Neurosurgery non-planned secondary operation risk assessment model and assessment method thereof

Abstract

The invention provides a neurosurgery non-planned secondary operation risk evaluation model and an evaluation method thereof, and the evaluation model comprises score contrast lines which are uniformly distributed from 0 to 100, and an age contrast line, a disease type contrast line and an operation duration contrast line which are horizontally aligned with the score contrast lines. The evaluation model further comprises a total risk evaluation contrast line, a linear prediction contrast line and a risk coefficient contrast line, wherein the total risk evaluation contrast line and the linear prediction contrast line are evenly distributed from 0 to 240, and the risk coefficient contrast line is respectively mapped with the total risk evaluation contrast line and the linear prediction contrast line. According to the method, effective prevention measures can be taken in advance by identifying high-risk factors, the occurrence rate of the non-planned secondary surgery is reduced, so that the overall safety of a patient is remarkably improved, in addition, the requirements of the non-planned secondary surgery are predicted and reduced in advance, a medical institution can more effectively manage and distribute limited medical resources, and the medical institution is facilitated. The medical cost is reduced, and the medical service efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of neurosurgery medical technology, and in particular to an evaluation model and an evaluation method for the risk of unplanned secondary neurosurgery surgery. Background Art

[0002] Judging the risk of unplanned secondary surgery after neurosurgery has important clinical value and practical significance. First, by identifying high-risk factors, effective preventive measures can be taken in advance to reduce the incidence of unplanned secondary surgery, thereby significantly improving the overall safety of patients. Secondly, understanding and analyzing the key factors that affect secondary surgery can help clinicians develop more accurate and personalized treatment plans before, during, and after surgery to minimize the occurrence of complications. In addition, predicting and reducing the need for unplanned secondary surgery in advance can help medical institutions more effectively manage and allocate limited medical resources, reduce medical costs, and improve the efficiency of medical services.

[0003] At the patient level, reducing unplanned secondary surgeries not only helps shorten hospital stays and reduce economic burdens, but also improves patient satisfaction with the treatment process and improves their quality of life. For clinical research, by systematically analyzing the risk factors of unplanned secondary surgeries, we can accumulate rich clinical data and promote academic research and technological progress in the field of neurosurgery. Finally, applying these research results to medical education and training will help cultivate the surgical skills and risk management capabilities of a new generation of neurosurgeons, thereby improving the overall level of medical care. Therefore, in-depth research and understanding of the risk factors for unplanned secondary surgeries after neurosurgery will have a profound positive impact on patients, medical institutions, and the entire medical field. Summary of the invention

[0004] The purpose of the present invention is to establish a probability model for predicting whether a patient undergoes an unplanned secondary surgery after surgery, so as to provide a basis for determining whether a patient undergoing elective neurosurgery undergoes an unplanned secondary surgery in the early postoperative period.

[0005] To achieve the above-mentioned purpose, in a first aspect, the present invention provides an assessment model for the risk of unplanned secondary surgery in neurosurgery, characterized in that: the assessment model includes a score control line evenly distributed from 0 to 100, an age control line, a disease type control line, and an operation duration control line horizontally aligned with the score control line, the assessment model also includes a total risk assessment control line evenly distributed from 0 to 240, a linear prediction control line, and a risk coefficient control line mapped respectively to the total risk assessment control line and the linear prediction control line, wherein the scale on the risk coefficient control line is logarithmically distributed from 0.01 to 0.11, 0.01 corresponds to a probability coefficient of 10%, and 0.11 corresponds to a probability coefficient of 110%.

[0006] As a further improvement of the present invention, the age control line is evenly distributed from 90 to 0 years old, and 90 is the starting position of the age control line aligned with the 0 scale line position of the score control line, and 0 is the ending position of the age control line aligned with the scale line position of the score control line 52.

[0007] As a further improvement of the present invention, the scales on the disease type control line include craniotomy without resection, lesion resection, lesion puncture biopsy, and external ventricular drainage, which are aligned with the 0, 44, 48, and 88 scale lines of the scoring control line respectively.

[0008] As a further improvement of the present invention, the operation duration control line is evenly distributed from 0 to 24 hours, 0 is the starting position of the operation duration control line aligned with the 0 scale line position of the scoring control line, and 24 is the ending position of the operation duration control line aligned with the 100 scale line of the scoring control line.

[0009] As a further improvement of the present invention, the starting position of the risk coefficient control line is mapped to the scale line position of the total risk assessment control line 106, and the ending position is mapped to the scale line position of the total risk assessment control line 206.

