Anesthesia preoperative risk assessment method and system

Abstract

The invention relates to the technical field of anesthesia patient physiological monitoring, in particular to an anesthesia preoperative risk assessment method and system, which continuously acquire physiological parameters of an anesthesia patient in a resting state, monitor physiological parameter changes in a dynamic physiological load state, screen physiological parameters exceeding a steady state range and generate a preoperative physiological monitoring data set. According to the method, the individual physiological steady state range is set through data comparison between the resting state and the dynamic physiological load state, accurate screening of parameters such as the heart rate, the blood oxygen saturation degree and the blood pressure is achieved, and the monitoring accuracy is improved. In combination with a load state fluctuation value, a resting mean value and a weight parameter, an individual physiological regulation range is calculated, so that evaluation is more personalized. And the preoperative risk adjustment coefficient is dynamically calculated, so that the risk assessment has the real-time adjustment capability. The heart rate recovery time, the blood oxygen fluctuation amplitude and the blood pressure recovery time are introduced, the physiological load ratio and dynamic load index classification are combined, and the risk assessment fineness is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of physiological monitoring of anesthesia patients, and in particular to a method and system for pre-anesthesia risk assessment. Background Art

[0002] The technical field of physiological monitoring of anesthesia patients involves real-time or periodic measurement and analysis of the patient's physiological parameters at different stages before, during, and after anesthesia to ensure the safety and controllability of the anesthesia process. The core content of this technical field includes the collection of physiological signals, data analysis, and risk prediction. By monitoring key indicators such as the patient's blood pressure, heart rate, blood oxygen saturation, and respiratory rate, their tolerance to anesthesia and potential risks are assessed. The overall technical field covers the standardized collection of physiological data, the combined analysis of historical medical history and real-time physiological signals, patient risk assessment methods based on data characteristics, and personalized management strategies under different anesthesia schemes to ensure the scientificity and accuracy of anesthesia decisions.

[0003] Among them, the pre-anesthesia risk assessment method refers to a procedure used to systematically analyze and predict the medical risks that patients may face before receiving anesthesia. This method collects the patient's basic physiological data, including blood pressure, heart rate, blood oxygen saturation, and lung function indicators, and combines previous anesthesia history, allergy history, cardiovascular disease history, and drug use to comprehensively assess preoperative risks. In the data processing link, this method uses principal component analysis or linear discriminant analysis to reduce the dimensionality of multidimensional physiological data to reduce redundant information, and extracts the patient's dynamic physiological characteristics based on time series analysis, such as parameters such as heart rate variability and blood pressure volatility. For the collected physiological indicators, this method matches the risk grading standards set by the clinical anesthesia risk assessment guidelines, calculates and classifies the patient's risk level, and common risk levels include low, medium, and high risk.

[0004] Existing technologies mainly rely on static physiological indicators and past medical history for preoperative risk assessment, lack of real-time analysis of dynamic physiological changes, and it is difficult to fully reflect the patient's preoperative physiological adaptability. The assessment of physiological parameters is based only on single or periodic measurements, ignoring physiological fluctuations under different states, resulting in individual differences in patients being difficult to accurately capture. Risk grading methods usually rely on fixed classification standards and fail to make dynamic adjustments based on the patient's individual physiological regulation range, which may lead to risk assessment deviations. For the processing of physiological signals, existing technologies mostly use dimensionality reduction analysis methods, but do not fully combine time series data, making it difficult to accurately characterize the physiological load response characteristics of patients in the preoperative state. Existing risk prediction relies on the matching of historical data, insufficient use of real-time monitoring data, and lack of individualized risk analysis for the preoperative state, which may lead to the underestimation or overestimation of preoperative risks for some patients, affecting the accurate formulation of anesthesia plans. Summary of the Invention

[0005] In order to solve the technical problems existing in the prior art, the embodiment of the present invention provides a method and system for pre-anesthesia risk assessment. The technical solution is as follows:

[0006] A method for risk assessment before anesthesia surgery, comprising the following steps:

[0007] S1: Continuously obtain physiological parameters of anesthetized patients in a resting state, monitor changes in physiological parameters under dynamic physiological load, screen physiological parameters that exceed the steady-state range, and generate a preoperative physiological monitoring data set;

[0008] S2: Based on the preoperative physiological monitoring data set, the maximum fluctuation value of the physiological parameters under load is compared with the resting mean value, the adaptability level is divided, the physiological regulation index is calculated, and the individual physiological regulation range is set with reference to the weight parameter to obtain the preoperative physiological regulation ability analysis result.

[0009] S3: Based on the preoperative physiological regulation ability analysis result, calling the patient's physiological regulation range, calculating the preoperative risk dynamic adjustment coefficient, and generating the patient's preoperative risk adjustment result;

[0010] S4: Based on the preoperative risk adjustment results and preoperative physiological monitoring data, assess the heart rate recovery time, blood oxygen fluctuation amplitude, and blood pressure recovery time, calculate the physiological load ratio, set the dynamic physiological load index and classify and mark it, and generate a preoperative physiological load classification result;

[0011] S5: Based on the preoperative physiological load classification results, calculate the preoperative risk factors, screen and adjust the priorities in combination with the changing trend of the dynamic physiological load index, divide the risk levels, and generate preoperative individualized risk assessment results.

[0012] The present invention has improvements in that the preoperative physiological monitoring data set includes the heart rate change rate, blood oxygen saturation change range, and blood pressure change rate; the preoperative physiological regulation ability analysis results include the adaptability level of physiological parameters, the physiological regulation index, and the individual physiological regulation range; the patient's preoperative risk adjustment results include the preoperative risk dynamic adjustment coefficient and the patient's preoperative risk adjustment value; the preoperative physiological load classification results include the dynamic physiological load index, the load state classification label, and the physiological load ratio; the preoperative individualized risk assessment results include the preoperative risk factor, the physiological load index change trend, and the risk level.

[0013] The present invention has been improved in that the specific steps of continuously acquiring physiological parameters of anesthetized patients in a resting state, monitoring changes in physiological parameters under a dynamic physiological load state, screening physiological parameters that exceed the steady-state range, and generating a preoperative physiological monitoring data set are as follows:

[0014] S101: Continuously obtain the heart rate, blood pressure, blood oxygen saturation, and weight parameters of the anesthetized patient at rest, calculate the mean value of each physiological parameter, set the physiological steady-state range based on the upper and lower floating ranges of the mean values corresponding to each physiological parameter, calibrate the range as the baseline limit of the patient's physiological parameter fluctuations at rest, and construct a resting physiological baseline range;

[0015] S102: Based on the resting physiological baseline interval, the patient's heart rate, blood oxygen saturation, and blood pressure are monitored in real time under a dynamic physiological load state, a variation range of each monitored physiological parameter within a specified time period is calculated, and a physiological parameter whose variation range exceeds a preset variation threshold of the resting physiological baseline interval is marked as an abnormal variation parameter, thereby generating abnormal physiological load data;

[0016] S103: Based on the abnormal physiological load data, physiological parameters that are within the physiological steady-state range and do not exceed the change threshold of the resting physiological reference interval are eliminated, and abnormal parameters are retained as preoperative evaluation data to obtain a preoperative physiological monitoring data set.

