Precise evaluation and intra-operative management optimization method for individualized optimal cardiac displacement in perioperative period

Abstract

The invention discloses a perioperative individualized optimal cardiac discharge volume accurate evaluation and intraoperative management optimization method, and belongs to the technical field of hemodynamic monitoring and management, and the method comprises the following steps: S1, adopting a multi-modal monitoring technology to carry out multi-physiological parameter acquisition on a patient meeting a group entering condition, and preprocessing the multi-physiological parameter acquisition; s2, analyzing the preprocessed data in ICM + software, and calculating threshold values of personalized parameters, including a parameter lower limit, a parameter optimal value and a parameter upper limit; s3, calculating a low perfusion degree and an over-perfusion degree; s4, analyzing the relationship between perfusion conditions corresponding to different parameter thresholds and different postoperative complications; according to the perioperative individual optimal cardiac discharge volume accurate evaluation and intraoperative management optimization method provided by the invention, an appropriate threshold value of a real-time hemodynamic management target parameter can be calculated in a short time by utilizing intraoperative data, a real-time calculation result is fed back, a doctor is reminded to pay attention to the condition change of a patient in time, and intervention is carried out when necessary.

Description

Technical Field

[0001] The present invention relates to the technical field of hemodynamic monitoring and management, and in particular to a method for accurately evaluating individualized optimal cardiac output during perioperative period and optimizing intraoperative management. Background Art

[0002] With the changes in human lifestyles and the increasing aging of the population, cardiovascular disease has gradually become the number one killer threatening human life and health. The number of deaths caused by cardiovascular disease each year accounts for more than 30% of the total global death toll.

[0003] Coronary heart disease (CAD), short for coronary atherosclerotic heart disease, is a type of heart disease characterized by the accumulation of cholesterol and other substances in the coronary artery walls, leading to narrowing or obstruction of the arterial wall and insufficient blood flow to the myocardium. Heart bypass surgery is the most effective treatment for CAD. This procedure creates a new pathway at either end of the narrowed area, allowing blood to continue flowing through the narrowed area, thereby improving myocardial ischemia and reducing the risk of myocardial infarction. The brain's ability to regulate itself during surgery can, to a certain extent, affect the risk of postoperative complications. Cerebral autoregulation is the brain's ability to maintain stable blood flow when arterial pressure fluctuates within a certain range, ensuring adequate oxygen supply to the brain. The cerebral autoregulation index (CAI) is a measure of CAI, calculated by correlation coefficients between specific physiological parameters. The CAI fluctuates between -1 and 1. Values ​​closer to 1 indicate more severe impairment of cerebral blood flow autoregulation, while values ​​closer to -1 indicate improved CAI. Mean arterial pressure (MAP) refers to the average value of arterial blood pressure during a cardiac cycle. It reflects the relationship between cardiac pumping and peripheral vascular resistance and is an important indicator of organ perfusion. The MAP range is approximately 70-105 mmHg. Cardiac output (CO) refers to the volume of blood pumped per minute by the left or right ventricle, and its magnitude influences the body's oxygen supply to a certain extent.

[0004] During heart bypass surgery, doctors usually lower the patient's arterial pressure to reduce collateral bleeding and provide a clearer field of vision. This can lead to insufficient cerebral perfusion and even inadequate perfusion of other organs, resulting in complications such as heart failure, ventricular fibrillation, and delirium after surgery, and even death in severe cases. The patient's various physiological parameters should be controlled within a range suitable for the patient. Controlling only one hemodynamic parameter during surgery is not sufficient to provide sufficient benefits to different patients undergoing heart bypass surgery. Therefore, multi-parameter hemodynamic management of patients during heart bypass surgery is extremely important, and physiological parameters such as MAP and CO should be controlled within an appropriate range.

[0005] Currently, during coronary bypass surgery, physicians perform traditional hemodynamic management based on historical experience. This approach fails to fully consider individual patient differences and the various changes that may occur during surgery, and cannot reflect the patient's personalized hemodynamic characteristics in real time. Intraoperative hypoperfusion or hyperperfusion increase the probability of a poor postoperative outcome. Therefore, we hope to provide reliable guidance for clinical practice through personalized hemodynamic management methods based on brain autoregulation, reduce the probability of poor postoperative outcomes, and offer new perspectives for the treatment of cardiovascular disease. Summary of the Invention

