Objective:To assess the predictive value of systemic immune inflammation index(SII)for sepsis in low-and medium-risk community-acquired pneumonia.Methods:A total of 589 elderly patients with low-and medium-risk commun...Objective:To assess the predictive value of systemic immune inflammation index(SII)for sepsis in low-and medium-risk community-acquired pneumonia.Methods:A total of 589 elderly patients with low-and medium-risk community-acquired pneumonia admitted to the Emergency Department of the Second Affiliated Hospital of Hainan Medical University from January 2020 to January 2023 were included as the research subjects,and the general information and laboratory test results of the patients were collected,and the optimal cut-off value of continuous variables for predicting sepsis in elderly patients with low-and medium-risk community-acquired pneumonia was determined by plotting the receiver work characteristic(ROC)curve,which was converted into dichotomous variables and univariate and multivariate logistic Regression analysis of the influencing factors of sepsis in elderly patients with low-and medium-risk community-acquired pneumonia.Based on this,a nomogram model is constructed to predict the risk of sepsis.The differentiation,consistency and accuracy of the model were verified by calibration curve and subject operating characteristic(ROC)curve,and the clinical utility of the model was determined by decision curve analysis.Results:A total of 589 elderly patients with low-and intermediate-risk community-acquired pneumonia were included in this study,of which 96(16.30%)developed sepsis.There were significant differences in age,diabetes mellitus and chronic obstructive pulmonary disease,Lac,PCT,SII and other indexes between sepsis and non-sepsis groups(P<0.05).Logistics regression analysis showed that age,diabetes mellitus and chronic obstructive pulmonary disease,Lac,and SII were independent risk factors for sepsis in elderly patients with low-and medium-risk community-acquired pneumonia.The nomogram prediction model was used to verify the results,and the AUC was 0.826(95%CI:0.780-0.872),and the calibration curve tended to the ideal curve with good accuracy.The decision curve shows that when the threshold of the model is between 0.10~0.78,the model has the advantage of clinical benefit.Conclusion:The nomogram prediction model constructed based on SII to predict sepsis in elderly patients with low-and medium-risk community-acquired pneumonia has good accuracy,which can predict the occurrence of sepsis early,help early identification of high-risk groups and timely intervention,and thus improve the prognosis of patients.展开更多
Objective:To investigate the predictive value of controlling nutritional status(CONUT)score for progression to chronic critical illness sepsis in elderly patients,and to construct a predictive model based on CONUT sco...Objective:To investigate the predictive value of controlling nutritional status(CONUT)score for progression to chronic critical illness sepsis in elderly patients,and to construct a predictive model based on CONUT score histogram.Methods:739 elderly patients with sepsis admitted from January 2020 to December 2022 were selected as the study objects,and were divided into chronic critical illness group(n=188)and non-chronic critical illness group(n=551)according to whether chronic critical illness disease occurred.Clinical data of the patients were collected and compared.The predictive value of CONUT score,PNI and NLR in the progression of senile sepsis to chronic severe disease was compared,and the optimal threshold value was determined,which was used to convert the numerical variables into binary variables.Through univariate analysis and multivariate Logistic regression analysis,the risk factors affecting the progression of elderly sepsis patients to chronic critical illness disease were screened out,and the prediction model was built based on the nomogram.The efficacy and clinical utility of the prediction model were evaluated by the area under the ROC curve(AUC),calibration curve and decision curve analysis(DCA).Results:The best cut-off value for CONUT score in predicting elderly sepsis progressing to chronic critical illness was 4 points.The predictive performance of CONUT score(AUC=0.739)was better than that of PNI(AUC=0.609)and NLR(AUC=0.582)in elderly sepsis progressing to chronic critical illness(CONUT score vs PNI:Z=5.960,P<0.001;CONUT score vs NLR:Z=6.119,P<0.001).Univariate analysis showed that age,CCI score,SOFA score,sepsis shock,serum Lac,CONUT score,mechanical ventilation(MV),and continuous renal replacement therapy(CRRT)treatment were related to elderly sepsis progressing to chronic critical illness(P<0.05).Multivariate logistic regression analysis showed that CONUT score≥4 points,age≥75 years,CCI score≥3 points,SOFA score>5 points,sepsis shock,and serum Lac≥4 mmol/L were independent risk factors for elderly sepsis progressing to chronic critical illness(P<0.05).The nomogram showed that CONUT score had the greatest contribution value in predicting elderly sepsis progressing to chronic critical illness.Based on this,the nomogram prediction model had an AUC of 0.846[95%CI(0.812~0.879)],with a sensitivity of 75.5%and specificity of 81.3%,indicating good predictive performance.The calibration curve was close to the ideal curve,and the DCA threshold had clinical utility advantages when ranging from 0.1 to 0.9.Conclusion:The nomographic prediction model based on CONUT score can effectively predict the risk probability of senile sepsis progressing to chronic critical illness,helpful for clinicians to identify the high risk group of chronic and severe senile sepsis,which is helpful for clinicians to identify high-risk populations of elderly sepsis with chronic critical illness.展开更多
