ceftobiprole for the treatment of infective endocarditis a case series

Abstract Objectives Ceftobiprole is a relatively new cephalosporin with broad-spectrum activity and good tolerability. Despite its promising characteristics, to our knowledge, only two case reports, previously published also by some of us, is available concerning its administration for the treatment of infective endocarditis. Hereby we report our experience in this field. Methods All the patients with infective endocarditis treated with ceftobiprole were enrolled. Results 12 cases of endocarditis were treated with ceftobiprole, 11/12 in combination with daptomycin and 1/12 as monotherapy. Gram-positive bacteria were isolated in 12/12 patients; 3 cases were polymicrobial. Cure rate was 83% (10/12 patients). In 9/12 (75%) cases, patients were switched to ceftobiprole following failure of previous antimicrobial regimen. In 3/3 patients in which ceftobiprole was administered because of persistently positive blood culture, bacteraemia clearance was rapidly achieved. Conclusions Ceftobiprole, especially in combination, could be a promising alternative treatment for infective endocarditis

In-label, off-label prescription, efficacy and tolerability of dalbavancin: report from a National Registry

PurposeAlthough dalbavancin is currently approved for the treatment of ABSSIs, several studies suggest its efficacy and tolerance as long-term therapy for other off-label indications requiring prolonged intravenous antibiotic administration.MethodsWe conducted a prospective nationwide study of dalbavancin use in real-life settings for both approved and off-label indications analysing for each case the clinical and microbiological characteristics of infection the efficacy and safety of treatments.ResultsDuring the study period (from December 2018 to July 2021), the ID specialists from 14 different centres enrolled 223 patients treated with dalbavancin [141 males (63%) and 82 females (37%); male/female ratio 1.72; mean age 59 (SD 17.2) years, (range 15-96). Most patients in the study population (136/223; 61.0%) came from community rather than health care facilities and most of them were visited in Infectious Diseases wards (93/223; 41.7%) and clinics (55/223; 24.7%) even though some patients were cured in other settings, such as surgery wards (18/223; 8.1%), orthopaedic wards (11/223; 4.9%), Emergency Rooms (7/223; 3.1%) and non-surgical other than ID wards (6/223; 2.7%). The most common ID diagnoses were osteomyelitis (44 cases/223; 19.7%; of which 29 acute and 15 chronic osteomyelitis), cellulitis (28/223; 12.5%), cutaneous abscess (23/223; 10.3%), orthopaedic prosthesis-associated infection (22/223; 9.9%), surgical site infection (20/223; 9.0%) and septic arthritis (15/223; 6.7%).ConclusionIn conclusion, by virtue of its PK/PD properties, dalbavancin represents a valuable option to daily in-hospital intravenous or outpatient antimicrobial regimens also for off-label indications requiring a long-term treatment of Gram-positive infections

Prolonged higher dose methylprednisolone vs. conventional dexamethasone in COVID-19 pneumonia: a randomised controlled trial (MEDEAS)

Dysregulated systemic inflammation is the primary driver of mortality in severe COVID-19 pneumonia. Current guidelines favor a 7-10-day course of any glucocorticoid equivalent to dexamethasone 6 mg·day-1. A comparative RCT with a higher dose and a longer duration of intervention was lacking

Gain- and Loss-of-Function CFTR Alleles Are Associated with COVID-19 Clinical Outcomes

Carriers of single pathogenic variants of the CFTR (cystic fibrosis transmembrane conductance regulator) gene have a higher risk of severe COVID-19 and 14-day death. The machine learning post-Mendelian model pinpointed CFTR as a bidirectional modulator of COVID-19 outcomes. Here, we demonstrate that the rare complex allele [G576V;R668C] is associated with a milder disease via a gain-of-function mechanism. Conversely, CFTR ultra-rare alleles with reduced function are associated with disease severity either alone (dominant disorder) or with another hypomorphic allele in the second chromosome (recessive disorder) with a global residual CFTR activity between 50 to 91%. Furthermore, we characterized novel CFTR complex alleles, including [A238V;F508del], [R74W;D1270N;V201M], [I1027T;F508del], [I506V;D1168G], and simple alleles, including R347C, F1052V, Y625N, I328V, K68E, A309D, A252T, G542*, V562I, R1066H, I506V, I807M, which lead to a reduced CFTR function and thus, to more severe COVID-19. In conclusion, CFTR genetic analysis is an important tool in identifying patients at risk of severe COVID-19

A genome-wide association study for survival from a multi-centre European study identified variants associated with COVID-19 risk of death

: The clinical manifestations of SARS-CoV-2 infection vary widely among patients, from asymptomatic to life-threatening. Host genetics is one of the factors that contributes to this variability as previously reported by the COVID-19 Host Genetics Initiative (HGI), which identified sixteen loci associated with COVID-19 severity. Herein, we investigated the genetic determinants of COVID-19 mortality, by performing a case-only genome-wide survival analysis, 60 days after infection, of 3904 COVID-19 patients from the GEN-COVID and other European series (EGAS00001005304 study of the COVID-19 HGI). Using imputed genotype data, we carried out a survival analysis using the Cox model adjusted for age, age2, sex, series, time of infection, and the first ten principal components. We observed a genome-wide significant (P-value < 5.0 × 10-8) association of the rs117011822 variant, on chromosome 11, of rs7208524 on chromosome 17, approaching the genome-wide threshold (P-value = 5.19 × 10-8). A total of 113 variants were associated with survival at P-value < 1.0 × 10-5 and most of them regulated the expression of genes involved in immune response (e.g., CD300 and KLR genes), or in lung repair and function (e.g., FGF19 and CDH13). Overall, our results suggest that germline variants may modulate COVID-19 risk of death, possibly through the regulation of gene expression in immune response and lung function pathways