[0006] The purpose of the present invention is to provide a method for accurate assessment of individualized optimal cardiac output during perioperative period and optimization of intraoperative management, so as to solve the problems existing in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention provides a method for accurate assessment of individualized optimal cardiac output during perioperative period and optimization of intraoperative management, comprising the following steps:

[0008] S1. Use multimodal monitoring technology to collect multiple physiological parameters from patients who meet the inclusion criteria and perform preprocessing on them;

[0009] S2. Analyze the preprocessed data in ICM+ software and calculate the thresholds of personalized parameters, including the lower limit, optimal value, and upper limit of the parameters;

[0010] S3. Calculate the degree of hypoperfusion and hyperperfusion;

[0011] S4. Analyze the relationship between the perfusion conditions corresponding to different parameter thresholds and different postoperative complications.

[0012] Preferably, step S1 specifically includes:

[0013] S11, respectively read the data files of physiological parameters collected clinically by different devices, analyze the specific time of data collection according to the time column of the data file, convert the text timestamp into a time format, and convert the table into a time series table;

[0014] S12. Synchronize multiple tables converted into time series tables according to the timestamp column to generate data on the same time axis. Fill missing values ​​with NaN. Use a forward filling function to process NaNs generated during timestamp synchronization. Convert uncollected data points to NaNs and perform forward filling again to ensure data continuity.

[0015] S13. Use the outlier replacement method to replace values ​​with values ​​greater than 5000 with the standard missing value NaN, use forward filling to fill the standard missing values, and write the processed data into a csv file for subsequent data calculations.

[0016] Preferably, in step S2, the collected data is used to calculate the patient's brain autoregulation index - cerebral blood oxygen saturation index COx using ICM+ software. The calculation principle is: the moving Pearson correlation coefficient of the patient's regional cortical oxygen saturation and mean arterial pressure, the time window is 300 seconds, and the calculation update frequency is 10 seconds; the moving Pearson correlation coefficient of the regional cortical oxygen saturation and mean arterial pressure collected within 300 seconds is calculated to obtain a COx value, the 300-second time window is moved backward by 10 seconds, and the moving Pearson correlation coefficient is calculated based on the new data points obtained after sliding.

[0017] Preferably, the calculation of the personalized threshold value of center displacement CO in step S2 adopts a U-shaped curve algorithm, specifically:

[0018] With CO as the horizontal axis (the horizontal axis range is 1 to 8) and COx as the vertical axis, the horizontal axis was evenly divided into 16 intervals. The COx mean and standard deviation corresponding to all data points falling within the interval were calculated and plotted at the midpoint of the corresponding interval. The above points were fitted into a U-shaped curve. The intersection of COx = 0.35 and the U-shaped curve was defined as the patient's personalized threshold. The horizontal axis corresponding to the intersection of the COx = 0.35 line and the left side of the U-shaped curve was defined as the lower limit of cardiac output autoregulation (CO_LLA), the horizontal axis corresponding to the lowest point of the U-shaped curve was defined as the optimal parameter value (COopt), and the horizontal axis corresponding to the intersection of the COx = 0.35 line and the U-shaped curve was defined as the upper limit of cardiac output autoregulation (CO_ULA).

[0019] Preferably, the specific calculation method of step S3 is:

[0020] The area enclosed by the patient's actual physiological parameters and the parameter threshold is calculated using the infinitesimal method, with unit time Δt = 1 min;

[0021] For low perfusion levels, the area under the curve was calculated as the sum of the areas of the microcells, where the area of ​​each microcell is the product of the difference between CO_LLA, COopt and the corresponding actual CO and the unit time, using the formula:

[0022] AUC = ∑(CO_LLA-CO)*Δt;

[0023] AUC = ∑(COopt-CO)*Δt;

[0024] For the degree of overperfusion, the area under the curve is calculated as the sum of the areas of the micro-units, where the area of ​​each micro-unit is the product of the difference between CO_ULA and the corresponding actual CO and the unit time, using the formula:

[0025] AUC = ∑(CO_ULA-CO)*Δt;

[0026] The percentage of time the patient spends with low perfusion and high perfusion is calculated using the following formula:

[0027]

[0028] For the percentage of low perfusion time and the percentage of high perfusion time, t1 represents the time when the patient's actual CO is lower than CO_LLA and COopt, and the time when the patient's actual CO is higher than CO_ULA, respectively; t2 represents the duration of the operation.

