Carbapenem-resistant gram-negative bacteria in Germany: incidence and distribution among specific infections and mortality: an epidemiological analysis using real-world data

A Systematic Literature Review of the Epidemiology of Complicated Urinary Tract Infection

INTRODUCTION

Urinary tract infections (UTIs) are common bacterial infections and present with heterogeneous clinical phenotypes. Whereas many uncomplicated UTIs resolve spontaneously or with antibiotic treatment, a complicated UTI (cUTI) presents with greater morbidity and a higher risk of treatment failures. The goal of this study was to estimate the real-world epidemiology of cUTI, including acute pyelonephritis (AP) and catheter-associated UTIs (CAUTIs), and its associated mortality internationally.

METHODS

A systematic literature search was conducted using PubMed, Embase, Cochrane, and EconLit databases for relevant articles published between July 2013 and July 2023 covering Europe and the following countries: France, Italy, Germany, Spain, the UK, China, Japan, and the US (US). Search terms relating to cUTI, AP, CAUTI, outcomes of interest (epidemiology), and real-world research designs were used. There were no language limitations (protocol registry: PROSPERO-CRD42023454794).

RESULTS

Database searches yielded 1014 unique records, of which 91 met the prespecified inclusion criteria; bibliography and conference abstract searches yielded 27 additional records for a total of 118 records for inclusion. Disease presentation and reported outcomes varied widely across studies, and most studies reporting incidence and prevalence of cUTI were from the US (21 of 29). No studies reporting incidence or prevalence rates of cUTI in China, Germany, or the UK were identified. Overall, high antibiotic resistance rates were reported in both inpatient and outpatient settings. The inpatient cohort mortality rates were highly variable (0-50%) depending on the patient population.

CONCLUSIONS

While disease presentation and reported outcomes varied widely across studies, cUTIs represent a considerable burden in terms of incidence, prevalence, drug resistance, and mortality, yet vast knowledge gaps remain in the literature. There is a crucial need to address these gaps to effectively evaluate new treatments and improve future analyses of cUTI burden and outcomes.

Evaluating the Efficacy of Inhaled Colistin via Two Nebulizer Types in Ventilator-Associated Pneumonia: Prospective Randomized Trial

Backgroud: This prospective randomized trial evaluated the clinical efficacy of inhaled colistin administered through two distinct nebulizer types, a vibrating mesh nebulizer (VMN) and a jet nebulizer (JN), in the treatment of ventilator-associated pneumonia caused by multidrug-resistant bacteria. In addition, an in vitro model was used to determine the optimal delivery of colistin. Method: Thirty-two patients prescribed intravenous (IV) colistin inhalation were randomized to receive either a VMN (n = 17) or a JN (n = 15), then compared to the control group (IV alone) over a 7-to 10-day period. The primary endpoint was the clinical pulmonary infection score (CPIS), and the secondary endpoints were the Sequential Organ Failure Assessment (SOFA) score, Acute Physiology and Chronic Health Evaluation (APACE) score, and duration of ventilator use. Results: Results from in vitro testing demonstrated that VMN delivered a significantly higher colistin dose than JN (35.68 ± 3.55% vs. 23.56 ± 3.31%; p < 0.001) when positioned at the humidifier inlet. Compared to the IV alone group, the IV with inhalation group yielded significant improvements in CPIS, SOFA score, and APACHE score on day 7; nevertheless, clinical outcomes between the two nebulizers were statistically indistinguishable. Conclusions: In conclusion, although VMN delivers a higher dose in vitro, both nebulizers yielded comparable clinical outcomes. This study was registered at US Clinical Trial Registration (NCT04633317).

Individuals at risk for severe COVID-19 in whom ritonavir-containing therapies are contraindicated or may lead to interactions with concomitant medications: a retrospective analysis of German health insurance claims data

Background

Nirmatrelvir/ritonavir is authorized for the treatment of COVID-19 but has several contraindications and potential drug-drug interactions (pDDIs) due to ritonavir-induced irreversible inhibition of cytochrome P450 3A4. We aimed to assess the prevalence of individuals with one or more risk factors for severe COVID-19 along with contraindications and pDDIs due to ritonavir-containing COVID-19 therapy.

Methods

Retrospective observational study of individuals with one or more risk factors according to Robert Koch Institute criteria for severe COVID-19 according to German statutory health insurance (SHI) claims data from the pre-pandemic years 2018-2019 based on the German Analysis Database for Evaluation and Health Services Research. Prevalence was extrapolated to the entire SHI population using age-adjusted and sex-adjusted multiplication factors.

Results

Nearly 2.5 million fully insured adults, representing 61 million people in the German SHI population, were included in the analysis. In 2019, prevalence of individuals that would have been at risk of severe COVID-19 was 56.4%. Amongst them, the prevalence of contraindications for treatment with ritonavir-containing COVID-19 therapy was approximately 2% according to presence of somatic comorbidities (severe liver or kidney disease). Prevalence of intake of medicines contraindicated for their potential interactions with ritonavir-containing COVID-19 therapy was 16.5% according to Summary of Product Characteristics and 31.8% according to previously published data. The prevalence of individuals at risk of pDDIs during ritonavir-containing COVID-19 therapy without adjustment of their concomitant therapy was 56.0% and 44.3%, respectively. Prevalence data for 2018 were similar.

