CN117497170A - Construction method and application of early warning model for converting acute kidney injury into chronic kidney disease - Google Patents
Construction method and application of early warning model for converting acute kidney injury into chronic kidney disease Download PDFInfo
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Abstract
The invention discloses a construction method and application of an early warning model for converting Acute Kidney Injury (AKI) into Chronic Kidney Disease (CKD), and relates to the technical field of biological medicines. The construction method of the early warning model for converting acute kidney injury into chronic kidney disease comprises the following steps: s1, collecting risk information of an AKI patient, wherein the risk information comprises clinical index data and marker data; s2, substituting the risk information into a multi-factor Logistic regression equation, and training a model to obtain an early warning model for converting acute kidney injury into chronic kidney disease. The constructed early warning model can effectively predict the progression risk of various etiologies AKI to CKD.
Description
Technical Field
The invention relates to the technical field of biological medicine, in particular to a construction method and application of an early warning model for converting acute kidney injury into chronic kidney disease.
Background
Acute Kidney Injury (AKI) is a serious complication that threatens the life of hospitalized patients, especially critically ill patients. AKI occurs in 1300 thousands of people worldwide each year, with mortality rates as high as 50% -80% for severe AKI patients requiring dialysis. AKI has become a "global health alert," and prevention of AKI and its adverse consequences is a significant need to ensure the life and health of the public. Basic and epidemiological studies have shown that the long-term prognosis of AKI is related to whether it progresses to Chronic Kidney Disease (CKD). AKI progression to CKD is a significant cause of increasing chronic renal failure disease burden and sanitary resource consumption. Early warning AKI progresses to CKD risk, screening high risk disease population with progression to CKD, is extremely important for accurate typing, accurate monitoring and accurate intervention of AKI.
Risk prediction of AKI progression to CKD currently lacks efficient methods and reliable indicators. Therefore, there is a need to build a risk early warning model that can be used to develop AKI to CKD.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art. Therefore, the invention provides a construction method of an early warning model for converting AKI into CKD.
The invention also provides an early warning model obtained based on the construction method.
The invention also provides an early warning system for converting AKI into CKD.
The invention also provides an analysis device for converting AKI into CKD.
The invention also provides a pre-warning method for evaluating the risk of transforming AKI into CKD for non-disease diagnosis and/or treatment.
The invention also provides application of the early warning model.
The invention also provides a kit.
The invention also provides computer equipment.
The invention also provides a computer readable storage medium.
According to an embodiment of the first aspect of the invention, the construction method of the early warning model for AKI conversion to CKD comprises the following steps:
s1, collecting risk information of an AKI patient, wherein the risk information comprises clinical index data and marker data; the clinical index data includes age, gender, baseline serum creatinine value, urinary albumin amount, AKI severity stage, serum creatinine value; the marker data comprises at least one of urine CK20 concentration, urine timp2×igfbp7 concentration, urine NGAL concentration, urine KIM1 concentration on the AKI diagnosis day;
s2, substituting the risk information into a multi-factor Logistic regression equation, and training a model to obtain an early warning model for converting AKI into CKD.
The construction method according to the embodiment of the invention has at least the following beneficial effects:
urine CK20 can be used as a noninvasive index for reflecting the severity of acute tubular injury, early warning the occurrence risk of chronic renal fibrosis and the progression risk of AKI of various etiologies to CKD, and can effectively reduce the dependence on invasive renal biopsy diagnosis. In addition, urine is convenient to obtain, can be used for detecting CK20 without special treatment, and is rapid and efficient in detection. Urine CK20 concentration is closely related to severe acute tubular injury diagnosed by renal puncture biopsy, and the diagnosis accuracy is 82%.
After the marker data (urine CK20 concentration, urine TIMP2 IGFBP7 concentration, urine NGAL concentration or urine KIM1 concentration) and other clinical indexes (age, sex, baseline serum creatinine value, urine albumin quantity, serum creatinine value in time of AKI severity stage and discharge), the early warning accuracy can be obviously improved, and the large-scale screening and dynamic monitoring of patients can be conveniently carried out. Especially, the prediction efficiency of the early warning model obtained by combining the urine CK20 concentration with other clinical indexes reaches 90 percent. Urine CK20 concentration is more than or equal to 5.0 mug/g Cr, and the risk of the early warning AKI after 90 days of developing CKD or advanced CKD after correcting clinical indexes is 8.8 times and 9.3 times of that of patients with less than 5.0 mug/g Cr respectively. Therefore, the early warning model for converting AKI into CKD provided by the invention can actually evaluate the risk of converting AKI into CKD by applying the corresponding urine CK20 concentration threshold value (5.0 mug/g Cr) and clinical indexes. This will greatly aid the clinician in more accurately finding out a dangerous patient who is progressing to CKD after AKI, performing close follow-up and accurate treatment, thereby improving AKI patient prognosis.
