CN110827992B - Preoperative prediction system for acute renal injury after hypertension operation - Google Patents
Preoperative prediction system for acute renal injury after hypertension operation Download PDFInfo
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Abstract
The invention relates to a preoperative prediction method of acute renal injury after a hypertensive surgery, which comprises the following steps: acquiring preoperative clinical data related to postoperative acute kidney injury of a patient; establishing a nomogram of postoperative acute kidney injury occurrence probability according to the preoperative clinical data, calculating a total risk score, and calculating a predicted value of the postoperative acute kidney injury occurrence probability of the hypertensive according to the total risk score; and outputting the predicted value of the occurrence probability of the postoperative acute kidney injury of the hypertensive. The invention provides a method for predicting postoperative acute renal injury, which is suitable for Chinese people and combines various potential preoperative risk factors such as preoperative blood pressure change, antihypertensive medication, laboratory examination and the like, and provides a risk quantitative value for a clinician to predict the occurrence probability of postoperative acute renal injury of a hypertensive before an operation more comprehensively and accurately.
Description
Technical Field
The invention relates to the technical field of clinical medicine, in particular to a preoperative prediction method of acute renal injury after a hypertensive surgery.
Background
Acute kidney injury is a syndrome characterized by an acute decline in renal function, with higher morbidity and associated increased mortality and increased medical costs. Previous studies have shown that acute kidney injury is a common complication after surgery. Acute kidney injury is an important risk factor for chronic kidney disease, while acute kidney injury causes other organ damage, further increasing morbidity and mortality. Even if the postoperative renal function is completely restored, the mortality rate of postoperative patients with acute renal injury is still high. Hypertension is a risk factor for postoperative acute kidney injury. Long-term hypertension is easy to cause renal arteriosclerosis, glomerular filtration rate is reduced, and renal function is damaged. Certain hypertension treatment medications may also cause or exacerbate kidney damage, making it more at risk for acute kidney injury.
Hypertension is an extremely common chronic non-infectious disease affecting as many as a billion people worldwide. Therefore, the method can accurately predict the occurrence of postoperative acute kidney injury of patients with hypertension, and has important clinical, scientific and social values. In clinical work, accurate prediction of postoperative acute kidney injury can guide a doctor to make a personalized examination and treatment scheme aiming at a hypertensive patient, help the doctor to make a reasonable review and follow-up plan, and further improve the quality of medical service. In scientific research, the accurate prediction of the acute kidney injury risk level of a patient can provide important basis for developing an effective treatment scheme aiming at a hypertensive patient, and can become an important method for testing a novel treatment effect. From the social perspective, the occurrence of postoperative acute kidney injury of a hypertensive can be accurately predicted, postoperative kidney injury risks can be scientifically clarified for patients and families, the hypertensive can be guided to follow a treatment plan, excessive medical treatment is avoided, family economic pressure is relieved, and doctor-patient relationship can be improved.
The important reasons for acute renal injury after surgery are tissue damage during surgery, hypotension caused by anesthesia, and the like. The research shows that the risk factors exist before, during and after the operation, which causes the acute kidney injury of the patient after the operation. At present, the risk prevention of postoperative acute kidney injury of hypertensive patients in clinical work is focused on various risk factors in and after operation. A simple and reliable preoperative prediction tool is still lacked in clinical practice for preventing postoperative acute kidney injury of a hypertensive patient, and prediction of postoperative acute kidney injury occurrence probability based on comprehensive preoperative factors can be provided before operation of the patient. Meanwhile, compared with common patients, hypertension patients receive more antihypertensive drug therapy before operation, and blood pressure fluctuation is more obvious, so that the influence on renal blood perfusion is larger, and preoperative factors causing postoperative acute renal injury are more complicated. At present, a prediction method combining preoperative factors such as preoperative blood pressure change, preoperative blood pressure reduction treatment and the like is lacked, and prediction of postoperative acute renal injury risks of hypertensive patients before an operation cannot be met.
Disclosure of Invention
The embodiment of the invention aims to solve the technical problem that: the existing prediction method neglects preoperative factors such as preoperative blood pressure change, preoperative blood pressure reduction treatment and the like, and cannot meet the prediction of postoperative acute kidney injury risks of hypertensive patients.