基金Natural Science Foundation of Hainan Province(No.819MS128)。
文摘Objective:To assess the predictive value of systemic immune inflammation index(SII)for sepsis in low-and medium-risk community-acquired pneumonia.Methods:A total of 589 elderly patients with low-and medium-risk community-acquired pneumonia admitted to the Emergency Department of the Second Affiliated Hospital of Hainan Medical University from January 2020 to January 2023 were included as the research subjects,and the general information and laboratory test results of the patients were collected,and the optimal cut-off value of continuous variables for predicting sepsis in elderly patients with low-and medium-risk community-acquired pneumonia was determined by plotting the receiver work characteristic(ROC)curve,which was converted into dichotomous variables and univariate and multivariate logistic Regression analysis of the influencing factors of sepsis in elderly patients with low-and medium-risk community-acquired pneumonia.Based on this,a nomogram model is constructed to predict the risk of sepsis.The differentiation,consistency and accuracy of the model were verified by calibration curve and subject operating characteristic(ROC)curve,and the clinical utility of the model was determined by decision curve analysis.Results:A total of 589 elderly patients with low-and intermediate-risk community-acquired pneumonia were included in this study,of which 96(16.30%)developed sepsis.There were significant differences in age,diabetes mellitus and chronic obstructive pulmonary disease,Lac,PCT,SII and other indexes between sepsis and non-sepsis groups(P<0.05).Logistics regression analysis showed that age,diabetes mellitus and chronic obstructive pulmonary disease,Lac,and SII were independent risk factors for sepsis in elderly patients with low-and medium-risk community-acquired pneumonia.The nomogram prediction model was used to verify the results,and the AUC was 0.826(95%CI:0.780-0.872),and the calibration curve tended to the ideal curve with good accuracy.The decision curve shows that when the threshold of the model is between 0.10~0.78,the model has the advantage of clinical benefit.Conclusion:The nomogram prediction model constructed based on SII to predict sepsis in elderly patients with low-and medium-risk community-acquired pneumonia has good accuracy,which can predict the occurrence of sepsis early,help early identification of high-risk groups and timely intervention,and thus improve the prognosis of patients.
基金Natural Science Foundation of Hainan Provincial(No.819MS128)。
文摘Objective:To investigate the predictive value of controlling nutritional status(CONUT)score for progression to chronic critical illness sepsis in elderly patients,and to construct a predictive model based on CONUT score histogram.Methods:739 elderly patients with sepsis admitted from January 2020 to December 2022 were selected as the study objects,and were divided into chronic critical illness group(n=188)and non-chronic critical illness group(n=551)according to whether chronic critical illness disease occurred.Clinical data of the patients were collected and compared.The predictive value of CONUT score,PNI and NLR in the progression of senile sepsis to chronic severe disease was compared,and the optimal threshold value was determined,which was used to convert the numerical variables into binary variables.Through univariate analysis and multivariate Logistic regression analysis,the risk factors affecting the progression of elderly sepsis patients to chronic critical illness disease were screened out,and the prediction model was built based on the nomogram.The efficacy and clinical utility of the prediction model were evaluated by the area under the ROC curve(AUC),calibration curve and decision curve analysis(DCA).Results:The best cut-off value for CONUT score in predicting elderly sepsis progressing to chronic critical illness was 4 points.The predictive performance of CONUT score(AUC=0.739)was better than that of PNI(AUC=0.609)and NLR(AUC=0.582)in elderly sepsis progressing to chronic critical illness(CONUT score vs PNI:Z=5.960,P<0.001;CONUT score vs NLR:Z=6.119,P<0.001).Univariate analysis showed that age,CCI score,SOFA score,sepsis shock,serum Lac,CONUT score,mechanical ventilation(MV),and continuous renal replacement therapy(CRRT)treatment were related to elderly sepsis progressing to chronic critical illness(P<0.05).Multivariate logistic regression analysis showed that CONUT score≥4 points,age≥75 years,CCI score≥3 points,SOFA score>5 points,sepsis shock,and serum Lac≥4 mmol/L were independent risk factors for elderly sepsis progressing to chronic critical illness(P<0.05).The nomogram showed that CONUT score had the greatest contribution value in predicting elderly sepsis progressing to chronic critical illness.Based on this,the nomogram prediction model had an AUC of 0.846[95%CI(0.812~0.879)],with a sensitivity of 75.5%and specificity of 81.3%,indicating good predictive performance.The calibration curve was close to the ideal curve,and the DCA threshold had clinical utility advantages when ranging from 0.1 to 0.9.Conclusion:The nomographic prediction model based on CONUT score can effectively predict the risk probability of senile sepsis progressing to chronic critical illness,helpful for clinicians to identify the high risk group of chronic and severe senile sepsis,which is helpful for clinicians to identify high-risk populations of elderly sepsis with chronic critical illness.
