System and method for generating blood pressure monitoring prediction of surgical patient according to BMI value

Abstract

The invention relates to a system and method for generating blood pressure monitoring prediction of a surgical patient according to a BMI value, and the system is characterized in that the system comprises a blood pressure prediction processing module used for collecting basic information of the patient and obtaining individual differentiated blood pressure threshold values of the patient, and a blood pressure monitoring module used for collecting real-time invasive / non-invasive blood pressure of the patient and obtaining individual differentiated blood pressure threshold values of the patient. The blood pressure background management module is used for comparing and analyzing data in combination with the personality differentiation threshold data provided by the blood pressure prediction processing module and the real-time data provided by the blood pressure monitoring module, and carrying out risk early warning and pre-intervention preparation according to an analysis result. And according to the condition of the patient, an accurate monitoring interval with individual difference is automatically generated to carry out automatic background data monitoring, and predicted abnormal risk data is converted into an alarm signal. According to the invention, the patient can obtain safer high-quality diagnosis and treatment service in the surgical treatment process, and the surgical risk is reduced.

Description

Technical Field

[0001] The present invention relates to a system and method, and in particular to a method suitable for generating blood pressure monitoring predictions for surgical patients according to BMI values. Background Art

[0002] Normal blood pressure values ​​were first proposed by medical scientists in 1984 as the concept of "high normal blood pressure". The healthiest blood pressure defined in the early days was: systolic upper limit 120mmHg, diastolic upper limit 80mmHg. In 1993, blood pressure below 17.3-11.3kPa (130-85mmHg) was further defined as normal blood pressure. Therefore, when systolic blood pressure is 17.3-18.6kPa (130-139mmHg), diastolic blood pressure is 11.3-11.9kPa (85-89mmHg), or as long as one of the two reaches this level, it is "high normal blood pressure", that is, blood pressure "normal high value". With the changes in people's living standards and eating habits, the blood pressure standard has also been adjusted to 140 / 90mmHg, that is, the invasive (IBP: systolic 95-140mmhg, diastolic 62-88mmhg) / non-invasive (NIBP: systolic 90-140mmhg, diastolic 60-90mmhg) reference range of normal blood pressure under the international standard. However, there is no clear boundary in medicine, and in most cases, it needs to be judged according to individual differences.

[0003] According to an article published in the Journal of the American Medical Association (JAMA) in August 2018, body index was positively correlated with blood pressure in tens of thousands of general populations, indicating that the increasing trend of BMI will be associated with the prevalence of hypertension in almost all populations. In other words, if the higher the BMI, the greater the chance of suffering from hypertension, the relationship between BMI and blood pressure is of great significance in countries such as China. Compared with December 2010, it is expected that by 2025, the prevalence of obesity in men will more than double (from 4.0% to 12.3%), and the prevalence of obesity in women will more than double (from 5.2% to 10.8%). And for every 1 percentage point increase in BMI, blood pressure will increase by 0.8 to 1.7 mmHg.

[0004] Currently, in outpatient surgeries, such as endoscopy centers (painless colonoscopy, painless gastroscopy), painless delivery, painless abortion, emergency trauma, etc., the mobility is relatively large. Anesthesiologists need to observe the vital signs of surgical patients while performing treatment operations. In surgical scenarios, they need to observe whether the patient's vital signs remain stable during the operation through real-time monitoring feedback, and judge whether to conduct therapeutic intervention based on the normal reference range of invasive (IBP: systolic blood pressure 95-140mmhg, diastolic blood pressure 62-88mmhg) / non-invasive (NIBP: systolic blood pressure 90-140mmhg, diastolic blood pressure 60-90mmhg) blood pressure under international standards and medical experience. The application of this method is relatively good in normal adult patients in large tertiary inpatient operating rooms with relatively abundant resources. When encountering infants, elderly patients, patients with special physical conditions, etc., there will be obvious distortion deviations, which can only be compensated by changing the instrument configuration or human experience. In outpatient operating room scenarios where resources are relatively scarce or medical and technical examinations and treatment operations need to be taken into account at the same time, due to the short operation time, frequent patient in and out, and large differences among groups, simply monitoring the patient's blood pressure in real time cannot support business applications, and it relies more on the experience of anesthesiologists for human judgment and post-intervention. Therefore, it is necessary to design a system and method that can generate blood pressure monitoring predictions for surgical patients based on BMI values, which can reduce the difficulty of anesthesiologists' work and improve the safety and quality of surgical patient services. Summary of the invention

