Orthopedic postoperative infection risk assessment method and system

Abstract

The invention relates to the field of postoperative infection risk assessment, in particular to an orthopedic postoperative infection risk assessment method and system. The method comprises the following steps: firstly, acquiring the erythrocyte sedimentation rates of a patient to be detected and uninfected normal patients of different ages every day after an operation and the age of the patient to be detected, and acquiring an initial normal erythrocyte sedimentation predicted value every day based on the erythrocyte sedimentation rates of all the normal patients on the same day after the operation; the first infection possibility of each day is obtained according to the difference between the red blood cell sedimentation rate and the initial normal erythrocyte sedimentation predicted value of the to-be-detected patient every day, the real normal erythrocyte sedimentation predicted value of each day is obtained by combining the age of the to-be-detected patient, and the first infection possibility of each day is obtained according to the difference between the red blood cell sedimentation rate and the real normal erythrocyte sedimentation predicted value of the to-be-detected patient every day. Obtaining a second infection possibility of every day, and evaluating the infection risk of the patient to be detected every day after the operation in combination with the first infection possibility and the second infection possibility. According to the invention, the accuracy of infection risk assessment of the patient after the orthopedic surgery is improved.

Description

Technical Field

[0001] The present invention relates to the field of postoperative infection risk assessment, and in particular to an orthopedic postoperative infection risk assessment method and system. Background Art

[0002] After orthopedic surgery, wound healing takes some time. During this period, the patient may be at risk of inflammation and infection due to environmental pollution or improper care. Severe infection may spread throughout the body and endanger life. Therefore, assessing whether the patient is at risk of infection after orthopedic surgery can detect potential infection in a timely manner, control the spread of inflammation and infection, and protect the patient's health.

[0003] The erythrocyte sedimentation rate is a key indicator for determining whether an inflammatory infection occurs in a patient's body. In related technologies, the erythrocyte sedimentation rate of patients after orthopedic surgery is usually analyzed to assess whether the patient is at risk of infection. However, due to differences in metabolic rates and other physical conditions among patients of different ages, the erythrocyte sedimentation rate levels of patients of different ages are different, which results in the inability of existing assessment methods that do not consider the patient's age to accurately assess the patient's infection risk after orthopedic surgery. Summary of the invention

[0004] In order to solve the technical problem that the existing assessment method that does not consider the patient's age cannot accurately assess the patient's infection risk after orthopedic surgery, the purpose of the present invention is to provide an orthopedic postoperative infection risk assessment method and system, and the technical solution adopted is as follows: The present invention proposes a method for assessing the risk of postoperative infection in orthopedics, the method comprising: Obtaining the daily erythrocyte sedimentation rate of each normal patient of different ages who is not infected within a preset historical period after surgery, and the daily erythrocyte sedimentation rate of the patient to be tested within a preset historical period after surgery, and obtaining the age of the patient to be tested; Fitting the overall level of the erythrocyte sedimentation rate of all normal patients on the same day after surgery, constructing an initial prediction model, and obtaining the initial normal erythrocyte sedimentation rate prediction value every day after surgery; obtaining the first infection possibility of the patient to be tested every day after surgery based on the difference between the erythrocyte sedimentation rate of the patient to be tested every day after surgery and the initial normal erythrocyte sedimentation rate prediction value every day after surgery; According to the erythrocyte sedimentation rate of all normal patients of the same age on the same day, a line graph showing the change of the erythrocyte sedimentation rate of normal patients with age is drawn every day, and according to the age of the patient to be tested, a true prediction model is constructed, and the true normal erythrocyte sedimentation rate prediction value is obtained every day after surgery; according to the difference between the erythrocyte sedimentation rate of the patient to be tested every day after surgery and the true normal erythrocyte sedimentation rate prediction value every day after surgery, the second infection possibility of the patient to be tested every day after surgery is obtained; the first infection possibility and the second infection possibility are combined to obtain the true infection possibility of the patient to be tested every day after surgery; Based on the actual infection possibility, the infection risk of the patient after surgery is assessed every day.

