| Objective:The prediction model of sepsis-associated acute kidney injury(SA-AKI)was established by the product of tissue inhibitor of metalloproteinase-2(TIMP-2)and insulin-like growth factor binding protein-7(IGFBP-7)([TIMP-2]×[IGFBP-7])combined with acute physiology and chronic health score(APACHEII)to predict the probability of sepsis-associated acute kidney injury(SA-AKI).It is expected to provide valuable information for the early prediction and diagnosis of SA-AKI,and contribute to the early treatment of SA-AKI and optimize the management of SA-AKI patients.Methods:The subjects of this study are patients with sepsis.The clinical data of 121 patients with sepsis treated in the emergency intensive care unit and department of critical care medicine of Shanxi Provincial people’s Hospital from February 2020 to September 2022 were collected and analyzed.According to the KDIGO guidelines issued in 2012,the patients were divided into sepsis-associated acute kidney injury group(SA-AKI group,n=37)and sepsis non-acute kidney injury group(N-SA-AKI group,n=84).The general data of the two groups were recorded,such as age,sex,complications,general vital signs,site of primary infection and clinical data,including blood cell analysis,blood gas analysis,procalcitonin(PCT),C-reactive protein(CRP)and other biochemical indexes measured within 24 hours of admission.APACHEII score were calculated according to the above laboratory indexes.0h and 24h urine samples were collected from the two groups,and ELISA kit was used to detect the stress markers TIMP-2 and IGFBP-7 in the pre-kidney injury stage.After the original data were inputted and checked by Excel form,R language(4.2.0)was used for statistical analysis.The quantitative data that obey normal distribution are expressed by mean ± standard deviation.The quantitative data that do not obey the normal distribution are expressed by the median(the first quartile,the third quartile),the statistical inference was performed by t test or Wilcoxon rank sum test.The number of cases(percentage)was used to describe the classification variables,and the statistical inference was analyzed by χ2 test.Binary logistic regression was used to explore the effects of APACHEII score,0h[TIMP-2]×[IGFBP-7]and 24h[TIMP-2]×[IGFBP-7]on the risk of SA-AKI.In addition,the three variables were used to construct the prediction model of the line graph of sepsis-associated acute kidney injury.Draw ROC curve and Calibration calibration curve to evaluate the discrimination and calibration of the line chart prediction model.The test level is α=0.05.Results:A total of 121 patients with sepsis were included,including 55 males(45.5%)and 66 females(54.5%).The median ages of SA-AKI group and N-SA-AKI group were 54 years old(48 years old,63 years old)and 75 years old(71 years old,81 years old)respectively.1.The general data of SA-AKI group and N-SA-AKI group were compared.the results showed that in terms of gender,the proportion of males in the SA-AKI group(59.5%)was significantly higher than N-SA-AKI group(39.3%),but the difference was not significant(P=0.064).In terms of age,the patients in the SA-AKI group[54(48,63)years old]were younger than those in the N-SA-AKI group[75(71,81)years old],and the difference was very significant(P<0.001).In terms of past medical history,the proportion of previous medical history in SA-AKI group was significantly higher than N-SA-AKI group(P=0.039).In terms of primary infection site,the proportion of intestinal and pulmonary infection in SA-AKI group[2(5.4%),1(2.7%)]was significantly lower than N-SA-AKI group[27(32.1%),17(20.2%)],while the ratio of other site infection[9(24.3%),25(67.6%)]was significantly higher than N-SA-AKI group[11(13.1%),29(34.5%)].The difference was very significant and statistically significant(P<0.001).In terms of consciousness,the proportion of drowsiness in SA-AKI group[7(18.9%)]was significantly higher than N-SA-AKI group[4(4.8%)],while the proportion of ambiguity[3(8.1%)]was significantly lower than N-SA-AKI group[16(19%)].The proportion of obscurity in SA-AKI group[1(2.7%)]was also significantly lower than that in N-SA-AKI group[6(7.1%)].See Table 1 below for details.2.The laboratory index data of patients in SA-AKI group and N-SA-AKI group showed that:The median white blood cell count in N-SA-AKI group was[15.32(6.80,23.00)×109/L],and that in SA-AKI group was[17.21(7.18,36.77)×109/L].There was significant difference between the two groups(P=0.043).It is suggested that the white blood cell count in SA-AKI group may be slightly higher