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. IMPORTANCE Antimicrobial 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. © 2024 Ibáñez-Prada et al

Rare predicted loss-of-function variants of type I IFN immunity genes are associated with life-threatening COVID-19.

We previously reported that impaired type I IFN activity, due to inborn errors of TLR3- and TLR7-dependent type I interferon (IFN) immunity or to autoantibodies against type I IFN, account for 15-20% of cases of life-threatening COVID-19 in unvaccinated patients. Therefore, the determinants of life-threatening COVID-19 remain to be identified in ~ 80% of cases. We report here a genome-wide rare variant burden association analysis in 3269 unvaccinated patients with life-threatening COVID-19, and 1373 unvaccinated SARS-CoV-2-infected individuals without pneumonia. Among the 928 patients tested for autoantibodies against type I IFN, a quarter (234) were positive and were excluded. No gene reached genome-wide significance. Under a recessive model, the most significant gene with at-risk variants was TLR7, with an OR of 27.68 (95%CI 1.5-528.7, P = 1.1 × 10 <sup>-4</sup> ) for biochemically loss-of-function (bLOF) variants. We replicated the enrichment in rare predicted LOF (pLOF) variants at 13 influenza susceptibility loci involved in TLR3-dependent type I IFN immunity (OR = 3.70[95%CI 1.3-8.2], P = 2.1 × 10 <sup>-4</sup> ). This enrichment was further strengthened by (1) adding the recently reported TYK2 and TLR7 COVID-19 loci, particularly under a recessive model (OR = 19.65[95%CI 2.1-2635.4], P = 3.4 × 10 <sup>-3</sup> ), and (2) considering as pLOF branchpoint variants with potentially strong impacts on splicing among the 15 loci (OR = 4.40[9%CI 2.3-8.4], P = 7.7 × 10 <sup>-8</sup> ). Finally, the patients with pLOF/bLOF variants at these 15 loci were significantly younger (mean age [SD] = 43.3 [20.3] years) than the other patients (56.0 [17.3] years; P = 1.68 × 10 <sup>-5</sup> ). Rare variants of TLR3- and TLR7-dependent type I IFN immunity genes can underlie life-threatening COVID-19, particularly with recessive inheritance, in patients under 60 years old

At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients. © The Author(s) 2024