Objective:To evaluate the efficacy and safety of a risk stratification-based model for prediction of acute kidney injury (AKI) combined with hemoperfusion (HP) in the treatment of patients with sepsis.Methods:A prospective, observational, pilot trial was conducted. The patients who met the Sepsis-3 diagnostic criteria admitted to intensive care unit of Lanzhou University Second Hospital from May to December in 2019 were enrolled as the research objects. Through the AKI early warning model established by the research group in the early stage, AKI risk > 30% was defined as AKI high risk. Patients with AKI high risk were enrolled in the observation group, and the remaining patients were enrolled in the control group. All patients were given conventional treatment, including the search and treatment of original infection sites, the use of antibiotics and main organ function support. Patients in the observation group were combined with HP treatment on the basis of conventional treatment, 2.5 hours per day for 3 days. The baseline data of gender, age, infection site, acute physiology and chronic health evaluation Ⅱ (APACHE Ⅱ) score, sequential organ failure assessment (SOFA) score, mean arterial pressure (MAP) and serum creatinine (SCr) were recorded. The inflammatory indexes such as interleukin-6 (IL-6), lipopolysaccharide (LPS) and procalcitonin (PCT) were detected at ICU admission, 24 hours and 72 hours after ICU admission, and the length of ICU stay, ICU mortality and bleeding were recorded.Results:Among the 49 patients with sepsis enrolled in this study, the main diagnosis was pneumonia, and Gram-negative (G -) bacilli were the main pathogenic bacteria [61.2% (30/49)]. Among them, 30 patients with AKI risk > 30% were in the observation group, and the remaining 19 patients were in the control group. There was no significant difference in gender, age, infection site, APACHE Ⅱ score, SOFA score, MAP or other baseline data between the two groups, but the baseline value of SCr in the observation group was significantly higher than that in the control group (μmol/L: 112.2±34.4 vs. 93.4±13.0, P < 0.05). At ICU admission, there was no significant difference in IL-6, LPS or PCT between the two groups. However, with the extension of ICU time, the inflammatory indexes of the two groups showed a downward trend. At 24 hours after ICU admission, there was no significant difference in IL-6, LPS or PCT between the two groups. At 72 hours after ICU admission, IL-6 in the experimental group decreased significantly as compared with the control group (ng/L: 90.9±38.1 vs. 119.1±41.9, P < 0.05), but there was no significant difference in LPS or PCT between the two groups. The length of ICU stay in the experimental group was significantly shorter than that in the control group (days: 9.77±2.76 vs. 12.47±3.85, P < 0.01), but there was no significant difference in the ICU mortality between the experimental group and control group (20.0% vs. 21.1%, P > 0.05). None of the 49 patients had severe bleeding events. Conclusions:The application of a risk stratification-based model for prediction of AKI combined with HP in septic patients is feasible both in theory and in clinical practice, and shortens the length of ICU stay, but fails to effectively remove inflammatory mediators or reduce sepsis mortality. A large sample, multicenter, randomized controlled study is still needed for further verification.

Objective:To investigate the risk factors affecting the prognosis of patients with acute kidney injury (AKI) in the intensive care unit (ICU) based on the Medical Information Mart for Intensive Care Ⅲ (MIMIC-Ⅲ) database, and to establish a prognostic model for AKI.Methods:Patients (aged ≥ 18 years) with acute renal failure, admitted to the ICU for the first time, and had complete hospital records (the RIFLE diagnostic criteria were used in the database, and the diagnosis was expressed as AKI in this article) were screened from MIMIC-Ⅲ database according to diagnostic codes. Patients were divided into two groups based on survival state at discharge, and the general information, underlying diseases, injury factors, vital signs and laboratory indicators within 24 hours after AKI, related intervention and prognostic indicators were analyzed. Univariate and multivariate Logistic regression analysis were used to determine the risk factors affecting mortality in patients with AKI and established a prediction model. The receiver operator characteristic curve (ROC curve) was used to analyze the predictive value of the prediction model for the prognosis of AKI patients.Results:There were 4 554 patients with AKI included and 862 died, with mortality of 18.93%. Univariate Logistic regression analysis was performed for factors that might be associated with death in AKI patients, and the results showed that age, hypertension, lymphoma, metastatic carcinoma, vancomycin, aspirin, coagulation abnormalities, cardiac arrest, sepsis or septic shock, invasive mechanical ventilation, white blood cell count (WBC), platelet count (PLT), K +, blood urea nitrogen (BUN), total bilirubin (TBil), renal replacement therapy (RRT) and length of stay (LOS) were independent risk factors [odds ratio ( OR) and 95% confidence interval (95% CI) were 1.002 (1.001-1.003), 0.764 (0.618-0.819), 1.749 (1.112-2.752), 2.606 (1.968-3.451), 1.779 (1.529-2.071), 0.689 (0.563-0.842), 1.871 (1.590-2.201), 2.468 (1.209-5.036), 2.610 (2.226-3.060), 2.154 (1.853-2.505), 1.105 (1.009-1.021), 0.998 (0.997-0.998), 1.132 (1.057-1.212), 1.008 (1.006-1.011), 1.061 (1.049-1.073), 2.142 (1.793-2.997), 0.805 (0.778-1.113), all P < 0.05]. Further binary Logistic regression analysis showed that lymphoma, metastatic cancer, vancomycin, cardiac arrest, sepsis or septic shock, coagulation dysfunction, invasive mechanical ventilation, increased BUN, increased TBil, increased or decreased blood K + and increased WBC were independent risk factors for death [β values were 0.636, 1.005, 0.207, 0.894, 0.787, 0.346, 0.686, 0.006, 0.051, 0.085, and 0.009; OR and 95% CI were 1.889 (1.177-3.031), 2.733 (2.027-3.683), 1.229 (1.040-1.453), 2.445 (1.165-5.133), 2.197 (1.850-2.610), 1.413 (1.183-1.689), 1.987 (1.688-2.338), 1.006 (1.003-1.009), 1.052 (1.039-1.065), 1.089 (1.008-1.176), and 1.009 (1.004-1.015), respectively, all P < 0.05]. The Hosmer-Lemeshow test showed that the AKI prognostic model was able to fit the observed data well ( P = 0.604). ROC curve analysis showed that the area under ROC curve (AUC) of the AKI prognostic model was 0.716 (95% CI was 0.697-0.735), when the cut-off value was 0.320, the sensitivity was 71.9%, the specificity was 60.1%, the positive likelihood ratio was 1.80, and the negative likelihood ratio was 0.47. Conclusion:The prognostic prediction model of AKI in critically ill patients established and based on the MIMIC-Ⅲ database may have practical significance for prognostic risk assessment of AKI and later intervention.