EXPRESS: Predictive Modeling of Mortality in Carbapenem-Resistant Acinetobacter baumannii Bloodstream Infections Using Machine Learning.

Abstract

Acinetobacter baumannii, a notable drug-resistant bacterium, often induces severe infections in healthcare settings, prompting a deeper exploration of treatment alternatives due to escalating carbapenem resistance. This study meticulously examined clinical, microbiological, and molecular aspects related to in-hospital mortality in patients with carbapenem-resistant A. baumannii (CRAB) bloodstream infections (BSI). From 292 isolates, 153 cases were scrutinized, reidentified through MALDI-TOF-MS, and evaluated for antimicrobial susceptibility and carbapenemase genes via multiplex PCR. Utilizing supervised machine learning, the study constructed models to predict 14-day and 30-day mortality rates, revealing the Naïve Bayes model's superior specificity (0.75) and area under the curve (AUC; 0.822) for 14-day mortality, and the Random Forest model's impressive recall (0.85) for 30-day mortality. These models delineated 8 and 9 significant features for 14-day and 30-day mortality predictions, respectively, with 'septic shock' as a pivotal variable. Additional variables such as neutropenia with neutropenic days prior to sepsis, mechanical ventilator support, chronic kidney disease, and heart failure were also identified as ranking features. However, empirical antibiotic therapy appropriateness and specific microbiological data had minimal predictive efficacy. This research offers foundational data for assessing mortality risks associated with CRAB BSI and underscores the importance of stringent infection control practices in the wake of the scarcity of new effective antibiotics against resistant strains. The advanced models and insights generated in this study serve as significant resources for managing the repercussions of A. baumannii infections, contributing substantially to the clinical understanding and management of such infections in healthcare environments.