[0005] Furthermore, the overall level of the erythrocyte sedimentation rate of all normal patients on the same day after surgery is fitted to construct an initial prediction model, and the initial normal erythrocyte sedimentation rate prediction value for each day after surgery is obtained, including: The average of the erythrocyte sedimentation rates of all normal patients on the same day after surgery was taken as the first overall normal erythrocyte sedimentation rate for each day after surgery; Performing exponential decay fitting processing on the first overall normal erythrocyte sedimentation rate value every day after surgery to construct an initial prediction model; The expression of the initial prediction model is: in, Indicates postoperative Initial normal ESR predicted value for the day; represents a natural constant; After surgery The serial number corresponding to the day Substitute it into the initial prediction model to obtain the initial normal erythrocyte sedimentation rate prediction value for each day after surgery.

[0006] Further, obtaining the first infection probability of the patient to be tested every day after surgery includes: The absolute value of the difference between the erythrocyte sedimentation rate of the patient to be tested every day after surgery and the initial normal erythrocyte sedimentation rate predicted value every day after surgery is normalized to obtain the first infection possibility of the patient to be tested every day after surgery.

[0007] Furthermore, the method of drawing a line graph showing the change of the erythrocyte sedimentation rate of normal patients with age on each day based on the erythrocyte sedimentation rate of all normal patients of the same age on the same day includes: Taking any age as the target age, taking any day after surgery as the target day, and taking the average of the erythrocyte sedimentation rates of all normal patients of the target age on the target day as the second overall normal erythrocyte sedimentation rate of the target age on the target day; The two-dimensional data points formed by each age and the second overall normal erythrocyte sedimentation rate of each age on the target day are mapped into a coordinate system, and the two-dimensional data points in the coordinate system are connected by broken lines to draw a line graph of the erythrocyte sedimentation rate of normal patients on the target day versus age, wherein the horizontal axis of the coordinate system represents age and the vertical axis represents the second overall normal erythrocyte sedimentation rate.

[0008] Furthermore, the construction of a true prediction model according to the age of the patient to be tested and obtaining the true normal erythrocyte sedimentation rate prediction value every day after surgery includes: The expression of the true prediction model is: in, Indicates postoperative The true normal ESR predicted value for the day; represents a natural constant; represents the age of the patient to be tested; After surgery The serial number corresponding to the day Substitute it into the real prediction model to obtain the real normal erythrocyte sedimentation rate prediction value for each day after surgery.

[0009] Further, obtaining the possibility of a second infection of the patient to be tested every day after surgery includes: The absolute value of the difference between the erythrocyte sedimentation rate of the patient to be tested every day after surgery and the true normal erythrocyte sedimentation rate predicted value every day after surgery is normalized to obtain the second infection possibility of the patient to be tested every day after surgery.

[0010] Furthermore, obtaining the actual infection probability of the patient to be tested every day after surgery includes: The sum of the first infection possibility and the second infection possibility is normalized to obtain the actual infection possibility of the patient to be tested every day after the operation.

[0011] Furthermore, the assessment of the infection risk of the patient after surgery every day includes: Any day after surgery is used as a test day. If the actual infection possibility of the test patient on the test day is less than a preset first possibility threshold, the infection risk level of the test patient on the test day is a first-level infection risk. If the actual infection probability of the patient to be tested on the day to be tested is greater than or equal to the preset first probability threshold and less than the preset second probability threshold, the infection risk level of the patient to be tested on the day to be tested is a secondary infection risk; If the actual infection possibility of the patient to be tested on the day to be tested is greater than or equal to the preset second possibility threshold, the infection risk level of the patient to be tested on the day to be tested is level three infection risk, among which level three infection risk has the highest severity, level two infection risk is second, and level one infection risk is the lowest.

[0012] Furthermore, the value range of the preset first possibility threshold is , the value range of the preset second possibility threshold is .

[0013] The present invention also proposes an orthopedic postoperative infection risk assessment system, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any step of an orthopedic postoperative infection risk assessment method.