[0017] The present invention has been improved in that, based on the preoperative physiological monitoring data set, the maximum fluctuation value of the physiological parameter under load is compared with the resting mean value, the adaptability level is divided, the physiological regulation index is calculated, and the individual physiological regulation range is set with reference to the weight parameter. The specific steps for obtaining the preoperative physiological regulation ability analysis result are as follows:

[0018] S201: Based on the preoperative physiological monitoring data set, the mean value of each physiological parameter in the resting state is retrieved, the maximum fluctuation value of the corresponding physiological parameter under the load state is extracted, the fluctuation amplitude of each physiological parameter is calculated, and the adaptive ability level is determined based on a preset fluctuation comparison threshold to obtain a physiological parameter fluctuation level distribution;

[0019] S202: Based on the physiological parameter fluctuation level distribution, a corresponding weight coefficient is set for each physiological parameter, a weighted fluctuation index of each physiological parameter is calculated, and the weighted fluctuation index is converted into a physiological regulation index of the corresponding physiological parameter. The physiological regulation range of the individual patient is set with reference to the individual patient's weight parameter to obtain the individual physiological regulation range;

[0020] S203: According to the individual physiological regulation range, the deviation degree of the measured value of each physiological parameter within the physiological regulation range is dynamically adjusted through real-time monitoring data, the current physiological regulation ability index is updated, and the preoperative physiological regulation ability analysis result is obtained.

[0021] The present invention has the following improvements: for calculating the physiological regulation index, the formula is used:

[0022] PI raw =WI HR +WI BP +WISpO2 ;

[0023] Among them, PI raw is the patient's physiological regulation index, WI HR is the patient's heart rate fluctuation amplitude, WI BP is the patient's blood pressure fluctuation amplitude, WI SpO2 is the fluctuation amplitude of the patient's blood oxygen saturation;

[0024] For calculation of individual physiological regulation range, the formula is used:

[0025] PI adjusted =PI raw ×TW;

[0026] Among them, PI adjusted is the individual physiological regulation range, and TW is the correction coefficient of the reference body weight parameter.

[0027] The present invention is improved in that, based on the preoperative physiological regulation ability analysis results, the patient's physiological regulation range is called, and the preoperative risk dynamic adjustment coefficient is calculated. The specific steps of generating the patient's preoperative risk adjustment result are as follows:

[0028] S301: Based on the preoperative physiological regulation ability analysis result, call the patient's physiological parameters within the current physiological steady-state range, calculate the proportion of each physiological parameter within the corresponding physiological regulation range, and construct a physiological regulation data set;

[0029] S302: Based on the physiological regulation data set, comparing the ratio deviation of each physiological parameter, selecting the maximum deviation ratio as a weight factor, and using the weight factor to perform weighted normalization processing on all ratios to obtain a preoperative risk adjustment coefficient;

[0030] S303: Based on the preoperative risk adjustment coefficient, the preoperative physiological regulation range of the individual patient is evaluated, the sensitivity index of the preoperative physiological parameters to the physiological regulation range is analyzed, the adjusted risk coefficient is applied to the individual patient's preoperative physiological parameters, and the adjusted risk weight ratio of each sensitivity index is calculated to obtain the patient's preoperative risk adjustment result.

[0031] The present invention has been improved in that, based on the preoperative risk adjustment results and preoperative physiological monitoring data, heart rate recovery time, blood oxygen fluctuation amplitude, and blood pressure recovery time are evaluated, the physiological load ratio is calculated, a dynamic physiological load index is set and classified and marked, and the specific steps of generating preoperative physiological load classification results are as follows:

[0032] S401: Based on the patient's preoperative risk adjustment result, the heart rate data, blood oxygen data, and blood pressure data under a dynamic physiological load state are retrieved to obtain the time interval between the heart rate and blood pressure recovery starting point and the stable point, as well as the difference between the maximum and minimum blood oxygen values, to obtain the physiological parameter recovery interval;

[0033] S402: Calculating a physiological load ratio based on the physiological parameter recovery interval, adjusting the ratio range by using the ratios of heart rate recovery time, blood oxygen fluctuation amplitude, and blood pressure recovery time, setting a dynamic physiological load index that matches the target patient, and obtaining a dynamic physiological load result for the patient;

[0034] S403: Based on the patient's dynamic physiological load results, the load classification standard is set with reference to the preoperative physiological monitoring data set, the heart rate recovery time distribution range, blood oxygen fluctuation range and blood pressure recovery time distribution range are screened, the distribution ratio of each physiological parameter at each physiological load level is calculated, the patient's load status is classified and marked, and the preoperative physiological load classification result is obtained.

[0035] The present invention has been improved in that, based on the preoperative physiological load classification results, the preoperative risk factors are calculated, and the priority is screened and adjusted in combination with the changing trend of the dynamic physiological load index, and the risk level is divided to generate the preoperative individualized risk assessment results. The specific steps are as follows:

[0036] S501: Based on the preoperative physiological load classification result, the preoperative risk dynamic adjustment coefficient and dynamic physiological load index of the corresponding patient are called to calculate the preoperative risk factor within the patient's physiological regulation range, the numerical variation range of the risk factor in each physiological load category is extracted, and the risk factor is aggregated into each physiological load state to obtain a preoperative risk factor set;

[0037] S502: Based on the preoperative risk factor set, calculate the risk ratio of each risk factor to the corresponding patient's preoperative physiological adjustment range, analyze the change trend of the risk factor in each physiological load category, screen the change characteristics of the risk factor in the time series, adjust the ranking sequence of the risk factor according to the change trend, and obtain the risk factor ranking result;

[0038] S503: Based on the risk factor ranking results, classification criteria are set with reference to the changing trends of the risk factors, the distribution of risk factors within each category is evaluated, the risk factors are divided into corresponding grade categories, and the risk grades are mapped to the patient's preoperative risk assessment system to obtain preoperative individualized risk assessment results.

[0039] The present invention has the following improvements: for calculating the preoperative risk factor R factor,init , using the formula:

[0040]

[0041] Among them, R adjust represents the preoperative risk dynamic adjustment coefficient, L dynamic represents the dynamic physiological load index, R baseline,static The resting baseline value representing the patient's preoperative physiological regulation range, Rvar,i represents the physiological parameter deviation of the i-th risk factor, T delay time-lagged parameters representing changes in patients' preoperative risk factors;

[0042] For each risk factor R factor,mod The risk ratio R of the corresponding patient's preoperative physiological adjustment range ratio , using the formula:

[0043]

[0044] Among them, R factor,mod represents the modified preoperative risk factor, R factor,mod,j represents the value of the modified preoperative risk factor, R baseline,dynamic,j Represents the dynamic baseline value of the preoperative physiological regulation range, T diff,j Represents the time difference of preoperative risk factors.

[0045] A pre-anesthesia risk assessment system, comprising:

[0046] The physiological monitoring data acquisition module obtains the patient's heart rate, blood pressure, blood oxygen saturation and weight at rest, calculates the rate of change of physiological parameters, filters out parameters that exceed the physiological steady-state range, and generates a preoperative physiological monitoring data set;

[0047] The individual physiological regulation ability analysis module calculates the maximum fluctuation value of the physiological parameters under load based on the preoperative physiological monitoring data set, compares it with the mean value in the resting state to determine the degree of fluctuation, divides the adaptability level according to the preset fluctuation comparison threshold, sets the weight coefficient of each physiological parameter, calculates the physiological regulation index, calculates the individual physiological regulation range in combination with the patient's weight parameter, and adjusts the physiological regulation range in combination with the real-time monitoring data to generate the preoperative physiological regulation ability analysis results;

[0048] The preoperative risk dynamic adjustment module extracts the ratio of the parameter within the physiological steady-state range to the physiological regulation range based on the preoperative physiological regulation ability analysis result, calculates the preoperative risk dynamic adjustment coefficient, and generates the patient's preoperative risk adjustment result;

[0049] The preoperative physiological load classification module calculates the heart rate recovery time, blood oxygen fluctuation amplitude, and blood pressure recovery time based on the patient's preoperative risk adjustment results and preoperative physiological monitoring data set, sets a dynamic physiological load index and classifies and marks it, and generates a preoperative physiological load classification result;

[0050] The preoperative individualized risk assessment module calculates preoperative risk factors based on the preoperative physiological load classification results, screens risk factors with larger dynamic physiological load index change trends, divides risk levels, and generates preoperative individualized risk assessment results.