According to a first aspect of embodiments of the present invention, there is provided a method for pre-operative prediction of acute renal injury after hypertensive surgery, comprising:
acquiring preoperative clinical data related to postoperative acute kidney injury of a patient;
establishing a nomogram of the occurrence probability of postoperative acute kidney injury according to the preoperative clinical data and calculating a total risk score;
calculating a predicted value of the occurrence probability of postoperative acute kidney injury of the hypertensive according to the total risk score;
and outputting the predicted value of the occurrence probability of the postoperative acute kidney injury of the hypertensive.
In some embodiments, a nomogram of the occurrence probability of the postoperative acute kidney injury is established by a computer host, a central processing unit or a network server, a total risk score is calculated, and a predicted value of the occurrence probability of the postoperative acute kidney injury of the hypertensive is calculated according to the total risk score.
In some embodiments, the predicted value of the probability of occurrence of the postoperative acute kidney injury in the hypertensive is output by a display, a printer, or an audio output device.
In some embodiments, said calculating a predictive value for the probability of occurrence of post-operative acute kidney injury in a hypertensive patient based on said total risk score comprises:
and drawing a vertical line at the position of the total risk score in the nomogram of the occurrence probability, wherein the intersection point of the vertical line and the occurrence probability line in the nomogram is the predicted value of the occurrence probability of the postoperative acute renal injury of the hypertensive.
In some embodiments, the clinical data comprises:
absolute value of neutrophil granulocytes before operation;
3 days before the operation, the variation degree of the systolic pressure;
preoperative alpha receptor blocker treatment profile;
estimating glomerular filtration rate before operation; and
before the operation, the patient is hospitalized.
In some embodiments, the total risk score is the cumulative sum of the preoperative neutrophil absolute value, the preoperative 3-day systolic blood pressure variability, the preoperative alpha blocker treatment profile, the preoperative estimated glomerular filtration rate, and the risk score on the preoperative hospitalization day.
In some embodiments, the preoperative 3-day systolic blood pressure variability is calculated as follows:
wherein a represents the average of the systolic pressure of 3 days before the operation, b represents the measured systolic pressure of 3 days before the operation, c represents the measured blood pressure of 3 days before the operation, and d represents the systolic pressure variation degree of 3 days before the operation.
According to a second aspect of the embodiments of the present invention, there is provided a pre-operative prediction system for acute renal injury after hypertensive surgery, including:
the acquisition module is used for acquiring preoperative clinical data related to postoperative acute kidney injury of a patient;
the analysis module is connected with the acquisition module and used for establishing a nomogram of the postoperative acute kidney injury occurrence probability according to the preoperative clinical data acquired by the acquisition module, calculating a total risk score and calculating a predicted value of the postoperative acute kidney injury occurrence probability of the hypertensive according to the total risk score;
and the output module is connected with the analysis module and used for outputting the predicted value of the postoperative acute kidney injury occurrence probability of the hypertensive.
In some embodiments, said calculating a predictive value for the probability of occurrence of post-operative acute kidney injury in a hypertensive patient based on said total risk score comprises:
and drawing a vertical line at the position of the total risk score in the nomogram of the occurrence probability, wherein the intersection point of the vertical line and the occurrence probability line in the nomogram is the predicted value of the occurrence probability of the postoperative acute renal injury of the hypertensive.
In some embodiments, the obtaining module comprises:
the first acquisition submodule is used for acquiring the absolute value of the neutrophil granulocytes before the operation;
The second acquisition submodule is used for acquiring the systolic pressure variation degree of the preoperative 3 days;
a third obtaining submodule, which is used for obtaining the treatment condition of the alpha receptor blocker before the operation;
the fourth acquisition submodule is used for acquiring the glomerular filtration rate estimated before the operation;
and the fifth acquisition submodule is used for acquiring the preoperative hospitalization days.
In some embodiments, the total risk score is the cumulative sum of the preoperative neutrophil absolute value, the preoperative 3-day systolic blood pressure variability, the preoperative alpha blocker treatment profile, the preoperative estimated glomerular filtration rate, and the risk score on the preoperative hospitalization day.
In some embodiments, the variation of systolic blood pressure 3 days before surgery is calculated as follows:
wherein, a represents the average value of the 3 days of the preoperative systolic blood pressure, b represents the measured value of the 3 days of the preoperative systolic blood pressure, c represents the measured times of the 3 days of the preoperative blood pressure, and d represents the variation degree of the 3 days of the preoperative systolic blood pressure.
In some embodiments, the parsing module is a computer host, a central processing unit, or a network server.
In some embodiments, the output module is a display, a printer, or an audio output device.
In some embodiments, the obtaining module and the parsing module are connected in a wired connection and/or a wireless connection.