[0014] The present invention has the following beneficial effects: The present invention takes into account that the existing assessment methods that do not consider the patient's age are unable to accurately assess the patient's infection risk after orthopedic surgery. Therefore, the daily erythrocyte sedimentation rate of each normal patient of different ages who is not infected within a preset historical time period after surgery, as well as the daily erythrocyte sedimentation rate of the patient to be tested within a preset historical time period after surgery, and the age of the patient to be tested is obtained at the same time. Subsequently, the infection risk of the patient to be tested can be accurately assessed based on the daily erythrocyte sedimentation rate of normal patient groups of different ages, combined with the daily erythrocyte sedimentation rate of the patient to be tested and the age of the patient to be tested. Since normal patients are patients who have not been infected, the erythrocyte sedimentation rate of normal patients is at a normal level. Therefore, the daily erythrocyte sedimentation rate in the absence of infection can be predicted by the constructed initial prediction model to obtain the initial normal erythrocyte sedimentation rate prediction value for each day after surgery. The difference between the erythrocyte sedimentation rate of the patient to be tested on a certain day and the initial normal erythrocyte sedimentation rate prediction value on that day is calculated. The larger the difference is, the more likely the patient to be infected on that day is. Therefore, the first infection possibility can preliminarily reflect the possibility of inflammatory infection in the patient to be tested every day after surgery. Taking into account the different levels of erythrocyte sedimentation rate in patients of different ages, if the age is not considered, the accuracy of infection risk assessment will be low. Therefore, the present invention first draws a line graph of the erythrocyte sedimentation rate of normal patients every day versus age, and reflects the relationship between the erythrocyte sedimentation rate and age through the line graph. The real prediction model that takes age factors into consideration is constructed to predict the erythrocyte sedimentation rate every day in the absence of infection, and obtain the real normal erythrocyte sedimentation rate prediction value for each day after surgery. The second infection possibility is used to further reflect the possibility of inflammatory infection in the patient to be tested every day after surgery. Then, the first infection possibility and the second infection possibility are combined, and the infection risk of the patient after orthopedic surgery is accurately assessed through the obtained real infection possibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. 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 paying creative work.

[0016] Figure 1 A flow chart of a method for assessing the risk of postoperative infection in orthopedics provided by one embodiment of the present invention; Figure 2 A schematic diagram of a line graph showing daily changes in erythrocyte sedimentation rate of a normal patient with age provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the orthopedic postoperative infection risk assessment method and system proposed by the present invention, its specific implementation method, structure, characteristics and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0019] The specific scheme of the orthopedic postoperative infection risk assessment method and system provided by the present invention is described in detail below with reference to the accompanying drawings.

[0020] See also Figure 1 , which shows a flow chart of a method for assessing the risk of postoperative infection in orthopedics provided by an embodiment of the present invention, the method comprising: Step S1: Obtain the daily erythrocyte sedimentation rate of each normal patient of different ages who is not infected within a preset historical time period after surgery, and the daily erythrocyte sedimentation rate of the patient to be tested within a preset historical time period after surgery, and obtain the age of the patient to be tested.

[0021] After a patient completes orthopedic surgery in the hospital, in order to prevent wound infection, the hospital usually uses medical equipment such as an erythrocyte sedimentation rate dynamic analyzer or a fully automatic erythrocyte sedimentation rate detector to test the patient's erythrocyte sedimentation rate every day, and stores the collected erythrocyte sedimentation rate data in the hospital data management system for subsequent data analysis.

[0022] Therefore, the embodiment of the present invention first extracts the daily erythrocyte sedimentation rate of each normal patient of different ages who is not infected within a preset historical time period after surgery from the hospital data management system, wherein the normal patients are patients who do not have inflammatory infection after orthopedic surgery, the erythrocyte sedimentation rate of normal patients every day after surgery is at a normal level, and there are multiple normal patients of each age.

[0023] At the same time, the embodiment of the present invention also needs to use medical equipment such as an erythrocyte sedimentation rate dynamic analyzer or a fully automatic erythrocyte sedimentation rate detector to collect the erythrocyte sedimentation rate of the patient to be tested every day within a preset historical time period after surgery in real time, and obtain the age of the patient to be tested. Among them, it is unknown whether the patient to be tested will develop inflammatory infection after orthopedic surgery. The subsequent steps need to evaluate the infection risk of the patient to be tested every day after surgery.

[0024] The preset time period is set to one month, and the specific value of the preset time period can also be set by the implementer according to the specific implementation scenario, which is not limited here.

[0025] Step S2: Fit the overall level of erythrocyte sedimentation rate of all normal patients on the same day after surgery, construct an initial prediction model, and obtain the initial normal erythrocyte sedimentation rate prediction value for each day after surgery; based on the difference between the erythrocyte sedimentation rate of the patient to be tested every day after surgery and the initial normal erythrocyte sedimentation rate prediction value for each day after surgery, obtain the first infection possibility of the patient to be tested every day after surgery.

