Antimicrobial treatment challenges in the era of carbapenem resistance

Peri, Anna Maria; Doi, Yohei; Potoski, Brian A.; Harris, Patrick N A; Paterson, David L.; Righi, Elda

doi:10.1016/j.diagmicrobio.2019.01.020

Cited by 56 publications

(43 citation statements)

References 153 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, recommendations and antimicrobial stewardship programs to position these therapeutic options are still lacking. Notably, additional data on clinical efficacy are needed for some of them (Peri et al, 2019). New β-lactamase inhibitors avibactam, relebactam, and vaborbactam can inhibit the activity of KPC-K. pneumoniae.…”

Section: Management Of Cr-infections In Hm Adult Patientsmentioning

confidence: 99%

Infections Due to Carbapenem-Resistant Bacteria in Patients With Hematologic Malignancies

et al. 2020

View full text Add to dashboard Cite

In developed countries, hematological malignancies (HM) account for 8 to 10% of cancers diagnosed annually and one-third of patients with HM (HMP) are expected to die from their disease. The former wide spectrum "magic bullet," imipenem, has been ousted by the emergence of carbapenem resistant (CR) pathogens. In endemic areas, infections with CR-bacteria occur in vulnerable patients, notably in HMP, who suffer from high mortality related to infectious complications. In this work, we reviewed epidemiologic and clinical factors associated with CR-infections in adult HMP and data on CR-related mortality and antibiotic treatments in this population. We found that resistance profile of strains involved in HMP infections, mainly bacteremia, reflect local epidemiology. Significant risk factors for infections with CR-bacteria include sex male, age around 50 years old, acute leukemia, selvage chemotherapy, neutropenia, and digestive colonization by CR-bacteria. Mortality rate is high in HMP infected with CR-Enterobacteriaceae, more particularly in case of acute myeloid leukemia and unresolved neutropenia, due to inappropriate empiric management and delayed administration of targeted antibiotics, such as tigecycline, colistin, or new associations of active drugs. Thus, we developed an algorithm for clinicians, assessing the incremental risk for CRbacterial infection occurrence and mortality in febrile HMP, to guide decisions related to empirical therapeutic strategies.

show abstract

Section: Management Of Cr-infections In Hm Adult Patientsmentioning

confidence: 99%

Infections Due to Carbapenem-Resistant Bacteria in Patients With Hematologic Malignancies

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Carbapenem-resistant Enterobacteriaceae (CRE) has attracted widespread attention due to its rapid growth, treatment di culty, high mortality and high economic burden [1][2][3]. The course of CRE bloodstream infection (BSI) in immunocompromised patients is usually abrupt and fatal [4].…”

Section: Introductionmentioning

confidence: 99%

Construction of a risk prediction model for subsequent bloodstream infection in intestinal carriers of carbapenem-resistant Enterobacteriaceae: a retrospective study in hematology department and intensive care unit

Wang

Lin

Chen

et al. 2020

Preprint

View full text Add to dashboard Cite

Background To establish a risk prediction model for carbapenem-resistant Enterobacteriaceae (CRE) bloodstream infection (BSI) in intestinal carriers. Methods CRE screenings were performed every two weeks in hematology department and intensive care unit (ICU). Patients with positive CRE rectal swab screening were identified using electronic healthcare records from 15 May 2018 to 31 December 2019. All CRE strains were collected and identified. Carriers who developed CRE BSI were compared with those who did not develop CRE infection. The control group 1:1 stratified randomly matched the case group. Univariate logistic analysis, multivariate logistic analysis and stepwise regression analysis were carried out. Results A total of 42 cases were included. Multivariate analysis showed that gastrointestinal injury (OR 86.82, 95%CI 2.58-2916.59, P = 0.013), tigecycline exposure (OR 14.99, 95%CI 1.82-123.74 P = 0.012) and carbapenem resistance score (OR 11.24, 95% CI 1.81–69.70, P = 0.009) were independent risk factors for CRE BSI in intestinal carriers (P < 0.05). They were included in the Logistic regression model to predict BSI. According to receiver operating characteristic (ROC) curve analysis, the cut-off value of the model was 0.72, and the sensitivity, specificity and area under the curve (AUC) were 90.5%, 85.7% and 0.92, respectively. Conclusions The risk prediction model based on gastrointestinal injury, tigecycline exposure and carbapenem resistance score of colonizing strain can effectively predict CRE BSI in patients with CRE colonization. Early CRE screening and detection for inpatients in key departments may early warning and reduce the risk of nosocomial infection of CRE.

show abstract

“…The purpose of establishing a 14-day mortality prediction model is that CRKP bloodstream infection deteriorates rapidly into severe multiple organ dysfunction syndromes, resulting in death. The establishment of a relatively short-term risk prediction model can help clinicians identify high-risk groups when bloodstream infection occurs, and guide the use of more active monitoring measures and anti-infection programs 16 .…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Algorithms to Predict the Mortality of Carbapenem Resistant Klebsiella Pneumoniae bacteremia

Liang

Qian

Chen

et al. 2019

Preprint

View full text Add to dashboard Cite

Purpose To establish mortality prediction models in 14 days of Carbapenem-Resistant Klebsiella Pneumoniae bacteremia using Machine learning.Materials and Methods It is a single-center retrospective study. We collect the relevant clinical information of all patients with Carbapenem-Resistant Klebsiella Pneumoniae (CRKP) bacteremia in the past 5 years using the local database. Data analysis and verification are carried out by multiple logical regression, decision tree, random forest, support vector machine (SVM), and XGBoost.Result This study includes 187 patients with 40 related variables. In multiple logical regression, acute renal injury (P=0.003), Apache II score (P=0.036), immunodeficiency (P=0.025), severe thrombocytopenia (P=0.025) and septic shock (P=0.044) are the high-risk factors for 14 days mortality of CRKP bloodstream infections. According to the importance of those parameters, risk scoring is established to predict the survival rate of CRKP bacteremia. The analysis of the five models, with 70% training set and 30% test set, show the comprehensive performance of random forest (AUROC=0.953, precision=91.85%) is slightly better than that of XGBoost (AUROC=0.912, precision=86.41%) and SVM (AUROC=0.936, precision=79.89%) in predicting 14-day mortality of CRKP bacteremia. The multiple logical regression model (AUROC=0.825, precision=81.52%) is the second, and the decision tree model (AUROC=0.712, precision=79.89%) is not very ideal.Conclusion Machine learning has good performances in predicting 14-day mortality of CRKP bacteremia than multiple logical regression. Acute renal injury, severe thrombocytopenia, and septic shock are the high-risk factors of CRKP bacteremia mortality.

show abstract

Antimicrobial treatment challenges in the era of carbapenem resistance

Cited by 56 publications

References 153 publications

Infections Due to Carbapenem-Resistant Bacteria in Patients With Hematologic Malignancies

Infections Due to Carbapenem-Resistant Bacteria in Patients With Hematologic Malignancies

Construction of a risk prediction model for subsequent bloodstream infection in intestinal carriers of carbapenem-resistant Enterobacteriaceae: a retrospective study in hematology department and intensive care unit

Machine Learning Algorithms to Predict the Mortality of Carbapenem Resistant Klebsiella Pneumoniae bacteremia

Contact Info

Product

Resources

About