Intraoperative individualized blood pressure management method and system

Abstract

The invention provides an intraoperative individualized blood pressure management method and system, and belongs to the technical field of blood pressure management.The method comprises the steps that clinical data and intraoperative blood pressure data of a patient are collected and counted, and a database is established; establishing a perioperative complication score card model; distinguishing low-risk patients, medium-risk patients and high-risk patients by using different risk scores; on the basis of the low-risk patients, the medium-risk patients and the high-risk patients, single-factor Logistic regression is adopted to calculate intraoperative blood pressure threshold values; respectively calculating intraoperative blood pressure duration threshold values of the low-risk patient, the medium-risk patient and the high-risk patient by adopting a restrictive cubic spline; and combining the intraoperative blood pressure duration thresholds of the low-risk patient, the medium-risk patient and the high-risk patient to form an intraoperative individualized blood pressure management strategy. The invention provides a scheme for establishing an intra-operative individualized blood pressure management strategy for complications in different perioperative periods aiming at the blank in the field of current intra-operative blood pressure management strategies.

Description

Technical Field

[0001] The present invention relates to the technical field of blood pressure management, and in particular to an intraoperative individualized blood pressure management method and system. Background Art

[0002] It is estimated that more than 300 million surgeries are performed worldwide each year, and this number is still increasing year by year. Hypotension is one of the most common risks for patients during the perioperative period. Studies have shown that up to 87% of patients experience one or more hypotensive events during surgery, and about 20% of surgical patients experience at least one postoperative hypotension. In addition, there is increasing evidence that hypotension is closely related to the incidence of perioperative organ damage (such as myocardial injury and acute kidney injury) and high perioperative mortality. Therefore, many recent medical consensuses have emphasized that minimizing the duration and degree of perioperative hypotension in patients is crucial to improving patient prognosis.

[0003] Currently, optimizing intraoperative blood pressure management has become an emerging research hotspot. However, there is still some controversy about how to maintain intraoperative blood pressure within a certain range to effectively protect organ function. This field urgently needs more research to verify the effects of different blood pressure levels on organ function and the optimal blood pressure management strategy in specific patient groups. Summary of the invention

[0004] The present invention provides an intraoperative individualized blood pressure management method and system, and aims to fill the gap in the current field of intraoperative blood pressure management strategies, and provides a solution for establishing an intraoperative individualized blood pressure management strategy for different perioperative complications.

[0005] This specification discloses a method for individualized blood pressure management during surgery, including: Collect and compile clinical data and intraoperative blood pressure data of patients and establish a database; Based on the database, a perioperative complication scorecard model is established; Based on the described perioperative complication scorecard model, different risk scores were used to distinguish low-risk patients, medium-risk patients, and high-risk patients; Univariate logistic regression was used to calculate the intraoperative blood pressure threshold based on low-risk patients, medium-risk patients, and high-risk patients; Based on the intraoperative blood pressure threshold, the intraoperative blood pressure duration thresholds for low-risk patients, medium-risk patients, and high-risk patients are calculated using restricted cubic splines; The intraoperative blood pressure duration thresholds for low-risk patients, medium-risk patients, and high-risk patients were combined to form an individualized intraoperative blood pressure management strategy.

[0006] In this specification, preoperative variables are extracted from the database, and the preoperative variables include categorical variables and continuous variables. The original classification of the categorical variables is retained, and the continuous variables are binned into quintiles using WOE. The perioperative complication scorecard model is established as follows: ; in, is the score; A is the benchmark score coefficient of the scorecard model, and B is the benchmark score coefficient; is the coefficient of the variable in the model, is the WOE value corresponding to the feature variable binning, is the characteristic variable, is a constant.

[0007] In this specification, the risk score corresponding to 90% specificity of the perioperative complication scoring card model is used as the critical value between low-risk patients and medium-risk patients; the risk score corresponding to 90% sensitivity of the perioperative complication scoring card model is used as the critical value between medium-risk patients and high-risk patients.

[0008] In this manual, the systolic blood pressure threshold is defined, with 5 mmHg as a step, and the proportion of time that the patient's intraoperative systolic blood pressure is lower than 100 mmHg, 95 mmHg...75 mmHg, and lower than 70 mmHg is calculated (duration / operation time); the mean arterial pressure threshold is defined, with 5 mmHg as a step, and the proportion of time that the patient's intraoperative mean arterial pressure is lower than 80 mmHg, 75 mmHg...60 mmHg, and lower than 55 mmHg is calculated.