[0029] Preferably, step S4 specifically includes:

[0030] Based on the analysis of the relationship between the patient's perfusion level and the occurrence of postoperative complications, maintaining the patient's CO at the COopt level during surgery is beneficial to reducing the probability of postoperative heart failure; CO_ULA is the threshold that increases the probability of atrial fibrillation, ventricular arrhythmia, and delirium; CO_LLA is the threshold that increases postoperative mortality.

[0031] Based on the analysis of CO threshold and postoperative complications, it is recommended to control CO within the range of COopt-CO_ULA during the perioperative period.

[0032] Therefore, the present invention adopts the above-mentioned perioperative individualized optimal cardiac output precise assessment and intraoperative management optimization method, which can use intraoperative data to calculate the appropriate threshold values ​​of real-time hemodynamic management target parameters in a short time, and feed back real-time calculation results to remind doctors to pay attention to changes in the patient's condition in a timely manner and intervene when necessary.

[0033] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a flow chart of the method for accurate assessment of individualized optimal cardiac output during perioperative period and optimization of intraoperative management according to the present invention;

[0035] Figure 2 A schematic diagram of a patient screening process according to an embodiment of the present invention;

[0036] Figure 3 Schematic diagram of the definition of personalized cardiac output threshold according to an embodiment of the present invention. DETAILED DESCRIPTION

[0037] The following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort shall fall within the scope of protection of the present invention.

[0038] See also Figure 1 , accurate assessment of individualized optimal cardiac output during perioperative period and optimization of intraoperative management, including the following steps:

[0039] S1. Use multimodal monitoring technology to collect multiple physiological parameters from patients who meet the inclusion criteria and perform preprocessing on them;

[0040] In this example, the clinical data of patients who underwent heart bypass surgery in a thoracic hospital were screened according to the following inclusion and exclusion criteria to select eligible patients. The process is as follows: Figure 2 shown.

[0041] Inclusion criteria: ① Age ≥ 18 years old; ② Underwent single or combined coronary artery bypass grafting surgery.

[0042] Exclusion criteria: ① Age < 18 years; ② Patients who have received lung or heart transplantation, have a ventricular assist device in their heart, or have kidney disease, lung disease, or heart failure before surgery; ③ Patients without recorded postoperative complications; ④ Patients with incomplete clinical information or collected data or too many missing data.

[0043] The collected information includes:

[0044] (1) Basic information: age, gender, medical history

[0045] (2) Intraoperative patient data: regional cortical oxygen saturation (rSO2), oxygen saturation, cardiac output, stroke volume, heart rate, mean arterial pressure, vascular resistance, pulse oximetry, tidal volume, and respiratory rate.

[0046] S11, respectively read the data files of physiological parameters collected clinically by different devices, analyze the specific time of data collection according to the time column of the data file, convert the text timestamp into a time format (yyyy-MM-dd HH:mm:ss), and convert the table into a time series table;

[0047] S12. Synchronize the multiple tables converted to time series tables along the timestamp column (synchronize function) to generate data on the same time axis (fill missing values ​​with NaN). Use the fillmissing function to perform forward filling to address NaNs generated during timestamp synchronization. Convert data points "\N" that were not collected during data collection to NaNs, and perform forward filling again to ensure data continuity. After the above processing, the data is resampled and missing value processing is completed.

[0048] S13. During the data collection process, poor patch contact, environmental noise, and other conditions can introduce significant outliers. For these outliers, we use a replacement method: replace values ​​exceeding 5000 with standard missing values ​​(NaN). We use forward filling to fill these missing values, and write the processed data to a CSV file for subsequent data calculations.

[0049] S2. Analyze the preprocessed data in ICM+ software and calculate the thresholds of personalized parameters, including the lower limit, optimal value, and upper limit of the parameters;

[0050] The collected data were used to calculate the patient's cerebral autoregulation index (CRO)—the cerebral oximetry index (COx)—using ICM+ software. The calculation principle is as follows: the moving Pearson correlation coefficient of the patient's regional cortical oxygen saturation and mean arterial pressure is calculated within a 300-second time window and updated every 10 seconds. The COx value is obtained by calculating the moving Pearson correlation coefficient of the regional cortical oxygen saturation and mean arterial pressure collected within 300 seconds. The 300-second time window is then shifted backward by 10 seconds, and the moving Pearson correlation coefficient is calculated based on the new data points obtained after sliding.