[0005] The purpose of the present invention is to provide a method for generating blood pressure monitoring predictions for surgical patients based on BMI values, which can ensure that patients receive safer and higher-quality diagnosis and treatment services during surgical treatment and reduce surgical risks.

[0006] In order to achieve the above-mentioned purpose, the first technical solution of the present invention is: a system for generating blood pressure monitoring predictions for surgical patients based on BMI values, the innovation of which is:

[0007] The blood pressure prediction processing module is used to collect the patient's basic information and obtain the patient's personalized differentiated blood pressure threshold based on the patient's basic information.

[0008] Blood pressure monitoring module, used to collect patients' real-time invasive / non-invasive blood pressure,

[0009] The blood pressure background management module combines the personalized differentiated threshold data provided by the blood pressure prediction processing module and the real-time data provided by the blood pressure monitoring module to compare and analyze the data, and conducts risk warning and pre-intervention preparation based on the analysis results.

[0010] Among them, the basic information of patients collected by the blood pressure prediction processing module includes three types of factor elements, namely BMI factor, age factor and gender factor. The three types of factors are combined to predict the patient's risk and make differentiated personalized corrections to the blood pressure range.

[0011] In the above-mentioned first technical solution, the blood pressure prediction processing module includes:

[0012] The patient information submodule is used to collect basic patient information as raw data for differentiated judgment and calculation.

[0013] The factor and risk submodule generates the patient's own BMI value based on the collected height and weight information to determine the risk of high / low blood pressure, and uses it as the first-level weight. It determines the high / low age risk based on the patient's age value and uses it as the second-level weight. It determines the gender difference range based on the patient's gender value and uses it as the third-level weight.

[0014] The blood pressure threshold calculation and prediction submodule performs personalized calculation and numerical matching of the patient's blood pressure according to the weights of three types of factors, namely, based on the blood pressure threshold BV provided by BMI, the blood threshold AV provided by age, and the blood pressure threshold SV provided by gender, it generates the patient's individually differentiated predicted normal blood pressure value, critical range interval alarm value and predicted blood pressure waveform trend.

[0015] In the above-mentioned first technical solution, the blood pressure background management module includes:

[0016] The data monitoring submodule receives and stores the real-time invasive / non-invasive blood pressure of the patient collected by the blood pressure monitoring module, including systolic pressure and diastolic pressure, and the patient's personalized differentiated blood pressure threshold output by the blood pressure prediction processing module. The data monitoring submodule compares the two sets of values ​​and outputs them as normal and abnormal data.

[0017] The early warning / alarm management submodule, if abnormal data is monitored in the patient's real-time invasive / non-invasive blood pressure, a mandatory alarm and mandatory intervention will be issued until the data monitored again returns to the normal threshold range, and the mandatory alarm will be lifted. If the patient's real-time invasive / non-invasive blood pressure is monitored to continuously increase / decrease, or continuously maintain a high / low state, a early warning alarm will be issued and intervention will be required until the data monitored again returns to the normal threshold range, and the early warning alarm will be lifted.

[0018] In order to achieve the above object, the second technical solution of the present invention is: a method for generating blood pressure monitoring prediction of surgical patients according to the above BMI value, the innovation of which is: the specific steps are:

[0019] Step a, predicting the blood pressure threshold, the blood pressure prediction processing module performs risk prediction based on the patient's personal diagnosis and treatment basic data, and makes personalized corrections to the blood pressure range.