[0051] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0052] In the present invention, by comparing the data of the resting state and the dynamic physiological load state, the individual physiological steady-state range is set, and the accurate screening of parameters such as heart rate, blood oxygen saturation, and blood pressure is achieved, thereby improving the monitoring accuracy. The individual physiological regulation range is calculated in combination with the load state fluctuation value, the resting mean and the weight parameters, making the assessment more personalized. The preoperative risk adjustment coefficient is dynamically calculated to enable the risk assessment to have real-time adjustment capabilities. The heart rate recovery time, blood oxygen fluctuation amplitude and blood pressure recovery time are introduced, combined with the physiological load ratio and dynamic load index classification to improve the precision of risk assessment. The risk factor priority is screened and adjusted according to the load index change trend, and the risk level is divided according to the degree of change to enhance the accuracy of preoperative risk prediction. The solution integrates multi-dimensional physiological parameter analysis, physiological regulation ability grading, and real-time risk adjustment to make preoperative assessment more accurate and effectively improve the individualized risk prediction capability. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0054] Figure 1 is a flow chart of the method of the present invention;

[0055] Figure 2 This is a detailed flow chart of step S1 of the present invention;

[0056] Figure 3 This is a schematic diagram of a detailed process of step S2 of the present invention;

[0057] Figure 4 This is a detailed flow chart of step S3 of the present invention;

[0058] Figure 5 This is a detailed flow chart of step S4 of the present invention;

[0059] Figure 6 This is a detailed flow chart of step S5 of the present invention. DETAILED DESCRIPTION

[0060] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0061] See also Figure 1 The present invention provides a technical solution: a method for risk assessment before anesthesia, comprising the following steps:

[0062] S1: Continuously obtain physiological parameters of anesthetized patients in a resting state, monitor changes in physiological parameters under dynamic physiological load, screen physiological parameters that exceed the steady-state range, and generate a preoperative physiological monitoring data set;

[0063] S2: Based on the preoperative physiological monitoring data set, the maximum fluctuation value of the physiological parameters under load is compared with the resting mean, the adaptability level is divided, the physiological regulation index is calculated, and the individual physiological regulation range is set with reference to the weight parameter to obtain the preoperative physiological regulation ability analysis results.

[0064] S3: Based on the preoperative physiological regulation ability analysis results, call the patient's physiological regulation range, calculate the preoperative risk dynamic adjustment coefficient, and generate the patient's preoperative risk adjustment result;

[0065] S4: Based on the preoperative risk adjustment results and preoperative physiological monitoring data, evaluate the heart rate recovery time, blood oxygen fluctuation amplitude, and blood pressure recovery time, calculate the physiological load ratio, set the dynamic physiological load index and classify it, and generate the preoperative physiological load classification results;

[0066] S5: Based on the preoperative physiological load classification results, calculate the preoperative risk factors, combine the changing trend of the dynamic physiological load index to screen and adjust the priority, divide the risk level, and generate the preoperative individualized risk assessment results.

[0067] The preoperative physiological monitoring data set includes the heart rate change rate, blood oxygen saturation change range, and blood pressure change rate. The preoperative physiological regulation ability analysis results include the adaptability level of physiological parameters, the physiological regulation index, and the individual physiological regulation range. The patient's preoperative risk adjustment results include the preoperative risk dynamic adjustment coefficient and the patient's preoperative risk adjustment value. The preoperative physiological load classification results include the dynamic physiological load index, the load state classification label, and the physiological load ratio. The preoperative individualized risk assessment results include the preoperative risk factor, the physiological load index change trend, and the risk level.

[0068] See also Figure 2 The specific steps for continuously acquiring physiological parameters of anesthetized patients in a resting state, monitoring changes in physiological parameters under dynamic physiological load, screening physiological parameters that exceed the steady-state range, and generating a preoperative physiological monitoring data set are as follows:

[0069] S101: Continuously obtain the heart rate, blood pressure, blood oxygen saturation, and weight parameters of the anesthetized patient at rest, calculate the mean value of each physiological parameter, set the physiological steady-state range based on the upper and lower floating ranges of the mean values corresponding to each physiological parameter, calibrate the range as the baseline limit of the patient's physiological parameter fluctuations at rest, and construct a resting physiological baseline range;

[0070] Continuously obtain the heart rate, blood pressure, blood oxygen saturation, and weight parameters of anesthetized patients in the resting state. For the collection process of these physiological parameters, the monitoring device (such as a multi-parameter monitor) is first connected to the patient and the data collection interval is set, for example, data is recorded every 5 minutes. During the data collection process, the monitoring device will measure the heart rate (unit: bpm), systolic and diastolic blood pressure (unit: mmHg), blood oxygen saturation (unit: %), and weight (unit: kg). The collection time point of each physiological parameter must be recorded uniformly to ensure data synchronization for subsequent analysis. After the collected data is stored in the database, the mean of each physiological parameter is calculated for a specific time window (such as 6 hours, 12 hours, and 24 hours). The mean is calculated using the arithmetic mean method, that is, all measured values in the time window are summed and divided by the number of measurements. For example, if the heart rate values of a patient measured within 6 hours are 72, 75, 78, 74, 76, and 73 bpm, respectively, the mean is calculated as follows:

[0071]

[0072] Similarly, the mean values of blood pressure, blood oxygen saturation, and weight can be calculated. After obtaining the mean values of each physiological parameter, the physiological steady-state range is set based on medical statistical data and individual physiological characteristics. The setting method is based on standard deviation calculation, that is, the standard deviation σ is calculated based on the patient's historical physiological parameter data, and the mean ±1.96σ is used as the 95% confidence interval to ensure that most data within the normal fluctuation range will not be misclassified as abnormal. For example, if the standard deviation of the patient's heart rate measurement data at rest in the past week is 2.5bpm, the heart rate steady-state range is calculated as follows:

[0073] HR range =[HR mean -1.96×σ,HR mean +1.96×σ];

[0074] After substituting the values:

[0075] HR range =[74.67-1.96×2.5,74.67+1.96×2.5]=[69.8,79.5]bpm;

[0076] The physiological steady-state ranges of blood pressure, blood oxygen saturation, and weight are also set in this way to ensure that the calculated results are consistent with the physiologically reasonable range and avoid misjudgments caused by individual differences. Ultimately, the baseline limits of all physiological parameters are stored in the database to form a resting physiological baseline range.

[0077] S102: Based on the resting physiological baseline interval, the patient's heart rate, blood oxygen saturation, and blood pressure are monitored in real time under a dynamic physiological load state, and the variation range of each monitored physiological parameter within a specified time period is calculated. Physiological parameters whose variation range exceeds a preset variation threshold of the resting physiological baseline interval are marked as abnormal variation parameters, thereby generating abnormal physiological load data;

[0078] Based on the resting physiological baseline interval, the patient's heart rate, blood oxygen saturation, and blood pressure are monitored in real time under dynamic physiological load conditions. During surgery or preoperative evaluation, the patient's physiological load state may fluctuate due to activity, stress, or medication. Therefore, the data collection frequency needs to be appropriately increased, for example, adjusted to once every 1 minute. During each measurement cycle, the system reads the current heart rate, blood oxygen saturation, and blood pressure values and stores them in the database. The variation range of each monitored physiological parameter is calculated within a specified time period (such as 30 minutes), that is, the maximum and minimum values within the time period are obtained, and the variation range is calculated. The calculation formula is as follows:

[0079] ΔHR=HR max -HR min ;

[0080] For example, within 30 minutes, a patient's heart rate fluctuation range is recorded as [78, 85, 83, 80, 82, 79, 81] bpm. The maximum and minimum values are:

[0081] HR max =85, HR min =78;

[0082] The change range is calculated as follows:

[0083] ΔHR=85-78=7bpm;

[0084] For blood oxygen saturation and blood pressure, their fluctuation ranges are similarly calculated. Physiological parameters whose fluctuation ranges exceed the preset change thresholds within the resting physiological baseline range are marked as abnormally changing parameters. The change threshold is set based on historical monitoring data and medical guidelines, and is usually defined as 10% of the mean physiological steady-state range, or as a reference to the statistical fluctuation range under a patient's specific state. For example, the standard deviation σ is calculated based on the past 24 hours of data, and the threshold is set at 2σ to cover more than 95% of normal physiological fluctuation values. If the heart rate baseline range is [69.8, 79.5] bpm, its change threshold is calculated as follows:

[0085] HR threshold=2×σ=2×2.5=5bpm;

[0086] If the calculated variation of 7bpm exceeds 5bpm, the patient's heart rate fluctuation is marked as abnormal, otherwise it is considered to be within the normal range. All abnormal variation parameters are stored in the database and abnormal physiological load data are generated.