[0051] The calculation of the personalized threshold of cardiac output CO uses the U-curve algorithm, such as Figure 3 As shown, specifically:

[0052] With CO as the horizontal axis (ranging from 1 to 8) and COx as the vertical axis, the horizontal axis was evenly divided into 16 intervals. The mean and standard deviation of COx corresponding to all data points falling within the interval were calculated and plotted at the midpoint of the corresponding interval. The above points were fitted into a U-shaped curve. The intersection of COx = 0.35 and the U-shaped curve was defined as the patient's personalized threshold. The horizontal axis corresponding to the intersection of the COx = 0.35 line and the left side of the U-shaped curve was defined as the lower limit of cardiac output autoregulation (CO_LLA). The horizontal axis corresponding to the lowest point of the U-shaped curve was defined as the optimal cardiac output (COopt). The horizontal axis corresponding to the intersection of the COx = 0.35 line and the right side of the U-shaped curve was defined as the upper limit of cardiac output autoregulation (CO_ULA).

[0053] This method can calculate the patient's brain autoregulation ability in real time and promptly update the personalized parameter threshold adapted to the patient.

[0054] S3. Calculate the degree of hypoperfusion and hyperperfusion;

[0055] The calculated degree of hypoperfusion and hyperperfusion reflects the patient's cardiac function and the body's oxygen supply level. The area enclosed by the patient's actual physiological parameters and the parameter threshold is calculated using the microelement method, with a unit time of Δt = 1 minute;

[0056] For low perfusion levels, the area under the curve was calculated as the sum of the areas of the microcells, where the area of ​​each microcell is the product of the difference between CO_LLA, COopt and the corresponding actual CO and the unit time, using the formula:

[0057] AUC = ∑(CO_LLA-CO)*Δt;

[0058] AUC = ∑(COopt-CO)*Δt;

[0059] For the degree of overperfusion, the area under the curve is calculated as the sum of the areas of the micro-units, where the area of ​​each micro-unit is the product of the difference between CO_ULA and the corresponding actual CO and the unit time, using the formula:

[0060] AUC = ∑(CO_ULA-CO)*Δt;

[0061] The percentage of time the patient spends with low perfusion and high perfusion is calculated using the following formula:

[0062]

[0063] For the percentage of low perfusion time and the percentage of high perfusion time, t1 represents the time when the patient's actual CO is lower than CO_LLA and COopt, and the time when the patient's actual CO is higher than CO_ULA, respectively; t2 represents the duration of the operation.

[0064] S4. Analyze the relationship between the perfusion conditions corresponding to different parameter thresholds and different postoperative complications.

[0065] GraphPad Prism (version 8.3) was used to analyze AUC, time%, and postoperative complications. Patients were divided into a group with or without postoperative complications, using postoperative heart failure, ventricular arrhythmia, atrial fibrillation, delirium, and death as outcome measures. Data within each group were compared using a logistic regression model adjusted for age, operative time, and EuroScore. All analyses were performed using t-tests for normal distribution, and data are presented as mean ± SD. Non-normal distributions were analyzed using nonparametric tests with a 95% confidence interval (CI). P values ​​< 0.05 were considered statistically significant.

[0066] Based on the analysis of the relationship between the patient's perfusion level and the occurrence of postoperative complications, maintaining the patient's CO at the COopt level during surgery is beneficial to reducing the probability of postoperative heart failure; CO_ULA is the threshold that increases the probability of atrial fibrillation, ventricular arrhythmia, and delirium; CO_LLA is the threshold that increases postoperative mortality.

[0067] Based on the analysis of CO threshold and postoperative complications, it is recommended to control CO within the range of COopt-CO_ULA during the perioperative period.

[0068] Therefore, the present invention adopts the above-mentioned perioperative individualized optimal cardiac output precise assessment and intraoperative management optimization method, which can use intraoperative data to calculate the appropriate threshold values ​​of real-time hemodynamic management target parameters in a short time, and feed back real-time calculation results to remind doctors to pay attention to changes in the patient's condition in a timely manner and intervene when necessary.

[0069] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. Accurate assessment of individualized optimal cardiac output during perioperative period and optimization of intraoperative management, characterized by: The following steps are involved: S1. Use multimodal monitoring technology to collect multiple physiological parameters from patients who meet the inclusion criteria and perform preprocessing on them; S2. Analyze the preprocessed data in ICM+ software and calculate the thresholds of personalized parameters, including the lower limit, optimal value, and upper limit of the parameters; S3. Calculate the degree of hypoperfusion and hyperperfusion; S4. Analyze the relationship between the perfusion conditions corresponding to different parameter thresholds and different postoperative complications.

