protective reflexes can suppress inspiratory muscle drive in specific volume segments, resulting in actual flow rates significantly lower than pain-free smooth flow rates. Furthermore, the deviation between the actual and pain-free smooth flow rates is substantial, leading to missing volume-related flow rate values and higher high-frequency oscillation intensity. Lung filling numerical characteristics can quantify the mechanical characteristics of different respiratory cycles on a uniform scale. Therefore, based on the distribution characteristics of theoretical smooth flow rate data and airway flow rate data at different times, as well as the numerical characteristics of lung filling, missing volume-related flow rate values and high-frequency oscillation intensity for different preset lung filling levels can be obtained.
[0043] Preferably, in one embodiment of the present invention, the method for obtaining the missing volume-related flow rate value includes: For any preset lung filling degree, if there exists a time when the lung filling degree is equal to the preset lung filling degree, the corresponding time is taken as the target time; the mean of the difference between the theoretical smoothed flow rate data and the airway flow rate data at all target times is obtained as the volume-related flow rate missing value for the corresponding preset lung filling degree. It should be noted that analyzing the difference between theoretical smoothed flow rate data and airway flow rate data can reflect the difference between ideal painless airflow and actual breathing. By calculating the mean, the overall deviation can be quantified, reflecting the missing volume-related flow rate values.
[0044] Iterate through all preset lung filling values. If there is no time when the lung filling value is equal to the preset lung filling value, perform linear interpolation based on the missing volume-related flow rate values of other preset lung filling values to obtain the missing volume-related flow rate values of the preset lung filling value.
[0045] It should be noted that, in the embodiments of the present invention, the lung filling degree is limited to a percentage coordinate system, that is, the lung filling degree is preset to traverse within the range of 1 to 100, and the integer is taken for analysis.
[0046] It should be noted that the missing values of volume-related flow rate reflect how much flow rate the patient lost when the lung was filled to the preset lung filling degree. Linear interpolation uses straight lines to connect adjacent known data points to fill the corresponding gaps and avoid overshoot. The specific methods are well known to those skilled in the art and will not be described in detail here.
[0047] Unlike the smooth restriction caused by pulmonary consolidation or pleural effusion, the restriction caused by pain is often accompanied by a neurological conflict between the agonist and antagonist muscles, which manifests as microscopic jitter or coarsening on the flow velocity waveform. Therefore, the greater the change in flow velocity data, the greater the jitter and the greater the intensity of high-frequency oscillations.
[0048] Preferably, in one embodiment of the present invention, the method for obtaining the high-frequency oscillation intensity includes: For airway flow rate data or theoretical smoothed flow rate data, the difference between the data at each time step and the previous time step is obtained as the rate of change of the data at each time step; the difference between the rate of change of the data at each time step and the previous time step is obtained as the data acceleration at each time step. It should be noted that data acceleration describes the increase or decrease of the rate of change of flow velocity data. That is, it reveals the dynamic characteristics hidden in the original waveform through mathematical differentiation. The greater the acceleration, the more violent the oscillation.
[0049] The average difference in acceleration between theoretical smooth flow velocity data and airway flow velocity data at all target times is obtained as the high-frequency oscillation intensity corresponding to the preset lung filling degree. It should be noted that the difference in data acceleration represents the absolute value of the difference between data accelerations, reflecting the degree to which the actual airflow deviates from the ideal smooth trajectory in terms of the rate of change of acceleration. The larger the absolute value of the difference, the greater the degree of deviation.
[0050] Iterate through all preset lung filling values. If there is no time when the lung filling value is equal to the preset lung filling value, perform linear interpolation based on the high-frequency oscillation intensity of other preset lung filling values to obtain the high-frequency oscillation intensity of the preset lung filling value.
[0051] Based on this, the intensity of high-frequency oscillations reflects the intensity of the tremor of the respiratory drive when the lungs are filled to a preset lung filling level.
[0052] The pain grading assessment module 103 is used to obtain the coupling block integral based on the high-frequency oscillation intensity of different lung filling degrees within a preset range of preset lung filling degree and the volume-related flow velocity missing value; and to obtain the painful respiratory baseline limitation coefficient based on the coupling block integral and the total inspiratory volume, and to perform pain grading assessment.
[0053] Incision pain has a significant end-enhancement effect on respiration, meaning that at the end of inspiration, the loss of flow rate and muscle tremors are most intense. By analyzing the high-frequency oscillation intensity and volume-related flow rate loss value, the overall efficiency loss of the respiratory system in transporting gas and responding to high-frequency signals is determined. Based on the high-frequency oscillation intensity and volume-related flow rate loss value of different lung filling degrees within a preset range of preset lung filling degree, the coupling block integral is obtained.