[0026] When the patient does not have an infection within a period of time after orthopedic surgery, the patient's erythrocyte sedimentation rate is shown in the following manner within a period of time after surgery: in the early postoperative period, the patient's erythrocyte sedimentation rate is high, and as time goes by, the patient's erythrocyte sedimentation rate gradually decreases, showing an exponential decay feature as a whole; and when the patient has an infection after surgery, the patient's erythrocyte sedimentation rate will gradually increase as time goes by, that is, the difference between the erythrocyte sedimentation rate when the infection occurs after surgery and the erythrocyte sedimentation rate when the infection does not occur after surgery is large. Therefore, the embodiment of the present invention first performs fitting processing on the overall level of the erythrocyte sedimentation rate of all normal patients on the same day after surgery, constructs an initial prediction model, and uses the initial prediction model to predict the erythrocyte sedimentation rate of each day without infection, thereby obtaining the initial normal erythrocyte sedimentation rate prediction value every day after surgery. The initial normal erythrocyte sedimentation rate prediction value is the normal value of the erythrocyte sedimentation rate when no infection occurs. Subsequently, based on the difference between the erythrocyte sedimentation rate of the patient to be tested every day after surgery and the initial normal erythrocyte sedimentation rate prediction value every day after surgery, the possibility of infection in the patient to be tested every day can be preliminarily analyzed.

[0027] Preferably, in one embodiment of the present invention, the method for obtaining the initial normal erythrocyte sedimentation rate prediction value every day after surgery specifically includes: First, the average erythrocyte sedimentation rate of all normal patients on the same day after surgery is taken as the first overall normal erythrocyte sedimentation value for each day after surgery. The first overall normal erythrocyte sedimentation value reflects the overall level of erythrocyte sedimentation rate of all normal patients on the same day after surgery.

[0028] Then, the first overall normal erythrocyte sedimentation value of each day after surgery is subjected to exponential decay fitting processing to construct an initial prediction model, wherein exponential decay fitting is a technical means well known to those skilled in the art and will not be described in detail here.

[0029] The expression of the initial prediction model is: in, Indicates postoperative Initial normal ESR predicted value for the day; Represents a natural constant.

[0030] Finally, the postoperative The serial number corresponding to the day Substitute it into the initial prediction model to obtain the initial normal ESR prediction value for each day after surgery. If the initial normal ESR value is predicted for the day Substitute it into the initial prediction model.

[0031] When the patient develops an infection after orthopedic surgery, the patient's erythrocyte sedimentation rate will exceed the normal level, and the more it exceeds the normal level, the more likely the patient is to develop an infection. Therefore, the difference between the patient's erythrocyte sedimentation rate every day after surgery and the initial normal erythrocyte sedimentation rate predicted value every day after surgery can be analyzed. The first infection possibility obtained can preliminarily reflect the possibility of inflammatory infection in the patient every day after surgery, thereby improving the data basis for subsequent infection risk assessment.

[0032] Preferably, in one embodiment of the present invention, the method for obtaining the first infection possibility of the patient to be tested every day after surgery specifically includes: The greater the difference between the erythrocyte sedimentation rate of the patient to be tested and the initial normal erythrocyte sedimentation rate predicted value after surgery, the greater the possibility of infection in the patient to be tested every day. Therefore, the absolute value of the difference between the erythrocyte sedimentation rate of the patient to be tested and the initial normal erythrocyte sedimentation rate predicted value after surgery can be normalized and the calculation result can be limited to The range is within which the first infection possibility of the patient to be tested every day after the operation is obtained. The greater the first infection possibility of the patient to be tested on a certain day, the more likely the patient to be tested is to have an inflammatory infection phenomenon on that day.

[0033] In one embodiment of the present invention, the normalization processing can be specifically, for example, maximum and minimum value normalization processing, and the normalization in subsequent steps can all adopt maximum and minimum value normalization processing. In other embodiments of the present invention, other normalization methods can be selected according to the specific range of numerical values, which will not be described in detail.

[0034] As an example, in one embodiment of the present invention, the expression for the first infection possibility of the patient to be tested every day after surgery can be specifically, for example, as follows: in, Indicates that the patient is The first probability of infection on the day; Indicates the patient's first Erythrocyte sedimentation rate per day; Indicates postoperative Initial normal ESR predicted value for the day; Represents the normalization function.