[0020] Specifically, the patient's personal information is collected, including height, weight, age and gender, where the patient's height and weight constitute the patient's BMI factor as the first-level weight, age constitutes the age factor as the second-level weight, and gender is the gender factor as the third-level weight.

[0021] According to the patient's height and weight, the patient's BMI value is obtained to determine the risk of high / low blood pressure. If BMI < 18.5, the patient is judged to be at risk of low blood pressure. If 18.5 ≤ BMI < 24, the patient is judged to be at no risk. If 24 ≤ BMI < 24 or BMI ≥ 28, the patient is judged to be at risk of high blood pressure.

[0022] If the patient is younger than 14 years old, the patient is considered to be of low age and low risk; if the patient is between 14 and 60 years old, the patient is considered to be of no risk; if the patient is between 60 and 80 years old, the patient is considered to be of high age and high risk.

[0023] The gender difference value is determined based on the patient's gender value, and the female value is lower than the male value.

[0024] According to the weight level and combined with the blood pressure threshold BV provided by the patient's BMI, the blood pressure threshold AV provided by age, and the blood pressure threshold SV provided by gender, the patient's individual differentiated blood pressure predicted normal value, critical value range interval alarm value, and predicted blood pressure waveform trend are generated.

[0025] Step b: real-time blood pressure monitoring. The patient obtains the current blood pressure in real time through the blood pressure monitoring module, including invasive / non-invasive systolic and diastolic blood pressure.

[0026] Step c, online blood pressure monitoring, the personalized differentiated threshold data provided in step a and the real-time data provided in step b are sent to the blood pressure background management module for storage, and the blood pressure background management module compares and analyzes the data, issues warnings for potential risks or predicted results of reaching / exceeding the alarm peak, and gives risk warnings and preview intervention preparation signals,

[0027] Specifically, if the blood pressure background management module detects abnormal data in the patient's real-time invasive / non-invasive blood pressure, a mandatory alarm and mandatory intervention will be issued until the data monitored again returns to the normal threshold range, and the mandatory alarm will be lifted. If the patient's real-time invasive / non-invasive blood pressure is monitored to continuously increase / decrease, or continuously maintain a high / low state, an early warning alarm will be issued and intervention will be required until the data monitored again returns to the normal threshold range, and the early warning alarm will be lifted.

[0028] In the above-mentioned second technical solution, in step a, the patient's BMI = weight / height. When BMI is less than 18.5, the patient is underweight. If 18.5≤BMI<24, the patient is normal. If 24≤BMI<24, the patient is overweight. If BMI≥28, the patient is determined to be obese.

[0029] The positive effect of the present invention is: after adopting the system and method of the present invention for generating blood pressure monitoring predictions for surgical patients based on BMI values, since the system of the present invention includes a blood pressure prediction processing module for collecting basic information of patients, and based on the basic information of patients, for obtaining individualized blood pressure thresholds for patients, a blood pressure monitoring module for collecting real-time invasive / non-invasive blood pressure of patients, and a blood pressure background management module, combining the individualized threshold data provided by the blood pressure prediction processing module and the real-time data provided by the blood pressure monitoring module, the blood pressure background management module performs data comparison and analysis, and performs risk warning and pre-intervention preparation based on the analysis results. The method of the present invention, while monitoring the vital signs of patients in real time, automatically generates individualized accurate monitoring intervals based on the patient's own situation for automated background data monitoring, and converts the predicted abnormal risk data into an alarm signal to notify the anesthesiologist, and focuses the anesthesiologist's attention more on the surgical treatment itself, reducing the doctor's workload, and at the same time ensuring that patients obtain safer and higher-quality diagnosis and treatment services during the surgical treatment, reducing surgical risks. At the research level, based on the accumulation of massive surgical data with the same ICD surgical name (such as painless colonoscopy), the invasive / non-invasive blood pressure fluctuation trend of patients under different influencing factors is predicted, providing more accurate auxiliary support for the abnormal risk data prediction function, and providing data support and models for scientific research projects in the field of anesthesia. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 is a flow chart of a specific implementation mode of the present invention;