In some embodiments, the parsing module and the output module are connected by a wired connection and/or a wireless connection.
In some embodiments, the wireless connection is a wireless local area network, bluetooth, or infrared; the wired connection is a fixed telephone network.
In some embodiments, the nomogram is a nomogram in which a Logistic regression model is completed using Stata statistical software.
The invention provides the method for predicting the postoperative acute kidney injury, which is suitable for Chinese people and combines various potential risk factors such as preoperative blood pressure change, antihypertensive medication, laboratory examination and the like, and provides a risk quantitative value of the postoperative acute kidney injury of a patient for preoperative prediction of a hypertension patient by a clinician more comprehensively and accurately. The clinician can in time adjust preceding art, postoperative diagnosis and treatment means and operation scheme according to the risk quantification value of patient's postoperative acute kidney injury, reduces the risk that patient's postoperative takes place acute kidney injury.
Drawings
FIG. 1 shows a flow diagram of one embodiment of a predictor of the present invention.
Fig. 2 is a schematic diagram showing a flow chart of calculation of the predicted value of the occurrence probability of postoperative acute kidney injury of a hypertensive patient.
Fig. 3 shows a nomogram of the probability of occurrence of postoperative acute kidney injury in hypertensive patients.
Figure 4 shows the subject work curve predicted by Logistic regression model for the post-operative occurrence of acute renal injury in hypertensive patients.
FIG. 5 is a schematic diagram of an embodiment of the prediction system of the present invention.
Detailed Description
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein, but rather should be construed as broadly as the present invention is capable of modification in various respects, all without departing from the spirit and scope of the present invention.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
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. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
Fig. 1 is a schematic flow chart illustrating a preoperative prediction method of acute renal injury after hypertensive surgery according to an embodiment of the present invention. As shown in fig. 2, the pre-operative prediction method of this embodiment includes:
step S101, acquiring preoperative clinical data related to postoperative acute kidney injury of a patient;
step S102, establishing a nomogram of the occurrence probability of postoperative acute kidney injury according to the clinical data and calculating a total risk score;
step S103, calculating a predicted value of the occurrence probability of postoperative acute kidney injury of the hypertensive according to the total risk score;
and step S104, outputting the predicted value of the occurrence probability of the postoperative acute kidney injury of the hypertensive.
By combining the preoperative prediction method of various potential risk factors such as preoperative blood pressure change, antihypertensive medication, laboratory examination and the like, the risk quantitative value can be more comprehensively and accurately provided for the clinician to predict the occurrence probability of postoperative acute renal injury of the hypertensive before operation. The clinician can in time adjust preceding art, postoperative diagnosis and treatment means and operation scheme according to the risk quantification value of patient's postoperative acute kidney injury, reduces the risk that patient's postoperative takes place acute kidney injury.
In step S101, in order to screen out preoperative variables affecting the occurrence of postoperative acute kidney injury of hypertensive patients, follow-up information from hospitalization to discharge of 1028 patients with cerebral hypertension was collected, wherein 92 patients suffered from postoperative acute kidney injury. Screening out part of pre-operation clinical data possibly related to the occurrence of the postoperative acute kidney injury of the hypertensive, as shown in table 1, including age, gender, BMI, smoking and drinking history, preoperative blood pressure, preoperative medication, laboratory examination and preoperative hospitalization days, and using the pre-operation clinical data for establishing a prediction model of the incidence rate of the subsequent acute kidney injury.
TABLE 1
Fig. 2 is a schematic diagram of a calculation flow of the predicted value of the occurrence probability of postoperative acute kidney injury of a hypertensive patient. The prediction model is established according to the relevant preoperative clinical data of the postoperative acute kidney injury occurrence of the hypertensive, and accurate and normalized prediction is carried out through the prediction model. The specific steps for establishing the prediction model are as follows:
(1) the method comprises the steps of (1) applying single-factor Logistic regression analysis to preoperative clinical data possibly related to the occurrence of acute kidney injury of a patient, wherein the variables listed in the table 1 comprise age, gender, BMI, smoking and drinking history, preoperative blood pressure, preoperative medication, laboratory examination, preoperative hospitalization date and the like, evaluating the prognostic value of the clinical data, and when the value of the variable p is less than 0.05, considering that the variable is obviously related to the occurrence of postoperative acute kidney injury of the hypertensive; 10 preoperative variables (preoperative 3-day systolic blood pressure variation, preoperative diuretic, preoperative alpha receptor blocker, preoperative estimated glomerular filtration rate, preoperative neutrophil absolute value, preoperative hematocrit, preoperative hemoglobin, diabetes and preoperative hospitalization) are screened out through single-factor Logistic regression analysis to be obviously related to postoperative acute renal injury of a patient. The results of the one-way Logistic regression analysis are shown in table 2.