[0035] At this point, the first infection probability of the patient every day after surgery is obtained.

[0036] Step S3: Based on the erythrocyte sedimentation rate of all normal patients of the same age on the same day, draw a line graph of the erythrocyte sedimentation rate of normal patients every day versus age, and build a true prediction model based on the age of the patient to be tested, and obtain the true normal erythrocyte sedimentation rate prediction value for each day after surgery; based on the difference between the erythrocyte sedimentation rate of the patient to be tested every day after surgery and the true normal erythrocyte sedimentation rate prediction value for each day after surgery, obtain the possibility of a second infection for the patient to be tested every day after surgery; combining the first infection possibility and the second infection possibility, obtain the true infection possibility for the patient to be tested every day after surgery.

[0037] Since the erythrocyte sedimentation rate levels of patients of different ages are different, if age is not taken into consideration, the accuracy of infection risk assessment will be low. Therefore, if the first infection possibility obtained above is directly used to evaluate whether the patient to be tested has an infection every day after surgery, the accuracy of the final evaluation result will be reduced. Therefore, the embodiment of the present invention first draws a line graph of the erythrocyte sedimentation rate of normal patients every day according to the erythrocyte sedimentation rate of all normal patients of the same age on the same day. Subsequently, a true prediction model can be constructed based on the characteristics of the normal erythrocyte sedimentation rate changing with age in the line graph, thereby improving the accuracy of the prediction of the normal value of the erythrocyte sedimentation rate every day after surgery in the absence of infection, thereby improving the accuracy of subsequent infection risk assessment of the patient to be tested every day after surgery.

[0038] Preferably, in one embodiment of the present invention, the method for obtaining a line graph of the daily changes in the erythrocyte sedimentation rate of a normal patient with age specifically includes: First, take any age as the target age, and any day after surgery as the target day. Take the average erythrocyte sedimentation rate of all normal patients of the target age on the target day as the second overall normal erythrocyte sedimentation rate of the target age on the target day. The second overall normal erythrocyte sedimentation rate reflects the overall level of erythrocyte sedimentation rate of all normal patients of the target age on the target day. The second overall normal erythrocyte sedimentation rate of each age on the target day can be obtained by the same method as above.

[0039] Furthermore, the two-dimensional data points consisting of each age and the second overall normal erythrocyte sedimentation rate value of each age on the target day are mapped to the coordinate system, and the two-dimensional data points in the coordinate system are connected by broken lines to draw a line graph of the erythrocyte sedimentation rate of normal patients on the target day as they change with age, wherein the horizontal axis of the coordinate system represents age, and the vertical axis represents the second overall normal erythrocyte sedimentation rate value. The same method as above can be used to obtain a line graph of the erythrocyte sedimentation rate of normal patients on the target day as they change with age.

[0040] See also Figure 2 , which shows a schematic diagram of a line graph of the daily changes in the erythrocyte sedimentation rate of a normal patient with age provided by an embodiment of the present invention, Figure 2Only the line graphs of the erythrocyte sedimentation rate of normal patients on the 1st, 7th, 14th and 28th days versus age are listed in the figure, where line A, line B, line C and line D are the line graphs of the erythrocyte sedimentation rate of normal patients on the 1st, 7th, 14th and 28th days versus age, respectively. It can be seen from the line graphs that with increasing age, the second overall normal erythrocyte sedimentation rate of all normal patients of all ages on the same day shows an upward trend, and the erythrocyte sedimentation rate of normal patients of the same age is higher in the early postoperative period, and the erythrocyte sedimentation rate gradually decreases with the passage of time. In other words, the stronger the correlation between the erythrocyte sedimentation rate of normal patients and age, the higher the erythrocyte sedimentation rate is in general for older normal patients in the early postoperative period, because the slower metabolism in the elderly causes the erythrocyte sedimentation rate to decrease more slowly. And the erythrocyte sedimentation rate is still at a relatively high level after recovery, while for normal patients of younger age, the erythrocyte sedimentation rate recovers faster after surgery and is closer to the normal level after recovery. Therefore, a true prediction model can be constructed according to the age of the patient to be tested. The true prediction model taking into account the age factor is used to predict the daily erythrocyte sedimentation rate in the absence of infection, thereby obtaining the true normal erythrocyte sedimentation rate prediction value for each day after surgery. The true normal erythrocyte sedimentation rate prediction value is the normal value of the erythrocyte sedimentation rate predicted in the absence of infection taking into account the age factor. Subsequently, based on the difference between the erythrocyte sedimentation rate of the patient to be tested every day after surgery and the true normal erythrocyte sedimentation rate prediction value for each day after surgery, a more accurate analysis can be made on the possibility of infection in the patient to be tested every day, thereby improving the accuracy of subsequent orthopedic postoperative infection risk assessment for the patient to be tested.