[0031] Figure 2 is a flow chart of a blood pressure prediction processing module of the present invention;

[0032] Figure 3 is a flow chart of the blood pressure background management module of the present invention;

[0033] Figure 4 This is the flow chart of Case 1;

[0034] Figure 5 This is the flow chart of Case 2;

[0035] Figure 6 This is a short-term systolic blood pressure fluctuation trend chart of a normal, risk-free patient;

[0036] Figure 7 It is a short-term systolic blood pressure fluctuation trend chart for patients with different risks. DETAILED DESCRIPTION

[0037] The present invention is further described below with reference to the accompanying drawings and given embodiments, but is not limited thereto.

[0038] Example 1

[0039] like Figure 1 As shown, a system for generating blood pressure monitoring predictions for surgical patients based on BMI values ​​includes:

[0040] The blood pressure prediction processing module is used to collect the patient's basic information and obtain the patient's personalized differentiated blood pressure threshold based on the patient's basic information.

[0041] Blood pressure monitoring module, used to collect patients' real-time invasive / non-invasive blood pressure,

[0042] The blood pressure background management module combines the personalized differentiated threshold data provided by the blood pressure prediction processing module and the real-time data provided by the blood pressure monitoring module to compare and analyze the data, and conducts risk warning and pre-intervention preparation based on the analysis results.

[0043] Among them, the basic information of patients collected by the blood pressure prediction processing module includes three types of factor elements, namely BMI factor, age factor and gender factor. The three types of factors are combined to predict the patient's risk and make differentiated personalized corrections to the blood pressure range.

[0044] like Figure 2 As shown, in order to determine the relevant risk level and monitoring threshold difference of the patient according to the patient's condition characteristics, the blood pressure prediction processing module includes:

[0045] The patient information submodule is used to collect basic patient information as raw data for differentiated judgment and calculation.

[0046] The factor and risk submodule generates the patient's own BMI value based on the collected height and weight information to determine the risk of high / low blood pressure, and uses it as the first-level weight. It determines the high / low age risk based on the patient's age value and uses it as the second-level weight. It determines the gender difference range based on the patient's gender value and uses it as the third-level weight.

[0047] The blood pressure threshold calculation and prediction submodule performs personalized calculation and numerical matching of the patient's blood pressure according to the weights of three types of factors, namely, based on the blood pressure threshold BV provided by BMI, the blood threshold AV provided by age, and the blood pressure threshold SV provided by gender, it generates the patient's individually differentiated predicted normal blood pressure value, critical range interval alarm value and predicted blood pressure waveform trend.

[0048] like Figure 3As shown, in order to realize the automatic generation of the predicted fluctuation trend of vital signs that conform to the patient's condition characteristics according to the background calculation matching, the blood pressure background management module includes:

[0049] The data monitoring submodule receives and stores the real-time invasive / non-invasive blood pressure of the patient collected by the blood pressure monitoring module 1, and the blood pressure includes systolic pressure, diastolic pressure, and the patient's personalized differentiated blood pressure threshold output by the blood pressure prediction processing module. The data monitoring submodule compares the two sets of values ​​and outputs them as normal and abnormal data.

[0050] The early warning / alarm management submodule, if abnormal data is monitored in the patient's real-time invasive / non-invasive blood pressure, a mandatory alarm and mandatory intervention will be issued until the data monitored again returns to the normal threshold range, and the mandatory alarm will be lifted. If the patient's real-time invasive / non-invasive blood pressure is monitored to continuously increase / decrease, or continuously maintain a high / low state, a early warning alarm will be issued and intervention will be required until the data monitored again returns to the normal threshold range, and the early warning alarm will be lifted.

