Early diagnosis and prognostic prediction of secondary bloodstream infections caused by Acinetobacter baumannii in critically ill patients by machine-learning algorithms

作者：Hengxin Chen, Wenjia Gan, Xianling Zhou, Pingjuan Liu, Tangdan Ding, Hui Xu, Peisong Chen, Y. Chen · 发表于：Frontiers in Cellular and Infection Microbiology · 年份：2026 · DOI：10.3389/fcimb.2025.1667176 · 被引用次数：1 · 研究领域：Antibiotic Resistance in Bacteria、Nosocomial Infections in ICU、Sepsis Diagnosis and Treatment

Background: (AB) are a major threat to patient safety in the Intensive Care Unit (ICU) due to their prevalence and severity. Developing accurate predictive models is crucial for enhancing clinical decision-making and improving patient outcomes. This study aimed to leverage machine learning (ML) to create a diagnostic model for predicting the risk of AB-sBSI in ICU patients and a prognostic model for assessing the associated 30-day mortality risk. Methods: secondary bloodstream infection (AB-sBSI) and 76 age and sex matched controls with non AB-sBSI. For 30-day mortality assessment, the AB-sBSI patients were categorized into non-survivors (n=39) and survivors (n=31). Demographic, microbiological, and laboratory data encompassing hematological, coagulation, and inflammatory markers were analyzed. Fourteen machine learning models were evaluated using the Deepwise and Beckman Coulter DxAI platforms with five-fold cross-validation. Model performance was assessed using five standard metrics, and the DeLong test was applied for AUC comparison. After data preprocessing, patients were enrolled to form an external validation cohort. Results: The AB-sBSI risk diagnosis model, constructed with 11 features, identified red cell distribution width as the most significant predictor. The AdaBoost model outperformed both comparative models (Linear Discriminant Analysis, Logistic Regression, LinearSVC) and the conventional biomarker C-reactive protein (AUC = 0.66), with AUCs of 0.937 in trainin...