1. The application of a prediction model in preparing a reagent or a kit for predicting the risk and prognosis of acute kidney injury caused by sepsis is characterized in that the judgment method of the prediction model comprises the following steps:

IN[P/(1-P)]＝-0.163Gender-0.022ATIII+0.030Cr+0.681HBP-0.002age-4.895；

wherein, Gender stands for Gender, Gender is male, Gender stands for 1, Gender is female, Gender stands for 0, ATIII stands for antithrombin III expression level, Cr stands for serum creatinine expression level, HBP stands for hypertension history, the patient has hypertension history before, HBP stands for 1, the patient has no hypertension history before, HBP stands for 0, age stands for age;

and when the calculation result is 0.5-1, the high risk of the acute kidney injury of the sepsis is represented, and the rest is low risk.

2. The use of claim 1, wherein the predictive model further comprises the following agents: normal human serum and positive control serum.

3. The use according to claim 1, wherein the prognosis is selected from any one or more of:

a) predicting the survival rate of the sepsis acute kidney injury individual for a certain time length;

b) predicting the survival time of the sepsis acute kidney injury individual;

c) and predicting the possibility of the sepsis acute kidney injury individual to relapse after the sepsis acute kidney injury individual is improved.

4. A diagnostic kit for predicting the risk of acute kidney injury caused by sepsis is characterized by comprising a reagent for detecting the expression level of ATIII and a reagent for detecting the expression level of serum creatinine, and the kit also comprises an instruction book, wherein the instruction book records the following formula:

IN[P/(1-P)]＝-0.163Gender-0.022ATIII+0.030Cr+0.681HBP-0.002age-4.895；

wherein, Gender stands for Gender, Gender is male, Gender stands for 1, Gender is female, Gender stands for 0, ATIII stands for antithrombin III expression level, Cr stands for serum creatinine expression level, HBP stands for hypertension history, the patient has hypertension history before, HBP stands for 1, the patient has no hypertension history before, HBP stands for 0, age stands for age;

when the calculated result is 0.5-1, it represents a high risk of sepsis acute kidney injury, and the rest is a low risk.

5. The kit of claim 4, further comprising the following reagents: normal human serum and positive control serum.

6. The kit according to claim 4, wherein the expression level of the gene or protein is measured for prognostic analysis.

7. The kit of claim 4, wherein the detection method for detecting the expression levels of ATIII and Cr is selected from RT-PCR, real-time fluorescent quantitative PCR, gene chip, high-throughput sequencing or immunological detection.

Background

Sepsis and sepsis-related AKI are involved in the dysregulation of inflammatory responses caused by interactions between the host immune system and microorganisms. Despite recent advances in clinical studies, sepsis and sepsis-associated AKI are still associated with high morbidity and mortality. While the value of ATIII in predicting sepsis-associated AKI has been suggested previously, our earlier studies found that male, low ATIII levels are independent risk factors for sepsis-associated AKI in the elderly, and whether these factors can be combined to predict sepsis-associated AKI at the same time would still merit further research.

Sex hormones are reported to have a modulating effect on immune responses. Estradiol induces the production of proinflammatory cytokines and the activation of macrophages, testosterone has an inhibitory effect on the immune response and increases susceptibility to infection. Furthermore, epidemiological studies have shown that men are more susceptible to sepsis than women, indicating that sex-specific hormone levels may affect susceptibility to severe illness. Gender differences after trauma or severe infection are reported. Limited clinical studies have also demonstrated a potential link between serum sex hormone levels and the onset and outcome of septic shock. There is currently a lack of information regarding the relationship between sex hormones and sepsis-associated multiple organ dysfunction.

A great deal of research is currently focused on sepsis-associated AKI predictive biomarkers. However, a single biomarker has certain defects and different sensibility and specificity for predicting diseases and prognosis, and recently, some researches prove that the establishment of a diagnosis and prediction model has a beneficial effect on AKI. However, a predictive model of sepsis-associated AKI is rarely reported. Thus, the potential role of combining plasma ATIII, gender, serum creatinine, urea nitrogen levels in sepsis-induced AKI or other organ dysfunction and prognosis deserves further investigation.