than that in N-SA-AKI group.The median percentage of central granulocytes in N-SA-AKI group was[90.50(89.10,94.10)%],and that in SA-AKI group was[91.40(84.90,94.80)%],indicating that there was no significant difference between the two groups(P=0.710).The median platelet count in N-SA-AKI group was[254.00(131.50,328.00)× 109/L],while that in SA-AKI group was[146.00(106.00,233.00)× 109/L].The difference between the two groups was statistically significant(P=0.002),indicating that the platelet count in SA-AKI group may be lower than N-SA-AKI group.The median of C-reactive protein in N-SA-AKI group was[186.00(123.00,284.00)mg/L],and SA-AKI group was[188.00(134.00,238.00)mg/L].There was no significant difference between the two groups(P=0.782).The median of procalcitonin in N-SA-AKI group was[5.09(4.90,41.03)ng/L],while in SA-AKI group was[25.004(58,67.00)ng/L].There was no significant difference between the two groups(P=0.206).The median of bilirubin in N-SA-AKI group was[22.65(12.74,46.00)ummol/L],and in SA-AKI group was[16.13(9.52,23.70)ummol/L].There was significant difference between the two groups(P=0.015).The median of albumin in N-SA-AKI group and SA-AKI group were[30.91(25.27,35.00)g/L]and[29.31(22.69,35.85)g/L]respectively.There was no significant difference between the two groups(P=0.180).The[IGFBP-7]×[TIMP-2]of 0h in patients with SA-AKI group[(34.69±3.89)ng/ml2/1000]was higher than N-SA-AKI group[(30.63±2.75)ng/ml2/1000],and the difference was statistically significant(P<0.001).The[IGFBP-7]×[TIMP-2]of 24h in patients with SA-AKI group[(32.64±4.17)ng/ml2/1000]was higher than N-SA-AKI group[(30.59±4.48)ng/ml2/1000],and the difference was statistically significant(P=0.019).See table 2 below for details.3.Logistic regression analysis of urine[TIMP-2]×[IGFBP-7]and APACHEII score showed that:When 0h and 24h of[TIMP-2]×[IGFBP-7]was constant,when the APACHEII score increased by 1 point,the risk of SA-AKI increased by 1.275 times(P=0.002;OR:0.99;95%CI 1.090～1.493).When the APACHEII score and 24 h[TIMP-2]×[IGFBP-7]remained unchanged,the risk of SA-AKI increased by 1.447 times for every 1(ng/ml)2/1000 of[TIMP-2]×[IGFBP-7]in o h(P<0.001;OR:1.447;95%CI 1.212～1.728).When the APACHEII score and 0h[TIMP-2]×[IGFBP-7]remained unchanged,the risk of SA-AKI increased by 1.186 times for every 1(ng/ml)2/1000 of[TIMP-2]×[IGFBP-7]in 24 h(P=0.006;OR:1.186;95%CI 1.051～1.338).4.Evaluation of Logistic regression prediction model:The two most commonly used indicators for the evaluation of the effectiveness of the prediction model are the degree of differentiation and the degree of calibration,and the most commonly used index is the C index,as shown in figure 2.The AUC scores of APACHEII score,0h[TIMP-2]×[IGFBP-7]and 24h[TIMP-2]×[IGFBP-7]were 0.751,0.818 and 0.623,respectively,while the AUC of the combination of the three indexes to distinguish sepsis-associated acute kidney injury can reach 0.873.It can be seen from Table 4 that the sensitivity and specificity of APACHEII score,0h[TIMP-2]×[IGFBP-7],24h[TIMP-2]×[IGFBP-7]and their combination in the diagnosis of sepsis-associated acute kidney injury are(0.784,0.643),(0.811,0.786),(0.757,0.583)and(0.703,0.905)respectively,indicating that the combination of the three can improve the diagnostic performance.In addition,a correction curve is drawn to measure the calibration of the logistic regression model.As you can see from figure 3,the Abscissa is the prediction probability,the ordinate is the actual probability,and 0-1 means that the probability of occurrence is 0%100%.As can be seen from the chart,the prediction probability of the model is close to the actual probability line(Apparent)and ideal line(Ideal),indicating that the prediction probability of sepsis-asssociated acute kidney injury by the model is close to the actual probability,and the model has a good consistency.Bias-corrected is to correct the deviation line.Average absolute error(Meanabsoluteerror):used to evaluate the closeness between the predicted results and the real data set,the value is 0.023,indicating that the model has a good performance.Conclusion:1.0-hour[TIMP-2]×[IGFBP-7]and 24-hour[TIMP-2]×[IGFBP-7]and APACHE Ⅱscores were independent risk factors for sepsis-associated acute kidney injury.2.0-hour[TIMP-2]×[IGFBP-7]and 24-hour[TIMP-2]×[IGFBP-7]and APACHE Ⅱscore can predict the occurrence of sepsis-associated acute kidney injury.The combination of the three can improve the predictive efficiency of sepsis-associated acute kidney injury. |
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