[0009] In this manual, univariate logistic regression was used to calculate the relationship between the proportion of duration exceeding different intraoperative blood pressure thresholds and perioperative complications, which was recorded as the p-value; the maximum intraoperative blood pressure threshold with a p-value <0.05 for patients in different risk levels was selected as the recommended intraoperative blood pressure threshold.

[0010] In this specification, restricted cubic spline is used to calculate the blood pressure duration ratio. According to the odds ratio converted by restricted cubic spline, the intersection point of the lower limit of the confidence interval of the odds ratio and the odds ratio = 1 is used as the intraoperative blood pressure duration threshold corresponding to the intraoperative blood pressure threshold.

[0011] In this specification, the Kolmogorov-Smirnov test was used to evaluate the data normality of the database, and the normally distributed data were expressed as mean ± standard deviation, and the non-normally distributed data were expressed as median and interquartile range.

[0012] In this manual, data statistics are completed using Python and R languages.

[0013] This specification also discloses an intraoperative individualized blood pressure management system, which is used to implement any of the intraoperative individualized blood pressure management methods described above, and the intraoperative individualized blood pressure management system includes: Database establishment module, used to collect and compile clinical data and intraoperative blood pressure data of patients and establish a database; A model building module, used to build a perioperative complication score card model based on the database; A patient classification module for different risk levels, used to distinguish low-risk patients, medium-risk patients and high-risk patients using different risk scores based on the perioperative complication score card model; An intraoperative blood pressure threshold calculation module is used to calculate the intraoperative blood pressure threshold based on low-risk patients, medium-risk patients and high-risk patients using univariate logistic regression; An intraoperative blood pressure duration threshold calculation module, used to calculate the intraoperative blood pressure duration thresholds of low-risk patients, medium-risk patients and high-risk patients respectively using restricted cubic splines based on the intraoperative blood pressure threshold; A strategy combination module is used to combine the intraoperative blood pressure duration thresholds of low-risk patients, medium-risk patients and high-risk patients to form an intraoperative individualized blood pressure management strategy.

[0014] This manual can at least achieve the following beneficial effects: The method for establishing an individualized intraoperative blood pressure management strategy proposed in the present invention realizes the stratification of perioperative complication risk factors by constructing a scoring model, and adopts univariate logistic regression and restricted cubic spline technology to formulate personalized blood pressure management plans for different types of perioperative complications, which is helpful to implement precision medicine. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0016] Figure 1 It is a schematic diagram of the process of the intraoperative individualized blood pressure management method involved in the present invention.

[0017] Figure 2 It is a schematic diagram of the spline curve of the duration of systolic blood pressure <80 mmHg and postoperative acute kidney injury in patients with low risk of postoperative acute kidney injury involved in the present invention.

[0018] Figure 3This is a schematic diagram of the intraoperative blood pressure threshold and duration threshold (systolic blood pressure) involved in the present invention that increase the risk of postoperative acute kidney injury in patients.

[0019] Figure 4 This is a schematic diagram of the intraoperative blood pressure threshold and duration threshold (mean arterial pressure) involved in the present invention that lead to an increased risk of postoperative acute kidney injury in patients. DETAILED DESCRIPTION

[0020] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the spirit or scope of the embodiments of the present invention. Therefore, the drawings and descriptions are considered to be exemplary and non-restrictive in nature.

[0021] The disclosure below provides many different embodiments or examples to implement different structures of the embodiments of the present invention. In order to simplify the disclosure of the embodiments of the present invention, the parts and settings of specific examples are described below. Of course, they are only examples, and the purpose is not to limit the embodiments of the present invention. In addition, the embodiments of the present invention can repeat reference numbers and / or reference letters in different examples, and this repetition is for the purpose of simplification and clarity, which itself does not indicate the relationship between the various embodiments and / or settings discussed.