The inventors have previously combined plasma and other relevant factors to generate a prediction model (literature: Yun Xie 1, Yi Zhang1, Rui Tian 1, Wei Jin, Jiang Du, Zhigan Zhou, Ruilan Wang. A prediction model of segmented-associated heart kit in segmented based on antithrombin III.

Clin Exp Med..2021 Feb; 21(1) 89-100IF2.644), but later experiments show that the prediction model obtained by the invention is obviously improved compared with the former results. Therefore, the main objective of this study was to establish a more accurate predictive model of the incidence of sepsis-related AKI based on plasma ATIII, gender, serum creatinine, age, and history of hypertension.

The model for predicting acute kidney injury caused by sepsis is not reported at present.

Disclosure of Invention

The invention aims to provide a model for predicting acute kidney injury caused by sepsis, aiming at the defects of the prior art.

In order to achieve the purpose, the invention adopts the technical scheme that:

in a first aspect, the invention provides an application of a prediction model in preparing a reagent or a kit for predicting the risk and prognosis of acute kidney injury caused by sepsis, wherein the judgment method of the prediction model comprises the following steps:

IN[P/(1-P)]＝-0.163Gender-0.022ATIII+0.030Cr+0.681HBP-0.002age-4.895；

wherein, Gender stands for Gender, Gender is male, Gender stands for 1, Gender is female, Gender stands for 0, ATIII stands for antithrombin III expression level, Cr stands for serum creatinine expression level, HBP stands for hypertension history, the patient has hypertension history before, HBP stands for 1, the patient has no hypertension history before, HBP stands for 0, age stands for age;

and when the calculation result is 0.5-1, the high risk of the acute kidney injury of the sepsis is represented, and the rest is low risk.

Preferably, the predictive model further comprises the following reagents: normal human serum and positive control serum.

Preferably, the prognosis is selected from any one or several of the following:

a) predicting the survival rate of the sepsis acute kidney injury individual for a certain time length;

b) predicting the survival time of the sepsis acute kidney injury individual;

c) and predicting the possibility of the sepsis acute kidney injury individual to relapse after the sepsis acute kidney injury individual is improved.

In a second aspect, the invention provides a diagnostic kit for predicting the risk of acute kidney injury caused by sepsis, which comprises a reagent for detecting the expression level of ATIII and a reagent for detecting the expression level of serum creatinine, and the kit also comprises an instruction book, wherein the instruction book records the following formula:

IN[P/(1-P)]＝-0.163Gender-0.022ATIII+0.030Cr+0.681HBP-0.002age-4.895；

wherein, Gender stands for Gender, Gender is male, Gender stands for 1, Gender is female, Gender stands for 0, ATIII stands for antithrombin III expression level, Cr stands for serum creatinine expression level, HBP stands for hypertension history, the patient has hypertension history before, HBP stands for 1, the patient has no hypertension history before, HBP stands for 0, age stands for age;

when the calculated result is 0.5-1, it represents a high risk of sepsis acute kidney injury, and the rest is a low risk.

Preferably, the kit further comprises the following reagents: normal human serum and positive control serum.

Preferably, the expression level of the gene or protein is measured for prognostic analysis.

Preferably, the detection method for detecting the expression levels of ATIII and Cr is selected from RT-PCR, real-time fluorescent quantitative PCR, gene chip, high-throughput sequencing or immunological detection.

The invention has the advantages that:

1. compared with the prior single diagnosis index or combined index, the invention has greatly improved discrimination and calibration degree, more objective diagnosis result, simpler determination method, easy operation and reliability, and provides basis for individualized administration of clinical patients.

2. The method selects other indexes to construct the model for predicting the acute kidney injury of the sepsis on the basis of the previous research, and compared with the previous prediction model, the method is more objective and reliable in detection result, and remarkably improves the discrimination and the calibration degree, so that the method has strong practicability.

Drawings

FIG. 1 is a study flow chart of the present invention.

FIG. 2 is a graphical representation of the ROC analysis of the sepsis AKI predictive model established in example 1 based on ATIII.