[0054] Preferably, in one embodiment of the present invention, the method for obtaining the coupling hindrance integral is described in [reference needed]. Figure 3 It shows a flowchart of a method for obtaining the coupling hindrance integral, including: Step S301: Select the maximum value of the high-frequency oscillation intensity among all preset lung filling degrees, and obtain the ratio of the high-frequency oscillation intensity of each preset lung filling degree to the corresponding maximum value, as the oscillation weighting coefficient.
[0055] It should be noted that when performing ratio calculations, considering that the denominator of the formula may be 0, rendering the formula meaningless, a very small positive number with consistent dimensions needs to be added to the denominator. The value of this number can be specifically set according to the range of values for the denominator; the specific methods are well-known to those skilled in the art and will not be elaborated here. Considering that flow velocity loss is accompanied by strong tremors, the greater the intensity of high-frequency oscillations, the more likely it is to be caused by pain, and therefore the oscillation weighting coefficient should be relatively larger.
[0056] Step S302: Adjust the gain of the missing volume-related flow velocity value according to the oscillation weighting coefficient of each preset lung filling degree to obtain the degree of local coupling blockage of each preset lung filling degree.
[0057] It should be noted that the gain adjustment is obtained as follows: the sum of the positive integer 1 and the oscillation weighting coefficient is obtained as the oscillation weight; the product of the oscillation weight and the missing volume-related velocity value is obtained as the degree of local coupling blockage. The larger the oscillation weighting coefficient, the larger the missing volume-related velocity value, the more likely it is caused by pain, and the greater the degree of local coupling blockage.
[0058] The formula is expressed as: ;in, Indicates the preset lung filling degree The degree of local coupling obstruction; Indicates the preset lung filling degree Missing values for volume-related flow rates; Indicates the preset lung filling degree The intensity of high-frequency oscillations; This represents the maximum value of the high-frequency oscillation intensity among all preset lung filling values; It represents a very small positive number.
[0059] Step S303: Obtain the cumulative value of the local coupling blockage degree of all preset lung filling degrees within the preset range, as the coupling blockage integral.
[0060] It should be noted that, in one embodiment of the present invention, the incision for thoracic surgery is usually located in the intercostal space. Inhalation is a process of thoracic expansion. When the lung filling reaches more than 60%, the intercostal space is significantly stretched. At this time, the tension of the skin, muscles and sutures at the incision site reaches its peak. When the lung filling exceeds 90%, the flow rate will naturally and rapidly decrease to zero near the end of inhalation. Therefore, based on the physiological and mechanical characteristics after thoracic surgery, the size of the preset range is set to be within the range of 60% to 90% of the preset lung filling. In other embodiments of the present invention, the size of the preset range can be specifically set according to the specific situation, which will not be limited or described in detail here.
[0061] The coupling block integral reflects the degree of pain influence; the larger the coupling block integral, the greater the pain influence. The total inspiratory volume reflects the total tidal volume; the larger the volume, the greater the inspiratory flow rate and the smaller the pain-induced respiratory restriction. Based on the coupling block integral and the total inspiratory volume, the pain-induced respiratory baseline restriction coefficient is obtained. Preferably, in one embodiment of the present invention, the method for obtaining the pain-induced respiratory baseline restriction coefficient includes: The product of the coupling hysteresis integral and the interval between adjacent time steps is obtained as the hysteresis volume; The percentage of blocked volume to total inspiratory volume is used as the painful respiratory limitation coefficient. Select other painful respiratory limitation coefficients (excluding the maximum and minimum values) within the neighborhood of the latest painful respiratory limitation coefficient, and calculate the mean of all other painful respiratory limitation coefficients as the baseline painful respiratory limitation coefficient.
[0062] It should be noted that, in one embodiment of the present invention, the method for obtaining the neighborhood range includes: using the latest painful respiratory limitation coefficient as a benchmark, selecting a preset number of historical painful respiratory limitation coefficients, the preset number being set to 10; if the historical painful respiratory limitation coefficients do not meet 10, selecting all historical painful respiratory limitation coefficients for analysis; in other embodiments of the present invention, the size of the neighborhood range can be specifically set according to the specific situation, and is not limited or elaborated here.
[0063] It should be noted that the painful respiratory limitation coefficient is a real-time changing physiological indicator, which may produce extremely high or low values due to sudden patient movements, coughing, brief postural adjustments, or momentary electrical signal interference from the instrument. By removing the maximum and minimum values, these non-representative transient events can be effectively filtered out, and the remaining data points are more likely to reflect the patient's relatively stable level of limitation over a neighboring time period. The painful respiratory baseline limitation coefficient reflects the patient's persistent and stable level of painful respiratory limitation within the most recent observation window. The larger the painful respiratory baseline limitation coefficient, the greater the pain, and the more helpful it is for pain grading assessment. Preferably, in one embodiment of the present invention, pain grading assessment includes: If the baseline respiratory restriction coefficient for pain is less than a preset first threshold, the patient is judged to be in the first level of pain. If the pain-related respiratory baseline limitation coefficient is greater than or equal to the preset first threshold and less than the preset second threshold, it is determined to be in the second pain level. If the pain-related respiratory baseline limitation coefficient is greater than or equal to the preset second threshold, the patient is judged to be in the third pain level; if the preset first threshold is less than the preset second threshold.