[0022] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0023] like Figure 1 As shown, this specification discloses a method for individualized blood pressure management during surgery, comprising: Collect and compile clinical data and intraoperative blood pressure data of patients and establish a database; Based on the database, a perioperative complication scorecard model is established; Based on the described perioperative complication scorecard model, different risk scores were used to distinguish low-risk patients, medium-risk patients, and high-risk patients; Univariate logistic regression was used to calculate the intraoperative blood pressure threshold based on low-risk patients, medium-risk patients, and high-risk patients; Based on the intraoperative blood pressure threshold, the intraoperative blood pressure duration thresholds for low-risk patients, medium-risk patients, and high-risk patients are calculated using restricted cubic splines; The intraoperative blood pressure duration thresholds for low-risk patients, medium-risk patients, and high-risk patients were combined to form an individualized intraoperative blood pressure management strategy.

[0024] In the embodiment of this specification, preoperative variables are extracted from the database, and the preoperative variables include categorical variables and continuous variables. The original classification of the categorical variables is retained, and the continuous variables are binned into quintiles using WOE, and the perioperative complication score card model is established as follows: ; in, is the score; A is the benchmark score coefficient of the scorecard model, and B is the benchmark score coefficient; is the coefficient of the variable in the model, is the WOE value corresponding to the feature variable binning, is the characteristic variable, is a constant.

[0025] In the embodiments of this specification, the risk score corresponding to the specificity of the perioperative complication scoring card model of 90% is used as the critical value between low-risk patients and medium-risk patients; the risk score corresponding to the sensitivity of the perioperative complication scoring card model of 90% is used as the critical value between medium-risk patients and high-risk patients.

[0026] In the embodiments of this specification, the blood pressure duration ratio is calculated using restricted cubic splines. According to the odds ratio converted by the restricted cubic spline, the intersection point of the lower limit of the confidence interval of the odds ratio and the odds ratio = 1 is used as the intraoperative blood pressure duration threshold corresponding to the intraoperative blood pressure threshold.

[0027] In the examples of this specification, the Kolmogorov-Smirnov test was used to evaluate the normality of the data in the database, and the normally distributed data were expressed as mean ± standard deviation, and the non-normally distributed data were expressed as median and interquartile range.

[0028] In the examples of this specification, data statistics are completed using Python and R languages.

[0029] In the examples of this specification, patients are divided into three levels according to the risk scores corresponding to a sensitivity of 90% and a specificity of 90%: low-risk patients, medium-risk patients and high-risk patients.

[0030] In the examples of this specification, univariate logistic regression is used to calculate the relationship between the proportion of duration exceeding different intraoperative blood pressure thresholds and perioperative complications, which is recorded as the p-value; the maximum intraoperative blood pressure threshold value with a p-value <0.05 for patients at different risk levels is selected as the recommended intraoperative blood pressure threshold.

[0031] This specification also discloses an intraoperative individualized blood pressure management system, which is used to implement any of the intraoperative individualized blood pressure management methods described above, and the intraoperative individualized blood pressure management system includes: Database establishment module, used to collect and compile clinical data and intraoperative blood pressure data of patients and establish a database; A model building module, used to build a perioperative complication score card model based on the database; A patient classification module for different risk levels, used to distinguish low-risk patients, medium-risk patients and high-risk patients using different risk scores based on the perioperative complication score card model; An intraoperative blood pressure threshold calculation module is used to calculate the intraoperative blood pressure threshold based on low-risk patients, medium-risk patients and high-risk patients using univariate logistic regression; An intraoperative blood pressure duration threshold calculation module, used to calculate the intraoperative blood pressure duration thresholds of low-risk patients, medium-risk patients and high-risk patients respectively using restricted cubic splines based on the intraoperative blood pressure threshold; A strategy combination module is used to combine the intraoperative blood pressure duration thresholds of low-risk patients, medium-risk patients and high-risk patients to form an intraoperative individualized blood pressure management strategy.