2. The method for accurate assessment of individualized optimal cardiac output during perioperative period and optimization of intraoperative management according to claim 1, characterized in that: Step S1 specifically includes: S11, respectively read the data files of physiological parameters collected clinically by different devices, analyze the specific time of data collection according to the time column of the data file, convert the text timestamp into a time format, and convert the table into a time series table; S12. Synchronize multiple tables converted into time series tables according to the timestamp column to generate data on the same time axis. Fill missing values ​​with NaN. Use a forward filling function to process NaNs generated during timestamp synchronization. Convert uncollected data points to NaNs and perform forward filling again to ensure data continuity. S13. Use the outlier replacement method to replace values ​​with values ​​greater than 5000 with the standard missing value NaN, use forward filling to fill the standard missing values, and write the processed data into a csv file for subsequent data calculations.

3. The method for accurate assessment of individualized optimal cardiac output during perioperative period and optimization of intraoperative management according to claim 1, characterized in that: In step S2, the collected data are used to calculate the patient's cerebral autoregulation index (COx) using ICM+ software. The calculation principle is: the moving Pearson correlation coefficient of the patient's regional cortical oxygen saturation and mean arterial pressure, with a time window of 300 seconds and a calculation update frequency of 10 seconds; The moving Pearson correlation coefficient of regional cortical oxygen saturation and mean arterial pressure collected within 300 seconds was calculated to obtain a COx value. The 300-second time window was shifted backward by 10 seconds, and the moving Pearson correlation coefficient was calculated based on the new data points obtained after sliding.

4. The method for accurate assessment of individualized optimal cardiac output during perioperative period and optimization of intraoperative management according to claim 3, characterized in that: In step S2, the calculation of the personalized threshold value of center displacement CO is performed using a U-shaped curve algorithm, specifically: With CO as the horizontal axis (ranging from 1 to 8) and COx as the vertical axis, the horizontal axis was evenly divided into 16 intervals. The COx mean and standard deviation corresponding to all data points falling within the interval were calculated and plotted at the midpoint of the corresponding interval. The above points were fitted into a U-shaped curve. The intersection of COx = 0.35 and the U-shaped curve was defined as the patient's personalized threshold. The horizontal axis corresponding to the intersection of the COx = 0.35 line and the left side of the U-shaped curve was defined as the lower limit of cardiac output automatic regulation CO_LLA. The horizontal axis corresponding to the lowest point of the U-shaped curve was defined as the optimal parameter value COopt. The horizontal axis corresponding to the intersection of the COx = 0.35 line and the U-shaped curve was defined as the upper limit of cardiac output automatic regulation CO_ULA.

5. The method for accurate assessment of individualized optimal cardiac output during perioperative period and optimization of intraoperative management according to claim 4, characterized in that: The specific calculation method of step S3 is: The area enclosed by the patient's actual physiological parameters and the parameter threshold is calculated using the infinitesimal method, with unit time Δt = 1 min; For low perfusion levels, the area under the curve was calculated as the sum of the areas of the microcells, where the area of ​​each microcell is the product of the difference between CO_LLA, COopt and the corresponding actual CO and the unit time, using the formula: AUC = ∑(CO_LLA-CO)*Δt; AUC = ∑(COopt-CO)*Δt; For the degree of overperfusion, the area under the curve is calculated as the sum of the areas of the micro-units, where the area of ​​each micro-unit is the product of the difference between CO_ULA and the corresponding actual CO and the unit time, using the formula: AUC = ∑(CO_ULA-CO)*Δt; The percentage of time the patient spends with low perfusion and high perfusion is calculated using the following formula: For the percentage of low perfusion time and the percentage of high perfusion time, t1 represents the time when the patient's actual CO is lower than CO_LLA and COopt, and the time when the patient's actual CO is higher than CO_ULA, respectively; t2 represents the duration of the operation.

6. The method for accurate assessment of individualized optimal cardiac output during perioperative period and optimization of intraoperative management according to claim 5, characterized in that: Step S4 specifically includes: Based on the analysis of the relationship between the patient's perfusion level and the occurrence of postoperative complications, maintaining the patient's CO at the COopt level during surgery is beneficial to reducing the probability of postoperative heart failure; CO_ULA is the threshold that increases the probability of atrial fibrillation, ventricular arrhythmia, and delirium; CO_LLA is the threshold that increases postoperative mortality. Based on the analysis of CO threshold and postoperative complications, it is recommended to control CO within the range of COopt-CO_ULA during the perioperative period.

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