Objective:To investigate the clinical characteristics of the severe trauma patients with Acute kidney injury (AKI) ,and analyze the risk factors and clinical prognosis.Methods:Clinical data of severe trauma patients admitted to ICU of Xiaolan Hospital of Southern Medical University, from July 2018 to December 2020 were retrospectively analyzed. Demographic data, basic diseases, critical disease score, serum creatinine, hemoglobin, treatment options, blood transfusion volume, and clinical outcomes were collected to establish a clinical database. AKI was diagnosed and graded according to the Kidney Disease Improving Global Outcomes (KDIGO) criterion, and trauma type was classified according to the main injury part. The clinical data and laboratory examination of different groups were compared to analyze the clinical characteristics and prognosis in severe trauma patients. The risk factors of AKI in severe trauma patients were analyzed by Logistic regression.Results:(1) A total of 175 patients with severe trauma were eligible for inclusion, and the incidence of AKI was 30.9%(54/175), including 29 patients with AKI stage 1(16.6%), 15 patients with AKI stage 2 (8.6%), and 10 patients with AKI stage 3 (5.7%). In the cohort, the rate of in-hospital renal replacement therapy was 4%, in-hospital mortality was 5.7%, and 28-day mortality was 16.6%. (2) The age, shock patients, ICU admission serum creatinine, APACHEⅡscore and ISS score of AKI group were significantly higher than those of non-AKI group ( P<0.05). There were no significant differences between the two groups in gender, underlying diseases (hypertension and diabetes), ICU admission hemoglobin level and contrast agent utilization rate( P>0.05). Compared with the non-AKI group, AKI group had higher rates of surgical treatment (63% vs. 44.6%), more blood transfusion [875(720,1110)mL & 670(610,750)mL], longer ICU stay [6(4,11)d & 4(2.5,7.5)d], and higher rates of mechanical ventilation (96.3% vs. 81%), renal replacement therapy rate (13% vs. 0), in-hospital mortality (13% vs. 2.5%) and 28-day mortality (25.9% vs. 12.4%), the differences were statistically significant ( P<0.05). (3) The incidence of AKI was different in patients with different types of severe trauma, and the abdominal trauma group with a highest rate (50%). The serum creatinine at ICU admission and the peak value during hospitalization in abdominal trauma group were significantly higher than those in other injury types ( P<0.05). (4) Logistic regression analysis showed Age [ OR=1.020, 95% CI(1.003,1.038), P=0.024], APACHEⅡscore [ OR=1.137, 95% CI(1.053,1.228), P=0.001], shock [ OR=1.102, 95% CI(0.906,1.208), P=0.034], ICU admission serum creatinine [ OR=1.068, 95% CI(1.036,1.102), P=0.000], surgical treatment [ OR=4.205, 95% CI(1.446,12.233), P=0.008], blood transfusion volume [ OR=1.006, 95% CI(1.002,1.009), P=0.001] were independent risk factors for AKI in severe trauma patients. Conclusions:Severe trauma patients yield a high incidence of AKI influencing clinical prognosis. The incidence of AKI varies with different types of severe trauma. Age, APACHEⅡscore, shock, ICU admission serum creatinine, surgical treatment, and blood transfusion volume are independent risk factors for AKI in severe trauma patients.

Objective:To investigate the clinical value of neuron specific enolase (NSE) and bispectral index (BIS ) in predicting the neurological prognosis in patients with severe intracerebral hemorrhage.Methods:Patients with severe intracerebral hemorrhage admitted to the ICU of Xiaolan Hospital of Southern Medical University from January 2019 to December 2020 were selected, and serum NSE detection and BIS monitoring were performed at an early stage. According to the Glasgow outcome scale (GOS) at 90 days after intracerebral hemorrhage, the patients were divided into the good neurologic prognosis group (GOS 4-5) and poor neurologic prognosis group (GOS 1-3). The levels of NSE and BIS between the two groups were compared. Receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to evaluate the predictive value of NSE, BIS and their combination in predicting neurological prognosis.Results:A total of 126 patients with severe intracerebral hemorrhage were enrolled in this study, and 32 patients (25.4%) had poor neurological prognosis. The level of NSC in the poor neurological prognosis group was significantly higher than that in the good neurologic prognosis group [28 (13.7, 50.4) ng/mL vs. 13.5 (9.6, 18.5) ng/mL, P < 0.05], while the BIS level was significantly lower than that in the good neurologic prognosis group [32 (25.2, 45) vs. 55 (48, 62.2), P <0.05]. For detection of poor neurologic outcome in patients with severe intracerebral hemorrhage, NSE and BIS yielded the AUC values of 0.768 (0.685, 0.839) and 0.866 (0.793, 0.920), respectively, with cut-off values of 21.7 ng/mL and 47, respectively. The combination of NSE and BIS yielded a remarkably higher AUC value of 0.927 (0.867, 0.966) for predicting poor neurologic outcome than each index alone ( P<0.05). Conclusions:Early monitoring of NSE and BIS can effectively predict the neurological prognosis of patients with severe intracerebral hemorrhage, and the combination of NSE and BIS can further improve the prediction efficiency.