[0032] The technical idea of ​​the present invention is to construct a prediction model for different perioperative complications to achieve patient risk factor stratification, calculate the relationship between the duration ratio (duration / operation time) exceeding different intraoperative blood pressure thresholds and perioperative complications in different risk stratifications based on univariate logistic regression, and extract the intraoperative blood pressure thresholds for patients at each risk level. Restricted cubic splines are used to calculate the duration thresholds corresponding to different intraoperative blood pressure thresholds, specifically including: (1) Collect and compile clinical data and intraoperative blood pressure data of patients to establish a database; (2) extracting preoperative variables from the database established in step (1), wherein the preoperative variables include categorical variables and continuous variables. The original classification of the categorical variables is retained, and the continuous variables are binned into quintiles using WOE; (3) Establish a perioperative complication scorecard model; (4) Based on the scorecard model established in step (3), different risk scores are used to define low-risk, medium-risk, and high-risk patients. The risk score corresponding to high specificity (90%) is selected as the cutoff value for low risk and medium risk; while the cutoff value for medium risk and high risk is the risk score corresponding to high sensitivity (90%); (5) Define the systolic blood pressure threshold, using 5 mmHg as an interval, and calculate the duration of the patient's systolic blood pressure below 100 mmHg, 95 mmHg...75 mmHg, and below 70 mmHg during surgery; (6) Define the mean arterial pressure threshold, using 5 mmHg as a step, and calculate the duration of the patient's mean arterial pressure below 80 mmHg, 75 mmHg, 60 mmHg, and below 55 mmHg during surgery; (7) Perform univariate logistic regression on patients with different risk stratifications in step (6), calculate the relationship between the proportion of duration exceeding different intraoperative blood pressure thresholds and the corresponding perioperative complications, and extract the intraoperative blood pressure thresholds corresponding to patients in each risk level; (8) Restricted cubic spline was used to calculate the blood pressure duration ratio. The odds ratio (OR) converted by restricted cubic spline was used, and the intersection point of the lower limit of the confidence interval of the OR value and the OR value = 1 was taken as the duration threshold corresponding to the intraoperative blood pressure threshold.

[0033] Furthermore, the variables in step (1) were evaluated for data normality using the Kolmogorov-Smirnov test. Normally distributed quantitative data were expressed as mean ± standard deviation, and non-normally distributed quantitative data were expressed as median and interquartile range.

[0034] Furthermore, all statistical analyses were performed using Python (3.7.0) and R (3.5).

[0035] In a specific embodiment, data of 34,668 adult patients who underwent non-cardiac surgery under general anesthesia in a hospital are taken as an example.

[0036] Postoperative acute kidney injury (PO-AKI) was used as the target perioperative complication. PO-AKI was defined as a creatinine increase of ≥0.3 mg / dl (≥26.5 μmol / L) within 48 hours after surgery compared with the preoperative creatinine or a creatinine increase of ≥1.5 times the preoperative creatinine within 7 days after surgery.

[0037] The clinical data and intraoperative blood pressure data of patients were collected and counted to establish a database.

[0038] Preoperative variables were extracted from the database, including categorical variables and continuous variables. The original classification of the categorical variables was retained, and the continuous variables were binned into quintiles using WOE.

[0039] A PO-AKI score card model was established based on the 27 preoperative variables using the logistic regression method, with an average AUROC of 0.80.

[0040] According to the risk scores corresponding to the high sensitivity (90%) and high specificity (90%) of the model, patients were divided into three strata: <15 points for low risk of PO-AKI; 15-67 points for moderate risk of PO-AKI; >67 points for high risk of PO-AKI.

[0041] The systolic blood pressure threshold was defined with 5 mmHg as the step, and the duration ratio of the patient's systolic blood pressure below 100 mmHg, 95 mmHg···75 mmHg, and below 70 mmHg during the operation was defined.

[0042] The mean arterial pressure threshold was defined with 5 mmHg as the step and the duration ratio of the patient's mean arterial pressure below 80 mmHg, 75 mmHg···60 mmHg and below 55 mmHg during surgery.

[0043] Univariate logistic regression was used to calculate the relationship between the proportion of duration exceeding different intraoperative blood pressure thresholds and PO-AKI, which was recorded as the p-value; the maximum intraoperative blood pressure threshold with a p-value < 0.05 in patients of different risk levels was selected as the recommended intraoperative blood pressure threshold (as shown in Table 1).

[0044] Restricted cubic splines were used to calculate the duration threshold corresponding to different intraoperative blood pressure thresholds, and the intersection point between the lower limit of the confidence interval of the OR value and the OR value = 1 was taken as the duration threshold corresponding to the intraoperative blood pressure threshold. Figure 2 As shown, the threshold duration of systolic blood pressure <80 mmHg in patients at low risk of PO-AKI was 10 minutes and 7 seconds.