[0064] It should be noted that, in one embodiment of the present invention, the preset first threshold is set to 20; the preset second threshold is set to 50; the first pain level is less than the second pain level, and the second pain level is less than the third pain level; in other embodiments of the present invention, the values of the preset first threshold and the preset second threshold can be set according to specific circumstances, and are not limited or elaborated here.
[0065] The analgesia protocol optimization module 104 is used to optimize the analgesia protocol based on the pain level.
[0066] It should be noted that the higher the pain level, the greater the respiratory restriction. For the first pain level, there is no significant obstruction at the end of the inhalation, indicating that the current analgesia regimen is sufficient to counteract the incision stimulation and lung compliance is maintained at a good level. The system sends a maintenance command. For the second pain level, the patient begins to experience flow restriction accompanied by muscle tremors, indicating that the neurological reflex has begun to inhibit deep breathing ability and there is a tendency for inadequate ventilation. The system sends a background gain command, reads the current background infusion rate and increases it by a fixed percentage set according to the specific situation, such as 10%. If the current background infusion rate is zero, the preset basal maintenance dose is started to be infused, aiming to moderately increase the baseline blood drug concentration to suppress the neurological reflex caused by incision traction and prevent pain escalation. For the third pain level, the patient experiences tonic spasm at the end of the inhalation, which severely restricts lung volume expansion and poses a very high risk of atelectasis. The system sends an intervention command, triggering the analgesia pump to perform a single supplemental dose infusion and simultaneously activating an audible and visual alarm to prompt medical staff to intervene.
[0067] Based on this, the system establishes a monotonic negative feedback mechanism that increases drug administration when the blockage worsens and maintains or reduces drug administration when the blockage is relieved, thus achieving automated optimization of analgesia regimens.
[0068] In summary, this invention obtains the total inspiratory volume based on the distribution of airway flow rate data at different times, and obtains the theoretical smoothed flow rate data and lung filling degree at each time point within the inspiratory time range. Based on the distribution characteristics of the theoretical smoothed flow rate data and airway flow rate data at different times, as well as the numerical characteristics of lung filling degree, it obtains the volume-related flow rate missing values and high-frequency oscillation intensity for different preset lung filling degrees. Based on the high-frequency oscillation intensity and volume-related flow rate missing values for different lung filling degrees within a preset range of preset lung filling degree, it obtains the coupling block integral. Finally, it obtains the painful respiratory baseline limitation coefficient for pain grading assessment and analgesia regimen optimization. This invention achieves pain grading assessment and optimizes analgesia regimens by accurately obtaining the painful respiratory baseline limitation coefficient.
[0069] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
[0070] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.
Claims
1. A thoracic surgery postoperative pain grading assessment and analgesic regimen optimization system, characterized in that, The system includes: The data acquisition module is used to acquire airway flow rate data at each moment, as well as the start and end times of inhalation. The feature decoupling module is used to obtain the total inspiratory volume for the inspiratory time range between adjacent inspiratory start and inspiratory end times, based on the distribution of airway flow rate data at different times, and to obtain the theoretical smoothed flow rate data and lung filling at each time within the inspiratory time range; based on the distribution characteristics of the theoretical smoothed flow rate data and airway flow rate data at different times, as well as the numerical characteristics of lung filling, it obtains the volume-related flow rate missing values and high-frequency oscillation intensity for different preset lung filling levels. The method for obtaining the theoretical smoothed flow velocity data includes: Based on the distribution of airway flow rate data at different times within the inspiratory time range, interpolated airway flow rate data for each time moment is obtained. Obtain the cumulative value of the airway flow rate interpolation data at all times, calculate the product of the cumulative value and the interval between adjacent times, and use it as the integration area; Obtain the ratio of total inspiratory volume to integral area, calculate the product of the ratio result and the interpolated airway flow rate data at each moment, and use it as the theoretical smooth flow rate data at each moment. The method for obtaining the high-frequency oscillation intensity includes: For airway flow rate data or theoretical smoothed flow rate data, the difference between the data at each time step and the previous time step is obtained as the rate of change of the data at each time step; the difference between the rate of change of the data at each time step and the previous time step is obtained as the data acceleration at each time step. The average difference in acceleration between theoretical smooth flow velocity data and airway flow velocity data at all target times is obtained as the high-frequency oscillation intensity corresponding to the preset lung filling degree. Iterate through all preset lung filling values. If there is no lung filling value equal to the