[0010] As a further improvement of the present invention, the starting position of the risk coefficient control line is mapped to the -4.6 scale mark position of the linear prediction control line, and the ending position is mapped to the -2.1 scale mark position of the linear prediction control line.

[0011] The present invention also provides an evaluation method for an evaluation model of the risk of unplanned secondary neurosurgery surgery, comprising the following steps:

[0012] a. Obtaining dependent variable data of patients before, during and after neurosurgery, including patient age, surgery type and duration of surgery;

[0013] b. Compare the specific types of each dependent variable data of the patient with the assessment model of the risk of unplanned secondary surgery in neurosurgery to obtain the scores corresponding to each dependent variable data of the patient;

[0014] c. Add up the scores corresponding to the data of each dependent variable to get the total score, and then compare the data with the assessment model of the risk of unplanned secondary surgery in neurosurgery to obtain the first probability of the patient undergoing unplanned secondary surgery in neurosurgery;

[0015] d. According to the patient's dependent variable data, substitute it into the following prediction formula to calculate the linear prediction result logitP. The prediction formula is as follows:

[0016] logitP=-4.7906+0.0876*t-0.0122*a+X

[0017] Among them, t is the duration of the operation, unit is h, a is age, unit is years, X is the influencing factor of the operation type, when the patient's operation type is lesion puncture biopsy, X = 0.0618, when the operation type is lesion resection, X = -0.0123 lesion resection, when the operation type is craniotomy without resection, X = -0.9545, when the operation type is extraventricular drainage, X = 0, after obtaining the patient's linear prediction results, the data will be compared with the assessment model of the risk of unplanned secondary surgery in neurosurgery to obtain the second probability of the patient's unplanned secondary surgery in neurosurgery;

[0018] e. Compare the first probability with the second probability. If one of the two probabilities exceeds 50%, it is judged that the patient needs a second operation and needs intensive care. If both probabilities do not exceed 50%, but the average of the first and second probabilities exceeds 30%, it is judged that the patient needs a second operation and needs intensive care.

[0019] As a further improvement of the present invention, in step b, the specific types of the patient's dependent variable data are compared with the assessment model of the risk of unplanned secondary surgery in neurosurgery, including finding corresponding scales for the patient's age, operation duration and operation type on the age control line, disease type control line and operation duration control line, and then finding corresponding scores for the scales corresponding to these three on the score control line.

[0020] As a further improvement of the present invention, the scores corresponding to the data of each dependent variable are added to obtain a total score, and then the data is compared with the assessment model of the risk of unplanned secondary surgery in neurosurgery to obtain the first probability of the patient undergoing unplanned secondary surgery in neurosurgery, which includes finding the corresponding scores of the patient's age, operation duration and operation type on the score control line and adding them to obtain the total score, and then finding the scale line position corresponding to the total score on the total risk assessment control line, and mapping it to the risk coefficient control line to find the first probability.

[0021] As a further improvement of the present invention, after the linear prediction result of the patient is obtained, the data will be compared with the assessment model of the risk of unplanned secondary surgery in neurosurgery to obtain the second probability of the patient undergoing unplanned secondary surgery in neurosurgery, which includes finding the position of the corresponding scale on the linear prediction control line after the linear prediction result of the patient is obtained, and mapping it to the risk coefficient control line to find the second probability.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] By identifying high-risk factors, the present invention can take effective preventive measures in advance to reduce the incidence of unplanned secondary surgeries, thereby significantly improving the overall safety of patients. Understanding and analyzing the key factors affecting secondary surgeries can help clinicians develop more accurate and personalized treatment plans before, during and after surgery to minimize the occurrence of complications. In addition, predicting and reducing the need for unplanned secondary surgeries in advance can help medical institutions more effectively manage and allocate limited medical resources, reduce medical costs, and improve the efficiency of medical services. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 The present invention is a flow chart of a method for assessing the risk of unplanned secondary neurosurgery surgery;

[0025] Figure 2 Schematic diagram of the assessment model for the risk of unplanned secondary neurosurgery surgery according to the present invention. DETAILED DESCRIPTION

[0026] The present invention is described in detail below in conjunction with the various embodiments shown in the accompanying drawings, but it should be noted that these embodiments are not limitations of the present invention, and any equivalent transformations or substitutions in functions, methods, or structures made by ordinary technicians in the field based on these embodiments are all within the scope of protection of the present invention.