[0041] Preferably, in one embodiment of the present invention, the method for obtaining the true normal erythrocyte sedimentation rate prediction value every day after surgery specifically includes: First, we build a true prediction model. The expression of the true prediction model is: in, Indicates postoperative The true normal ESR predicted value for the day; represents a natural constant; Indicates the age of the patient to be tested.

[0042] Then, the postoperative The serial number corresponding to the day Substitute it into the real prediction model to obtain the real normal erythrocyte sedimentation rate prediction value for each day after surgery.

[0043] Similarly, the greater the difference between the erythrocyte sedimentation rate of the patient to be tested every day after surgery and the true normal erythrocyte sedimentation rate predicted value every day after surgery, the greater the possibility that the patient to be tested will develop an infection every day after surgery. Therefore, the difference between the erythrocyte sedimentation rate of the patient to be tested every day after surgery and the true normal erythrocyte sedimentation rate predicted value every day after surgery can be analyzed, and the second infection possibility obtained can be further reflected to further reflect the possibility of inflammatory infection in the patient to be tested every day after surgery. Subsequently, the second infection possibility and the first sensing possibility obtained above can be combined to accurately assess the infection risk of the patient to be tested every day after surgery.

[0044] Preferably, in one embodiment of the present invention, the method for obtaining the possibility of a second infection of the patient to be tested every day after surgery specifically includes: The absolute value of the difference between the daily erythrocyte sedimentation rate of the patient after surgery and the actual normal erythrocyte sedimentation rate predicted value after surgery was normalized, and the calculation result was limited to range, thereby obtaining the possibility of a second infection for the patient every day after surgery.

[0045] As an example, in one embodiment of the present invention, the expression for the possibility of a second infection of the patient to be tested every day after surgery may be specifically, for example, as follows: in, Indicates that the patient is Probability of second infection on day 1; Indicates the patient's first Erythrocyte sedimentation rate per day; Indicates postoperative Initial normal ESR predicted value for the day; Represents the normalization function.

[0046] The greater the first infection possibility and the second infection possibility of the patient to be tested every day after surgery, the greater the risk of inflammatory infection of the patient to be tested every day after surgery. Therefore, the first infection possibility and the second infection possibility can be further combined to obtain the actual infection possibility of the patient to be tested every day after surgery. Subsequently, based on the actual infection possibility, the infection risk of the patient to be tested every day after surgery can be accurately assessed.

[0047] Preferably, in one embodiment of the present invention, the method for obtaining the actual infection probability of the patient to be tested every day after surgery specifically includes: The sum of the first infection possibility and the second infection possibility is normalized to obtain the actual infection possibility of the patient after surgery every day.

[0048] As an example, in one embodiment of the present invention, the expression of the actual infection probability of the patient to be tested every day after surgery can be specifically, for example, as follows: in, Indicates that the patient is The actual probability of infection per day; Indicates that the patient is The first probability of infection on the day; Indicates that the patient is Probability of second infection on day 1; Represents the normalization function.

[0049] In other embodiments of the present invention, the product value of the first infection possibility and the second infection possibility may also be normalized to obtain the actual infection possibility of the patient to be tested every day after the operation, which is not limited here.

[0050] At this point, the actual infection probability of the patients after surgery is obtained.

[0051] Step S4: Based on the actual infection possibility, the infection risk of the patient after surgery is assessed every day.

[0052] The greater the actual possibility of infection for the patient to be tested on a certain day after surgery, the more likely the patient to be tested is to be at risk of infection on that day. Therefore, based on the actual possibility of infection, the infection risk of the patient to be tested every day after surgery can be evaluated, thereby improving the accuracy of the infection risk assessment of the patient to be tested every day after surgery.