The term "baseline serum creatinine value" refers to the daily serum creatinine level of an AKI patient prior to a definitive diagnosis of AKI. For example: the outpatient measurements or the average of multiple outpatient measurements over the 7-365 days prior to diagnosis of AKI may be determined for the AKI patient.
The term "urinary albumin amount" refers to urinary albumin concentration corrected for urinary creatinine in mg/g Cr.
The term "on discharge blood creatinine value" refers to the blood creatinine value of an AKI patient at or near discharge when the risk of AKI conversion to CKD is analyzed. For reflecting how well AKI is restored when the patient is discharged from the hospital. For example: the risk of AKI to CKD conversion at discharge of the patient may be the value of creatinine at discharge after treatment (it will be appreciated that it is not limited to the value of creatinine on discharge but may be the value of creatinine closest to the time of discharge).
According to some embodiments of the invention, the early warning model is an early warning model of progression to CKD 90 days after AKI onset (90 days after AKI confirmation). According to the international guidelines, the progression of AKI to CKD is clinically established 90 days after AKI.
According to some embodiments of the invention, the early warning model is an early warning model of progression to late CKD 90 days after AKI onset.
According to some embodiments of the invention, the constructing method further comprises: and evaluating the early warning model by adopting an ROC curve.
According to the embodiment of the second aspect of the invention, the early warning model for converting AKI into CKD is obtained based on the construction method.
According to some embodiments of the invention, when the marker data is urine CK20 concentration, the early warning model is logic [ p/(1-p) ]=0.166×1+0.032×2-0.516×3+0.023×4+0.007×5+0.771×6+0.006×7;
and/or, when the marker data is urine TIMP2 xigfbp 7 concentration, the early warning model is Logit [ p/(1-p) ]=0.001 x1+0.027 x2-0.431 x3+0.012 x4+0.007 x5+1.061x6+0.006 x7;
and/or, when the marker data is urine NGAL concentration, the early warning model is logic [ p/(1-p) ]=0.002×x1+0.031×2-0.374×3+0.025×4+0.007×5+1.074×6+0.005×x7;
and/or, when the marker data is the concentration of urine KIM1, the early warning model is logic [ p/(1-p) ]=0.162×x1+0.032×2-0.510×3+0.015×4+0.006×5+1.098×6+0.006×7;
wherein the predicted probability value is Logit [ p/(1-p) ], urine CK20 concentration or urine TIMP2 IGFBP7 concentration or urine NGAL concentration or urine KIM1 concentration is X1, age is X2, sex is X3, baseline serum creatinine value is X4, urine albumin amount is X5, AKI severity stage is X6, serum creatinine value at discharge is X7; x3, X6 are categorical numerical variables and X1, X2, X4, X5 and X7 are continuous numerical variables. When the AKI patient is male, X3 is 1; when the AKI patient is female, X3 is 0. X6 is 0 during AKI 1 phase; x6 is 1 in AKI phase 2 or 3.
Urine CK20 concentration is a value corrected for urinary creatinine concentration.
An early warning system for AKI conversion to CKD according to an embodiment of the third aspect of the present invention includes:
the risk information acquisition system is used for acquiring risk information of the AKI patient, wherein the risk information comprises clinical index data and marker data; the clinical index data includes age, sex, baseline creatinine value, urine albumin amount, AKI severity stage, creatinine value at discharge; the marker data comprises at least one of urine CK20 concentration, urine timp2×igfbp7 concentration, urine NGAL concentration, urine KIM1 concentration on the AKI diagnosis day;
the risk analysis system comprises the early warning model.
An analysis device for AKI conversion to CKD according to an embodiment of the fourth aspect of the present invention comprises:
the risk information acquisition unit is used for acquiring risk information of the AKI patient, wherein the risk information comprises clinical index data and marker data; the clinical index data includes age, sex, baseline creatinine value, urine albumin amount, AKI severity stage, creatinine value at discharge; the marker data comprises at least one of urine CK20 concentration, urine timp2×igfbp7 concentration, urine NGAL concentration, urine KIM1 concentration on the AKI diagnosis day;
and the risk analysis unit is used for substituting the risk information into the early warning model and calculating a predicted probability value of the risk of the AKI patient from AKI to CKD so as to analyze the risk of the AKI patient from AKI to CKD.
According to some embodiments of the invention, analyzing the risk of transformation of AKI to CKD in the AKI patient comprises: when the predicted probability value is more than or equal to a probability threshold, the AKI patient is or is candidate to be a progressor, and is easy to progress into CKD or advanced CKD in the future;
when the predicted probability value < probability threshold, the AKI patient is or is candidate as a non-progressor, and is not prone to progress to CKD or advanced CKD in the future.