[0027] In the field of neurosurgery, different surgical methods have different risks of unplanned secondary surgery, which may mainly exist in the following aspects:

[0028] Lesion resection surgery: This type of surgery usually involves precise resection of specific diseased tissues. The surgical procedure is complex and requires high technical skills from the doctor. Therefore, the risk of operational errors or complications during the surgery is relatively high, resulting in a high incidence of unplanned secondary surgery.

[0029] Non-lesion excision craniotomy: This type of surgery may involve the exploration or treatment of the surrounding structures of the brain tissue. Although it does not directly remove the lesion, it also requires high surgical skills and delicate operations. If the operation is not handled properly, it may also cause complications and lead to unplanned secondary surgery.

[0030] Biopsy: Biopsy is usually performed to obtain diseased tissue for pathological diagnosis, and the scope of the operation is relatively small. However, the incidence of unplanned secondary surgery for biopsy is not uncommon in clinical practice.

[0031] External ventricular drainage surgery: This type of surgery is mainly used to treat diseases such as hydrocephalus. The cerebrospinal fluid in the ventricles is drained by placing drainage tubes. Although the operation is relatively simple, if it is not performed properly, it may also lead to unplanned secondary surgery.

[0032] There may be differences in the patient groups for different surgeries, such as age, underlying diseases, and severity of illness. These factors may affect the surgical results and the incidence of complications, and thus affect the incidence of unplanned secondary surgeries. Therefore, we selected gender, age, BMI, history of hypertension, duration of surgery, extent of lesion resection, and location of lesions as potential influencing factors.

[0033] Then, by analyzing the potential influencing factors of patients in tens of thousands of clinical cases in this field, these potential influencing factors are analyzed by using the univariate logistics regression method in R language for each data type in the original data, and the dependent variable data type with statistical significance is screened out. The formula is as follows:

[0034] Logit(pi)=1 / (1+exp(-pi))

[0035] ln(pi / (1-pi))=Beta_0+Beta_1*X_1+…+B_k*K_k, where pi refers to the score of the i-th data type, and Beta_i refers to the percentage value of the i-th data type.

[0036] The linear regression method described above yields the dependent variable data types related to the present invention as age, surgery duration, and surgery type.

[0037] Ginseng Figure 1-2 As shown, this embodiment provides an assessment model for the risk of unplanned secondary neurosurgery surgery, the assessment model includes a score control line evenly distributed from 0 to 100, an age control line, a disease type control line, and an operation duration control line horizontally aligned with the score control line, the assessment model also includes a total risk assessment control line evenly distributed from 0 to 240, a linear prediction control line, and a risk coefficient control line mapped to the total risk assessment control line and the linear prediction control line, respectively, wherein the scale on the risk coefficient control line is logarithmically distributed from 0.01 to 0.11, 0.01 corresponds to a probability coefficient of 10%, and 0.11 corresponds to a probability coefficient of 110%.

[0038] The age control line is evenly distributed from 90 to 0 years old, and 90 is the starting position of the age control line aligned with the 0 scale line position of the score control line, and 0 is the ending position of the age control line aligned with the scale line position of the score control line 52.

[0039] The scales on the disease type control line include craniotomy without resection, lesion resection, lesion puncture biopsy, and external ventricular drainage, which are aligned with the 0, 44, 48, and 88 scale lines of the scoring control line, respectively.

[0040] The operation duration control line is evenly distributed from 0 to 24 hours, 0 is the starting position of the operation duration control line aligned with the 0 scale line position of the scoring control line, and 24 is the ending position of the operation duration control line aligned with the 100 scale line of the scoring control line.

[0041] The starting position of the risk coefficient control line is mapped to the scale line position of the total risk assessment control line 106 , and the ending position is mapped to the scale line position of the total risk assessment control line 206 .

[0042] The starting position of the risk coefficient control line is mapped to the -4.6 scale mark position of the linear prediction control line, and the ending position is mapped to the -2.1 scale mark position of the linear prediction control line.

