ABSTRACT
Trauma registration is an important tool to record the process and timeline in the treatment of trauma patients. The operation of trauma database is of great significance for reducing the mortality of patients, promoting the construction of trauma treatment system, and providing reference for policy-making. Trauma registration system has been established in the United States, United Kingdom, Germany and other developed countries for many years. However, the domestic system is still at an initial stage, and there are problems like data deficiencies, data incoherence, no item of complications, no treatment data after discharge and limits of human and financial resources. Therefore, there is room for improvements in terms of personnel fixation, financial support and continuous data monitoring should be further improved. In this study, the authors summarize the traum registration system from aspects of basic situation both at home and abroad, data analysis, clinical value, operation mechanism and challenges so as to provide important data for clinical researches.
ABSTRACT
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.
ABSTRACT
Objective To observe the changes of TNF-α and NF-κB after different doses of nalmefene hydrochloride (NAL) therapy for traumatic brain injury (TBI) in an effort to identify the effect of NAL on TBI-induced inflammatory response and the possible mechanism.Methods A model of TBI in the rat was produced using the improved Feeney' s free-fall impact method.The animals were randomly divided into sham group,TBI group,TBI + large dose of NAL (ip,0.2 mg/kg) group (TBI + NAL1group),TBI + medial dose of NAL (ip,0.14 mg/kg) group (TBI + NAL2 group),TBI + small dose of NAL (ip,0.07 mg/kg) group (TBI + NAL3 group).Form of brain tissues in each group was observed and mRNA levels of TNF-α and NF-κB were measured by real-time quantitative PCR assay.Results HE staining revealed severe injury and inflammatory infiltration of brain parenchyma in TBI group ;on the contrary,the situation ameliorated in TBI + NAL1 group,TBI + NAL2 group and TBI + NAL3group,with especially obvious improvement in TBI + NAL2 group.In PCR assay,significant expression of NF-κB and TNF-α was observed at post-TBI days 1,3,5 and 7 (P ＜ 0.05),followed by great reverse after NAL therapy (P ＜ 0.05),particularly in TBI + NAL2 group.Conclusions NAL can reduce the inflammation response to TBI and promote post-injury recovery.Moreover,there exists a NAL concentration window.