RESUMO
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.
RESUMO
PURPOSE: This study aims to investigate the impact of post-transplant diabetes mellitus (PTDM) on cardiovascular events, graft survival, and mortality and to determine the risk factors involved in developing PTDM. METHODS: A total of 703 patients who underwent kidney transplantation were included in the study. The total sample was subdivided into three groups: (i) patients with PTDM; (ii) patients who had diabetes before the transplantation (DM); and (iii) patients without diabetes (NoDM). The data on graft failure, cardiovascular events, all-cause mortality, and the potential risk factors that play a role in developing PTDM were recorded and analyzed. RESULTS: The patients were followed for a median of 80 (6-300) months after transplantation. Out of all patients, 41 (5.8%) had DM before transplantation, and 101 (14.4%) developed PTDM. Recipient BMI, post-transplant fasting plasma glucose, and hepatitis C seropositivity were independent risk factors for PTDM development. The incidence of cardiovascular events was 6.1% in the NoDM group, 14.9% in the PTDM group, and 29.3% in the DM group (p < 0.001). In PTDM patients, hepatitis C seropositivity and the recipient's age at transplant were independent predictors of a cardiovascular event. There were no significant differences between the groups regarding the risk of graft loss. PTDM had no significant effect on all-cause mortality. However, the survival rates of DM patients were significantly reduced compared to those with NoDM or PTDM. CONCLUSIONS: PTDM had no impact on patient survival. Hepatitis C seropositivity and recipient age at transplant predicted cardiovascular events in PTDM patients.