[0043] The present invention also provides an evaluation method for an evaluation model of the risk of unplanned secondary neurosurgery surgery, comprising the following steps:

[0044] a. Obtaining dependent variable data of patients before, during and after neurosurgery, including patient age, surgery type and duration of surgery;

[0045] b. Compare the specific types of each dependent variable data of the patient with the assessment model of the risk of unplanned secondary surgery in neurosurgery to obtain the scores corresponding to each dependent variable data of the patient;

[0046] c. Add up the scores corresponding to the data of each dependent variable to get the total score, and then compare the data with the assessment model of the risk of unplanned secondary surgery in neurosurgery to obtain the first probability of the patient undergoing unplanned secondary surgery in neurosurgery;

[0047] d. According to the patient's dependent variable data, substitute it into the following prediction formula to calculate the linear prediction result logitP. The prediction formula is as follows:

[0048] logitP=-4.7906+0.0876*t-0.0122*a+X

[0049] Among them, t is the duration of the operation, unit is h, a is age, unit is years, X is the influencing factor of the operation type, when the patient's operation type is lesion puncture biopsy, X = 0.0618, when the operation type is lesion resection, X = -0.0123 lesion resection, when the operation type is craniotomy without resection, X = -0.9545, when the operation type is extraventricular drainage, X = 0, after obtaining the patient's linear prediction results, the data will be compared with the assessment model of the risk of unplanned secondary surgery in neurosurgery to obtain the second probability of the patient's unplanned secondary surgery in neurosurgery;

[0050] e. Compare the first probability with the second probability. If one of the two probabilities exceeds 50%, it is judged that the patient needs a second operation and needs intensive care. If both probabilities do not exceed 50%, but the average of the first and second probabilities exceeds 30%, it is judged that the patient needs a second operation and needs intensive care.

[0051] In step b, the specific types of the patient's dependent variable data are compared with the assessment model of the risk of unplanned secondary surgery in neurosurgery, including finding corresponding scales for the patient's age, operation duration and operation type on the age control line, disease type control line and operation duration control line, and then finding corresponding scores for the scales corresponding to these three on the score control line.

[0052] The scores corresponding to the data of each dependent variable are added to obtain a total score, and then the data is compared with the assessment model of the risk of unplanned secondary surgery in neurosurgery to obtain the first probability of the patient undergoing unplanned secondary surgery in neurosurgery, which includes finding the corresponding scores of the patient's age, operation duration and operation type on the score control line and adding them to obtain the total score, and then finding the scale line position corresponding to the total score on the total risk assessment control line, and mapping it to the risk coefficient control line to find the first probability.

[0053] After the linear prediction result of the patient is obtained, the data will be compared with the assessment model of the risk of unplanned secondary surgery in neurosurgery to obtain the second probability of the patient undergoing unplanned secondary surgery in neurosurgery. This includes finding the position of the corresponding scale on the linear prediction control line after the linear prediction result of the patient is obtained, and mapping it to the risk coefficient control line to find the second probability.

[0054] In order to verify the reliability of the evaluation model of the present invention, some patient cases from tens of thousands of clinical medical records were selected for experimental verification. Through experimental verification, it was found that the evaluation model and evaluation method provided by the present invention can identify high-risk factors, and effective preventive measures can be taken in advance to reduce the incidence of unplanned secondary operations, thereby significantly improving the overall safety of patients, understanding and analyzing the key factors affecting secondary operations, which is helpful for clinicians to formulate more accurate and personalized treatment plans before, during and after operations, and minimize the occurrence of complications; in addition, predicting and reducing the need for unplanned secondary operations in advance can help medical institutions to more effectively manage and allocate limited medical resources, reduce medical costs, and improve the efficiency of medical services.

[0055] The series of detailed descriptions listed above are only specific descriptions of feasible implementation methods of the present invention. They are not intended to limit the scope of protection of the present invention. Any equivalent implementation methods or changes that do not deviate from the technical spirit of the present invention should be included in the scope of protection of the present invention.

[0056] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0057] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A model for assessing the risk of unplanned secondary neurosurgery surgery, characterized by: The evaluation model includes a score control line evenly distributed from 0 to 100, an age control line, a disease type control line, and an operation duration control line horizontally aligned with the score control line. The evaluation model also includes a total risk assessment control line evenly distributed from 0 to 240, a linear prediction control line, and a risk coefficient control line mapped to the total risk assessment control line and the linear prediction control line, respectively, wherein the scale on the risk coefficient control line is logarithmically distributed from 0.01 to 0.11, 0.01 corresponds to a probability coefficient of 10%, and 0.11 corresponds to a probability coefficient of 110%.

2. The assessment model for the risk of unplanned secondary neurosurgery surgery according to claim 1, characterized in that: The age control line is evenly distributed from 90 to 0 years old, and 90 is the starting position of the age control line aligned with the 0 scale line position of the score control line, and 0 is the ending position of the age control line aligned with the scale line position of the score control line 52.