[0053] Preferably, in one embodiment of the present invention, the method for evaluating the infection risk of the patient to be tested every day after surgery specifically includes: Any day after surgery is taken as a test day. If the actual infection possibility of the test patient on the test day is less than the preset first possibility threshold, the infection risk level of the test patient on the test day is level one infection risk. If the actual infection probability of the patient to be tested on the day to be tested is greater than or equal to the preset first probability threshold and less than the preset second probability threshold, the infection risk level of the patient to be tested on the day to be tested is a secondary infection risk; If the actual infection possibility of the patient to be tested on the testing day is greater than or equal to the preset second possibility threshold, the infection risk level of the patient to be tested on the testing day is level three infection risk, among which level three infection risk has the highest severity, level two infection risk is second, and level one infection risk is the lowest. Therefore, according to different infection risk levels, the hospital can adopt different medical measures to deal with them.

[0054] The value range of the preset first possibility threshold is , the value range of the preset second possibility threshold is In one embodiment of the present invention, the preset first possibility threshold is set to 0.2, and the preset second possibility threshold is set to 0.7. The preset first possibility threshold and the preset second possibility threshold can also be set by the implementer according to the specific implementation scenario, which is not limited here.

[0055] An embodiment of the present invention provides an orthopedic postoperative infection risk assessment system, which includes a memory, a processor and a computer program, wherein the memory is used to store the corresponding computer program, and the processor is used to run the corresponding computer program. When the computer program runs in the processor, it can implement the method described in steps S1 to S4.

[0056] In summary, the embodiment of the present invention first obtains the erythrocyte sedimentation rate of each normal patient of different ages who is not infected within a preset historical time period after surgery, as well as the erythrocyte sedimentation rate of the patient to be tested within a preset historical time period after surgery, and obtains the age of the patient to be tested at the same time; performs fitting processing on the overall level of the erythrocyte sedimentation rate of all normal patients on the same day after surgery, constructs an initial prediction model, and obtains the initial normal erythrocyte sedimentation rate prediction value every day after surgery; obtains the first infection possibility of the patient to be tested every day after surgery based on the difference between the erythrocyte sedimentation rate of the patient to be tested every day after surgery and the initial normal erythrocyte sedimentation rate prediction value every day after surgery; and calculates the infection possibility of the patient to be tested every day after surgery based on the corresponding The erythrocyte sedimentation rate of all normal patients of the same age on the same day is used to draw a line graph of the erythrocyte sedimentation rate of normal patients changing with age every day, and a true prediction model is constructed according to the age of the patient to be tested, and the true normal erythrocyte sedimentation rate prediction value for each day after surgery is obtained; based on the difference between the erythrocyte sedimentation rate of the patient to be tested every day after surgery and the true normal erythrocyte sedimentation rate prediction value for each day after surgery, the possibility of a second infection for the patient to be tested every day after surgery is obtained; the true possibility of infection for the patient to be tested every day after surgery is obtained by combining the first possibility of infection and the second possibility of infection; based on the true possibility of infection, the infection risk of the patient to be tested every day after surgery is evaluated.

[0057] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0058] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for assessing the risk of postoperative infection in orthopedics, characterized in that: The method comprises: Obtaining the daily erythrocyte sedimentation rate of each normal patient of different ages who is not infected within a preset historical period after surgery, and the daily erythrocyte sedimentation rate of the patient to be tested within a preset historical period after surgery, and obtaining the age of the patient to be tested; Fitting the overall level of the erythrocyte sedimentation rate of all normal patients on the same day after surgery, constructing an initial prediction model, and obtaining the initial normal erythrocyte sedimentation rate prediction value every day after surgery; obtaining the first infection possibility of the patient to be tested every day after surgery based on the difference between the erythrocyte sedimentation rate of the patient to be tested every day after surgery and the initial normal erythrocyte sedimentation rate prediction value every day after surgery; According to the erythrocyte sedimentation rate of all normal patients of the same age on the same day, a line graph showing the change of the erythrocyte sedimentation rate of normal patients with age is drawn every day, and according to the age of the patient to be tested, a true prediction model is constructed, and the true normal erythrocyte sedimentation rate prediction value is obtained every day after surgery; according to the difference between the erythrocyte sedimentation rate of the patient to be tested every day after surgery and the true normal erythrocyte sedimentation rate prediction value every day after surgery, the second infection possibility of the patient to be tested every day after surgery is obtained; the first infection possibility and the second infection possibility are combined to obtain the true infection possibility of the patient to be tested every day after surgery; Based on the actual infection possibility, the infection risk of the patient to be tested is assessed every day after surgery; The overall level of the erythrocyte sedimentation rate of all normal patients on the same day after surgery is fitted to construct an initial prediction model, and the initial normal erythrocyte sedimentation rate prediction value for each day after surgery is obtained, including: The average of the erythrocyte sedimentation rates of all normal patients on the same day after surgery was taken as the first overall normal erythrocyte sedimentation rate for each day after surgery; Performing exponential decay fitting processing on the first overall normal erythrocyte sedimentation rate value every day after surgery to construct an initial prediction model; The expression of the initial prediction model is: in, Indicates postoperative Initial normal ESR predicted value for the day; represents a natural constant; After surgery The serial number corresponding to the day Substitute it into the initial prediction model to obtain the initial normal ESR prediction value for each day after surgery; The method of constructing a true prediction model according to the age of the patient to be tested and obtaining the true normal erythrocyte sedimentation rate prediction value every day after surgery includes: The expression of the true prediction model is: in, Indicates postoperative The true normal ESR predicted value for the day; represents a natural constant; represents the age of the patient to be tested; After surgery The serial number corresponding to the day Substitute it into the real prediction model to obtain the real normal erythrocyte sedimentation rate prediction value for each day after surgery.