Host genetics and COVID-19 severity: increasing the accuracy of latest severity scores by Boolean quantum features

The impact of common and rare variants in COVID-19 host genetics has been widely studied. In particular, in Fallerini et al. (Human genetics, 2022, 141, 147–173), common and rare variants were used to define an interpretable machine learning model for predicting COVID-19 severity. First, variants were converted into sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. After that, the Boolean features, selected by these logistic models, were combined into an Integrated PolyGenic Score (IPGS), which offers a very simple description of the contribution of host genetics in COVID-19 severity.. IPGS leads to an accuracy of 55%–60% on different cohorts, and, after a logistic regression with both IPGS and age as inputs, it leads to an accuracy of 75%. The goal of this paper is to improve the previous results, using not only the most informative Boolean features with respect to the genetic bases of severity but also the information on host organs involved in the disease. In this study, we generalize the IPGS adding a statistical weight for each organ, through the transformation of Boolean features into “Boolean quantum features,” inspired by quantum mechanics. The organ coefficients were set via the application of the genetic algorithm PyGAD, and, after that, we defined two new integrated polygenic scores (IPGSph1 and IPGSph2). By applying a logistic regression with both IPGS, (IPGSph2 (or indifferently IPGSph1) and age as inputs, we reached an accuracy of 84%–86%, thus improving the results previously shown in Fallerini et al. (Human genetics, 2022, 141, 147–173) by a factor of 10%

Nelfinavir: An Old Ally in the COVID-19 Fight?

After almost three years of the pandemic, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is still spreading around the world, causing notable sanitary and social issues. New antiviral therapies are constantly under investigation. However, few options have been approved for the treatment of COVID-19. Clinical trials are currently ongoing to evaluate the efficacy of nelfinavir on mild&ndash;moderate COVID-19. This study aims to investigate the activity of this compound on SARS-CoV-2 &ldquo;Variants of Concern&rdquo; (VOCs), comparing its effectiveness with the approved drugs remdesivir and molnupiravir. The experiments were conducted in a biosafety level 3 facility. In this study, we used a Vero-E6-cell-based infection assay to investigate the in vitro activity of nelfinavir, molnupiravir, and remdesivir. Four strains of SARS-CoV-2 were tested: 20A.EU1, B.1.1.7, P.1, and B.1.617.2. All compounds reached micromolar/submicromolar EC50, EC90, and EC99. Furthermore, the Cmax/EC50 and Cmax/EC90 ratios were &gt;1 for all compounds and all variants tested. Our study demonstrated that nelfinavir, as molnupiravir, and remdesivir are effective in vitro on SARS-CoV-2 variants

KPC-producing Klebsiella pneumoniae gut decolonisation following ceftazidime/avibactam-based combination therapy: A retrospective observational study

KPC-producing Klebsiella pneumoniae (KPC-Kp) gut colonization is a major risk factor for developing systemic infections. Ceftazidime/avibactam could have a role as decolonization therapy in special situations PATIENTS AND METHODS: retrospective, observational, multicenter study. We compared KPC-Kp gut decolonization rate of ceftazidime/avibactam-based therapy (Group A) and other antimicrobial regimens (Group B) in patients with a KPC-Kp infection

Personalized machine learning approach to predict candidemia in medical wards

Purpose Candidemia is a highly lethal infection; several scores have been developed to assist the diagnosis process and recently different models have been proposed. Aim of this work was to assess predictive performance of a Random Forest (RF) algorithm for early detection of candidemia in the internal medical wards (IMWs). Methods A set of 42 potential predictors was acquired in a sample of 295 patients (male: 142, age: 72 +/- 15 years; candidemia: 157/295; bacteremia: 138/295). Using tenfold cross-validation, a RF algorithm was compared with a classic stepwise multivariable logistic regression model; discriminative performance was assessed by C-statistics, sensitivity and specificity, while calibration was evaluated by Hosmer-Lemeshow test. Results The best tuned RF algorithm demonstrated excellent discrimination (C-statistics = 0.874 +/- 0.003, sensitivity = 84.24% +/- 0.67%, specificity = 91% +/- 2.63%) and calibration (Hosmer-Lemeshow statistics = 12.779 +/- 1.369,p = 0.120), markedly greater than the ones guaranteed by the classic stepwise logistic regression (C-statistics = 0.829 +/- 0.011, sensitivity = 80.21% +/- 1.67%, specificity = 84.81% +/- 2.68%; Hosmer-Lemeshow statistics = 38.182 +/- 15.983,p &lt; 0.001). In addition, RF suggests a major role of in-hospital antibiotic treatment with microbioma highly impacting antimicrobials (MHIA) that are found as a fundamental risk of candidemia, further enhanced by TPN. When in-hospital MHIA therapy is not performed, PICC is the dominant risk factor for candidemia, again enhanced by TPN. When PICC is not used and MHIA therapy is not performed, the risk of candidemia is minimum, slightly increased by in-hospital antibiotic therapy. Conclusion RF accurately estimates the risk of candidemia in patients admitted to IMWs. Machine learning technique might help to identify patients at high risk of candidemia, reduce the delay in empirical treatment and improve appropriateness in antifungal prescription