Efficacy of ceftazidime-avibactam in solid organ transplant recipients with bloodstream infections caused by carbapenemase-producing Klebsiella pneumoniae.

We aimed to compare the efficacy of ceftazidime-avibactam (CAZ-AVI) versus the best available therapy (BAT) in solid organ transplant (SOT) recipients with bloodstream infection caused by carbapenemase-producing Klebsiella pneumoniae (CPKP-BSI). A retrospective (2016-2021) observational cohort study was performed in 14 INCREMENT-SOT centers (ClinicalTrials.gov identifier: NCT02852902; Impact of Specific Antimicrobials and MIC Values on the Outcome of Bloodstream Infections Due to ESBL- or Carbapenemase-producing Enterobacterales in Solid Organ Transplantation: an Observational Multinational Study). Outcomes were 14-day and 30-day clinical success (complete resolution of attributable manifestations, adequate source control, and negative follow-up blood cultures) and 30-day all-cause mortality. Multivariable logistic and Cox regression analyses adjusted for the propensity score to receive CAZ-AVI were constructed. Among 210 SOT recipients with CPKP-BSI, 149 received active primary therapy with CAZ-AVI (66/149) or BAT (83/149). Patients treated with CAZ-AVI had higher 14-day (80.7% vs 60.6%, P = .011) and 30-day (83.1% vs 60.6%, P = .004) clinical success and lower 30-day mortality (13.25% vs 27.3%, P = .053) than those receiving BAT. In the adjusted analysis, CAZ-AVI increased the probability of 14-day (adjusted odds ratio [aOR], 2.65; 95% confidence interval [CI], 1.03-6.84; P = .044) and 30-day clinical success (aOR, 3.14; 95% CI, 1.17-8.40; P = .023). In contrast, CAZ-AVI therapy was not independently associated with 30-day mortality. In the CAZ-AVI group, combination therapy was not associated with better outcomes. In conclusion, CAZ-AVI may be considered a first-line treatment in SOT recipients with CPKP-BSI.

Heteroresistance to colistin in wild-type Klebsiella pneumoniae isolates from clinical origin.

IMPORTANCE: Colistin is one of the last remaining therapeutic options for dealing with Enterobacteriaceae. Unfortunately, heteroresistance to colistin is also rapidly increasing. We described the prevalence of colistin heteroresistance in a variety of wild-type strains of Klebsiella pneumoniae and the evolution of these strains with colistin heteroresistance to a resistant phenotype after colistin exposure and withdrawal. Resistant mutants were characterized at the molecular level, and numerous mutations in genes related to lipopolysaccharide formation were observed. In colistin-treated patients, the evolution of K. pneumoniae heteroresistance to resistance phenotype could lead to higher rates of therapeutic failure.

Prognostic Significance of the Relative Load of KPC-Producing Klebsiella pneumoniae within the Intestinal Microbiota in a Prospective Cohort of Colonized Patients.