According to some embodiments of the invention, analyzing the risk of AKI patient to CKD conversion from AKI when the marker data is urine CK20 concentration further comprises:
s21, when an AKI patient is diagnosed, the concentration of urine CK20 is more than or equal to a tangential point value, the AKI patient is or is candidate to be a progressor, and the patient is easy to progress into CKD or advanced CKD in the future;
urine CK20 concentration < cut-off value when AKI patients are diagnosed, who are or are candidates for non-progressors, and are not likely to progress to CKD or advanced CKD in the future;
s22, combining the relation that the concentration of urine CK20 is more than or equal to the tangential point value in the step S21 with the clinical index data, establishing an early warning model according to the construction method, and analyzing the risk that the AKI of the AKI patient which is the progressor or candidate is progressed to the CKD or the advanced CKD. And the tangent point value is calculated based on an early warning model used by the risk analysis unit.
According to some embodiments of the invention, in the step S22, the early warning model is configured to analyze that when the AKI patient experiences AKI progression to CKD, the early warning model is:
Logit[p/(1-p)]＝2.289*X1+0.029*X2-0.676*X3+0.022*X4+0.006*X5+0.767*X6+0.006*X7；
ind (uCK 20.gtoreq.5.0 μg/g Cr) is X1, age is X2, sex is X3, baseline serum creatinine value is X4, urine albumin amount is X5, AKI severity stage is X6, serum creatinine value at discharge is X7; x1, X3, X6 are categorical numerical variables and X2, X4, X5 and X7 are continuous numerical variables. When the concentration of urine CK20 is more than or equal to the tangential point value, X1 is 1; when the concentration of urine CK20 is less than the tangential point value, X1 is 0. When the AKI patient is male, X3 is 1; when the AKI patient is female, X3 is 0. X6 is 0 during AKI 1 phase; x6 is 1 in AKI phase 2 or 3. 5.0. Mu.g/g Cr is the tangent point value.
According to some embodiments of the invention, in the step S22, the early warning model is used to analyze that when the AKI patient develops AKI to late CKD, the early warning model is:
Logit[p/(1-p)]＝1.826*X1+0.016*X2-1.055*X3+0.034*X4+0.005*X5+19.05*X6+0.005*X7；
ind (uCK 20.gtoreq.5.0 μg/g Cr) is X1, age is X2, sex is X3, baseline serum creatinine value is X4, urine albumin amount is X5, AKI severity stage is X6, serum creatinine value at discharge is X7; x1, X3, X6 are categorical numerical variables and X2, X4, X5 and X7 are continuous numerical variables. When the concentration of urine CK20 is more than or equal to the tangential point value, X1 is 1; when the concentration of urine CK20 is less than the tangential point value, X1 is 0. When the AKI patient is male, X3 is 1; when the AKI patient is female, X3 is 0. X6 is 0 during AKI 1 phase; x6 is 1 in AKI phase 2 or 3. 5.0. Mu.g/g Cr is the tangent point value.
An early warning method for assessing the risk of AKI conversion to CKD for non-disease diagnosis and/or treatment according to an embodiment of the fifth aspect of the present invention comprises the steps of:
a1, acquiring risk information of an AKI patient to be tested, wherein the risk information comprises clinical index data and marker data; the clinical index data includes age, sex, baseline creatinine value, urine albumin amount, AKI severity stage, creatinine value at discharge; the marker data comprises at least one of urine CK20 concentration, urine timp2×igfbp7 concentration, urine NGAL concentration, urine KIM1 concentration on the AKI diagnosis day;
a2, substituting the risk information into a multi-factor Logistic regression equation, training a model to obtain an early warning model, and determining a predicted probability value of the patient to be tested AKI;
a3, analyzing whether the AKI of the tested AKI patient is at risk of converting AKI into CKD according to the predicted probability value.
The reagent for detecting the concentration of urine CK20 or the application of the early warning model in the preparation of products is provided in the sixth aspect of the invention; the use of the product comprising at least one of B1) to B3),
b1 Predicting and/or aiding in predicting whether the patient's AKI is converted to CKD;
b2 Diagnosing and/or aiding in diagnosing tubular injury;
b3 Predicting and/or aiding in predicting whether a patient will progress to advanced CKD in the future.
According to some embodiments of the invention, B1) comprises progression to CKD 90 days after AKI onset.
According to some embodiments of the invention, B1) comprises progression to advanced CKD 90 days after AKI onset.
A kit according to an embodiment of the seventh aspect of the present invention, the kit comprising reagents for detecting the concentration of urine CK 20;
the use of the kit comprises at least one of B1) to B3),
b1 Predicting and/or aiding in predicting whether the patient's AKI is converted to CKD;
b2 Diagnosing and/or aiding in diagnosing tubular injury;
b3 Predicting and/or aiding in predicting whether a patient will progress to advanced CKD in the future.
According to some embodiments of the invention, B1) comprises progression to CKD 90 days after AKI onset.
