AUTHOR=Yan Jia , Yilin Huang , Di Wu , Jie Wang , Hanyue Wang , Ya Liu , Jie Peng 

TITLE=A nomogram for predicting the risk of mortality in patients with acute pancreatitis and Gram-negative bacilli infection

JOURNAL=Frontiers in Cellular and Infection Microbiology

VOLUME=Volume 12 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2022.1032375

DOI=10.3389/fcimb.2022.1032375

ISSN=2235-2988

ABSTRACT=Objective: Gram-negative bacilli (GNB) are common pathogens of infection in severe acute pancreatitis (SAP), and their occurrence increases the mortality of SAP. Early identification of disease severity and prognosis is of great significance. This study explored risk factors for mortality in GNB infected SAP patients and established a model for early prediction of the risk of death in GNB infected SAP patients. 
Methods: Patients diagnosed with SAP from 1st January 2016 to 31st March 2022 were included, and their baseline clinical characteristics were collected. Univariate logistic regression analysis was performed to screen for death-related variables, simultaneously, Boruta analysis was performed to identify potentially important clinical features associated with mortality. The intersection of the two results was taken for further multivariate logistic regression analysis. A logistic regression model was built according to the independent risk factors of death and visualized with a nomogram. The performance of the model was further validated in the training and validation cohort.
Results: A total of 151 SAP patients developed GNB infection. Univariate logistic regression analysis identified 11 variables associated with mortality. Boruta analysis identified 11 clinical features. And 4 out of 9 clinical variables shared by both were further selected as independent risk factors by multivariate logistic regression analysis, including platelets (OR 0.99, 95% CI 0.99 - 1.00 p = 0.007), hemoglobin (OR 0.96, 95% CI 0.92 - 1 p = 0.037), septic shock (OR 6.33, 95 % CI 1.12 - 43.47, p = 0.044), and carbapenem resistance (OR 7.99, 95% CI 1.66 - 52.37, p = 0.016). And a nomogram was used to visualize. The model demonstrated good performance in both training and validation cohorts, with recognition sensitivity and specificity of 96% and 80% in the training cohort and 92.8% and 75% in the validation cohort, respectively.
Conclusion: The nomogram can accurately predict the mortality risk of SAP patients with GNB infection. The clinical application of this model allows early identification of the severity and prognosis for GNB infected SAP patients and identification of patients requiring urgent management, thus to rationalize treatment options and improve clinical outcomes