Objective:To construct and evaluate a decision tree based on biomarkers for predicting severe acute kidney injury (AKI) in critical patients.Methods:A prospectively study was conducted. Critical patients who had been admitted to the department of critical care medicine of Xiaolan Hospital of Southern Medical University from January 2017 to June 2018 were enrolled. The clinical data of the patients were recorded, and the biomarkers, including serum cystatin C (sCys C) and urinary N-acetyl-β-D-glucosaminidase (uNAG) were established immediately after admission to intensive care unit (ICU), and the end points were recorded. The test cohort was established with patient data from January to December 2017. The decision tree classification and regression tree (CART) algorithm was used, and the best cut-off values of biomarkers were used as the decision node to construct a biomarker decision tree model for predicting severe AKI. The accuracy of the decision tree model was evaluated by the overall accuracy and the receiver operating characteristic (ROC) curve. The validation cohort, established on patient data from January to June 2018, was used to further validate the accuracy and predictive ability of the decision tree.Results:In test cohort, 263 patients were enrolled, of whom 57 developed severe AKI [defined as phase 2 and 3 of Kidney Disease: Improving Global Outcomes (KDIGO) criterion]. Compared with patients without severe AKI, severe AKI patients were older [years old: 64 (49, 74) vs. 52 (41, 66)], acute physiology and chronic health evaluation Ⅱ (APACHEⅡ) score were higher [23 (19, 27) vs. 15 (11, 20)], the incidence of hypertension, diabetes and other basic diseases and sepsis were higher (64.9% vs. 40.3%, 28.1% vs. 10.7%, 63.2% vs. 29.6%), the levels of sCys C and uNAG were higher [sCys C (mg/L): 1.38 (1.12, 2.02) vs. 0.79 (0.67, 0.98), uNAG (U/mmol Cr): 5.91 (2.43, 10.68) vs. 2.72 (1.60, 3.90)], hospital mortality and 90-day mortality were higher (21.1% vs. 4.4%, 52.6% vs. 13.1%), the length of ICU stay was longer [days: 6.0 (4.0, 9.5) vs. 3.0 (1.0, 6.0)], and renal replacement therapy requirement was higher (22.8% vs. 1.9%), with statistically significant differences (all P < 0.05). ROC curve analysis showed that the areas under ROC curve (AUC) of sCys C and uNAG in predicting severe AKI were 0.857 [95% confidence interval (95% CI) was 0.809-0.897) ] and 0.735 (95% CI was 0.678-0.788), and the best cut-off values were 1.05 mg/L and 5.39 U/mmol Cr, respectively. The structure of the biomarker decision tree model constructed by biomarkers were intuitive. The overall accuracy in predicting severe AKI was 86.0%, and AUC was 0.905 (95% CI was 0.863-0.937), the sensitivity was 0.912, and the specificity was 0.796. In validation cohort of 130 patients, this decision tree yielded an excellent AUC of 0.909 (95% CI was 0.846-0.952), the sensitivity was 0.906, and the specificity was 0.816, with an overall accuracy of 81.0%. Conclusion:The decision tree model based on biomarkers for predicting severe AKI in critical patients is highly accurate, intuitive and executable, which is helpful for clinical judgment and decision.

Autoimmune diseases (AID) are a group of complex disorders due to antibodies acting on self-antigens causing damage to the body. AID has long been considered as the outcome of genetic and environmental interactions. In recent years, studies have shown that increased susceptibility to AID may be associated with single nucleotide polymorphisms and copy number variations of Toll like receptor 7 (TLR7) gene, which provided a clue to further understanding of the pathogenesis of AID. This paper provides a review of the recent advances in understanding of the roles of TLR7 gene single nucleotide polymorphisms and copy number variations in AID.
Animals ; Autoimmune Diseases ; genetics ; DNA Copy Number Variations ; Genetic Predisposition to Disease ; Humans ; Polymorphism, Single Nucleotide ; Toll-Like Receptor 7 ; genetics

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