According to some embodiments of the invention, B1) comprises progression to advanced CKD 90 days after AKI onset.
A computer device according to an eighth aspect of the embodiment of the invention comprises a memory, a processor; the memory stores a computer program, and the construction method or the early warning method can be realized when the computer program is executed.
A computer-readable storage medium according to an embodiment of the ninth aspect of the present invention stores a computer program for executing the above-described construction method or the above-described early warning method when executed by a processor.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
FIG. 1 shows CK20 expression in kidney tissue of human AKI patients in a murine model of AKI induced by different causes; wherein A, B is the immunohistochemical staining result and Western blotting detection result of kidney tissues of IRI mouse model with different kidney injury degrees, C is the immunohistochemical staining result of kidney tissues of folic acid kidney injury mouse model with different kidney injury degrees, D is the immunohistochemical staining result of kidney biopsy tissues at different time points after the AKI patient is diagnosed with acute tubular necrosis;
FIG. 2 shows the expression of CK20 in urine from an IRI mouse model and a human AKI patient; wherein, the A graph is the Western blotting detection result of urine of IRI mouse model with different kidney injury degrees, the B graph is the Western blotting detection result of urine at different time points after the patient is diagnosed with acute tubular necrosis, the C graph is the correlation relation analysis result of semi-quantitative result of urine immunoblotting experiment and kidney tissue tubular injury score at the 1 st day after IRI mouse model operation;
FIG. 3 is a ROC curve of urine CK20, NGAL, TIMP2 XIGFBP 7 predicted severe ATI;
fig. 4 shows the accuracy of the different predictive models for predicting progression to CKD 90 days after AKI diagnosis; wherein, a graph a shows urine CK20 detection results of a progressor and a non-progressor at different time points (x represents a significant difference (P < 0.05)), x represents a significant difference (P < 0.01), x represents a very significant difference (P < 0.001)), B graph a ROC curve of a different biomarker predicted AKI to progress to CKD 90 days after diagnosis, C graph a ROC curve of a different early warning model predicted AKI to progress to CKD 90 days after diagnosis, D graph a ROC curve of a different biomarker predicted AKI to progress to late CKD 90 days after diagnosis, E graph a ROC curve of a different early warning model predicted AKI to progress to late CKD 90 days after diagnosis;
fig. 5 is a plot of optimal cut-point value region versus risk of future progression to CKD (a) or advanced CKD (B) in a patient.
Detailed Description
The conception and the technical effects produced by the present invention will be clearly and completely described in conjunction with the embodiments below to fully understand the objects, features and effects of the present invention. It is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments, and that other embodiments obtained by those skilled in the art without inventive effort are within the scope of the present invention based on the embodiments of the present invention.
The specific conditions are not noted in the examples and are carried out according to conventional conditions or conditions recommended by the manufacturer. The reagents or apparatus used were conventional products commercially available without the manufacturer's attention.
In the description of the present invention, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
24C 57 mice (8-10 weeks old) were randomly divided into 2 groups. One group was a 20-minute ischemic group (mild kidney injury), after anesthesia, the right kidney was opened and removed (as a self-control), and the left kidney was subjected to 20-minute renal pedicle-clamping ischemia treatment; one group was 40 minutes of ischemia (severe kidney injury to chronic fibrosis), right kidneys were harvested (as self-control) after anesthesia and left kidneys were subjected to 40 minutes of renal pedicle clamp ischemia. Each group was prepared by randomly taking 6 mice 1 day and 3 days after surgery, taking out the left kidney after sacrifice, extracting total RNA after separating the renal cortex fraction (with the pre-extracted right kidney as a control), constructing a library after oligo dT enrichment (rRNA removal) of total RNA after quality control, identifying library quality using Agilent 2100Bioanalyzer, and quantifying library using qPCR. Sequencing the mixed different sample library by using a sequencer; sequencing quality evaluation is carried out on reads after the decomplex by FastQC, the reads are compared with a reference genome (GRCm 38) through Hisat2, and FPKM calculation and expression difference conditions of gene level or transcript level are carried out by using R software Ballgown, so that genes expressed differentially between samples or groups are screened out; setting Fold change greater than 2 and p less than 0.05 indicates a significant difference.
The results of the gene correlation of the first three expression upregulation amplitudes of each group are shown in Table 1.
TABLE 1
In both the 20-min and 40-min ischemia groups (either 1 day or 3 days after surgery), the Krt20 gene was the gene whose up-regulation was the first compared to the native control. And the Krt20 gene was 42-fold and 25-fold different in 1 day and 3 days, respectively, in the 40-minute ischemia group compared with the 20-minute ischemia group. This suggests that the Krt20 gene is an important indicator reflecting the severity of kidney injury and even progression to fibrosis, which is significantly upregulated in a mouse model of kidney Ischemia Reperfusion Injury (IRI) at different injury levels.
