The Value Of Sepsis-3.0 To Predict The Mortality Of Patients With Sepsis In Intensive Care Units In China&A Multifactor Model For Predicting Mortality In Critically Ill Patients

Part I The value of Sepsis-3.0 to predict mortality of patients with sepsis in intensive care units in ChinaBackground:Sepsis is the leading cause of death of critically ill patients.In 1991,the international consensus meeting defined sepsis as systemic inflammatory response syndrome caused by infection,which is known as Sepsis-1.O.The definition of sepsis was updated to Sepsis-3.0 in February 2016 and it is modified tolife-threatening organ dysfunction due to a dysregulated host response to infection.However,the value of Sepsis-3.Oto predict mortality of patients with sepsis need to be elucidated in intensive care units of ChinaObjective:To estimate the value of Sepsis-3.0 to predictmortality of patients with sepsis and compare it with Sepsis-1.0 to improve the early recognition?diagnosis and treatment of infected patients in intensive care units.Methods:From May 1,2016 to June 1,2016,496 patients were enrolled consecutively in six intensive care units from five university-affiliated hospitals in China.Data were extracted from the electronic clinical records.We collected baseline characteristics and laboratory examination to complete sequential organ failure assessment score and the acute physiology and chronic health evaluation ? score.This is a retrospective multicenter study.We evaluated the performance of Sepsis-1.0 and Sepsis-3.0 by measuring the area under the receiver operating characteristic curves of it's predicting 28-day mortality rates respectively.Results:Of 496 enrolled patients,186(37.5%)were diagnosed with sepsis according to Sepsis-1.0,while 175(35.3%)fulfilled the criteria of Sepsis-3.0.Thearea under the receiver operating characteristic curves of systemic inflammatory response syndrome is significantly smaller than that of sequential organ failure assessment 0.55[95%confidence interval,0.46-0.64]vs.0.69(95%confidence interval,0.61-0.77],P=0.008)to predict 28-daymortality rates of infected patients.Moreover,5.9%infected patients(11 patients)were diagnosed as sepsis according to Sepsis-1.0 but not to Sepsis-3.0.The acute physiology and chronic health evaluation ?,sequential organ failure assessment scores,and mortality rate of the 11 patients were significantly lower than of patients whose sepsis was defined by both the previous and new criteria(8.6±3.5 vs.16.3±6.2,P?0.001;1(0-1)vs.6(4-8),P?0.001;0.0 vs.33.1%,P?0.019).In addition,the APACHE ?,length of stay in ICU,and 28-day mortality rate of septic patients rose gradually corresponding with the raise insequential organ failure assessment score(but not with the systemic inflammatory response syndromecriteria).Conclusions:Sepsis-3.0 performed better than Sepsis-1.0 in the study samples of intensive care units.Part ? A multifactormodel for predicting mortality in critically ill patientsBackground:Mortality in critically ill patients has not decreased after improving medical conditions in intensive care units and increasing the financial burden of treatment.Although there are many assessment systems,there seems to be a large margin in which the prediction capacity can be raised.Objective:The objective of this study was to develop a model using a combination of routine clinical variables topredict mortality in critically ill patients.Methods:A cohort of 500 patients recruited fromeight university hospital intensive care units(ICUs)was used todevelop a model via logistic regression analyses.Discrimination and calibration analyses were performed to assess the model.Results:The model included the lactate level(odds ratio[OR]=1.11,95%confidence interval[Cl]1.01 to 1.22,P=0.029),neutrophil-to-lymphocyte ratio(OR=1.03,95%Cl 1.01 to 1.04,P=0.002),acute physiology score(OR=1.11,95%CI 1.06 to 1.15,P<0.001),Charlson comorbidity index(OR=1.36,95%CI 1.15 to 1.60,P<0.001)andsurgery type(OR:selective=Ref,no surgery=8.04,95%CI 3.74 to 17.30,P b 0.001,emergency=3.66,95%CI1.60 to 8.36,P = 0.002).The model showed good discrimination(area under receiver operating characteristiccurve:0.84,95%CI:0.80 to 0.87)and calibration(Hosmer-Lemeshow test P = 0.137)for predicting in-hospitalmortality.Conclusion:The developedmultifactormodel can be used to effectively predictmortality in critically ill patients atICU admission.