Detailed Description

The invention will be further illustrated with reference to specific embodiments. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Furthermore, it should be understood that various changes and modifications can be made by those skilled in the art after reading the disclosure of the present invention, and equivalents fall within the scope of the appended claims.

Example 1

1 case selection

This is an observational study. According to "sepsis 3.0", we recruited adult patients 18 years old or older who suffered sepsis. This prospective study included data from a third class A teaching hospital (Shanghai first people hospital). This prospective observation study was conducted between 3 months in 2018 and 12 months in 2020. We recruited patients meeting inclusion criteria, followed hospitalization information, and performed a 28 day follow-up survey over the phone. All patients enrolled in the study signed informed consent. The study was approved by the ethical review committee of the first national hospital in shanghai.

1.1 inclusion criteria

(1) The age is more than or equal to 18 and less than or equal to 85 years old, and the male and the female are unlimited;

(2) a patient living in an ICU;

(3) confirmed or clinical diagnosis of infection;

(4) the acute change in sequential organ failure score was greater than or equal to 2 points, i.e., the Δ SOFA score was greater than or equal to 2 points (0 on baseline SOFA scores for patients with unknown prior organ dysfunction).

1.2 exclusion criteria:

(1) patients with stage 4 or 5 Chronic Kidney Disease (CKD) (creatinine clearance <30 ml/min);

(2) a renal transplant patient;

(3) ICU patients stay <24 h;

(4) patients already had AKI at the time of admission to the ICU;

(5) end stage tumor disease;

(6) patients who refuse all rescue measures;

(7) history of antibiotic or other drug allergies;

(8) a pregnant or lactating patient;

(9) there is a clear history of use of nephrotoxic drugs.

1.3 related concepts and definitions

AKI is defined as an increase in serum creatinine or a decrease in urine volume based on global prognosis for renal disease improvement (KDIGO) criteria. Baseline glomerular filtration rate was estimated using the renal disease dietary Modification (MDRD) study equation. Baseline creatinine was defined as the lowest serum creatinine value for the last 6 months prior to the onset of AKI, or for patients without such measurements, the lowest value reached during hospitalization without dialysis.

1.4 ethical criteria

The study meets medical ethical standards and has been approved by the ethical committee of the first-person hospital in shanghai. We will pay attention to protect the privacy of all patients participating in the study, remove personal information such as patient names during the study, and only register the hospitalization number information for use only in item data verification.

2 clinical Observation index

The deadline for data collection of clinical data of a patient to be observed is 28 days after the patient is observed, and the clinical data information is from a Haitai electronic medical record system, an HIS (medical advice system) and an original paper medical record of a first-person hospital in Shanghai.

2.1 basic data Collection

Basic data (gender, age, ethnicity, address, telephone, etc.).

History data (etiology, preliminary diagnosis, past history, medical/surgical patient, site of infection, pathogen outcome, complications, smoking history, drinking history, family history, etc.).

The major collection of complications: hypertension, diabetes, other cardiovascular diseases, immune diseases, liver diseases, chronic obstructive pulmonary diseases.

2.2 clinical data Collection

(1) Continuous acute physiology and chronic health assessment (APACHE) II scores and Sequential Organ Failure Assessment (SOFA) scores were calculated on the first ICU day.

(2) Laboratory data was collected on the first ICU day. Vital signs, hemodynamic and laboratory data were collected daily during ICU hospitalizations. Renal function was assessed daily by creatinine levels and urine volume.

(3) AKI was staged according to KDIGO stage 7 days after AKI diagnosis.

2.3 outcome index

The main results are: sepsis-associated AKI was diagnosed within 28 days.

Secondary outcome: 28 day mortality, incidence of CKD, total hospital stay, ICU hospital stay, mechanical ventilation, CRRT usage, vasoactive drug usage.