[0087] S103: Based on the abnormal physiological load data, physiological parameters that are within the physiological steady-state range and do not exceed the change threshold of the resting physiological reference interval are eliminated, and abnormal parameters are retained as preoperative evaluation data to obtain a preoperative physiological monitoring data set;

[0088] Based on abnormal physiological load data, physiological parameters that are within the physiological steady-state range and do not exceed the change threshold of the resting physiological baseline interval are eliminated. That is, for each parameter, check whether it is within the steady-state range. If it is within the steady-state range and the change amplitude does not exceed the threshold, it is excluded and not counted. For example, the aforementioned heart rate data is considered to be within the physiological steady-state range because the change amplitude does not exceed the threshold of 5bpm. If a patient's blood oxygen saturation baseline interval is [95.0, 99.0]%, but the actual monitoring data shows a minimum value of 91.0% within 30 minutes, the change amplitude is calculated as follows:

[0089] ΔSpO2=99.0-91.0=8.0%;

[0090] If the blood oxygen saturation change threshold is set to 4.2%, the standard deviation σ of the threshold is calculated based on the patient's data from the previous week, and 2σ is taken as the change range threshold, that is:

[0091] SpO2threshold=2×σ=2×2.1=4.2%;

[0092] 8.0% is far beyond the threshold range, and the parameter is marked as abnormal. All abnormal parameters are stored in the database as preoperative evaluation data, and a preoperative physiological monitoring data set is constructed. The specific data examples are as follows:

[0093] Table 1 Preoperative physiological monitoring data

[0094]

[0095] As shown in Table 1, the fluctuation ranges of heart rate, blood pressure, and blood oxygen saturation all exceed the thresholds and are therefore marked as abnormal and stored in the preoperative physiological monitoring data set. The threshold setting is strictly based on historical monitoring data, avoiding misjudgments caused by fixed empirical values and improving data accuracy. Ultimately, this data set is used for preoperative evaluation to determine whether the patient has an abnormal physiological load state.

[0096] See also Figure 3Based on the preoperative physiological monitoring data set, the maximum fluctuation value of the physiological parameters under load is compared with the resting mean, the adaptability level is divided, the physiological regulation index is calculated, and the individual physiological regulation range is set with reference to the weight parameter. The specific steps for obtaining the preoperative physiological regulation ability analysis results are as follows:

[0097] S201: Based on the preoperative physiological monitoring data set, the mean value of each physiological parameter in the resting state is retrieved, the maximum fluctuation value of the corresponding physiological parameter under the load state is extracted, the fluctuation amplitude of each physiological parameter is calculated, and the adaptive ability level is determined based on the preset fluctuation comparison threshold to obtain the physiological parameter fluctuation level distribution;

[0098] Based on the preoperative physiological monitoring data set, the mean value of each physiological parameter in the resting state is called, and the baseline values of the patient's heart rate (HR), blood pressure (BP), blood oxygen saturation (SpO2) and other key physiological parameters are read from the database, that is, the mean value of each physiological parameter in the resting state X μ and extract the maximum fluctuation value X of the corresponding physiological parameter under load state from the preoperative dynamic monitoring data max and the minimum fluctuation value X min , calculate the fluctuation amplitude ΔX of the physiological parameters as the fluctuation index, that is:

[0099] ΔX=X max -X min ;

[0100] For example, in the preoperative monitoring data of a patient, the maximum and minimum values of heart rate under dynamic physiological load are HR max =110bpm and HR min =78bpm, then the heart rate fluctuation amplitude is calculated as follows:

[0101] ΔHR=110-78=32bpm;

[0102] Similarly, if a patient's systolic blood pressure range is SBP max =145 mmHg and SBP min =115mmHg, then the blood pressure fluctuation range is: ΔSBP=145-115=30mmHg;

[0103] Then, the fluctuation amplitude ΔX of each physiological parameter is normalized to facilitate comparison between different patients. The normalization method is based on the mean value in the resting state, that is:

[0104] If the mean blood oxygen saturation of a patient at rest is The maximum fluctuation range is ΔSpO2 = 5%, and the normalized fluctuation amplitude is calculated as follows:

[0105] Set the fluctuation contrast threshold THR X As a threshold, its setting basis is based on the statistical data of the patient group, usually taking twice the standard deviation of physiological parameters in the resting state (2σ) as the upper limit fluctuation threshold, that is: THR X =2×σ X ;

[0106] Assume that the standard deviation of heart rate at rest is σ HR =6bpm, then the fluctuation threshold is calculated as follows: THR HR =2×6=12bpm;

[0107] Then, the normalized fluctuation amplitude is compared with the preset fluctuation threshold THR X Make judgments and classify the adaptability level. The set adaptability level is based on physiological standards and preoperative monitoring data statistics, for example:

[0108]

[0109] Finally, the calculated normalized fluctuation amplitude of physiological parameters is used to classify the adaptability level, thereby obtaining the physiological parameter fluctuation level distribution.

[0110] S202: Based on the physiological parameter fluctuation level distribution, a corresponding weight coefficient is set for each physiological parameter, a weighted fluctuation index of each physiological parameter is calculated, and the weighted fluctuation index of each physiological parameter is converted into a physiological regulation index of the corresponding physiological parameter. The physiological regulation range of the individual patient is set with reference to the individual patient's weight parameter to obtain the individual physiological regulation range;

[0111] Based on the fluctuation level distribution of physiological parameters, a corresponding weight coefficient W is set for each physiological parameter. X The weight coefficient is set based on the importance of physiological parameters to the overall regulatory ability of the human body. The following setting values are obtained from the analysis of physiological data:

[0112]

[0113] Then, the weighted fluctuation index WI of each physiological parameter is calculated X :WI X =ΔX norm ×W X ;

[0114] For example, assuming that a patient's normalized heart rate fluctuation range is 42.67%, blood pressure fluctuation range is 25%, and blood oxygen saturation fluctuation range is 5.15%, the weighted fluctuation index is calculated as follows:

[0115] WI HR =42.67%×0.4=17.07%;

[0116] WI BP =25%×0.35=8.75%;

[0117]

[0118] Then, all weighted fluctuation indices are summed to obtain the patient's physiological regulation index PI raw :

[0119]

[0120] Next, the individual physiological regulation range is set based on the patient's weight parameter, and the weight correction coefficient TW is set to correct the individual's influence on the physiological regulation ability:

[0121]

[0122] Assuming the patient weighs 68 kg, the corresponding correction factor TW = 1.0, then the final individual physiological adjustment range is calculated as follows:

[0123] PI adjusted =PI raw ×TW=27.11%×1.0=27.11%;

[0124] Ultimately, the individual physiological regulation range is obtained.

