Dose-related effects of dexmedetomidine on immunomodulation and mortality to septic shock in rats

BACKGROUND: Dexmedetomidine has already been used in septic patients as a new sedative agent, few studies have examined its effects on immunomodulation. Therefore, the authors have designed a controlled experimental study to characterize the immunomodulation effects of dexmedetomidine in the cecal ligation and puncture(CLP) model in rats. METHODS: After CLP, 48 Wistar rats were randomly allocated into four groups:(1) CLP group;(2) small-dose treatment group(2.5 g·kg^(-1)·h^(-1));(3) medium-dose treatment group(5.0 g·kg^(-1)·h^(-1)); and(4) large-dose treatment group(10.0 g·kg^(-1)·h^(-1)). HLA-DR and plasma cytokine(IL-4, IL-6, IL-10 and TNF-α) levels were measured, and the mean arterial blood pressure(MAP), heart rate(HR), arterial blood gases, lactate concentrations and mortality were also documented. RESULTS: The HLA-DR level, inflammatory mediator levels, MAP and HR had no obvious changes among Dexmedetomidine treatment groups(DEX groups). Compared with the CLP group, the DEX groups exhibited decreased HLA-DR levels(P_(group)=0.0202) and increased IL-6 production, which was increased at 3 h(P= 0.0113) and was then attenuated at 5 h; additionally, the DEX groups exhibited decreased HR(P<0.001) while maintaining MAP(P_(group)=0.1238), and remarkably improving lactate(P<0.0001). All of these factors led to a significant decrease in the mortality, with observed rates of 91.7%, 66.7%, 25% and 18% for the CLP, DEX2.5, DEX5.0, DEX10.0 groups, respectively.CONCLUSION: Dexmedetomidine treatment in the setting of a CLP sepsis rat model has partially induced immunomodulation that was initiated within 5 h, causing a decreased HR while maintaining MAP, remarkably improving metabolic acidosis and improving mortality dosedependently.

Lung-protective Ventilation in Patients with Brain Injury： A Multicenter Cross-sectional Study and Questionnaire Survey in China

Background： Over the years, the mechanical ventilation （MV） strategy has changed worldwide. The aim of the present study was to describe the ventilation practices, particularly lung-protective ventilation （LPV）, among brain-injured patients in China. Methods： This study was a multicenter, 1-day, cross-sectional study in 47 Intensive Care Units （ICUs） across China. Mechanically ventilated patients （18 years and older） with brain injury in a participating ICU during the time of the study, including traumatic brain injury, stroke, postoperation with intracranial tumor, hypoxic-ischemic encephalopathy, intracranial infection, and idiopathic epilepsy, were enrolled. Demographic data, primary diagnoses, indications for MV, MV modes and settings, and prognoses on the 60th day were collected. Multivariable logistic analysis was used to assess factors that might affect the use of LPV. Results： A total of 104 patients were enrolled in the present study, 87 （83.7%） of whom were identified with severe brain injury based on a Glasgow Coma Scale 〈8 points. Synchronized intermittent mandatory ventilation （SIMV） was the most frequent ventilator mode, accounting for 46.2% of the entire cohort. The median tidal volume was set to 8.0 ml/kg （interquartile range [IQR], 7.0-8.9 ml/kg） of the predicted body weight; 50 （48.1%） patients received LPV. The median positive end-expiratory pressure （PEEP） was set to 5 cmH20 （IQR, 5-5 cmH20）. No PEEP values were higher than 10 cmH20. Compared with partially mandatory ventilation, supportive and spontaneous ventilation practices were associated with LPV. There were no significant differences in mortality and MV duration between patients subjected to LPV and those were not. Conclusions： Among brain-injured patients in China, SIMV was the most frequent ventilation mode. Nearly one-half of the brain-injured patients received LPV. Patients under supportive and spontaneous ventilation were more likely to receive LPV.

Value of Kidney Disease Improving Global Outcomes Urine Output Criteria in Critically III Patients： A Secondary Analysis of a Multicenter Prospective Cohort Study

Background： Urine output （UO） is an essential criterion of the Kidney Disease Improving Global Outcomes （KD1GO） definition and classification system tbr acute kidney injury （AKI）, of which the diagnostic value has not been extensively studied. We aimed to determine whether AKI based on KDIGO UO criteria （KDtGOLro） could improve the diagnostic and prognostic accuracy, compared with KDIGO serum creatinine criteria （KDIGOscr）.Methods： We conducted a secondary analysis of the database of a previous study conducted by China Critical Care Clinical Trial Group （CCCCTG）, which was a 2-month prospective cohort study （July 1,2009 to August 31,2009） involving 3063 patients in 22 tertiary Intensive Care Units in Mainland of China. AKI was diagnosed and classified separately based on KDIGOt,o and KDlGOsc,. Hospital mortality of patients with more severe AKI classification based on KDIGOvo was compared with other patients by univariate and multivariate regression analyses. Results： The prevalence of AKl increased from 52.4% based on KDIGOscr to 55.4% based on KD1GOsc~ combined with KDIGOuo. KDIGOv~~ also restllted in an upgrade of AKI classification in 7.3% of patients, representing those with more severe AK1 classification based on KDIGOvo. Compared with non-AKI patients or those with maximum AKI classification by KDIGOscr, those with maximum AKI classification by KDIGOuo had a significantly higher hospital mortality of 58.4% （odds ratio [OR]： 7.580, 95% confidence interval [CI]： 4.141-13.873, P 〈 0.001）. In a multivariate logistic regression analysis, AKI based on KDIGOuo （OR： 2.891, 95% CI： 1.964-4.254, P 〈 0.001）, but not based on KDIGOscr （OR： 1.322, 95% CI： 0.902-1.939, P = 0.152）, was an independent risk factor for hospital mortality. Conclusion： UO was a criterion with additional value beyond creatinine criterion for AKI diagnosis and classification, which can help identify a group of patients with high risk of death.

Current status of China’s critical care medicine big data platform and future prospects

Overview The term“Big Data”originated at the 11th Electronic Materials Conference World Annual Conference,which originally referred to the large amount of data generated by the application of technology.[1]Medical big data includes not only the medical history and examination data accumulated during patient hospitalization,but also patient-related follow-up data,prognostic data from outpatient,emergency,and medical insurance settlement departments as well as clinical experiment centers.So far,it has profound applications in the various specialties of medicine.[2-4]However,intensive care medicine(ICU)is different from other medical fields.In comparison with clinical practice data,medical data in ICU have the following characteristics:large scale,rapid production,diverse dimensions,inaccuracies,heterogeneity,incompleteness,complexity,and privacy concerns.[5]In fact,in the process of constructing major ICU databases in China and worldwide,these databases have been optimized at great length.Taking heterogeneity as an example。