Example 2
The Krt20 gene is a protein-encoding gene, mainly encoding cytokeratin 20 (CK 20), which is an epithelial cytoskeletal protein component.
An IRI mouse model was constructed by the method of reference example 1 (wherein, 30 mice with 20 minutes of ischemia, 6 sham mice, right kidney pre-picked from 20 minutes of ischemia were used as kidney control, sham mouse urine was used as urine control; 30 mice with 40 minutes of ischemia, 6 sham mice, right kidney pre-picked from 40 minutes of ischemia were used as kidney control, sham mouse urine was used as urine control); meanwhile, 30 folic acid kidney injury mice models (the construction method is that 250mg/kg folic acid is injected into the abdominal cavity of a C57 mouse) and 6 mice in a control group (only the same amount of solvent is injected). Each mouse model randomly selects 6 mice on 1, 3, 7, 14, 30 days after surgery to obtain kidney tissue samples and urine samples within 24 hours of the day. And selecting kidney tissue and urine specimens at days 3, 7 and 14 after a patient (n=3) with severe tubular injury has been diagnosed in clinic by kidney biopsy; the kidney tissue control was paraneoplastic normal kidney tissue (i.e., normal kidney tissue without tubular injury, n=3) following a renal tumor resection in a patient with a simple kidney tumor, and the urine control was urine (n=3) from healthy volunteers. And (3) comparing the correlation of the CK20 expression quantity with clinical indexes and pathological injuries through immunohistochemical staining and Western blotting immunoblotting detection.
The results are shown in fig. 1 and 2.
In the IRI mouse model, kidney tissue staining suggests that CK20 is barely expressed in the intact kidney (control), whereas significant up-regulation of expression occurs after injury, mainly in the renal tubules; as shown in figure 1, panel a. The expression amount of CK20 in each time point in the 40-minute ischemia group is higher than that in the 20-minute ischemia group, and the expression duration is longer; as shown in figure 1B. In a folic acid kidney injury mouse model, the staining condition is similar to that of a 40-minute ischemia mouse model, the CK20 staining is obvious, and the expression duration is long; as shown in figure 1, panel C. This suggests that CK20 can be significantly up-regulated in animal models of severe kidney injury caused by different etiologies without damaging the specificity of the cause.
CK20 is expressed positively in kidney biopsy tissue of patients with kidney tissue pathology manifesting as acute tubular necrosis. As shown in figure 1D. It is similar to the results in animal experiments. The closer to the point in time when kidney damage was hit, the higher the CK20 expression level was.
In the IRI mouse model, CK20 was not expressed in normal mouse urine, whereas significant up-regulation of expression occurred after injury. As shown in figure 2, panel a. And the expression amount of CK20 at each time point in the 40-minute ischemia mouse model is higher than that of the 20-minute ischemia mouse model, and the expression duration is longer.
Positive expression of CK20 was also found in the urine of patients with renal tissue pathology manifested as acute tubular necrosis as shown in panel B of fig. 2. Similar to the results in animal experiments, the closer to the point in time when kidney damage was hit, the higher the CK20 expression level.
In addition, through correlation analysis of the semi-quantitative result of the urine immunoblotting experiment within 3 days after the operation of the IRI mouse model and the renal tissue tubular injury score, a remarkable correlation exists between the semi-quantitative result and the renal tissue tubular injury score, and the correlation coefficient is 0.75. As shown in figure 2, panel C. This suggests that urine CK20 levels may reflect the severity of kidney injury.
Taken together, the intensity and duration of CK20 expression in the kidney and urine are related to the mouse model of AKI induced by different causes and the extent of kidney injury and chronicity in human AKI patients.
Example 3
To examine the manifestation of urine CK20 concentration in patient cohorts, we established an AKI cohort containing 169 patients enrolled in the nephrology ICU, 102 of whom received a kidney biopsy. Clinical data of patients after admission are collected, urine specimens are reserved, and urine CK20 concentration level detection is carried out by using a commercial ELISA kit, and the steps are strictly carried out according to instructions.
The urine CK20 concentration on the current day of AKI diagnosis was subjected to a three-dimensional hierarchical analysis and Log10 shift linear analysis for observation of the correlation of urine CK20 concentration with severe acute tubular injury (ATI, defining that the area of damaged tubular is more than 25% severe) on the histopathology of kidney biopsies.
The results are shown in Table 2.
TABLE 2
Corrected for various clinical parameters (age, sex, AKI-confirmed day creatinine, urinary protein, AKI-grade, discharge creatinine values), the incidence of severe ATI in patients with highest urine CK20 concentrations was 91-fold higher than in patients with lowest grades. The incidence of severe ATI increased by a factor of 4 for every increase in urine CK20 concentration by one standard deviation. This suggests that among 102 AKI patients receiving kidney biopsy, the urinary CK20 concentration on the day of AKI diagnosis correlated significantly with the severity of ATI.
