Establishment of a scoring model for early diagnosis of infection associated with liver failure

Introduction and ObjectivesInfection is a common complication of liver failure. Serum inflammatory markers used to diagnose infection have sufficient diagnostic sensitivity but low specificity. This study aimed to improve the early diagnosis of infections in liver failure patients by developing a diagnostic model and evaluating its predictive ability.Patients and MethodsA retrospective analysis of clinical data from liver failure patients. Cases were divided into infected and non-infected groups according to their clinical diagnosis. Nine infection-related predictors (age, body temperature, neutrophil ratio (NE%), procalcitonin (PCT), C-reactive protein (CRP), lactic acid (Lac), serum albumin (Alb), model of end-stage liver disease (MELD) score, and sequential organ failure assessment (SOFA) score) were included in multivariate logistic regression analysis. The diagnostic model was validated, and the receiver operating characteristic (ROC) curve was used to analyze its predictive accuracy.ResultsIn the model group, multivariate logistic regression analysis showed that age, body temperature, PCT, CRP, Lac, and SOFA score were independent predictors of infection associated with liver failure (P < 0.05). The area under the ROC curve (AUC) of the model was 0.899 (95% confidence interval CI] 0.846–0.939), and the sensitivity and specificity were 86.2% and 80.4%, respectively. The AUC for the validation group was 0.953 (95% CI 0.899–0.983), and the sensitivity and specificity were 91.7% and 84.2%, respectively.ConclusionsThis study reports a model for early diagnosis of infection in liver failure patients. The model had high overall accuracy and showed good reproducibility and reliability in patients from different centers in the same region.