TABLE 2
(2) Whether the 10 variables which are obviously related to the postoperative acute kidney injury occurrence period of the hypertensive are independent prognostic factors or not is analyzed by applying stepwise multi-factor Logistic regression, the factors P >0.05 are included, the factors P <0.01 are discharged, 5 variables which are preoperative 3-day systolic blood pressure variation, preoperative alpha receptor retarder, preoperative estimated glomerular filtration rate, preoperative neutrophil absolute value and preoperative hospitalization day are finally screened out to be independently related to the postoperative acute kidney injury occurrence period of the hypertensive, and a multi-factor Logistic regression model is established for the 5 factors, wherein specific results are shown in a table 3.
TABLE 3
The above Logistic regression model containing 5 factors has the following polynomial:
Y＝0.079*X a +1.63*X b +0.65*X c +-0.15*X d +0.14*X e
wherein Y represents the occurrence of post-operative AKI, X a Representative of preoperative hospitalization day, X b Representing the preoperative use of an alpha receptor blocker, X c Representing the variation of systolic pressure at 3 days before operation, X d Representing the preoperative estimation of glomerular filtration rate, X e Representing absolute values of neutrophils before surgery.
(3) Establishing a post-operation acute kidney injury occurrence probability Nomogram of the hypertensive correspondingly as shown in figure 3 according to a post-operation acute kidney injury Logistic regression model of the hypertensive, wherein the process specifically comprises the step of converting the obtained Logistic regression model into a visualized acute kidney injury occurrence probability Nomogram by applying a Stata software Nomogram operation package. The specific commands are as follows:
Building Logistic regression model
logistic AKI prelosαCVSBP eGFR neutrophil
Plotting alignment graph
nomolog
Through the above steps, a postoperative acute kidney injury occurrence probability nomogram shown in fig. 3 can be obtained, the preoperative 3-day systolic blood pressure variation degree, the preoperative alpha receptor blocking agent, the preoperative estimated glomerular filtration rate, the preoperative neutrophil absolute value and the preoperative hospitalization day correspond to different risk score ranges respectively, so as to calculate the total risk score, and the total risk score is the sum of the preoperative 3-day systolic blood pressure variation degree, the preoperative alpha receptor blocking agent, the preoperative estimated glomerular filtration rate, the preoperative neutrophil absolute value and the preoperative hospitalization day risk score. The following conditions exist before the patient operation: firstly, the hospitalization day before operation is less than 1 day; ② the variation degree of the systolic pressure is less than 0.32 percent in 3 days before the operation. If the condition exists before the operation of the patient, the risk score corresponding to the systolic pressure variation degree of the day of the hospitalization before the operation and the day 3 before the operation is 0. And drawing a vertical line at the position of the total risk score in the nomogram of the occurrence probability, wherein the intersection point of the vertical line and the occurrence probability line in the nomogram is the predicted value of the occurrence probability of the postoperative acute renal injury of the hypertensive.
A Logistic regression model constructed by using preoperative neutrophil absolute value, preoperative 3-day systolic blood pressure variation, preoperative alpha receptor blocker treatment condition, preoperative estimated glomerular filtration rate and 5 factors on preoperative hospitalization days is used for predicting the postoperative acute renal injury occurrence of a hypertensive patient, the working curve of a subject is shown in figure 4, at the moment, the area under the curve is 0.7636, and generally, the model is considered to have statistical significance for prediction of disease prognosis if the area under the curve is higher than 0.5.
Patients with the following clinical conditions before surgery would not be suitable for the present prediction method: firstly, the hospitalization day before operation is more than 62 days; ② the variation degree of the systolic pressure is more than 30.22 percent in 3 days before the operation; ③ estimating the glomerular filtration rate to be more than 196.57ml/min/1.73m before operation 2 Or the glomerular filtration rate before operation is less than 15 ml/min/1.73m 2 (ii) a Fourthly, the absolute value of the neutrophil granulocytes before the operation is less than 0.52 multiplied by 10 9 /L, or absolute value of neutrophil over 20.89X 10 before operation 9 /L。
A preoperative prediction system for acute renal injury after hypertensive surgery according to one embodiment of the present invention is described below with reference to fig. 5.
