Objective:To explore the predictive value of serum microRNA (miR)-155-5p on prognosis in sepsis patients with acute liver injury.Methods:The clinical data of 103 sepsis patients with acute liver injury from March 2017 to March 2019 in Rongjun Hospital, Zhejiang Province, were retrospectively analyzed. Among them, 57 patients were improved (survival group) and 46 patients died in hospital (death group). The clinical data and serum miR-155-5p were compared between 2 groups, and the influencing factors of prognosis in sepsis patients with acute liver injury were analyzed. The receiver operating characteristic (ROC) curve was used to evaluate the prognosis value of miR-155-5p.Results:The incidence of septic shock, age, procalcitonin (PCT), C-reactive protein (CRP), D-dimer, acute physiology and chronic health evaluation Ⅱ (APACHE Ⅱ), sequential organ failure score (SOFA) and miR-155-5p in death group were significantly higher than those in survival group: 95.65% (44/46) vs. 45.61% (26/57), (50.82 ± 10.52) years vs. (43.84 ± 7.32) years, (16.42 ± 4.97) μg/L vs. (7.20 ± 2.19) μg/L, (23.21 ± 8.59) mg/L vs. (16.73 ± 5.04) mg/L, (10.84 ± 3.17) mg/L vs. (4.16 ± 2.15) mg/L, (22.37 ± 3.16) scores vs. (16.72 ± 4.10) scores, (10.98 ± 3.74) scores vs. (6.84 ± 2.47) scores and 3.10 ± 0.97 vs. 2.25 ± 0.63, and there were statistical differences ( P<0.05). Multivariate Logistic regression analysis result showed that the APACHE Ⅱ, SOFA, PCT and miR-155-5p were the independent risk factors of prognosis in sepsis patients with acute liver injury ( OR=3.173, 2.732, 2.553 and 2.153; 95% CI 2.127 to 6.312, 2.018 to 6.056, 1.249 to 4.466 and 1.234 to 4.153; P<0.01 or <0.05). ROC curve analysis result showed that the cut-off value of miR-155-5p was 2.89, and the area under curve was 0.871 (95% CI 0.782 to 0.951), with a sensitivity of 86.1% and a specificity of 80.4%. Conclusions:Serum miR-155-5p is closely related to the clinical prognosis in sepsis patients with acute liver injury. It can be used as one of the potential prognostic indicators for patients, and is worthy of further clinical study and analysis.

Objective:To establish an early prediction Nomogram model for severe acute pancreatitis(SAP) complicated with acute renal injury (AKI), and evaluate the prediction efficiency of the model.Methods:The clinical data of 295 SAP patients hospitalized in Zhejiang Rongjun Hospital from July 2017 to June 2021 were retrospectively analyzed, and the patients were divided into AKI group ( n=61) and non-AKI group ( n=234) according to whether complicated with AKI. The common characters, clinical data and laboratory examination results were compared. The risk factors for SAP complicated with AKI was analyzed by multivariate logistic regression analysis, and a nomogram prediction model was established by R software. The receiver operating characteristic (ROC) curve was drawn and the area under the curve (AUC) was calculated to evaluate its prediction performance. Results:The acute physiology and chronic health assessment Ⅱ (APACHEⅡ) and Ranson score, the incidence of abdominal compartment syndrome (ACS) and systemic inflammatory response syndrome (SIRS), the cases of shock and mechanical ventilation, and the levels of blood lactic acid (BLA), blood creatinine (Scr), urea nitrogen (BUN), C-reactive protein (CRP), procalcitonin (PCT) and cystatin C(Cys C) in peripheral blood were significantly higher in AKI group than those in non-AKI group, while the levels of blood calcium were lower than those in non-AKI group, and the differences were statistically significant (all P value <0.05). Multivariate logistic regression analysis showed that APACHEⅡ score ( OR=1.185, 95% CI 1.074-1.308, P=0.001), Ranson score ( OR=12.668, 95% CI 5.102-31.456, P<0.001), Scr ( OR=1.028, 95% CI 1.002-1.054, P=0.034), PCT ( OR=4.298, 95% CI 1.379-13.395, P<0.001) and Cys C ( OR=38738.38, 95% CI 43.190-347459.41, P<0.001) were independent risk factors for SAP complicated AKI. Serum calcium ( OR=0.0001, 95% 0.000-0.048, P<0.001) was an independent protective factor for SAP complicated AKI. A Nomogram prediction model based on the six factors above were established, and its AUC, sensitivity and specificity to predict AKI were 0.987, 99.0% and 98.5% in the training set, and were 0.976, 98.6% and 94.2% in the validation set. Conclusions:This study successfully established an early prediction model with high predict value for SAP complicated with AKI, which can efficiently predict the risk of SAP with concurrent AKI.