[0051] Example 2

[0052] like Figure 1 As shown, a method for generating blood pressure monitoring predictions for surgical patients based on BMI values, the specific steps of which are:

[0053] Step a, predicting the blood pressure threshold, the blood pressure prediction processing module performs risk prediction based on the patient's personal diagnosis and treatment basic data, and makes personalized corrections to the blood pressure range.

[0054] Specifically, the patient's personal information is collected, including height, weight, age and gender, where the patient's height and weight constitute the patient's BMI factor as the first-level weight, age constitutes the age factor as the second-level weight, and gender is the gender factor as the third-level weight.

[0055] According to the patient's height and weight, the patient's BMI value is obtained to determine the risk of high / low blood pressure. If BMI < 18.5, the patient is judged to be at risk of low blood pressure. If 18.5 ≤ BMI < 24, the patient is judged to be at no risk. If 24 ≤ BMI < 24 or BMI ≥ 28, the patient is judged to be at risk of high blood pressure.

[0056] If the patient is younger than 14 years old, the patient is considered to be of low age and low risk; if the patient is between 14 and 60 years old, the patient is considered to be of no risk; if the patient is between 60 and 80 years old, the patient is considered to be of high age and high risk.

[0057] The gender difference value is determined based on the patient's gender value, and the female value is lower than the male value.

[0058] According to the weight level and combined with the blood pressure threshold BV provided by the patient's BMI, the blood pressure threshold AV provided by age, and the blood pressure threshold SV provided by gender, the patient's individual differentiated blood pressure predicted normal value, critical value range interval alarm value, and predicted blood pressure waveform trend are generated.

[0059] Step b: real-time blood pressure monitoring. The patient obtains the current blood pressure in real time through the blood pressure monitoring module, including invasive / non-invasive systolic and diastolic blood pressure.

[0060] Step c, online blood pressure monitoring, the personalized differentiated threshold data provided in step a and the real-time data provided in step b are sent to the blood pressure background management module for storage, and the blood pressure background management module compares and analyzes the data, issues warnings for potential risks or predicted results of reaching / exceeding the alarm peak, and gives risk warnings and preview intervention preparation signals,

[0061] Specifically, if the blood pressure background management module detects abnormal data in the patient's real-time invasive / non-invasive blood pressure, a mandatory alarm and mandatory intervention will be issued until the data monitored again returns to the normal threshold range, and the mandatory alarm will be lifted. If the patient's real-time invasive / non-invasive blood pressure is monitored to continuously increase / decrease, or continuously maintain a high / low state, an early warning alarm will be issued and intervention will be required until the data monitored again returns to the normal threshold range, and the early warning alarm will be lifted.

[0062] In step a, the patient's BMI = weight / height. When BMI < 18.5, the patient is underweight. If 18.5 ≤ BMI < 24, the patient is normal. If 24 ≤ BMI < 24, the patient is overweight. If BMI ≥ 28, the patient is obese.

[0063] Further, the present invention is explained:

[0064] The blood pressure monitoring module of the present invention can measure and continuously monitor the vital signs of surgical patients through the ECG monitor next to the operating bed. Since the ECG monitor has been set by professionals before leaving the factory, the threshold is unchanged. If the monitored value exceeds the preset value in the ECG monitor, an alarm can be issued.

[0065] Since patients undergoing outpatient surgeries are under anesthesia and cannot communicate effectively, and the patient mobility is relatively high and the duration of the operation is relatively short, it is impossible to adjust the threshold of the ECG monitor individually and in a targeted manner. Therefore, the threshold setting of the monitor is always at the original threshold, that is, between invasive (IBP: systolic blood pressure 95-140mmhg, diastolic blood pressure 62-88mmhg) and non-invasive (NIBP: systolic blood pressure 90-140mmhg, diastolic blood pressure 60-90mmhg. Since outpatient surgeries are mostly based on non-invasive blood pressure data, the default threshold of the present invention uses non-invasive blood pressure as a reference.