[0125] S203: Based on the individual physiological regulation range, dynamically adjust the deviation of the measured value of each physiological parameter within the physiological regulation range through real-time monitoring data, update the current physiological regulation ability index, and obtain a preoperative physiological regulation ability analysis result;

[0126] According to the individual physiological regulation range, the deviation degree of each physiological parameter measurement value within the physiological regulation range is dynamically adjusted through real-time monitoring data, and the latest measurement values of the patient's heart rate, blood pressure, and blood oxygen saturation are obtained in real time. t , calculate its deviation D relative to the individual physiological regulation range X :

[0127] Assume that the average resting heart rate of a patient before surgery is 75 bpm, and the current real-time measured heart rate is X t =85bpm, then the deviation is calculated as follows:

[0128] Then, calculate the current physiological regulation ability index CI t :

[0129] Assume that the physiological regulation ability index CI at the previous momentt；1 =80%, then the patient's heart rate impact is calculated as follows:

[0130] CI t =80%-0.4×13.33%=74.67%;

[0131] Finally, the calculated physiological regulation ability index was used for preoperative physiological regulation ability analysis results.

[0132] See also Figure 4 Based on the preoperative physiological regulation ability analysis results, the patient's physiological regulation range is called, and the preoperative risk dynamic adjustment coefficient is calculated. The specific steps for generating the patient's preoperative risk adjustment results are as follows:

[0133] S301: Based on the preoperative physiological regulation ability analysis results, call the patient's physiological parameters within the current physiological steady-state range, calculate the proportion of each physiological parameter within the corresponding physiological regulation range, and construct a physiological regulation data set;

[0134] According to the results of preoperative physiological regulation analysis, call the physiological parameters within the patient's current physiological steady-state range, extract the range of key physiological parameters such as heart rate (HR), blood pressure (BP), blood oxygen saturation (SpO2) of the patient in the resting state from the database, and obtain the individual physiological regulation range PI in the preoperative regulation analysis results adjusted , calculate the proportion of each physiological parameter in the corresponding physiological regulation range, that is:

[0135] Among them, X stable Represents the median of the steady-state range of the physiological parameter in the resting state. The setting method of this benchmark value is based on the median of multiple measurements of the patient in the resting state, and outliers exceeding the mean ± 1.96σ are eliminated to ensure its rationality. For example, if a patient's heart rate steady-state range is [70,80] bpm, the median X is taken. stable =75bpm, and the individual physiological regulation range PI adjusted is 27.11%, then the ratio is calculated as follows:

[0136] Similarly, for blood pressure and blood oxygen saturation, the calculations are as follows:

[0137] Then, the ratios of all physiological parameters were used to construct a ratio data set, as shown in Table 2.

[0138] Table 2 Physiological regulation dataset

[0139]

[0140] As shown in Table 2, the calculated ratio data set is used for subsequent weight normalization processing.

[0141] S302: Based on the physiological regulation data set, the ratio deviation of each physiological parameter is compared, the maximum deviation ratio is selected as the weight factor, and the weight factor is used to perform weighted normalization processing on all ratios to obtain a preoperative risk adjustment coefficient;

[0142] Based on the physiological regulation data set, the ratio deviation of each physiological parameter is compared and the ratio deviation D of each parameter is calculated. X :

[0143] D X =|R X -1|;

[0144] The threshold is set based on the physiological regulation range PI adjusted The range of change, that is, if R X If the value is >1, it means that the relative adjustment range of the physiological parameter is too large, otherwise it is too small. Therefore, 1 is used as the ideal benchmark value to calculate the deviation degree of each physiological parameter. For example, if the ratio data set of a patient is:

[0145] R HR =2.77, R BP =4.43,

[0146] The ratio deviation is calculated as follows: D HR =|2.77-1|=1.77; D BP =|4.43-1|=3.43;

[0147]

[0148] Then, select the maximum deviation ratio D max As the weight factor W F :

[0149] W F =D max =max(1.77,3.43,2.61)=3.43;

[0150] The weight factor is set in such a way that the adjustment calculation of all physiological parameters is based on the maximum deviation ratio to maintain data consistency. The weight factor is called to perform weighted normalization on all ratios, namely:

[0151] The calculated weighted normalized ratio is as follows:

[0152]

[0153] Finally, the preoperative risk adjustment coefficient was calculated, as shown in Table 3.

[0154] Table 3 Preoperative risk adjustment coefficients

[0155]

[0156] As shown in Table 3 , the ratios of all physiological parameters were normalized to uniformly assess the patient's preoperative physiological regulation range.

[0157] S303: Based on the preoperative risk adjustment coefficient, the patient's individual preoperative physiological regulation range is assessed, the sensitivity index of the preoperative physiological parameters to the physiological regulation range is analyzed, the adjusted risk coefficient is applied to the patient's individual preoperative physiological parameters, and the adjusted risk weight ratio of each sensitivity index is calculated to obtain the patient's preoperative risk adjustment result;

[0158] According to the preoperative risk adjustment coefficient, the patient's individual preoperative physiological regulation range is evaluated, and the sensitivity index S of the physiological parameters to the physiological regulation range is calculated. X :

[0159]

[0160] The sensitivity index is used to quantify the impact of each physiological parameter on the individual's regulatory range. A larger value indicates a greater impact on the physiological regulatory range. For HR, BP, and SpO2, the calculation is as follows:

[0161]

[0162] Then, the adjusted risk coefficient is applied to the individual patient's preoperative physiological parameters to calculate the risk weight ratio RW after the sensitivity index is adjusted X :

[0163]

[0164] It is calculated as follows: RW HR =0.52×0.81=0.42; RW BP =1.00×1.29=1.29;

[0165] Finally, the preoperative risk adjustment results of the patients were calculated, as shown in Table 4.

[0166] Table 4 Preoperative risk adjustment results

[0167]

[0168] As shown in Table 4 , the calculated preoperative risk adjustment results were used to finally assess the patient's preoperative risk.

[0169] See also Figure 5 Based on the preoperative risk adjustment results and preoperative physiological monitoring data, the heart rate recovery time, blood oxygen fluctuation amplitude, and blood pressure recovery time are evaluated, the physiological load ratio is calculated, the dynamic physiological load index is set and classified and marked, and the specific steps for generating the preoperative physiological load classification results are as follows:

[0170] S401: Based on the patient's preoperative risk adjustment results, the heart rate data, blood oxygen data, and blood pressure data under dynamic physiological load are retrieved to obtain the time interval between the heart rate and blood pressure recovery starting point and the stable point, as well as the difference between the maximum and minimum blood oxygen values, to obtain the physiological parameter recovery interval;

[0171] Based on the patient's preoperative risk adjustment results, the heart rate data, blood oxygen data and blood pressure data under dynamic physiological load are called, and the monitoring data of the patient under exercise, stress or other dynamic physiological load conditions are extracted from the database to obtain the dynamic changes of the patient's heart rate (HR), blood oxygen saturation (SpO2) and blood pressure (BP), and determine the heart rate recovery starting point HR respectively. start and stable point HR stable , blood pressure recovery starting point BP start and stable point BP stable , calculate the time required for the two to recover, and calculate the maximum blood oxygen level With minimum value The fluctuation range between heart rate recovery time T HR The calculation is as follows:

[0172]

[0173] Assuming a patient is under dynamic physiological load, their heart rate reaches 120 bpm at 12:00 (recovery starting point) and recovers to 85 bpm at 12:05 (stabilization point). The recovery time is calculated as follows:

[0174] T HR =12:05-12:00=5min;

[0175] Similarly, blood pressure recovery time T BP The calculation method is the same. Assuming that it takes 7 minutes for the patient's blood pressure to recover from 140 / 90 mmHg to 120 / 80 mmHg, then: T BP =7min;

[0176] The fluctuation range of blood oxygen saturation ΔSpO2 is calculated as follows:

[0177] If a patient's blood oxygen saturation drops from 99% to 93% under dynamic physiological stress, the fluctuation amplitude is calculated as follows:

[0178] ΔSpO2=99-93=6%;

[0179] Set the physiological recovery threshold T for heart rate and blood pressure recovery time recovery,thresh This value is set based on the statistical range of cardiovascular recovery time in healthy people. Generally, heart rate recovery time is within the normal range of 3-7 minutes, and blood pressure recovery time is within the normal range of 4-8 minutes. If it exceeds this range, it is considered abnormal recovery. For example:

[0180] T recovery,thresh,HR =7min; T recovery,thresh,BP =8min;

[0181] Finally, the calculated recovery time and blood oxygen fluctuation data were used to construct the physiological parameter recovery interval, as shown in Table 5.