The concentration of urine CK20 on the current day was further diagnosed using AKI to predict severe ATI and a ROC curve was drawn.
The results are shown in FIG. 3.
The predicted diagnostic efficacy of urine CK20 concentration is 0.82, which is superior to the currently commonly used diagnostic markers of renal tubular injury NGAL and TIMP2 xIGFBP 7.
Example 4
1. In the process of establishing a patient queue early warning model, collecting information of 6 clinical indexes (age, sex, baseline creatinine value, urine albumin amount, AKI severity stage and creatinine value at discharge) of an AKI patient; meanwhile, the concentration value of urine CK20, the concentration of urine TIMP2 and the concentration of IGFBP7, the concentration of urine NGAL and the concentration of urine KIM-1 of the patient with AKI are detected.
Whether or not to progress to CKD 90 days after diagnosis with AKI (egffr persistence)<60mL/min/m 2 ) To be classified into two kinds of endThe stepwise regression multi-factor Logistic method establishes an early warning model of AKI progress to CKD. The independent variables consisted of urinary CK20 concentration, age, sex, baseline serum creatinine value, urinary albumin amount, AKI severity stage, serum creatinine value at discharge on the day of AKI diagnosis. Constructing a multi-factor Logistic regression equation according to seven independent variables, substituting seven variables of urine CK20 concentration (X1, continuous numerical variable), age (X2, continuous numerical variable), sex (X3, classified numerical variable, male 1, female 0), baseline blood creatinine value (X4, continuous numerical variable), urine albumin amount (X5, continuous numerical variable), AKI severity stage (X6, classified numerical variable, AKI 1 stage is 0, AKI 2 stage or 3 stage is 1) and discharge blood creatinine value (X7, continuous numerical variable) into equation (1), and calculating a predicted probability value (Logit [ p/(1-p)])。
Such as: the concentration of urine CK20 on the AKI definite diagnosis day is 8.0 mu g/g Cr, the age of 65 years, the baseline blood creatinine value of 100 mu mol/L, the urine albumin amount of 1000mg/g and the blood creatinine value of 150 mu mol/L at the discharge of the patient in AKI 3; namely: x1 is 8.0, X2 is 65, X3 is 1, X4 is 100, X5 is 1000, X6 is 1, and X7 is 150.
The early warning model (uCK 20+clinical model) of the clinical model predicting AKI progression to CKD was:
logit [ p/(1-p) ]=0.166×1+0.032×2-0.516×3+0.023×4+0.007×5+0.771×6+0.006×7, equation (1).
The early warning model uCK20+clinical model, uTIMP 2. Times. IGFBP7+clinical model, uNGAL+clinical model, uKIM1+clinical model, clinical model alone are constructed with reference to the above steps.
The early warning model uTIMP 2. IGFBP7+ clinical model is:
Logit[p/(1-p)]＝0.001*X1+0.027*X2-0.431*X3+0.012*X4+0.007*X5+1.061*X6+0.006*X7；
wherein X1 is urine timp2×igfbp7 concentration (continuous variable), X2 is age (continuous variable), X3 is gender (classified variable, male 1, female 0), X4 is baseline serum creatinine value (continuous variable), X5 is urine albumin amount (continuous variable), X6 is AKI severity stage (classified variable, AKI 1 stage is 0, AKI 2 stage or 3 stage is 1), X7 is serum creatinine value at discharge (continuous variable).
The early warning model uNGAL+clinical model is:
Logit[p/(1-p)]＝0.002*X1+0.031*X2-0.374*X3+0.025*X4+0.007*X5+1.074*X6+0.005*X7；
where X1 is urine NGAL concentration (continuous variable), X2 is age (continuous variable), X3 is gender (categorical variable, male 1, female 0), X4 is baseline serum creatinine value (continuous variable), X5 is urine albumin amount (continuous variable), X6 is AKI severity stage (categorical variable, AKI 1 stage 0, AKI 2 or 3 stage 1), X7 is serum creatinine value at discharge (continuous variable).
The early warning model uKIM1+ clinical model is:
Logit[p/(1-p)]＝0.162*X1+0.032*X2-0.510*X3+0.015*X4+0.006*X5+1.098*X6+0.006*X7；
wherein X1 is urine KIM1 concentration (continuous variable), X2 is age (continuous variable), X3 is gender (categorical variable, men 1, women 0), X4 is baseline serum creatinine value (continuous variable), X5 is urine albumin amount (continuous variable), X6 is AKI severity stage (categorical variable, AKI 1 stage 0, AKI 2 or 3 stage 1), X7 is serum creatinine value at discharge (continuous variable).