Conclusion

Administering ritonavir-containing COVID-19 therapy can be challenging as thorough medical record review and close monitoring are required. In some cases, ritonavir-containing treatment may not be appropriate due to contraindications, risk of pDDIs, or both. For those individuals, an alternative ritonavir-free treatment should be considered.

Carbapenem-resistant gram-negative bacterial infection in intensive care unit patients: Antibiotic resistance analysis and predictive model development

In this study, we analyzed the antibiotic resistance of carbapenem-resistant gram-negative bacteria (CR-GNB) in intensive care unit (ICU) patients and developed a predictive model. We retrospectively collected the data of patients with GNB infection admitted to the ICU of the First Affiliated Hospital of Fujian Medical University, who were then divided into a CR and a carbapenem-susceptible (CS) group for CR-GNB infection analysis. Patients admitted between December 1, 2017, and July 31, 2019, were assigned to the experimental cohort (n = 205), and their data were subjected to multivariate logistic regression analysis to identify independent risk factors for constructing the nomogram-based predictive model. Patients admitted between August 1, 2019, and September 1, 2020, were assigned to the validation cohort for validating the predictive model (n = 104). The Hosmer-Lemeshow test and receiver operating characteristic (ROC) curve analysis were used to validate the model's performance. Overall, 309 patients with GNB infection were recruited. Of them, 97 and 212 were infected with CS-GNB and CR-GNB, respectively. Carbapenem-resistant Klebsiella pneumoniae (CRKP), carbapenem-resistant Acinetobacter baumannii (CRAB) and carbapenem-resistant Pseudomonas aeruginosa (CRPA) were the most prevalent CR-GNB. The multivariate logistic regression analysis results of the experimental cohort revealed that a history of combination antibiotic treatments (OR: 3.197, 95% CI: 1.561-6.549), hospital-acquired infection (OR: 3.563, 95% CI: 1.062-11.959) and mechanical ventilation ≥ 7 days (OR: 5.096, 95% CI: 1.865-13.923) were independent risk factors for CR-GNB infection, which were then used for nomogram construction. The model demonstrated a good fit of observed data (p = 0.999), with an area under the ROC curve (AUC) of 0.753 (95% CI: 0.685-0.820) and 0.718 (95% CI: 0.619-0.816) for the experimental and validation cohort, respectively. The decision curve analysis results suggested that the model has a high practical value for clinical practice. The Hosmer-Lemeshow test indicated a good fit of the model in the validation cohort (p-value, 0.278). Overall, our proposed predictive model exhibited a good predictive value in identifying patients at high risk of developing CR-GNB infection in the ICU and could be used to guide preventive and treatment measures.

Risk Factors for Carbapenem Resistant Gram Negative Bacteria (CR-GNB) Carriage Upon Admission to the Gastroenterology Department in a Tertiary First Class Hospital of China: Development and Assessment of a New Predictive Nomogram

Background

With the increasing number of critically ill patients in the gastroenterology department (GED), infections associated with Carbapenem resistant gram-negative bacteria (CR-GNB) are of great concern in GED. As the turn-around time (TAT) for a positive screening culture result is slow, contact precaution and pre-emptive isolation, cohorting methods should be undertaken immediately on admission for high-risk patients. Accurate prediction tools for CR-GNB colonization in GED can help determine target populations upon admission. And thus, clinicians and nurses can implement preventive measures more timely and effectively.

Objective

The purpose of the current study was to develop and internally validate a CR-GNB carrier risk predictive nomogram for a Chinese population in GED.

Methods

Based on a training dataset of 400 GED patients collected between January 2020 and December 2021, we developed a model to predict CR-GNB carrier risk. A rectal swab was used to evaluate the patients' CR-GNB colonization status microbiologically. We optimized features selection using the least absolute shrinkage and selection operator regression model (LASSO). In order to develop a predicting model, multivariable logistic regression analysis was then undertaken. Various aspects of the predicting model were evaluated, including discrimination, calibration, and clinical utility. We assessed internal validation using bootstrapping.

Results

The prediction nomogram includes the following predictors: Transfer from another hospital (Odds ratio [OR] 3.48), High Eastern Cooperative Oncology Group (ECOG) performance status (OR 2.61), Longterm in healthcare facility (OR 10.94), ICU admission history (OR 9.03), Blood stream infection history (OR 3.31), Liver cirrhosis (OR 4.05) and Carbapenem usage history within 3 month (OR 2.71). The model demonstrated good discrimination and good calibration.

Conclusion

With an estimate of individual risk using the nomogram developed in this study, clinicians and nurses can take more timely infection preventive measures on isolation, cohorting and medical interventions.