Objective:To explore the risk factors for death within 90 days after artificial liver treatment in patients with liver failure,and to construct a nomogram model for predicting the probability of death.Methods:A retrospective analysis was conducted on the clinical data of 50 patients with liver failure who underwent artificial liver treatment at Yichang Central People's Hospital from January 2018 to October 2023.Based on the survival status within 90 days after artificial liver treatment,the patients were divided into survival group(n=26)and death group(n=24).Laboratory indicators and general clinical data before treatment were compared between the two groups.Statistically significant indicators from univariate analysis were included in multivariate Logistic regression analysis to construct a nomogram prediction model and evaluate its accuracy and effectiveness.Results:Univariate Logistic regression analysis showed significant differences in white blood cell(WBC),neutrophil(NEU),neutrophil-lymphocyte ratio(NLR),total bilirubin(TBIL),alpha fetoprotein(AFP),c-reactive protein(CRP),serum creatinine(SCR),blood urea nitrogen(BUN),Child-Pugh liver function grade,model for end-stage liver disease(MELD),MELD-Na score,and Chinese group on the study of severe hepatitis B acute-on-chronic liver failure score(COSSH-ACLFs)between the two groups(all P＜0.05).Multivariate Logistic regression analysis showed that NLR(OR=1.708,95％CI:1.088,2.683),AFP(OR=0.987,95％CI:0.976,0.999),CRP(OR=1.258,95％CI:1.031,1.534),and COSSH-ACLFs score(OR=4.043,95％CI:1.185,13.788)before artificial liver treatment were independent factors influencing death within 90 days in patients with liver failure undergoing artificial liver treatment(all P＜0.05).The nomogram prediction model constructed based on these influencing factors had high accuracy(AUC=0.931).Conclusion:NLR,AFP,CRP,and COSSH-ACLFs score are independent factors influencing death within 90 days after artificial liver treatment for liver failure.The nomogram prediction model constructed based on these risk factors has good predictive efficacy and can guide clinical diagnosis and treatment.
liver failureartificial liverprognosisnomogram prediction model
维普
万方数据
