Novel and simplified model for the precise identification of concurrent bacterial infections in patients aged 60 years and older with acute-on-chronic liver diseases: a nationwide, multicentre, prospective cohort study.

objectiveWe aimed to develop an effective model to identify the risk of concurrent bacterial infections in older patients with acute-on-chronic liver disease (AoCLD).

methodsData from 809 individuals aged 60-80 sourced from the CATCH-LIFE cohort were analyzed. Participants were randomly assigned to training and internal validation groups at a ratio of 7:3. An independent cohort of 336 older inpatients with AoCLD from Xiangya Hospital, Central South University was used to conduct an external validation of the model. Independent risk factors were identified using LASSO and logistic regression analysis in the training cohort and were subsequently used to develop a user-friendly model. Model performance was evaluated using area under the curve (AUC), calibration plots, and decision curve analysis in the internal and external validation cohorts. Two different cutoff values were determined to stratify infection risk in older patients with AoCLD.

resultsThe infection rate among older patients with AoCLD was 30.28%. Pulmonary infections were predominant, accounting for 93% of all infections. Gram-negative bacteria were the most frequently isolated pathogens, representing 64% of cases in this population. The novel model developed to identify bacterial infections included three variables: cirrhosis, absolute neutrophil count, and C-reactive protein (CRP) level. The AUC for the training, internal, and external validation datasets demonstrated high accuracy in identifying bacterial infections (AUC of the training dataset = 0.805, AUC of the internal validation dataset = 0.848, and AUC of the external validation dataset = 0.838). The model significantly outperformed neutrophil count, CRP level, and procalcitonin level alone in detecting bacterial infections among older patients with AoCLD. To facilitate clinical decision-making, we defined two cutoff values of prediction probability: a low cutoff of 32.2% to rule out bacterial infections and a high cutoff of 47.9% to confidently confirm bacterial infections.

conclusionOur model aids in the early and precise diagnosis of bacterial infections in older patients with AoCLD, thereby facilitating prompt interventions to prevent adverse outcomes.