[0182] Table 5 Recovery interval of physiological parameters

[0183]

[0184] As shown in Table 5, the calculated physiological parameter recovery intervals were used to further analyze the patient's dynamic physiological load.

[0185] S402: Calculate the physiological load ratio based on the physiological parameter recovery interval, adjust the ratio range by using the ratios of heart rate recovery time, blood oxygen fluctuation amplitude, and blood pressure recovery time, set a dynamic physiological load index that matches the target patient, and obtain the patient's dynamic physiological load result;

[0186] Calculate the physiological load ratio R according to the physiological parameter recovery interval load , call the ratio adjustment range of heart rate recovery time, blood oxygen fluctuation amplitude and blood pressure recovery time, and set the dynamic physiological load index that matches the target patient. The calculation method is as follows:

[0187] Assume that the physiological parameter data of a patient are as follows: T HR =5min; T BP =7min; ΔSpO2=6%;

[0188] The physiological load ratio is calculated as follows:

[0189] Then, set the ratio adjustment ratio range R adj , used to correct the individual physiological load level of the patient. The ratio is set according to the patient's weight (WT) with different correction factors W T :

[0190]

[0191] Assuming the patient weighs 65 kg, the corresponding correction factor W is T =1.0, then calculate the dynamic physiological load index L dynamic :

[0192]

[0193] Assume that the reference load time T is set baseline =6min, then the calculation is as follows:

[0194] Finally, the patient's dynamic physiological load results are calculated.

[0195] S403: Based on the patient's dynamic physiological load results, reference is made to the preoperative physiological monitoring data set to set load classification standards, screen the heart rate recovery time distribution range, blood oxygen fluctuation range, and blood pressure recovery time distribution range, calculate the distribution ratio of each physiological parameter at each physiological load level, classify and label the patient's load status, and obtain the preoperative physiological load classification results;

[0196] Based on the patient's dynamic physiological load results, the load classification standard was set with reference to the preoperative physiological monitoring data set, the heart rate recovery time distribution range, blood oxygen fluctuation range and blood pressure recovery time distribution range were screened, and the distribution ratio of each physiological parameter at each physiological load level was calculated. The distribution ratio P of the physiological parameter was calculated. X :

[0197]

[0198] Assume that the distribution of different load levels in the sample data set is as follows:

[0199]

[0200] The measurement data of a patient is: T HR =5min; ΔSpO2=6%; T BP =7min;

[0201] According to the classification standards, the patient's heart rate recovery time belongs to medium load, the blood oxygen fluctuation amplitude belongs to medium load, and the blood pressure recovery time belongs to medium load. Therefore, the patient's overall load status is judged to be medium load.

[0202] Finally, the preoperative physiological load classification results were calculated, and the patient's physiological load classification results were determined to be a moderate load state, which was ultimately used for preoperative physiological load assessment.

[0203] See also Figure 6Based on the preoperative physiological load classification results, the preoperative risk factors are calculated, and the changing trend of the dynamic physiological load index is combined to screen and adjust the priority, divide the risk level, and generate the preoperative individualized risk assessment results. The specific steps are as follows:

[0204] S501: Based on the preoperative physiological load classification results, the preoperative risk dynamic adjustment coefficient and dynamic physiological load index of the corresponding patient are called to calculate the preoperative risk factor within the patient's physiological regulation range, the numerical variation range of the risk factor in each physiological load category is extracted, and the risk factor is aggregated into each physiological load state to obtain a preoperative risk factor set;

[0205] Based on the preoperative physiological load classification results, the corresponding patient's preoperative risk dynamic adjustment coefficient R is called adjust Dynamic physiological load index L dynamic Calculate the patient's preoperative risk factor within the physiological adjustment range and call the patient's preoperative physiological adjustment range R baseline,static As a reference benchmark, and calculate the individual preoperative risk factor R factor,init The optimization formula is as follows:

[0206]

[0207] Among them, R adjust Represents the preoperative risk dynamic adjustment coefficient, which is used to correct the impact of individual physiological load status on preoperative risk factors. This parameter is set based on the risk adjustment amplitude of the patient under dynamic physiological load status, and its value range is set to 0.8≤R adjust ≤1.5. If the value is less than 0.8, it indicates that the patient's preoperative risk is lower than the baseline level. If it is greater than 1.5, it indicates that the patient's preoperative risk is significantly increased. For example, if a patient's preoperative physiological load fluctuates greatly, then his R adjust =1.3, while the other patient's preoperative physiological load was relatively stable, so his R adjust =0.9, L dynamic Represents the dynamic physiological load index, which is calculated based on the patient's heart rate recovery time, blood oxygen fluctuation amplitude and blood pressure change trend, and the set range is 0.5≤L dynamic ≤1.5, if L dynamic >1.2 indicates that the patient takes too long to recover under dynamic physiological load and has a higher risk. baseline,static The resting baseline value represents the patient's preoperative physiological adjustment range, and the setting range is 0.9≤R baseline,static ≤1.1. If the value exceeds the set range, it indicates that the patient's physiological parameters at rest are abnormal. var,i Represents the physiological parameter deviation of the i-th risk factor, which is calculated based on the patient's historical monitoring data and is set in the range of 0≤R var,i≤0.5. If the value is larger, it indicates that the patient's physiological parameters fluctuate greatly. delay The time lag parameter representing the change of the patient's preoperative risk factors is used to characterize the sensitivity of preoperative risk factors to time, and the setting range is 1≤T delay ≤5. If the value is higher, it indicates that the fluctuation of risk factors has a longer lag effect.

[0208] Assume that a patient's preoperative risk adjustment coefficient R adjust =1.2, dynamic physiological load index L dynamic =1.1, physiological regulation reference value R baseline,static =1.0, its physiological parameter deviation set R var,i Take [0.1, 0.05, 0.08], a total of n = 3 parameters, the time lag parameter T delay =3, then calculate:

[0209]

[0210] The patient's initial preoperative risk factor was 0.275, which was at a low risk level.

[0211] S502: Based on the preoperative risk factor set, calculate the risk ratio of each risk factor to the corresponding patient's preoperative physiological adjustment range, analyze the change trend of the risk factor in each physiological load category, screen the change characteristics of the risk factor in the time series, adjust the ranking sequence of the risk factor according to the change trend, and obtain the risk factor ranking result;

[0212] According to the preoperative risk factor set, calculate each risk factor R factor,mod The risk ratio R of the corresponding patient's preoperative physiological adjustment range ratio , the optimization formula is as follows:

[0213]

[0214] Among them, R factor,mod represents the modified preoperative risk factor, which is calculated based on R factor,init , and combined with the adjustment range of preoperative risk factors, the range was set to 0.8≤R factor,mod ≤1.4. If the value is less than 0.8, it indicates that the preoperative physiological regulation ability is strong. If the value is greater than 1.4, it indicates that the preoperative risk is high. baseline,dynamic Represents the dynamic reference value of the preoperative physiological adjustment range, with a setting range of 0.85≤R baseline,dynamic ≤1.15. If the value exceeds the set range, it indicates that the patient's dynamic physiological regulation ability is abnormal. diff,j Represents the time difference of preoperative risk factors, with a setting range of 1≤T diff,j≤4. If the value is larger, it indicates that the change of risk factors has a longer duration.

