Construction of a predictive model of short-term death risk in patients with sepsis based on NMR metabolomics characteristics and its efficacy
Objective To investigate the effect of building a predictive model of short-term mortality risk in sepsis patients based on NMR metabolomics characteristics.Methods Sixty patients with sepsis admitted to ICU of our hospital from January 2020 to December 2022 were selected as the study objects.All patients were divided into survival group and death group based on the survival status at 28 d after treatment.General data and laboratory examination data of patients were collected.Serum samples were collected for proton nuclear magnetic resonance specific metabolic markers when patients were enrolled.The top 4 products were selected by LASSO regression to construct a short-term death risk prediction model for sepsis patients.Finally,patients with sepsis treated in ICU of our hospital from January 2023 to December 2023 were included to verify the predictive effect of the model.Results APACHE II scores in the death group were higher than those in the survival group,and the difference was statistically significant(P<0.05).Multivariate statistical analysis method shows that the prediction ability of the original model is greater than that of any one random arrangement of Y variables,which proves that the model is effective.The scatter plot of unsupervised PCA explained 57%of the variables(PC1=50%,PC2=7%),and the metabolic profile was different between the control group and the model group,with significant changes in serum endogenous metabolites in sepsis patients after PCPA modeling.OPLS-DA graphical model has good fitting effect,there are no special points,and the distribution areas of the two groups are completely separated.As shown in the loading matrix,the levels of L-aspartate,indoleacetic acid and alanine increased,while the levels of isoleucine and leucine decreased.The top 50 variables with the largest VIP value in the OPLS-DA model were extracted for non-parametric test.Finally,34 variables with statistical significance were obtained(P<0.05),and a total of 19 characteristic metabolites were most likely to be associated with sepsis death.Serum phenylalanine,creatine,acetoacetic acid,glutamic acid,methionine,urea,lactic acid and trimethylamine oxide in death group were significantly higher than those in survival group(P<0.05).The C-index of short-term death risk in sepsis patients was 0.993(95%CI:0.931-0.999)in an eriograms of four highly correlated metabolites selected by LASSO.The Nomogram model was of certain value in predicting the short-term mortality risk of sepsis patients(AUC=0.993,95%CI:0.978-1.000,P<0.001).The short-term mortality risk in model-validated sepsis patients with a Nomogram model(AUC=0.934,95%CI:0.863-1.000,P<0.001)was useful.The accuracy,sensitivity and specificity of the model to the training set were 96.66%,94.11%and 97.60%,respectively.The accuracy,sensitivity and specificity of the model to the validation set were 90.62%,88.23%and 91.48%,respectively.Conclusion The prediction model of short-term death risk in sepsis patients based on serum NMR metabolomic characteristics has a good value in predicting the 28 d death risk of sepsis patients.
sepsisprognosisproton nuclear magnetic resonanceserum metabolomics
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