preset lung filling value at any given time, perform linear interpolation based on the high-frequency oscillation intensity of other preset lung filling values to obtain the high-frequency oscillation intensity of the preset lung filling value. The pain grading assessment module is used to obtain the coupling block integral based on the high-frequency oscillation intensity of different lung filling degrees within a preset range of preset lung filling degree and the volume-related flow rate missing value; and to obtain the painful breathing baseline limitation coefficient based on the coupling block integral and the total inspiratory volume, and to perform pain grading assessment. The method for obtaining the coupling hysteresis integral includes: The maximum value of the high-frequency oscillation intensity among all preset lung filling values is selected, and the ratio of the high-frequency oscillation intensity of each preset lung filling value to the corresponding maximum value is obtained as the oscillation weighting coefficient. Gain adjustment is performed on the volume-related flow velocity missing value based on the oscillation weighting coefficient of each preset lung filling degree to obtain the degree of local coupling blockage of each preset lung filling degree. The cumulative value of the degree of local coupling blockage for all preset lung filling degrees within the preset range is obtained as the coupling blockage integral; The method for obtaining the baseline respiratory limitation coefficient for painful breathing includes: The product of the coupling blockade integral and the interval between adjacent time points is obtained as the blockade volume; the percentage of the blockade volume to the total inspiratory volume is obtained as the painful respiratory limitation coefficient. Select the other painful breathing restriction coefficients within the neighborhood of the latest painful breathing restriction coefficient, excluding the maximum and minimum values, and calculate the mean of all other painful breathing restriction coefficients as the painful breathing baseline restriction coefficient; The analgesia protocol optimization module is used to optimize analgesia protocols based on pain levels.
2. A postoperative pain grading assessment and analgesic regimen optimization system for thoracic surgery patients according to claim 1, characterized in that, The method for obtaining the total inhalation volume includes: The average value of airway flow rate data at adjacent time points is obtained as the local airway flow rate level; The cumulative value of the local airway flow rate level at all adjacent moments within the inspiratory time range is obtained as the overall airway flow rate level; The product of the overall airway flow rate level and the interval between adjacent time points is obtained as the total inspiratory volume.
3. A postoperative pain assessment and analgesic regimen optimization system for thoracic surgery patients according to claim 1, wherein, The method for obtaining the airway flow rate interpolation data includes: The moment when the airway flow rate data is at its maximum within the inspiratory time range is taken as the peak moment. Using time as the horizontal axis and airway flow rate data as the vertical axis, interpolation is performed based on the airway flow rate data corresponding to the start time, peak time, and end time of inspiration to obtain interpolated airway flow rate data for each time moment.
4. A postoperative pain assessment and analgesic regimen optimization system for thoracic surgery patients according to claim 2, wherein, The method for obtaining lung filling includes: For the inspiratory time range, the cumulative value of the local airway flow rate level of all adjacent time points within the historical range of each time point is obtained as the instantaneous airway flow rate level at each time point; the product of the instantaneous airway flow rate level and the interval between adjacent time points is obtained as the instantaneous cumulative volume at each time point. Obtain the percentage of instantaneous cumulative volume to total inspiratory volume at each moment, and round up to obtain the lung filling degree at each moment.
5. A postoperative pain assessment and analgesic regimen optimization system for thoracic surgery patients as defined in claim 1, wherein, The method for obtaining the missing volume-related flow velocity values includes: For any preset lung filling degree, if there exists a time when the lung filling degree is equal to the preset lung filling degree, the corresponding time is taken as the target time; the mean of the difference between the theoretical smoothed flow rate data and the airway flow rate data at all target times is obtained as the volume-related flow rate missing value for the corresponding preset lung filling degree. Iterate through all preset lung filling values. If there is no time when the lung filling value is equal to the preset lung filling value, perform linear interpolation based on the missing volume-related flow rate values of other preset lung filling values to obtain the missing volume-related flow rate values of the preset lung filling value.
6. A postoperative pain assessment and analgesic regimen optimization system for thoracic surgery patients as defined in claim 1, wherein, The pain grading assessment includes: If the pain-related respiratory baseline restriction coefficient is less than a preset first threshold, the patient is judged to be in the first level of pain. If the pain-related respiratory baseline limitation coefficient is greater than or equal to the preset first threshold and less than the preset second threshold, it is determined to be in the second pain level. If the pain-related respiratory baseline restriction coefficient is greater than or equal to a preset second threshold, the patient is judged to be in the third pain level; the preset first threshold is less than the preset second threshold.