FIG. 5 is a block diagram of one embodiment of a prediction system of the present invention. As shown in fig. 5, the pre-operative prediction system implemented includes:
the acquisition module is used for acquiring preoperative clinical data related to postoperative acute kidney injury of a patient;
the analysis module is connected with the acquisition module and used for establishing a nomogram of the occurrence probability of postoperative acute kidney injury according to the clinical data acquired by the acquisition module and calculating a total risk score, wherein the total risk score is the sum of the absolute value of preoperative neutrophils, the systolic blood pressure variation degree of 3 days before an operation, the preoperative alpha receptor blocker treatment condition, the preoperative estimated glomerular filtration rate and the risk score of a preoperative hospitalization day; calculating a predicted value of the occurrence probability of postoperative acute kidney injury of the hypertensive according to the total risk score, wherein the predicted value is calculated by the following method: drawing a vertical line at the position of the total risk score in the nomogram of the occurrence probability, wherein the intersection point of the vertical line and the occurrence probability line in the nomogram is the predicted value of the occurrence probability of the postoperative acute kidney injury of the hypertensive;
And the output module is connected with the analysis module and used for outputting the predicted value of the postoperative acute kidney injury occurrence probability of the hypertensive.
In the system, the acquisition module includes: the first acquisition submodule is used for acquiring the absolute value of the neutrophil granulocytes before the operation; the second acquisition submodule is used for acquiring the systolic pressure variation degree of the preoperative 3 days; a third obtaining submodule, which is used for obtaining the treatment condition of the alpha receptor blocker before the operation; the fourth acquisition submodule is used for acquiring the glomerular filtration rate estimated before the operation; and the fifth acquisition submodule is used for acquiring the preoperative hospitalization days.
Further, the calculation formula of the variation of the systolic blood pressure at 3 days before the operation is as follows:
wherein a represents the average of the systolic pressure of 3 days before the operation, b represents the measured systolic pressure of 3 days before the operation, c represents the measured blood pressure of 3 days before the operation, and d represents the systolic pressure variation degree of 3 days before the operation.
In one embodiment of the present invention, the parsing module is a computer host, a central processing unit or a network server. The connection mode of the analysis module and the acquisition module is wired connection and/or wireless connection. Further, the wireless connection is a wireless local area network, bluetooth or infrared; the wired connection is a fixed telephone network.
In one embodiment of the invention, the output module is a display, a printer, or an audio output device. The output module and the analysis module are connected in a wired connection and/or a wireless connection mode. Further, the wireless connection is a wireless local area network, bluetooth or infrared; the wired connection is a fixed telephone network. By adopting the connection mode, the use of the prediction system by a user can be greatly facilitated, and meanwhile, the occurrence probability of the postoperative acute kidney injury of the hypertensive can be accurately predicted by means of increasingly developed information technology and increasingly popularized network resources.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (9)
1. A preoperative prediction system of acute renal injury after hypertensive surgery, comprising:
the system comprises an acquisition module, a data processing module and a data processing module, wherein the acquisition module is used for acquiring preoperative clinical data related to postoperative acute kidney injury of a hypertensive, and the preoperative clinical data comprise age, gender, BMI (total body mass index), smoking and drinking history, preoperative blood pressure, preoperative medication, laboratory examination and preoperative hospitalization days, the preoperative blood pressure comprises preoperative 3-day blood pressure and preoperative 3-day blood pressure variation, and the preoperative medication is preoperative oral antihypertensive medication;
the analysis module is connected with the acquisition module and used for establishing a nomogram of the postoperative acute kidney injury occurrence probability according to the preoperative clinical data acquired by the acquisition module, calculating a total risk score and calculating a predicted value of the postoperative acute kidney injury occurrence probability of the hypertensive according to the total risk score; the analysis module comprises a first screening module, a second screening module and a calculation module;
the first screening module is used for evaluating the prognostic value of pre-operative clinical data possibly related to the occurrence of acute kidney injury of a patient by applying single-factor Logistic regression analysis, and when the value of a variable p is smaller than 0.05, the variable is considered to be remarkably related to the occurrence of postoperative acute kidney injury of the hypertensive patient, and 10 pre-operative variables are screened to be remarkably related to the postoperative occurrence of acute kidney injury of the patient;
