RESUMEN
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.
RESUMEN
BACKGROUND: Understanding the dynamics of aerial spread of Acinetobacter may provide useful information for production of effective control measurements. We investigated genetic relationships between air and clinical isolates of Acinetobacter baumannii in an intensive care unit (ICU) setting. METHODS: We conducted a prospective surveillance study in a tertiary care hospital for 8 months. A total of 186 air samples were taken from 2 ICUs. Clonal characteristics of air isolates were compared with the prospective clinical strains and the previously isolated strains of ICU patients over a 23-month period. RESULTS: Twenty-six (11.4%) air samples yielded A baumannii, of which 24 (92.3%) isolates were carbapenem-resistant. The Acinetobacter concentration was the highest in bedside sampling areas of infected patients (0.39 CFU/m3). Air isolates were clustered in 13 genotypes, and 7 genotypes (including 18 air strains) were clonally related to the clinical strains of 9 ICU patients. One clone continued to be cultured over 27 days in ICU air, and air isolates could be clonally related to 7-week retrospective and approximately 15-week prospective clinical strains. CONCLUSIONS: The results of this study suggest that infected patients could spread significant amounts of Acinetobacter to ICU air. These strains could survive in air for some weeks and could likely still infect new patients after some months. Special control measurements may be required against the airborne spread of Acinetobacter in ICUs.