[0045] Individualized blood pressure management strategies during combined surgery (eg Figure 3 , Figure 4 As shown in the table above, for patients at low risk of PO-AKI, the systolic blood pressure threshold during surgery was <80 mmHg, the duration threshold was 10 minutes and 7 seconds, the mean arterial pressure threshold was 55 mmHg, and the duration threshold was 15 minutes and 1 second. For patients at medium risk of PO-AKI, the systolic blood pressure threshold was <85 mmHg, the duration threshold was 15 minutes and 23 seconds, the mean arterial pressure threshold was 65 mmHg, and the duration threshold was 64 minutes and 21 seconds. For patients at high risk of PO-AKI, the systolic blood pressure threshold was <100 mmHg, the duration threshold was 44 minutes and 9 seconds, the mean arterial pressure threshold was 80 mmHg, and the duration threshold was 110 minutes and 36 seconds.

[0046] Table 1 Relationship between the proportion of duration exceeding the intraoperative blood pressure threshold and PO-AKI (P value)

[0047] Note: * represents p<0.05, ** represents p<0.01, *** represents p<0.001.

[0048] The above-described embodiments are used to illustrate the present invention, not to limit the present invention, so changes in the exemplified values ​​or replacement of equivalent elements should still fall within the scope of the present invention.

[0049] From the above detailed description, it can be understood by those skilled in the art that the present invention can indeed achieve the aforementioned objectives and is in compliance with the provisions of the Patent Law.

[0050] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they are aware of the basic creative concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention. The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. It should be pointed out that any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

[0051] It should be noted that the above description of the relevant process is only for example and explanation, and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to the process under the guidance of this specification. However, these modifications and changes are still within the scope of this specification.

[0052] The basic concepts have been described above. Obviously, for those of ordinary skill in the art who have read this application, the above invention disclosure is only for example and does not constitute a limitation of this application. Although not explicitly stated here, those of ordinary skill in the art may make various modifications, improvements and amendments to this application. Such modifications, improvements and amendments are suggested in this application, so such modifications, improvements and amendments still belong to the spirit and scope of the exemplary embodiments of this application.

[0053] At the same time, the present application uses specific words to describe the embodiments of the present application. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of the present application. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, some features, structures or characteristics in one or more embodiments of the present application can be appropriately combined.

[0054] In addition, it will be understood by those skilled in the art that various aspects of the present application may be illustrated and described by a number of patentable categories or situations, including any new and useful process, machine, product or combination of substances, or any new and useful improvement thereof. Therefore, various aspects of the present application may be implemented entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software may be referred to as a "unit", "module" or "system". In addition, various aspects of the present application may take the form of a computer program product embodied in one or more computer-readable media, wherein computer-readable program code is contained therein.

[0055] The computer program code required for the operation of each part of the present application can be written in any one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc., conventional procedural programming languages ​​such as C programming language, VisualBasic, Fortran2103, Perl, COBOL2102, PHP, ABAP, dynamic programming languages ​​such as Python, Ruby and Groovy, or other programming languages. The program code can be run entirely on the user's computer, or run on the user's computer as an independent software package, or run partially on the user's computer and partially on the remote computer, or run entirely on the remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any network form, such as a local area network (LAN) or a wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or used as a service such as software as a service (SaaS).

[0056] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this application, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this application. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this application. For example, although the implementation of the various components described above can be embodied in a hardware device, it can also be implemented as a pure software solution, for example, installation on an existing server or mobile device.

[0057] Similarly, it should be noted that in order to simplify the description disclosed in this application and thus help understand one or more embodiments of the invention, in the above description of the embodiments of the application, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this method of the application should not be interpreted as reflecting the intention that the claimed object requires more features than those explicitly stated in each claim. On the contrary, the subject of the invention should have fewer features than the above single embodiment.