2. A method for assessing the risk of postoperative infection in orthopedics according to claim 1, characterized in that: The method of obtaining the first infection probability of the patient to be tested every day after surgery comprises: The absolute value of the difference between the erythrocyte sedimentation rate of the patient to be tested every day after surgery and the initial normal erythrocyte sedimentation rate predicted value every day after surgery is normalized to obtain the first infection possibility of the patient to be tested every day after surgery.

3. A method for assessing the risk of postoperative infection in orthopedics according to claim 1, characterized in that: The method of drawing a line graph of the erythrocyte sedimentation rate of normal patients on each day according to the erythrocyte sedimentation rate of all normal patients of the same age on the same day includes: Taking any age as the target age, taking any day after surgery as the target day, and taking the average of the erythrocyte sedimentation rates of all normal patients of the target age on the target day as the second overall normal erythrocyte sedimentation rate of the target age on the target day; The two-dimensional data points formed by each age and the second overall normal erythrocyte sedimentation rate of each age on the target day are mapped into a coordinate system, and the two-dimensional data points in the coordinate system are connected by broken lines to draw a line graph of the erythrocyte sedimentation rate of normal patients on the target day versus age, wherein the horizontal axis of the coordinate system represents age and the vertical axis represents the second overall normal erythrocyte sedimentation rate.

4. A method for assessing the risk of postoperative infection in orthopedics according to claim 1, characterized in that: The method of obtaining the possibility of a second infection of the patient to be tested every day after surgery comprises: The absolute value of the difference between the erythrocyte sedimentation rate of the patient to be tested every day after surgery and the true normal erythrocyte sedimentation rate predicted value every day after surgery is normalized to obtain the second infection possibility of the patient to be tested every day after surgery.

5. The method for assessing the risk of postoperative infection in orthopedics according to claim 1, characterized in that: The method of obtaining the actual infection probability of the patient to be tested every day after surgery includes: The sum of the first infection possibility and the second infection possibility is normalized to obtain the actual infection possibility of the patient to be tested every day after the operation.

6. A method for assessing the risk of postoperative infection in orthopedics according to claim 1, characterized in that: The infection risk assessment of the patient every day after surgery includes: Any day after surgery is used as a test day. If the actual infection possibility of the test patient on the test day is less than a preset first possibility threshold, the infection risk level of the test patient on the test day is a first-level infection risk. If the actual infection probability of the patient to be tested on the day to be tested is greater than or equal to the preset first probability threshold and less than the preset second probability threshold, the infection risk level of the patient to be tested on the day to be tested is a secondary infection risk; If the actual infection possibility of the patient to be tested on the day to be tested is greater than or equal to the preset second possibility threshold, the infection risk level of the patient to be tested on the day to be tested is level three infection risk, among which level three infection risk has the highest severity, level two infection risk is second, and level one infection risk is the lowest.

7. A method for assessing the risk of postoperative infection in orthopedics according to claim 6, characterized in that: The value range of the preset first possibility threshold is , the value range of the preset second possibility threshold is .

8. A system for assessing the risk of postoperative infection in orthopedics, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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