[0066] The blood pressure prediction processing module can collect the patient's weight and height information through a weight meter and a height ruler, and generate influencing factors that affect the systolic / diastolic pressure thresholds based on the patient's own basic information and condition, namely, weight values ​​such as BMI, age, gender, past history, and comorbidities, and re-update the differentiated personal systolic / diastolic pressure thresholds based on the patient's own body.

[0067] Take BMI value as an example:

[0068]

[0069] The blood pressure background management module: uses the host computer (human-computer interaction machine) as a carrier, and automatically sets differentiated thresholds for the blood pressure monitoring module in the system based on the data generated by the blood pressure prediction processing module, replacing the equipment's indiscriminate monitoring value and set value comparison, and the alarm function of exceeding the set value, and instead adopts differentiated targeted monitoring, and adds early warning judgment of critical values ​​on the basis of the set value. Based on a large amount of data accumulation, the system automatically determines whether the patient's next vital signs will reach or exceed the set value, so that the anesthesiologist can intervene in advance.

[0070] Here are some specific examples:

[0071] Case 1: A male patient enters the operating room of the endoscopy center for painless gastroscopy + painless colonoscopy. The system extracts the patient's basic preoperative information, including: height 180cm, weight 99.5kg, age 65 years old, etc. The blood pressure prediction processing module obtains the patient's BMI value = 30.71> 28.0, which belongs to the obese population. He has a very high risk of hypertension. The NIBP monitoring range is predicted to be between systolic pressure 90-159mmhg and diastolic pressure 60-99mmhg; the patient is 65 years old and has a high risk of aging. The NIBP monitoring range is predicted to be between systolic pressure 90-150mmhg and diastolic pressure 60-89mmhg; according to the comparison of the two weight levels, the age monitoring range is included in the BMI monitoring range, and the BMI (hypertension) monitoring range is used as the final monitoring standard output, and the age monitoring range is output as the critical interval. (Assuming that the BMI value of the male patient is within the normal range and there is no risk, the age monitoring range is used as the final monitoring standard output according to the weight logic, and the BMI (normal) monitoring range is used as the critical interval output) During the patient's surgery, the real-time data provided by the monitor showed that under normal circumstances, the patient's systolic blood pressure is maintained between 120-140mmhg and the diastolic blood pressure fluctuates between 68-90mmhg. Event 1: During the operation, when the patient's systolic blood pressure rises to 150mmhg, the system issues a warning event reminder based on the calculation of the blood pressure background management module. Event 2: During the operation, when the patient's systolic blood pressure rises to 159mmhg, the system issues an alarm event warning based on the calculation of the blood pressure background management module. Figure 4 shown.

[0072] Case 2: A male patient entered the operating room of the endoscopy center for painless gastroscopy + painless colonoscopy. The system extracted the patient's basic preoperative information, including: height 180cm, weight 55kg, age 42 years old, etc. The blood pressure prediction processing module showed that the patient's BMI value = 16.98 < 18.5, which means he is a thin person and is at risk of hypotension. It is predicted that his NIBP monitoring range is between systolic 80-140mmhg and diastolic 50-90mmhg; the patient is 42 years old and is risk-free. It is predicted that his NIBP monitoring range is between systolic 90-140mmhg and diastolic 60-90mmhg. According to the large amount of historical data that meets the patient's condition characteristics accumulated in the blood pressure background management module, calculation and analysis are performed to simulate the patient's NIBP blood pressure fluctuation trend under normal surgical procedures, and the blood pressure data generated by the real-time blood pressure monitoring module are compared with the difference, which is used as a reference standard for judgment. When the deviation is obvious, an early warning is issued, such as Figure 5 shown.