Claims

1. A method for individualized blood pressure management during surgery, characterized in that: include: Collect and compile clinical data and intraoperative blood pressure data of patients and establish a database; Based on the database, a perioperative complication scorecard model is established; Based on the described perioperative complication scorecard model, different risk scores were used to distinguish low-risk patients, medium-risk patients, and high-risk patients; Univariate logistic regression was used to calculate the intraoperative blood pressure threshold based on low-risk patients, medium-risk patients, and high-risk patients; Based on the intraoperative blood pressure threshold, the intraoperative blood pressure duration thresholds for low-risk patients, medium-risk patients, and high-risk patients are calculated using restricted cubic splines; The intraoperative blood pressure duration thresholds for low-risk patients, medium-risk patients, and high-risk patients were combined to form an individualized intraoperative blood pressure management strategy.

2. The intraoperative individualized blood pressure management method according to claim 1, characterized in that: Preoperative variables were extracted from the database, including categorical variables and continuous variables. The original classification of the categorical variables was retained, and the continuous variables were binned into quintiles using weight of evidence. The perioperative complication scorecard model was established as follows: ； in, is the score; A is the benchmark score coefficient of the scorecard model, and B is the benchmark score coefficient; is the coefficient of the variable in the model, is the WOE value corresponding to the feature variable binning, is the characteristic variable, is a constant.

3. The intraoperative individualized blood pressure management method according to claim 1, characterized in that: The risk score corresponding to 90% specificity of the perioperative complication scorecard model was used as the critical value between low-risk and medium-risk patients; the risk score corresponding to 90% sensitivity of the perioperative complication scorecard model was used as the critical value between medium-risk and high-risk patients.

4. The intraoperative individualized blood pressure management method according to claim 1, characterized in that: The systolic blood pressure threshold was defined, with 5 mmHg as a step, and the proportion of time that the patient's systolic blood pressure was below 100 mmHg, 95 mmHg...75 mmHg, and below 70 mmHg during the operation was calculated. The mean arterial pressure threshold was defined, with 5 mmHg as a step, and the proportion of time that the patient's mean arterial pressure was below 80 mmHg, 75 mmHg...60 mmHg, and below 55 mmHg during the operation was calculated.

5. The intraoperative individualized blood pressure management method according to claim 1, characterized in that: Univariate logistic regression was used to calculate the relationship between the proportion of duration exceeding different intraoperative blood pressure thresholds and perioperative complications, which was recorded as the p value; the maximum intraoperative blood pressure threshold with a p value < 0.05 in patients of different risk levels was selected as the recommended intraoperative blood pressure threshold.

6. The intraoperative individualized blood pressure management method according to claim 1, characterized in that: Restricted cubic spline was used to calculate the blood pressure duration ratio. According to the odds ratio converted by restricted cubic spline, the intersection point of the lower limit of the confidence interval of the odds ratio and the odds ratio = 1 was taken as the intraoperative blood pressure duration threshold corresponding to the intraoperative blood pressure threshold.

7. The intraoperative individualized blood pressure management method according to claim 1, characterized in that: The Kolmogorov-Smirnov test was used to assess the normality of the data in the database. Normally distributed data were expressed as mean ± standard deviation, and non-normally distributed data were expressed as median and interquartile range.

8. The intraoperative individualized blood pressure management method according to claim 1, characterized in that: Data statistics were completed using Python and R languages.

9. An intraoperative individualized blood pressure management system, characterized in that: The method for implementing the intraoperative individualized blood pressure management according to any one of claims 1 to 8, wherein the intraoperative individualized blood pressure management system comprises: Database establishment module, used to collect and compile clinical data and intraoperative blood pressure data of patients and establish a database; A model building module, used to build a perioperative complication score card model based on the database; A patient classification module for different risk levels, used to distinguish low-risk patients, medium-risk patients and high-risk patients using different risk scores based on the perioperative complication score card model; An intraoperative blood pressure threshold calculation module is used to calculate the intraoperative blood pressure threshold based on low-risk patients, medium-risk patients and high-risk patients using univariate logistic regression; An intraoperative blood pressure duration threshold calculation module, used to calculate the intraoperative blood pressure duration thresholds of low-risk patients, medium-risk patients and high-risk patients respectively using restricted cubic splines based on the intraoperative blood pressure threshold; A strategy combination module is used to combine the intraoperative blood pressure duration thresholds of low-risk patients, medium-risk patients and high-risk patients to form an intraoperative individualized blood pressure management strategy.

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