[0215] Assume that the patient's modified preoperative risk factor R factor,mod =0.3, preoperative physiological adjustment baseline value R baseline,dynamic =1.02, preoperative risk factor R at the past m=3 time points factor,mod,j They are [0.28, 0.31, 0.33], and the corresponding R baseline,dynamic,j They are [1.01, 1.03, 1.02] in sequence, and the time difference T diff,j The order is [2,1,3], then calculate:

[0216]

[0217] The patient's hazard ratio R ratio =1.56, indicating a high preoperative risk ratio.

[0218] S503: Based on the risk factor ranking results, classification criteria are set with reference to the risk factor change trends, the distribution of risk factors within each category is evaluated, the risk factors are classified into corresponding level categories, and the risk levels are mapped to the patient's preoperative risk assessment system to obtain preoperative individualized risk assessment results;

[0219] Based on the risk factor ranking results, the classification criteria are set with reference to the changing trends of the risk factors. The distribution of risk factors within each category is evaluated, and the corresponding level categories of risk factors are divided. The risk levels are then mapped to the patient's preoperative risk assessment system. The optimization formula is as follows:

[0220]

[0221] Among them, R factor,final Represents the final calculated preoperative risk factor, which is calculated based on the patient's preoperative risk ratio and dynamic physiological adjustment range, and is set within the range of 0.7≤R factor,final ≤1.5. If the value is greater than 1.5, it indicates a higher preoperative risk. If the value is less than 0.7, it indicates a stronger preoperative physiological regulation ability. p represents the number of time points used to calculate the distribution of risk factors. The setting range is 3≤p≤10. If p is too small, the temporal trend of risk factors may not be accurately assessed. q represents the number of time points used to calculate the trend of risk factor changes. The setting range is 3≤q≤8. If q is too small, the rate of change of risk factors may not be accurately assessed. Finally, the preoperative individualized risk assessment results are calculated.

[0222] Assume that the patient's final preoperative risk factor R at the past p = 4 time points factor,final,k They are [0.32, 0.35, 0.37, 0.40], and the corresponding Rbaseline,dynamic,k The order is [1.00, 1.02, 1.01, 1.03], calculate the first sum:

[0223]

[0224] Assume that the risk factor changes R at q = 3 time points factor,final,l The sequence is [0.35, 0.37, 0.40], calculate the second sum:

[0225]

[0226] Finally, calculate R level :

[0227]

[0228] The patient's risk level was 0.44, indicating that his preoperative risk was at a moderate level.

[0229] A pre-anesthesia risk assessment system, comprising:

[0230] The physiological monitoring data acquisition module obtains the patient's heart rate, blood pressure, blood oxygen saturation and weight at rest, calculates the rate of change of physiological parameters, filters out parameters that exceed the physiological steady-state range, and generates a preoperative physiological monitoring data set;

[0231] The individual physiological regulation capacity analysis module calculates the maximum fluctuation value of physiological parameters under load based on the preoperative physiological monitoring data set, compares it with the mean value in the resting state to determine the degree of fluctuation, divides the adaptability level according to the preset fluctuation comparison threshold, sets the weight coefficient of each physiological parameter, calculates the physiological regulation index, calculates the individual physiological regulation range based on the patient's weight parameter, and adjusts the physiological regulation range based on the real-time monitoring data to generate the preoperative physiological regulation capacity analysis results;

[0232] The preoperative risk dynamic adjustment module extracts the ratio of the parameters within the physiological steady-state range to the physiological regulation range based on the preoperative physiological regulation ability analysis results, calculates the preoperative risk dynamic adjustment coefficient, and generates the patient's preoperative risk adjustment results;

[0233] The preoperative physiological load classification module calculates the heart rate recovery time, blood oxygen fluctuation amplitude, and blood pressure recovery time based on the patient's preoperative risk adjustment results and preoperative physiological monitoring data set, sets the dynamic physiological load index and classifies it, and generates the preoperative physiological load classification results;

[0234] The preoperative individualized risk assessment module calculates preoperative risk factors based on the preoperative physiological load classification results, screens risk factors with a large trend of change in dynamic physiological load index, divides risk levels, and generates preoperative individualized risk assessment results.

[0235] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for risk assessment before anesthesia, characterized in that: The following steps are involved: S1: Continuously obtain physiological parameters of anesthetized patients in a resting state, monitor changes in physiological parameters under dynamic physiological load, screen physiological parameters that exceed the steady-state range, and generate a preoperative physiological monitoring data set; S2: Based on the preoperative physiological monitoring data set, the maximum fluctuation value of the physiological parameters under load is compared with the resting mean value, the adaptability level is divided, the physiological regulation index is calculated, and the individual physiological regulation range is set with reference to the weight parameter to obtain the preoperative physiological regulation ability analysis result. S3: Based on the preoperative physiological regulation ability analysis result, calling the patient's physiological regulation range, calculating the preoperative risk dynamic adjustment coefficient, and generating the patient's preoperative risk adjustment result; S4: Based on the preoperative risk adjustment results and preoperative physiological monitoring data, assess the heart rate recovery time, blood oxygen fluctuation amplitude, and blood pressure recovery time, calculate the physiological load ratio, set the dynamic physiological load index and classify and mark it, and generate a preoperative physiological load classification result; S5: Based on the preoperative physiological load classification results, calculate the preoperative risk factors, screen and adjust the priorities in combination with the changing trend of the dynamic physiological load index, divide the risk levels, and generate preoperative individualized risk assessment results.

2. The method for preoperative risk assessment of anesthesia according to claim 1, characterized in that: The preoperative physiological monitoring data set includes the heart rate change rate, blood oxygen saturation change range, and blood pressure change rate. The preoperative physiological regulation ability analysis results include the adaptability level of physiological parameters, the physiological regulation index, and the individual physiological regulation range. The patient's preoperative risk adjustment results include the preoperative risk dynamic adjustment coefficient and the patient's preoperative risk adjustment value. The preoperative physiological load classification results include the dynamic physiological load index, the load state classification label, and the physiological load ratio. The preoperative individualized risk assessment results include the preoperative risk factor, the physiological load index change trend, and the risk level.

3. The method for preoperative risk assessment of anesthesia according to claim 1, characterized in that: The specific steps for continuously acquiring physiological parameters of anesthetized patients in a resting state, monitoring changes in physiological parameters under dynamic physiological load, screening physiological parameters that exceed the steady-state range, and generating a preoperative physiological monitoring data set are as follows: S101: Continuously obtain the heart rate, blood pressure, blood oxygen saturation, and weight parameters of the anesthetized patient at rest, calculate the mean value of each physiological parameter, set the physiological steady-state range based on the upper and lower floating ranges of the mean values corresponding to each physiological parameter, calibrate the range as the baseline limit of the patient's physiological parameter fluctuations at rest, and construct a resting physiological baseline range; S102: Based on the resting physiological baseline interval, the patient's heart rate, blood oxygen saturation, and blood pressure are monitored in real time under a dynamic physiological load state, a variation range of each monitored physiological parameter within a specified time period is calculated, and a physiological parameter whose variation range exceeds a preset variation threshold of the resting physiological baseline interval is marked as an abnormal variation parameter, thereby generating abnormal physiological load data; S103: Based on the abnormal physiological load data, physiological parameters that are within the physiological steady-state range and do not exceed the change threshold of the resting physiological reference interval are eliminated, and abnormal parameters are retained as preoperative evaluation data to obtain a preoperative physiological monitoring data set.

