Molecular characterization and descriptive analysis of carbapenemase-producing Gram-negative rod infections in Bogota, Colombia.

In this study, the genetic differences and clinical impact of the carbapenemase-encoding genes among the community and healthcare-acquired infections were assessed. This retrospective, multicenter cohort study was conducted in Colombia and included patients infected with carbapenem-resistant Gram-negative rods between 2017 and 2021. Carbapenem resistance was identified by Vitek, and carbapenemase-encoding genes were identified by whole-genome sequencing (WGS) to classify the alleles and sequence types (STs). Descriptive statistics were used to determine the association of any pathogen or gene with clinical outcomes. A total of 248 patients were included, of which only 0.8% (2/248) had community-acquired infections. Regarding the identified bacteria, the most prevalent pathogens were Pseudomonas aeruginosa and Klebsiella pneumoniae. In the WGS analysis, 228 isolates passed all the quality criteria and were analyzed. The principal carbapenemase-encoding gene was blaKPC, specifically blaKPC-2 [38.6% (88/228)] and blaKPC-3 [36.4% (83/228)]. These were frequently detected in co-concurrence with blaVIM-2 and blaNDM-1 in healthcare-acquired infections. Notably, the only identified allele among community-acquired infections was blaKPC-3 [50.0% (1/2)]. In reference to the STs, 78 were identified, of which Pseudomonas aeruginosa ST111 was mainly related to blaKPC-3. Klebsiella pneumoniae ST512, ST258, ST14, and ST1082 were exclusively associated with blaKPC-3. Finally, no particular carbapenemase-encoding gene was associated with worse clinical outcomes. The most identified genes in carbapenemase-producing Gram-negative rods were blaKPC-2 and blaKPC-3, both related to gene co-occurrence and diverse STs in the healthcare environment. Patients had several systemic complications and poor clinical outcomes that were not associated with a particular gene.IMPORTANCEAntimicrobial resistance is a pandemic and a worldwide public health problem, especially carbapenem resistance in low- and middle-income countries. Limited data regarding the molecular characteristics and clinical outcomes of patients infected with these bacteria are available. Thus, our study described the carbapenemase-encoding genes among community- and healthcare-acquired infections. Notably, the co-occurrence of carbapenemase-encoding genes was frequently identified. We also found 78 distinct sequence types, of which two were novel Pseudomonas aeruginosa, which could represent challenges in treating these infections. Our study shows that in low and middle-income countries, such as Colombia, the burden of carbapenem resistance in Gram-negative rods is a concern for public health, and regardless of the allele, these infections are associated with poor clinical outcomes. Thus, studies assessing local epidemiology, prevention strategies (including trials), and underpinning genetic mechanisms are urgently needed, especially in low and middle-income countries.

A novel model to predict severe COVID-19 and mortality using an artificial intelligence algorithm to interpret chest radiographs and clinical variables.

Background: Patients with coronavirus disease 2019 (COVID-19) could develop severe disease requiring admission to the intensive care unit (ICU). This article presents a novel method that predicts whether a patient will need admission to the ICU and assesses the risk of in-hospital mortality by training a deep-learning model that combines a set of clinical variables and features in chest radiographs. Methods: This was a prospective diagnostic test study. Patients with confirmed severe acute respiratory syndrome coronavirus 2 infection between March 2020 and January 2021 were included. This study was designed to build predictive models obtained by training convolutional neural networks for chest radiograph images using an artificial intelligence (AI) tool and a random forest analysis to identify critical clinical variables. Then, both architectures were connected and fine-tuned to provide combined models. Results: 2552 patients were included in the clinical cohort. The variables independently associated with ICU admission were age, fraction of inspired oxygen (F iO2 ) on admission, dyspnoea on admission and obesity. Moreover, the variables associated with hospital mortality were age, F iO2 on admission and dyspnoea. When implementing the AI model to interpret the chest radiographs and the clinical variables identified by random forest, we developed a model that accurately predicts ICU admission (area under the curve (AUC) 0.92±0.04) and hospital mortality (AUC 0.81±0.06) in patients with confirmed COVID-19. Conclusions: This automated chest radiograph interpretation algorithm, along with clinical variables, is a reliable alternative to identify patients at risk of developing severe COVID-19 who might require admission to the ICU.