3. The assessment model for the risk of unplanned secondary neurosurgery surgery according to claim 1, characterized in that: The scales on the disease type control line include craniotomy without resection, lesion resection, lesion puncture biopsy, and external ventricular drainage, which are aligned with the 0, 44, 48, and 88 scale lines of the scoring control line, respectively.

4. The assessment model for the risk of unplanned secondary neurosurgery surgery according to claim 1, characterized in that: The operation duration control line is evenly distributed from 0 to 24 hours, 0 is the starting position of the operation duration control line aligned with the 0 scale line position of the scoring control line, and 24 is the ending position of the operation duration control line aligned with the 100 scale line of the scoring control line.

5. The assessment model for the risk of unplanned secondary neurosurgery surgery according to claim 1, characterized in that: The starting position of the risk coefficient control line is mapped to the scale line position of the total risk assessment control line 106 , and the ending position is mapped to the scale line position of the total risk assessment control line 206 .

6. The assessment model for the risk of unplanned secondary neurosurgery surgery according to claim 1, characterized in that: The starting position of the risk coefficient control line is mapped to the -4.6 scale mark position of the linear prediction control line, and the ending position is mapped to the -2.1 scale mark position of the linear prediction control line.

7. An evaluation method for the evaluation model of the risk of unplanned secondary neurosurgery surgery according to any one of claims 1 to 6, characterized in that: The following steps are involved: a. Obtaining dependent variable data of patients before, during and after neurosurgery, including patient age, surgery type and duration of surgery; b. Compare the specific types of each dependent variable data of the patient with the assessment model of the risk of unplanned secondary surgery in neurosurgery to obtain the scores corresponding to each dependent variable data of the patient; c. Add up the scores corresponding to the data of each dependent variable to get the total score, and then compare the data with the assessment model of the risk of unplanned secondary surgery in neurosurgery to obtain the first probability of the patient undergoing unplanned secondary surgery in neurosurgery; d. According to the patient's dependent variable data, substitute it into the following prediction formula to calculate the linear prediction result logitP. The prediction formula is as follows: logitP=-4.7906+0.0876*t-0.0122*a+X Among them, t is the duration of the operation, unit is h, a is age, unit is years, X is the influencing factor of the operation type, when the patient's operation type is lesion puncture biopsy, X = 0.0618, when the operation type is lesion resection, X = -0.0123 lesion resection, when the operation type is craniotomy without resection, X = -0.9545, when the operation type is extraventricular drainage, X = 0, after obtaining the patient's linear prediction results, the data will be compared with the assessment model of the risk of unplanned secondary surgery in neurosurgery to obtain the second probability of the patient's unplanned secondary surgery in neurosurgery; e. Compare the first probability with the second probability. If one of the two probabilities exceeds 50%, it is judged that the patient needs a second operation and needs intensive care. If both probabilities do not exceed 50%, but the average of the first and second probabilities exceeds 30%, it is judged that the patient needs a second operation and needs intensive care.

8. The method for assessing the risk of unplanned secondary neurosurgery surgery according to claim 7, characterized in that: The following steps are involved: In step b, the specific types of the patient's dependent variable data are compared with the assessment model of the risk of unplanned secondary surgery in neurosurgery, including finding corresponding scales for the patient's age, operation duration and operation type on the age control line, disease type control line and operation duration control line, and then finding corresponding scores for the scales corresponding to these three on the score control line.

9. The method for assessing the risk of unplanned secondary neurosurgery surgery according to claim 8, characterized in that: The following steps are involved: The scores corresponding to the data of each dependent variable are added to obtain a total score, and then the data is compared with the assessment model of the risk of unplanned secondary surgery in neurosurgery to obtain the first probability of the patient undergoing unplanned secondary surgery in neurosurgery, which includes finding the corresponding scores of the patient's age, operation duration and operation type on the score control line and adding them to obtain the total score, and then finding the scale line position corresponding to the total score on the total risk assessment control line, and mapping it to the risk coefficient control line to find the first probability.

10. The method for assessing the risk of unplanned secondary neurosurgery surgery according to claim 8, characterized in that: The following steps are involved: After the linear prediction result of the patient is obtained, the data will be compared with the assessment model of the risk of unplanned secondary surgery in neurosurgery to obtain the second probability of the patient undergoing unplanned secondary surgery in neurosurgery. This includes finding the position of the corresponding scale on the linear prediction control line after the linear prediction result of the patient is obtained, and mapping it to the risk coefficient control line to find the second probability.

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