Objective In the study, we planned to investigate the association of red blood cell distribution(RDW) with outcomes in patients with acute pancreatitis(AP) based on the MIMIC-Ⅳ database. Methods AP patients who were ...Objective In the study, we planned to investigate the association of red blood cell distribution(RDW) with outcomes in patients with acute pancreatitis(AP) based on the MIMIC-Ⅳ database. Methods AP patients who were admitted in intensive care unit(ICU) in MIMIC-Ⅳ were enrolled. We used different models to analyze the association between RDW and in-hospital mortality in AP. The smooth fitting curve was performed to illuminate the relationship between RDW and in-hospital mortality. Receiver operator characteristic(ROC) curve analysis was used for evaluating the prognostic value of RDW.Results 1068 AP patients were included. In-hospital mortality was 12.17%(n=130). With per 1% increasement in RDW, the risk of in-hospital mortality increased by 24%(OR=1.24, 95%CI:1.08~1.43,P=0.0025) after adjusted for all potential confounders. The non-linear association between RDW and in-hospital mortality was confirmed by smoothing fitting curve. The turning point of RDW was 15.0%. For the prediction of in-hospital mortality, the area under the ROC curve(AUC) of RDW was 0.645 and the cut-off value of RDW was 15.45%.Conclusions RDW was associated with in-hospital mortality in AP patients admitted in ICU. RDW had a prognostic value and could be regarded as an index of clinical outcomes in AP.展开更多
Background A model which can early and sensitively identify poor clinical outcome in short-term and long-term could be a useful tool to help physicians to assess the severity of the disease and early onset of therapeu...Background A model which can early and sensitively identify poor clinical outcome in short-term and long-term could be a useful tool to help physicians to assess the severity of the disease and early onset of therapeutic measures would be implemented in order to improve the prognosis of sepsis patients.This present study aimed to develop early predictive models for clinical outcomes based on a public database.Methods In the Medical Information Mart for Intensive Care-Ⅲ(MIMIC-Ⅲ)database,patients with severe sepsis or septic shock were included.Clinical variables were compared between survivor group and non-survivor group.Risk factors were identified by logistic regression model.Results A total of 2057 patients with severe sepsis or septic shock were finally enrolled.Mortality in 30-day and 180-day were 35.39%and 48.47%,respectively.Four independent factors including age,RDW,lactate and albumin for 30-day and 180-day mortality were identified in multivariate analysis.The accuracy of 30-day mortality model and 180-day mortality model were 0.702 and0.716,respectively.The area under the receiver operating characteristic curves(AUCs)of two models were 0.711 and 0.722,respectively.Conclusions In our study,apredictive model with four independent factors including age,RDW,lactate and albumin was performed by logistic regression,which could be applied for early identification in both 30-day and 180-day mortality in severe sepsis or septic shock.展开更多
Background Streptococcus pneumoniae,as a respiratory tract common pathogen,can cause invasive disease and sepsis.This study aimed to investigate the association of factors with clinical outcomes in sepsis with strepto...Background Streptococcus pneumoniae,as a respiratory tract common pathogen,can cause invasive disease and sepsis.This study aimed to investigate the association of factors with clinical outcomes in sepsis with streptococcus pneumoniae infection based on MIMIC-IV database.Methods The sepsis patients with streptococcus pneumoniae infection were included.Different variables between the survivor group and the non-survivor group were analyzed.Multivariable logistic regression was applied to identify the factors which were associated with clinical outcomes.Results A total of 80 sepsis patients with streptococcus pneumoniae infection were included.The in-hospital mortality was 23.75%(n=19).Significant differences were found in heart rate,white blood cell,RDW,MCV and hematocrit between the survivor group and the non-survivor group.The area under the ROC curve of hematocrit was 0.758 with a sensitivity of 73.7%and a specificity of 72.1%.The cut-off value of hematocrit was 30.8%.Conclusions Hematocrit level was identified to be negatively associated with in-hospital mortality in sepsis with streptococcus pneumoniae infection.展开更多
基金National Key Clinical Specialty Scientific Research Project(No.Z2023047)Changsha Natural Science Foundation(No.kq2208445)Changsha Central Hospital(No.YNKY202306)
文摘Objective In the study, we planned to investigate the association of red blood cell distribution(RDW) with outcomes in patients with acute pancreatitis(AP) based on the MIMIC-Ⅳ database. Methods AP patients who were admitted in intensive care unit(ICU) in MIMIC-Ⅳ were enrolled. We used different models to analyze the association between RDW and in-hospital mortality in AP. The smooth fitting curve was performed to illuminate the relationship between RDW and in-hospital mortality. Receiver operator characteristic(ROC) curve analysis was used for evaluating the prognostic value of RDW.Results 1068 AP patients were included. In-hospital mortality was 12.17%(n=130). With per 1% increasement in RDW, the risk of in-hospital mortality increased by 24%(OR=1.24, 95%CI:1.08~1.43,P=0.0025) after adjusted for all potential confounders. The non-linear association between RDW and in-hospital mortality was confirmed by smoothing fitting curve. The turning point of RDW was 15.0%. For the prediction of in-hospital mortality, the area under the ROC curve(AUC) of RDW was 0.645 and the cut-off value of RDW was 15.45%.Conclusions RDW was associated with in-hospital mortality in AP patients admitted in ICU. RDW had a prognostic value and could be regarded as an index of clinical outcomes in AP.
文摘Background A model which can early and sensitively identify poor clinical outcome in short-term and long-term could be a useful tool to help physicians to assess the severity of the disease and early onset of therapeutic measures would be implemented in order to improve the prognosis of sepsis patients.This present study aimed to develop early predictive models for clinical outcomes based on a public database.Methods In the Medical Information Mart for Intensive Care-Ⅲ(MIMIC-Ⅲ)database,patients with severe sepsis or septic shock were included.Clinical variables were compared between survivor group and non-survivor group.Risk factors were identified by logistic regression model.Results A total of 2057 patients with severe sepsis or septic shock were finally enrolled.Mortality in 30-day and 180-day were 35.39%and 48.47%,respectively.Four independent factors including age,RDW,lactate and albumin for 30-day and 180-day mortality were identified in multivariate analysis.The accuracy of 30-day mortality model and 180-day mortality model were 0.702 and0.716,respectively.The area under the receiver operating characteristic curves(AUCs)of two models were 0.711 and 0.722,respectively.Conclusions In our study,apredictive model with four independent factors including age,RDW,lactate and albumin was performed by logistic regression,which could be applied for early identification in both 30-day and 180-day mortality in severe sepsis or septic shock.
文摘Background Streptococcus pneumoniae,as a respiratory tract common pathogen,can cause invasive disease and sepsis.This study aimed to investigate the association of factors with clinical outcomes in sepsis with streptococcus pneumoniae infection based on MIMIC-IV database.Methods The sepsis patients with streptococcus pneumoniae infection were included.Different variables between the survivor group and the non-survivor group were analyzed.Multivariable logistic regression was applied to identify the factors which were associated with clinical outcomes.Results A total of 80 sepsis patients with streptococcus pneumoniae infection were included.The in-hospital mortality was 23.75%(n=19).Significant differences were found in heart rate,white blood cell,RDW,MCV and hematocrit between the survivor group and the non-survivor group.The area under the ROC curve of hematocrit was 0.758 with a sensitivity of 73.7%and a specificity of 72.1%.The cut-off value of hematocrit was 30.8%.Conclusions Hematocrit level was identified to be negatively associated with in-hospital mortality in sepsis with streptococcus pneumoniae infection.