4. The method for preoperative risk assessment of anesthesia according to claim 1, wherein: Based on the preoperative physiological monitoring data set, the maximum fluctuation value of the physiological parameter under load is compared with the resting mean, the adaptability level is divided, the physiological regulation index is calculated, and the individual physiological regulation range is set with reference to the weight parameter. The specific steps for obtaining the preoperative physiological regulation ability analysis result are as follows: S201: Based on the preoperative physiological monitoring data set, the mean value of each physiological parameter in the resting state is retrieved, the maximum fluctuation value of the corresponding physiological parameter under the load state is extracted, the fluctuation amplitude of each physiological parameter is calculated, and the adaptive ability level is determined based on a preset fluctuation comparison threshold to obtain a physiological parameter fluctuation level distribution; S202: Based on the physiological parameter fluctuation level distribution, a corresponding weight coefficient is set for each physiological parameter, a weighted fluctuation index of each physiological parameter is calculated, and the weighted fluctuation index is converted into a physiological regulation index of the corresponding physiological parameter. The physiological regulation range of the individual patient is set with reference to the individual patient's weight parameter to obtain the individual physiological regulation range; S203: According to the individual physiological regulation range, the deviation degree of the measured value of each physiological parameter within the physiological regulation range is dynamically adjusted through real-time monitoring data, the current physiological regulation ability index is updated, and the preoperative physiological regulation ability analysis result is obtained.

5. The method for preoperative risk assessment of anesthesia according to claim 1, wherein: For the calculation of the physiological regulation index, the formula is used: MORE raw =YES HR +YES BP +YES SpO2 ; Among them, WI HR is the patient's heart rate fluctuation amplitude, WI BP is the patient's blood pressure fluctuation amplitude, WI SpO2 is the fluctuation amplitude of the patient's blood oxygen saturation; For calculating individual physiological regulation range PI adjusted , using the formula: PI adjusted =PI raw ×TW; Wherein, TW is the correction coefficient of the reference weight parameter.

6. The method for preoperative risk assessment of anesthesia according to claim 1, characterized in that: Based on the preoperative physiological regulation ability analysis results, the specific steps of calling the patient's physiological regulation range, calculating the preoperative risk dynamic adjustment coefficient, and generating the patient's preoperative risk adjustment results are as follows: S301: Based on the preoperative physiological regulation ability analysis result, call the patient's physiological parameters within the current physiological steady-state range, calculate the proportion of each physiological parameter within the corresponding physiological regulation range, and construct a physiological regulation data set; S302: Based on the physiological regulation data set, comparing the ratio deviation of each physiological parameter, selecting the maximum deviation ratio as a weight factor, and using the weight factor to perform weighted normalization processing on all ratios to obtain a preoperative risk adjustment coefficient; S303: Based on the preoperative risk adjustment coefficient, the preoperative physiological regulation range of the individual patient is evaluated, the sensitivity index of the preoperative physiological parameters to the physiological regulation range is analyzed, the adjusted risk coefficient is applied to the individual patient's preoperative physiological parameters, and the adjusted risk weight ratio of each sensitivity index is calculated to obtain the patient's preoperative risk adjustment result.

7. The method for preoperative risk assessment of anesthesia according to claim 1, characterized in that: Based on the preoperative risk adjustment results and preoperative physiological monitoring data, the heart rate recovery time, blood oxygen fluctuation amplitude, and blood pressure recovery time are evaluated, the physiological load ratio is calculated, the dynamic physiological load index is set and classified and marked, and the specific steps for generating the preoperative physiological load classification results are as follows: S401: Based on the patient's preoperative risk adjustment result, call the heart rate data and blood oxygen data under the dynamic physiological load state, obtain the time interval between the heart rate and blood pressure recovery starting point and the stable point, and the difference between the maximum and minimum blood oxygen values, and obtain the physiological parameter recovery interval; S402: Calculating a physiological load ratio based on the physiological parameter recovery interval, adjusting the ratio range by using the ratios of heart rate recovery time, blood oxygen fluctuation amplitude, and blood pressure recovery time, setting a dynamic physiological load index that matches the target patient, and obtaining a dynamic physiological load result for the patient; S403: Based on the patient's dynamic physiological load results, the load classification standard is set with reference to the preoperative physiological monitoring data set, the heart rate recovery time distribution range, blood oxygen fluctuation range and blood pressure recovery time distribution range are screened, the distribution ratio of each physiological parameter at each physiological load level is calculated, the patient's load status is classified and marked, and the preoperative physiological load classification result is obtained.

8. The method for preoperative risk assessment of anesthesia according to claim 1, characterized in that: Based on the preoperative physiological load classification results, the preoperative risk factors are calculated, and the priority is screened and adjusted in combination with the changing trend of the dynamic physiological load index, and the risk level is divided to generate the preoperative individualized risk assessment results. The specific steps are as follows: S501: Based on the preoperative physiological load classification result, the preoperative risk dynamic adjustment coefficient and dynamic physiological load index of the corresponding patient are called to calculate the preoperative risk factor within the patient's physiological regulation range, the numerical variation range of the risk factor in each physiological load category is extracted, and the risk factor is aggregated into each physiological load state to obtain a preoperative risk factor set; S502: Based on the preoperative risk factor set, calculate the risk ratio of each risk factor to the corresponding patient's preoperative physiological adjustment range, analyze the change trend of the risk factor in each physiological load category, screen the change characteristics of the risk factor in the time series, adjust the ranking sequence of the risk factor according to the change trend, and obtain the risk factor ranking result; S503: Based on the risk factor ranking results, classification criteria are set with reference to the changing trends of the risk factors, the distribution of risk factors within each category is evaluated, the risk factors are divided into corresponding grade categories, and the risk grades are mapped to the patient's preoperative risk assessment system to obtain preoperative individualized risk assessment results.

9. The method for preoperative risk assessment of anesthesia according to claim 1, characterized in that: To calculate the preoperative risk factor R factor,init , using the formula: Among them, R adjust represents the preoperative risk dynamic adjustment coefficient, L dynamic represents the dynamic physiological load index, R baseline,static The resting baseline value representing the patient's preoperative physiological regulation range, R var,i represents the physiological parameter deviation of the i-th risk factor, T delay time-lagged parameters representing changes in patients' preoperative risk factors; For each risk factor R factor,mod The risk ratio R of the corresponding patient's preoperative physiological adjustment range ratio , using the formula: Among them, R factor,mod represents the modified preoperative risk factor, R factor,mod,j represents the value of the modified preoperative risk factor, R baseline,dynamic,j Represents the dynamic baseline value of the preoperative physiological regulation range, T diff,j Represents the time difference of preoperative risk factors.

10. A pre-anesthesia risk assessment system, characterized in that: The method for pre-anesthesia risk assessment according to any one of claims 1 to 9 is implemented, wherein the system comprises: The physiological monitoring data acquisition module obtains the patient's heart rate, blood pressure, blood oxygen saturation and weight at rest, calculates the rate of change of physiological parameters, filters out parameters that exceed the physiological steady-state range, and generates a preoperative physiological monitoring data set; The individual physiological regulation ability analysis module calculates the maximum fluctuation value of the physiological parameters under load based on the preoperative physiological monitoring data set, compares it with the mean value in the resting state to determine the degree of fluctuation, divides the adaptability level according to the preset fluctuation comparison threshold, sets the weight coefficient of each physiological parameter, calculates the physiological regulation index, calculates the individual physiological regulation range in combination with the patient's weight parameter, and adjusts the physiological regulation range in combination with the real-time monitoring data to generate the preoperative physiological regulation ability analysis results; The preoperative risk dynamic adjustment module extracts the ratio of the parameter within the physiological steady-state range to the physiological regulation range based on the preoperative physiological regulation ability analysis result, calculates the preoperative risk dynamic adjustment coefficient, and generates the patient's preoperative risk adjustment result; The preoperative physiological load classification module calculates the heart rate recovery time, blood oxygen fluctuation amplitude, and blood pressure recovery time based on the patient's preoperative risk adjustment results and preoperative physiological monitoring data set, sets a dynamic physiological load index and classifies and marks it, and generates a preoperative physiological load classification result; The preoperative individualized risk assessment module calculates preoperative risk factors based on the preoperative physiological load classification results, screens risk factors with larger dynamic physiological load index change trends, divides risk levels, and generates preoperative individualized risk assessment results.

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