Increased relative bacterial load of KPC-producing Klebsiella pneumoniae (KPC-KP) within the intestinal microbiota has been associated with KPC-KP bacteremia. Prospective observational study of KPC-KP adult carriers with a hospital admission at recruitment or within the three prior months (January 2018 to February 2019). A qPCR-based assay was developed to measure the relative load of KPC-KP in rectal swabs (RLKPC, proportion of blaKPC relative to 16S rRNA gene copy number). We generated Fine-Gray competing risk and Cox regression models for survival analysis of all-site KPC-KP infection and all-cause mortality, respectively, at 90 and 30 days. The median RLKPC at baseline among 80 KPC-KP adult carriers was 0.28% (range 0.001% to 2.70%). Giannella Risk Score (GRS) was independently associated with 90-day and 30-day all-site infection (adjusted subdistribution hazard ratio [aHR] 1.23, 95% CI = 1.15 to 1.32, P < 0.001). RLKPC (adjusted hazard ratio [aHR] 1.04, 95% CI = 1.01 to 1.07, P = 0.008) and age (aHR 1.05, 95% CI = 1.01 to 1.10, P = 0.008) were independent predictors of 90-day all-cause mortality in a Cox model stratified by length of hospital stay (LOHS) ≥20 days. An adjusted Cox model for 30-day all-cause mortality, stratified by LOHS ≥14 days, included RLKPC (aHR 1.03, 95% CI = 1.00 to 1.06, P = 0.027), age (aHR 1.10, 95% CI = 1.03 to 1.18, P = 0.004), and severe KPC-KP infection (INCREMENT-CPE score >7, aHR 2.96, 95% CI = 0.97 to 9.07, P = 0.057). KPC-KP relative intestinal load was independently associated with all-cause mortality in our clinical setting, after adjusting for age and severe KPC-KP infection. Our study confirms the utility of GRS to predict infection risk in patients colonized by KPC-KP. IMPORTANCE The rapid dissemination of carbapenemase-producing Enterobacterales represents a global public health threat. Increased relative load of KPC-producing Klebsiella pneumoniae (KPC-KP) within the intestinal microbiota has been associated with an increased risk of bloodstream infection by KPC-KP. We developed a qPCR assay for quantification of the relative KPC-KP intestinal load (RLKPC) in 80 colonized patients and examined its association with subsequent all-site KPC-KP infection and all-cause mortality within 90 days. Giannella Risk Score, which predicts infection risk in colonized patients, was independently associated with the development of all-site KPC-KP infection. RLKPC was not associated with all-site KPC-KP infection, possibly reflecting the large heterogeneity in patient clinical conditions and infection types. RLKPC was an independent predictor of all-cause mortality within 90 and 30 days in our clinical setting. We hypothesize that KPC-KP load may behave as a surrogate marker for the severity of the patient's clinical condition.

Integrative Clinical, Molecular, and Computational Analysis Identify Novel Biomarkers and Differential Profiles of Anti-TNF Response in Rheumatoid Arthritis.

Background: This prospective multicenter study developed an integrative clinical and molecular longitudinal study in Rheumatoid Arthritis (RA) patients to explore changes in serologic parameters following anti-TNF therapy (TNF inhibitors, TNFi) and built on machine-learning algorithms aimed at the prediction of TNFi response, based on clinical and molecular profiles of RA patients. Methods: A total of 104 RA patients from two independent cohorts undergoing TNFi and 29 healthy donors (HD) were enrolled for the discovery and validation of prediction biomarkers. Serum samples were obtained at baseline and 6 months after treatment, and therapeutic efficacy was evaluated. Serum inflammatory profile, oxidative stress markers and NETosis-derived bioproducts were quantified and miRNomes were recognized by next-generation sequencing. Then, clinical and molecular changes induced by TNFi were delineated. Clinical and molecular signatures predictors of clinical response were assessed with supervised machine learning methods, using regularized logistic regressions. Results: Altered inflammatory, oxidative and NETosis-derived biomolecules were found in RA patients vs. HD, closely interconnected and associated with specific miRNA profiles. This altered molecular profile allowed the unsupervised division of three clusters of RA patients, showing distinctive clinical phenotypes, further linked to the TNFi effectiveness. Moreover, TNFi treatment reversed the molecular alterations in parallel to the clinical outcome. Machine-learning algorithms in the discovery cohort identified both, clinical and molecular signatures as potential predictors of response to TNFi treatment with high accuracy, which was further increased when both features were integrated in a mixed model (AUC: 0.91). These results were confirmed in the validation cohort. Conclusions: Our overall data suggest that: 1. RA patients undergoing anti-TNF-therapy conform distinctive clusters based on altered molecular profiles, which are directly linked to their clinical status at baseline. 2. Clinical effectiveness of anti-TNF therapy was divergent among these molecular clusters and associated with a specific modulation of the inflammatory response, the reestablishment of the altered oxidative status, the reduction of NETosis, and the reversion of related altered miRNAs. 3. The integrative analysis of the clinical and molecular profiles using machine learning allows the identification of novel signatures as potential predictors of therapeutic response to TNFi therapy.