[0073] Case 1 represents the short-term systolic blood pressure fluctuation trend of normal risk-free patients under different surgeries, such as Figure 6As shown in Figure 2, under normal risk-free conditions, the patient's NIBP systolic pressure does not change significantly, and only changes according to the length of the operation. Case 2 represents the short-term systolic pressure fluctuation trend of patients with different risks under the same operation, such as Figure 7 As shown in the figure, under painless gastroenteroscopy, the NIBP systolic blood pressure of patients with normal / hypertension / hypotension will change significantly, and the more verified the abnormal blood pressure symptoms are, the more obvious the changes will be.

[0074] It should be noted that according to the ratio of anesthesiologists to patients in China, the country is currently overloaded with work, with an average of 0.6 anesthesiologists for every 1,000 patients, compared to 3.5 anesthesiologists for every 1,000 patients in the United States. At the same time, German hospitals at the same level as China have an average of about 287 anesthesiologists, far exceeding the number of anesthesiologists in China. And this data does not include the fact that domestic anesthesiologists need to take into account both hospitalization and outpatient anesthesia work, and their workload far exceeds the average work pressure abroad. At the same time, in outpatient settings such as endoscopy, anesthesiologists have to undertake medical and technical examinations while maintaining the patient's vital signs. At the same time, their attention and judgment will be relatively scattered, and the system needs to provide more intelligent assistance to reduce their work intensity and burden.

[0075] Therefore, the method of the present invention not only monitors the patient's vital signs in real time, but also automatically generates accurate monitoring intervals with individual differences according to the patient's own situation for automated background data monitoring, and converts the predicted abnormal risk data into an alarm signal to notify the anesthesiologist, so that the anesthesiologist's attention can be more focused on the surgical treatment itself. At the research level, based on the accumulation of a large amount of surgical data with the same ICD surgical name (such as painless colonoscopy), the invasive / non-invasive blood pressure fluctuation trend of the patient under different influencing factors is predicted, providing more accurate auxiliary support for the abnormal risk data prediction function.

[0076] Based on the above ideal embodiments of the present invention, the relevant staff can make various changes and modifications without departing from the technical concept of the present invention through the above description. The technical scope of the present invention is not limited to the contents of the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. A system for generating blood pressure monitoring predictions for surgical patients based on BMI values, characterized in that: include: The blood pressure prediction processing module is used to collect the patient's basic information and obtain the patient's personalized differentiated blood pressure threshold based on the patient's basic information. Blood pressure monitoring module, used to collect patients' real-time invasive / non-invasive blood pressure, The blood pressure background management module combines the personalized differentiated threshold data provided by the blood pressure prediction processing module and the real-time data provided by the blood pressure monitoring module to compare and analyze the data, and conducts risk warning and pre-intervention preparation based on the analysis results. Among them, the basic information of patients collected by the blood pressure prediction processing module includes three types of factor elements, namely BMI factor, age factor and gender factor. The three types of factors are combined to predict the patient's risk and make differentiated personalized corrections to the blood pressure range.

2. The system for generating blood pressure monitoring predictions for surgical patients based on BMI values ​​according to claim 1, characterized in that: The blood pressure prediction processing module comprises: The patient information submodule is used to collect basic patient information as raw data for differentiated judgment and calculation. The factor and risk submodule generates the patient's own BMI value based on the collected height and weight information to determine the risk of high / low blood pressure, and uses it as the first-level weight. It determines the high / low age risk based on the patient's age value and uses it as the second-level weight. It determines the gender difference range based on the patient's gender value and uses it as the third-level weight. The blood pressure threshold calculation and prediction submodule performs personalized calculation and numerical matching of the patient's blood pressure according to the weights of three types of factors, namely, based on the blood pressure threshold BV provided by BMI, the blood threshold AV provided by age, and the blood pressure threshold SV provided by gender, it generates the patient's individually differentiated predicted normal blood pressure value, critical range interval alarm value and predicted blood pressure waveform trend.