The second screening module analyzes whether the 10 preoperative variables which are obviously related to the postoperative acute kidney injury of the hypertensive are independent prognostic factors by applying stepwise multi-factor Logistic regression, incorporates factors P >0.05 and discharges factors P <0.01, and finally screens 5 variables which are independently related to the postoperative acute kidney injury of the hypertensive, wherein the method comprises the following steps: 3 days before the operation, the variation degree of systolic pressure, the application of alpha receptor blocker before the operation, the estimation of glomerular filtration rate, the absolute value of neutrophil granulocytes before the operation and the hospitalization days before the operation; the above polynomial of Logistic regression model containing 5 factors is as follows:
Y＝0.079*X a +1.63*X b +0.65*X c +-0.15*X d +0.14*X e
wherein Y represents the occurrence of postoperative acute renal loss, X a Representative of preoperative hospitalization day, X b Representing the preoperative use of an alpha receptor blocker, X c Representing the variation of systolic pressure at 3 days before operation, X d Representing the preoperative estimation of glomerular filtration rate, X e Represents the absolute value of neutrophils before operation;
the calculating module is used for establishing a post-operation acute kidney injury occurrence probability nomogram of the hypertensive according to a post-operation acute kidney injury Logistic regression model of the hypertensive, and 5 factors respectively correspond to different risk score ranges to calculate a total risk score; the total risk score is the cumulative sum of the preoperative neutrophil absolute value, the preoperative 3-day systolic blood pressure variation degree, the preoperative alpha receptor blocker treatment condition, the preoperative estimated glomerular filtration rate and the risk score of the preoperative hospitalization day;
The output module is connected with the analysis module and used for outputting a predicted value of the postoperative acute kidney injury occurrence probability of the hypertensive;
patients with the following clinical conditions before surgery would not be suitable for the present prediction system: the hospitalization day before operation is more than 62 days; the variation degree of the systolic pressure is more than 30.22% 3 days before the operation; preoperatively estimated glomerular filtration rate of more than 196.57ml/min/1.73m 2 Or the glomerular filtration rate before operation is less than 15ml/min/1.73m 2 (ii) a The absolute value of neutrophil granulocytes before operation is less than 0.52 multiplied by 10 9 /L, or absolute value of neutrophil over 20.89X 10 before operation 9 /L。
2. The pre-operative prediction system of acute renal injury after surgery with hypertension according to claim 1, wherein the calculating the prediction value of the probability of occurrence of acute renal injury after surgery for hypertensive patient according to the total risk score comprises:
and drawing a vertical line at the position of the total risk score in the nomogram of the occurrence probability, wherein the intersection point of the vertical line and the occurrence probability line in the nomogram is the predicted value of the occurrence probability of the postoperative acute renal injury of the hypertensive.
3. The pre-operative prediction system for acute renal injury after hypertensive surgery of claim 1, wherein the obtaining module comprises:
the first acquisition submodule is used for acquiring the absolute value of the neutrophil granulocytes before the operation;
The second acquisition submodule is used for acquiring the systolic pressure variation degree of the preoperative 3 days;
a third obtaining submodule, which is used for obtaining the treatment condition of the alpha receptor blocker before the operation;
the fourth acquisition submodule is used for acquiring the glomerular filtration rate estimated before the operation;
and the fifth acquisition submodule is used for acquiring the preoperative hospitalization days.
4. The pre-operative prediction system of acute renal injury after hypertensive surgery as set forth in claim 3, wherein the pre-operative 3-day systolic blood pressure variation is calculated as follows:
wherein a represents the average of the systolic pressure of 3 days before the operation, b represents the measured systolic pressure of 3 days before the operation, c represents the measured blood pressure of 3 days before the operation, and d represents the systolic pressure variation degree of 3 days before the operation.
5. The pre-operative prediction system of acute renal injury after hypertension operation according to claim 1, wherein the parsing module is a computer host, a central processing unit or a network server.
6. The pre-operative prediction system of acute renal injury after hypertensive surgery of claim 1, wherein the output module is a display, a printer, or an audio output device.
7. The pre-operative prediction system for acute renal injury after hypertensive surgery of claim 1, wherein the obtaining module and the analyzing module are connected by a wired connection and/or a wireless connection.
8. The pre-operative prediction system for acute renal injury after hypertensive surgery of claim 1, wherein the parsing module and the output module are connected by a wired connection and/or a wireless connection.
9. The pre-operative prediction system for acute renal injury after hypertensive surgery of claim 7 or 8, wherein the wireless connection is a wireless local area network, bluetooth or infrared; the wired connection is a fixed telephone network.
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| CN113782183B (en) * | 2021-07-29 | 2023-07-14 | 甘肃省人民医院 | Device and method for predicting risk of pressure injury based on multi-algorithm fusion |
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