The early warning model clinical model alone is:
Logit[p/(1-p)]＝0.029*X2-0.464*X3+0.013*X4+0.007*X5+1.252*X6+0.006*X7；
where X2 is age (continuous variable), X3 is gender (categorical variable, male 1, female 0), X4 is baseline creatinine value (continuous variable), X5 is urine albumin amount (continuous variable), X6 is AKI severity stage (categorical variable, AKI 1 is 0, AKI 2 or 3 is 1), X7 is the creatinine value at discharge (continuous variable).
Meanwhile, the accuracy of the early warning model for advanced CKD to 90 days after the AKI diagnosis was evaluated by the area under the working curve (AUC) method of the subject.
The results are shown in FIG. 4.
With follow-up, 59 of 169 AKI patients (progressors) progressed to CKD 90 days after onset. The urine CK20 concentration was significantly increased on the day of the patient's AKI diagnosis compared to the non-progressors, and continued for 1 week after the AKI occurrence. As shown in figure 4, panel a.
Furthermore, urine CK20 concentration was independently correlated with the risk of AKI progression to CKD, predicted to be 0.78, superior to other reported urine biomarkers (e.g., NGAL, KIM-1 and TIMP2 xigfbp 7). As shown in figure 4B.
The early warning model (uCK 20+clinical model) established by combining clinical factors and urine CK20 concentration during AKI diagnosis can be used for efficiently predicting the risk of converting AKI into CKD 90 days after onset, and the overall accuracy reaches 90%. As shown in figure 4, panel C.
Urine CK20 concentration was independently correlated with the risk of AKI progression to advanced CKD, predicted to correlate at 0.79, superior to other reported urine biomarkers (e.g., NGAL, KIM-1 and TIMP2 xigfbp 7). As shown in figure 4D.
The early warning model (uCK 20+clinical model) established by combining clinical factors and urine CK20 concentration during AKI diagnosis can be used for efficiently predicting the risk of converting AKI into advanced CKD 90 days after onset, and the overall accuracy reaches 92%. As shown in figure 4E.
2. And (3) taking the urine CK20 concentration value on the current day of AKI diagnosis as an index, calculating an optimal tangent point value according to the about sign index of the ROC curve corresponding to the early warning model (uCK 20+clinical model), and evaluating whether the tangent point value can be used as an early warning model for the AKI to progress to the CKD risk.
The results are shown in FIG. 5.
The optimal tangent point value calculated by the Johnson index method is 5.0 mug/g Cr. Seven variables of Ind (uCK. Gtoreq.5.0. Mu.g/g Cr) (X1, categorical value variable, ind (uCK. Gtoreq.5.0. Mu.g/g Cr) 1, ind (uCK. Gtoreq.5.0. Mu.g/g Cr) 0), age (X2, continuous value variable), sex (X3, categorical value variable, male 1, female 0), baseline serum creatinine value (X4, continuous value variable), urine albumin amount (X5, continuous value variable), AKI severity stage (X6, categorical value variable, KI 1 phase 0, AKI 2 phase or 3 phase 1), serum creatinine value (X7, continuous value variable) were taken into the probability model, and the predicted probability value (Logit [ p/(1-p) ]wascalculated.
The probability model for predicting AKI progression to CKD is:
Logit[p/(1-p)]＝2.289*X1+0.029*X2-0.676*X3+0.022*X4+0.006*X5+0.767*X6+0.006*X7；
the probability model for predicting AKI progression to advanced CKD is:
Logit[p/(1-p)]＝1.826*X1+0.016*X2-1.055*X3+0.034*X4+
0.005*X5+19.05*X6+0.005*X7。
urine CK20 concentrations were divided into high and low level groups at this cut-off value. It was found that after correction of clinical signs, patients with a urinary CK20 concentration of 5.0. Mu.g/g Cr at the time of AKI diagnosis had a risk of developing CKD of 8.8 times the risk of patients with < 5.0. Mu.g/g Cr in the future. As shown in figure 5, panel a.
Further studies have found that patients with urine CK20 concentrations of 5.0 μg/g Cr at the time of AKI diagnosis are at 9.3 times the risk of future progression to advanced CKD (estimated glomerular filtration rate < 30 mL/min) of < 5.0 μg/g Cr. As shown in figure 5B.
The embodiments of the present invention have been described in detail with reference to the embodiments, but the present invention is not limited to the embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the spirit of the present invention.
Claims (10)
1. The construction method of the early warning model for converting acute kidney injury into chronic kidney disease is characterized by comprising the following steps of:
s1, collecting risk information of an AKI patient, wherein the risk information comprises clinical index data and marker data; the clinical index data includes age, sex, baseline creatinine value, urine albumin amount, AKI severity stage, creatinine value at discharge; the marker data comprises at least one of urine CK20 concentration, urine timp2×igfbp7 concentration, urine NGAL concentration, urine KIM1 concentration on the AKI diagnosis day;
s2, substituting the risk information into a multi-factor Logistic regression equation, and training a model to obtain an early warning model for converting acute kidney injury into chronic kidney disease.