2.4 sample detection method

Blood samples were collected within the first 48 hours after ICU entry and assayed for antithrombin ATIII. The samples were centrifuged and stored at-80 ℃ and subsequently analyzed. ATIII is determined by a chromogenic substrate chromogenic method, a detection kit is purchased from Siemens Berichrom ATIII, and the specific operation steps are as follows:

(1) taking the concentration of the standard substance as a horizontal coordinate (logarithmic coordinate), and taking the optical density (OD value) as a vertical coordinate (common coordinate), so as to obtain a standard curve;

(2) diluting the plasma to be detected by 40 times of buffer solution, and adding 100ul of the diluted plasma into a hole of an enzyme-labeled plate;

(3) adding 50ul of thrombin liquid into the holes with the standard plasma and the plasma to be detected respectively, oscillating and uniformly mixing, and preserving moisture in a 37 ℃ moisture box for about 150-180 minutes;

(4) adding 50ul of chromogenic substrate into the holes, uniformly mixing, and standing at room temperature for 10 minutes;

(5) adding excessive thrombin into the detected plasma, wherein the thrombin and ATIII form a 1:1 complex, the rest thrombin hydrolyzes a chromogenic substrate Tos-Gly-Pro-Arg-ANBA-IPA to release a chromogenic gene ANBA-IPA, and the color development depth is in positive correlation with the rest thrombin and in negative correlation with the ATIII activity;

(6) adding 50ul of stop solution into each well in sequence to stop the reaction (at the moment, the blue color turns to yellow immediately);

(7) the corresponding plasma ATIII activity to be determined is determined from the OD values of the samples by means of a calibration curve.

3 statistical analysis

Study data were statistically analyzed using SPSS 20.0. Quantitative measures conforming to normal distribution are expressed as mean ± standard deviation, and non-conforming are expressed as median (quartile). The classification index is described by the number of cases and percentage of each class. For data with normal distributions, the difference between the two sets of data was compared using independent sample t-test. For data with non-normal distributions, the differences were compared using a non-parametric test. Chi-square test or Fisher test was used for qualitative data. Logistic regression analysis was used to evaluate the results based on the risk factors selected by univariate analysis. The diagnostic value of ATIII in predicting AKI and mortality was assessed by calculating the area under the subject's operating characteristic curve (AUC-ROC). The AUC-ROC analysis was performed by comparing AKI patients to non-AKI patients and by comparing survivor patients to non-survivor patients. The optimal cut-off was determined by the highest value of the Johnson index for the sensitivity and specificity calculations shown in the AUC-ROC analysis. P <0.05 was considered statistically significant. All statistical tests were performed using a two-sided test. And screening independent influence factors by adopting a Logistic regression model under single-factor and multi-factor conditions, and calculating an OR value of AKI occurrence risk and a 95% confidence interval of the OR value.

And forming a prediction model based on a plurality of prediction indexes through a multi-factor Logistic regression model, and calculating a new joint prediction factor. And (3) determining an optimal critical value by taking whether acute kidney injury occurs as a result and comparing the combined prediction factor with the area under each original index ROC curve (AUC), calculating working performance parameters such as sensitivity, specificity, prediction accuracy and the like, and finally carrying out individual prediction by substituting the individual values. And (3) adopting Stata 12.0 software to carry out prediction model building statistical analysis and statistical charting, and outputting Stata 12.0 command statements, operation flows and results. And performing predictive model verification statistical analysis and statistical charting by adopting R version 3.6.2 software.

4 results

4.1 comparison of demographic and baseline profiles in two groups of patients

The study time started at month 3 in 2018 and ended at month 12 in 2020. During the study, a total of 333 patients were selected for the study. 34 patients were excluded according to the exclusion criteria of the study (10 cases had been diagnosed with AKI, 9 cases of previous chronic kidney disease, 7 cases of end-stage tumor, 6 cases rejected all rescue measures, 2 cases had a history of antibiotics or other drug allergy). A total of 333 patients (90.7%) were enrolled. Of these, 78 were diagnosed with AKI, 33 deaths among AKI patients, and 47 deaths among non-diagnosed AKI patients (see flow sheet, figure 1).

