摘要
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 fa
基金
Natural Science Foundation of Hainan Provincial(No.819MS128)。
