Construction and Validation of a Predictive Model for Mortality Risk in Patients with Acinetobacter baumannii Bloodstream Infection

Abstract

Purpose To develop and validate a predictive model for the risk of death in patients with Acinetobacter baumannii ( A. baumannii ) bloodstream infection (BSI) for clinical decision-making and patient management. Methods In this study, we included demographic and clinical data from 206 patients with Acinetobacter baumannii BSI in China between January 2013 and December 2023. Variables were screened by least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression, and prognostic models and nomograms were constructed. The models were evaluated using the area under curve (AUC) of Receiver Operating Characteristic (ROC), decision curve analysis (DCA), and standard curves to evaluate the model. Results Comorbid septic shock, an elevated neutrophil/lymphocyte ratio (NLR), low hemoglobin (HGB) levels, and low platelet counts (PLT) were found to be independent risk factors for death in patients with A. baumannii BSI. With the models constructed from these four variables, the AUCs of the ROC curves of the test and validation cohorts for the prognostic scenarios at 7, 14, and 28 days were not less than 0.850, and the AUCs of the ROC curves of the risk-of-death prediction model were the highest for both groups at 7 days, at 0.907 and 0.886, respectively. The two sets of calibration curves show that the calibration curves oscillate around a 45° diagonal line at 7, 14, and 28 days, and there is a good correlation between the actual risk and the predicted risk, with a high degree of calibration.The clinical decision curve shows that the model has a strong discriminatory ability when the probability is between 10% and 70%. Conclusion Septic shock status, NLR, HGB and PLT are independent risk factors for 28-day mortality in patients with A. baumannii BSI. These variables are conveniently and readily available, and in patients with A. baumannii BSI these indicators can be closely monitored in clinical practice and timely interventions can be made to improve prognosis.