3. The system for generating blood pressure monitoring predictions for surgical patients based on BMI values ​​according to claim 1, characterized in that: The blood pressure background management module includes: The data monitoring submodule receives and stores the real-time invasive / non-invasive blood pressure of the patient collected by the blood pressure monitoring module, including systolic pressure and diastolic pressure, and the patient's personalized differentiated blood pressure threshold output by the blood pressure prediction processing module. The data monitoring submodule compares the two sets of values ​​and outputs them as normal and abnormal data. The early warning / alarm management submodule, if abnormal data is monitored in the patient's real-time invasive / non-invasive blood pressure, a mandatory alarm and mandatory intervention will be issued until the data monitored again returns to the normal threshold range, and the mandatory alarm will be lifted. If the patient's real-time invasive / non-invasive blood pressure is monitored to continuously increase / decrease, or continuously maintain a high / low state, a early warning alarm will be issued and intervention will be required until the data monitored again returns to the normal threshold range, and the early warning alarm will be lifted.

4. A method for generating blood pressure monitoring predictions for surgical patients based on BMI values ​​according to claim 1, characterized in that: The specific steps are: Step a, predicting the blood pressure threshold, the blood pressure prediction processing module performs risk prediction based on the patient's personal diagnosis and treatment basic data, and makes personalized corrections to the blood pressure range. Specifically, the patient's personal information is collected, including height, weight, age and gender, where the patient's height and weight constitute the patient's BMI factor as the first-level weight, age constitutes the age factor as the second-level weight, and gender is the gender factor as the third-level weight. According to the patient's height and weight, the patient's BMI value is obtained to determine the risk of high / low blood pressure. If BMI < 18.5, the patient is judged to be at risk of low blood pressure. If 18.5 ≤ BMI < 24, the patient is judged to be at no risk. If 24 ≤ BMI < 24 or BMI ≥ 28, the patient is judged to be at risk of high blood pressure. If the patient is younger than 14 years old, the patient is considered to be of low age and low risk; if the patient is between 14 and 60 years old, the patient is considered to be of no risk; if the patient is between 60 and 80 years old, the patient is considered to be of high age and high risk. The gender difference value is determined based on the patient's gender value, and the female value is lower than the male value. According to the weight level and combined with the blood pressure threshold BV provided by the patient's BMI, the blood pressure threshold AV provided by age, and the blood pressure threshold SV provided by gender, the patient's individual differentiated blood pressure predicted normal value, critical value range interval alarm value, and predicted blood pressure waveform trend are generated. Step b: real-time blood pressure monitoring. The patient obtains the current blood pressure in real time through the blood pressure monitoring module, including invasive / non-invasive systolic and diastolic blood pressure. Step c, online blood pressure monitoring, the personalized differentiated threshold data provided in step a and the real-time data provided in step b are sent to the blood pressure background management module for storage, and the blood pressure background management module compares and analyzes the data, issues warnings for potential risks or predicted results of reaching / exceeding the alarm peak, and gives risk warnings and preview intervention preparation signals, Specifically, if the blood pressure background management module detects abnormal data in the patient's real-time invasive / non-invasive blood pressure, a mandatory alarm and mandatory intervention will be issued until the data monitored again returns to the normal threshold range, and the mandatory alarm will be lifted. If the patient's real-time invasive / non-invasive blood pressure is monitored to continuously increase / decrease, or continuously maintain a high / low state, an early warning alarm will be issued and intervention will be required until the data monitored again returns to the normal threshold range, and the early warning alarm will be lifted.

5. The method for generating blood pressure monitoring prediction for surgical patients according to BMI value according to claim 4, characterized in that: In step a, the patient's BMI = weight / height. When BMI < 18.5, the patient is underweight. If 18.5 ≤ BMI < 24, the patient is normal. If 24 ≤ BMI < 24, the patient is overweight. If BMI ≥ 28, the patient is obese.

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