2. An early warning model for converting acute kidney injury into chronic kidney disease, which is characterized by being obtained based on the construction method of claim 1.
3. The early warning model of claim 2, wherein when the marker data is urine CK20 concentration, the early warning model is Logit [ p/(1-p) ] = 0.166 x1+0.032 x2-0.516 x3+0.023 x4+0.007 x5+0.771 x6+0.006 x7;
and/or, when the marker data is urine TIMP2 xigfbp 7 concentration, the early warning model is Logit [ p/(1-p) ]=0.001 x1+0.027 x2-0.431 x3+0.012 x4+0.007 x5+1.061x6+0.006 x7;
and/or, when the marker data is urine NGAL concentration, the early warning model is logic [ p/(1-p) ]=0.002×x1+0.031×2-0.374×3+0.025×4+0.007×5+1.074×6+0.005×x7;
and/or, when the marker data is the concentration of urine KIM1, the early warning model is logic [ p/(1-p) ]=0.162×x1+0.032×2-0.510×3+0.015×4+0.006×5+1.098×6+0.006×7;
wherein the predicted probability value is Logit [ p/(1-p) ], urine CK20 concentration or urine TIMP2 IGFBP7 concentration or urine NGAL concentration or urine KIM1 concentration is X1, age is X2, sex is X3, baseline serum creatinine value is X4, urine albumin amount is X5, AKI severity stage is X6, serum creatinine value at discharge is X7; x3, X6 are categorical numerical variables and X1, X2, X4, X5 and X7 are continuous numerical variables.
4. An early warning system for the conversion of acute kidney injury to chronic kidney disease, comprising:
the risk information acquisition system is used for acquiring risk information of the AKI patient, wherein the risk information comprises clinical index data and marker data; the clinical index data includes age, sex, baseline creatinine value, urine albumin amount, AKI severity stage, creatinine value at discharge; the marker data comprises at least one of urine CK20 concentration, urine timp2×igfbp7 concentration, urine NGAL concentration, urine KIM1 concentration on the AKI diagnosis day;
a risk analysis system comprising the early warning model of claim 2 or 3.
5. An apparatus for analyzing the conversion of acute kidney injury to chronic kidney disease, comprising:
the risk information acquisition unit is used for acquiring risk information of the AKI patient, wherein the risk information comprises clinical index data and marker data; the clinical index data includes age, sex, baseline creatinine value, urine albumin amount, AKI severity stage, creatinine value at discharge; the marker data comprises at least one of urine CK20 concentration, urine timp2×igfbp7 concentration, urine NGAL concentration, urine KIM1 concentration on the AKI diagnosis day;
a risk analysis unit for substituting the risk information into the early warning model according to claim 2 or 3, and analyzing the risk of the patient with AKI from acute kidney injury to chronic kidney disease.
6. A method of non-disease diagnosis and/or treatment for pre-warning to assess the risk of conversion of acute kidney injury to chronic kidney disease comprising the steps of:
a1, acquiring risk information of an AKI patient to be tested, wherein the risk information comprises clinical index data and marker data; the clinical index data includes age, sex, baseline creatinine value, urine albumin amount, AKI severity stage, creatinine value at discharge; the marker data comprises at least one of urine CK20 concentration, urine timp2×igfbp7 concentration, urine NGAL concentration, urine KIM1 concentration on the AKI diagnosis day;
a2, determining a predicted probability value of the AKI patient to be tested according to the early warning model of claim 2 or 3;
a3, analyzing whether the patient to be tested has the risk of converting acute kidney injury into chronic kidney disease according to the predicted probability value.
7. Use of a reagent for detecting urine CK20 concentration or an early warning model according to any one of claims 2 to 3 in the manufacture of a product; the use of the product comprising at least one of B1) to B3),
b1 Predicting and/or aiding in predicting whether a patient's acute kidney injury is transforming into chronic kidney disease;
b2 Diagnosing and/or aiding in diagnosing tubular injury;
b3 Predicting and/or aiding in predicting whether a patient will progress to advanced CKD in the future.
8. A kit comprising reagents for detecting the concentration of urine CK 20;
the use of the kit comprises at least one of B1) to B3),
b1 Predicting and/or aiding in predicting whether a patient's acute kidney injury is transforming into chronic kidney disease;
b2 Diagnosing and/or aiding in diagnosing tubular injury;
b3 Predicting and/or aiding in predicting whether a patient will progress to advanced CKD in the future.
9. A computer device comprising a memory, a processor; the memory stores a computer program, and the computer program is executed to implement the construction method according to claim 1 or the early warning method according to claim 6.
10. A computer-readable storage medium storing a computer program for executing the construction method of claim 1 or the early warning method of claim 6 when executed by a processor.
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