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

Wilke, Michael; Preisendörfer, Birgit; Seiffert, Anna; Kleppisch, Maria; Schweizer, Caroline; Rauchensteiner, Stephan

doi:10.1007/s15010-022-01843-6

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…For this purpose, the age and sex distribution in the DADB was compared with BAS data to determine age-adjusted and sex-adjusted multiplication factors. 36 The age and sex distribution of individuals in the DADB and the SHI population in the years of study (2018 and 2019) were comparable ( Supplementary Material 2 ).…”

Section: Methodsmentioning

confidence: 95%

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

Lübbert,

Dykukha,

Pelz

et al. 2023

DIC

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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.

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Section: Methodsmentioning

confidence: 95%

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

Lübbert,

Dykukha,

Pelz

et al. 2023

DIC

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“…Klebsiella pneumoniae , Acinetobacter baumannii and Pseudomonas aeruginosa are the main organisms that may invade the respiratory tract, urinary tract, peritoneal cavity and bloodstream of patients to cause pneumonia, and urinary tract, peritoneal and bloodstream infections. These infections can considerably increase treatment costs and patients’ mortality risks ( Brink, 2019 ; Wilke et al., 2022 ). In the absence of new and effective antibiotics, the strong antibiotic resistance of CR-GNB limits pharmacological options and exacerbates treatment difficulty ( Garg et al., 2019 ).…”

Section: Introductionmentioning

confidence: 99%

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

Liao

Zhi

et al. 2023

Front. Cell. Infect. Microbiol.

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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.

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“… 1 As a result of high levels of antimicrobial resistance (AMR), these bacteria are difficult to treat and are also associated with high mortality. 2 Importantly, due to mobile genetic elements, they may be able to transmit resistance widely. 3 Over 2.8 million cases and 35,000 deaths per year are reported by the disease control and prevention (CDC) as a result of infections caused by resistant bacteria.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

et al. 2022

IDR

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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.

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

Cited by 5 publications

References 24 publications

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

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

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

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

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