Patient demographics and baseline characteristics (based on AKI cohort) are shown in table 1. 216 patients (64.9%) were male, 117 (35.1%) were female, and all 333 patients were included in the final data analysis (fig. 1). The mean age was 60.16 ± 13.92 years, with most suffering from complications (65.8%, n ═ 219). Hypertension, diabetes and other cardiovascular diseases are the most common complications. APACHE II score 15.8 + -7.52, SOFA score 6.36 + -3.64. 78 patients (23.4%) had AKI with 31.2% acute renal replacement therapy and 24.0% mortality. With respect to age, diabetes, immune diseases, liver disease and COPD, the characteristics of non-AKI (n 255) and the AKI group (n 78) are similar. Men, hypertension and cardiovascular disease are higher in proportion to AKI. Mortality in patients with AKI was higher (42.3% vs 18.4%, p < 0.001). Table 1 shows the clinical and laboratory characteristics of the population based on the AKI cohort. The two groups required mechanical ventilation and vasoactive drug use were not statistically different, APACHE II scores (14.54 + -6.861 vs19.90 + -8.125, p <0.0001),

the SOFA score (5.720 ± 3.267vs8.460 ± 4.022, p <0.001), these indices were significantly higher in AKI patients (table 1).

TABLE 1 patient demographics and Baseline characteristics (based on AKI grouping)

Table 1.Patients demographics and clinical characteristics(n＝333)based on AKI

P <0.05, there was a statistical difference. AKI, acute kidney injury; COPD, chronic obstructive pulmonary disease; APACHE, acute physiological and chronic health assessment; SOFA, sequential organ failure assessment.

4.2AKI Risk factors multifactor regression analysis

Multifactorial regression analysis of AKI risk showed that men (OR 2.324, 95% CI 1.256-4.301, p 0.007), hypertension (OR 2.940, 95% CI 1.327-6.516, p 0.008), ATIII (OR 0.979, 95% CI 0.967-0.992, p 0.001), several of these risk factors were identified as independent risk factors for sepsis-related AKI (table 2).

TABLE 2AKI Risk factors Multi-factor regression analysis

Table 2.Multivariable analysis for AKI and death risk(n＝333)

P <0.05, there was a statistical difference.

4.3 establishment of ATIII-based sepsis AKI prediction model

Fig. 2 shows that the sepsis AKI prediction model established based on ATIII is:

IN[P/(1-P)]＝-0.163Gender-0.022ATIII+0.030Cr+0.681HBP-0.002age-4.895

model goodness of fit test P is 0.000 and the area under the model ROC curve is 0.9906 (see fig. 2). The results suggest that the model has a higher degree of discrimination and calibration and is more accurate than previously published models.

The diagnostic threshold is 0.5, in the range of 0.5-1, a high risk of sepsis AKI, the remainder being a low risk.

Example 2

The embodiment aims to carry out internal verification, and verifies the prediction effect of the model by using the data of the model by adopting a Bootstrap self-sampling method. The accuracy of the histogram model was evaluated using boottrap self-sampling of the new samples and the R software was used for internal validation to obtain a validation ROC curve for sepsis-related AKI, area under the curve 0.9903.

Example 3

This example is directed to performing external validation, re-selecting a new sample data set (test, n 159), and using the R software to validate the validation ROC curve for sepsis-related AKI, the area under the curve 0.9897.

Example 4

The embodiment provides a kit for predicting the risk and prognosis of acute kidney injury caused by sepsis, which contains a kit for detecting the expression levels of ATIII and Cr, and the kit comprises an operating instruction and is described as follows: detecting the expression quantity of ATIII and Cr according to a conventional sequencing method, and substituting into a risk scoring formula: IN [ P/(1-P) ] -0.163Gender-0.022ATIII +0.030Cr +0.681HBP-0.002 age-4.895;

wherein, Gender stands for Gender, Gender is male, Gender stands for 1, Gender is female, Gender stands for 0, ATIII stands for antithrombin III expression level, Cr stands for serum creatinine expression level, HBP stands for hypertension history, the patient has hypertension history before, HBP stands for 1, the patient has no hypertension history before, HBP stands for 0, age stands for age;

when the calculated result is 0.5-1, it represents a high risk of sepsis acute kidney injury, and the rest is a low risk.

The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and additions can be made without departing from the principle of the present invention, and these should also be considered as the protection scope of the present invention.