Developing A Risk Prediction Model For Multidrug-Resistant Bacterial Infection In Patients With Biliary Tract Infection

Background/Aims: The aim of this study was to develop a tool to predict multidrug-resistant bacteria infections among patients with biliary tract infection for targeted therapy. Patients and Methods: We conducted a single-center retrospective descriptive study from January 2016 to December 2018. Univariate and multivariable logistic regression analysis were used to identify independent risk factors of multidrug-resistant bacterial infections. A nomogram was constructed according to multivariable regression model. Moreover, the clinical usefulness of the nomogram was estimated by decision curve analysis. Results: 121 inpatients were randomly divided into a training cohort (n = 79) and validation cohort (n = 42). In multivariate analysis, 5 factors were associated with biliary tract infections caused by multidrug-resistant bacterial infections: aspartate aminotransferase (Odds ratio (OR), 13.771; 95% confidence interval (CI), 3.747-64.958; P <0.001), previous antibiotic use within 90 days (OR, 4.130; 95% CI, 1.192-16.471; P = 0.032), absolute neutrophil count (OR, 3.491; 95% CI, 1.066-12.851; P = 0.046), previous biliary surgery (OR, 3.303; 95% CI, 0.910-13.614; P = 0.079), and hemoglobin (OR, 0.146; 95% CI, 0.030-0.576; P = 0.009). The nomogram model was constructed based on these variables, and showed good calibration and discrimination in the training set [area under the curve (AUC), 0.86] and in the validation set (AUC, 0.799). The decision curve analysis demonstrated the clinical usefulness of our nomogram. Using the nomogram score, high risk and low risk patients with multidrug-resistant bacterial infection could be differentiated. Conclusions: This simple bedside prediction tool to predict multidrug-resistant bacterial infection can help clinicians identify low versus high risk patients as well as choose appropriate, timely initial empirical antibiotics therapy. This model should be validated before it is widely applied in clinical settings. we can differentiate between