Development and validation of a scoring system for predicting cancer patients at risk of extended-spectrum b-lactamase-producing Enterobacteriaceae infections

Martinez-Valencia, Alvaro Jose; Martínez, B Ricardo; Ayala, Anita M Montañez; García, Katherin Cabrera; Pedraza, Ricardo Sánchez; Cetina, Leydy Jiménez; Gómez-Rincón, Julio César; Maldonado, Sonia Isabel Cuervo

doi:10.1186/s12879-020-05280-4

Cited by 6 publications

(5 citation statements)

References 33 publications

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“…Eight of 144 (6%) reporting and modelling several microorganisms focused only on Gram-negative bacteria [58–62,120–122] and 1/144 (1%) focused only on fungi [63]. Twelve of 144 (8%) studies studied the family Enterobacterales (especially Escherichia coli and Klebsiella pneumoniae combined), either ESBL- or carbapenemase-producing [38,64–68,123–128]. The most commonly studied single organism was Clostridioides difficile with 27/144 (19%) studies [39–41,69–81,129–139].…”

Section: Resultsmentioning

confidence: 99%

“…Worth noting, we found two clinical prediction models. In one of them, the IRONIC group developed a scoring system for the risk of multidrug resistance in bloodstream infections by P. aeruginosa , and in another, Colombian researchers developed a scoring system for the risk of ESBL-producing Enterobactericaea (124, 140).…”

Section: Discussionmentioning

confidence: 99%

“…Eight of 144 (6%) reporting and modelling several microorganisms focused only on Gram-negative bacteria [58][59][60][61][62][120][121][122] and 1/144 (1%) focused only on fungi [63]. Twelve of 144 (8%) studies studied the family Enterobacterales (especially Escherichia coli and Klebsiella pneumoniae combined), either ESBL-or carbapenemase-producing [38,[64][65][66][67][68][123][124][125][126][127][128]. The most commonly studied single organism was Clostridioides difficile with 27/144 (19%) studies [39][40][41][69][70][71][72][73][74][75][76][77][78][79][80][81][129][130][131][132][133][134][135][136][137][138]…”

Section: Study Characteristicsmentioning

confidence: 99%

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Clinical outcomes of antimicrobial resistance in cancer patients: A systematic review of multivariable models

Danielsen

Franconeri

Page

et al. 2022

Preprint

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Background Infections are major causes of disease in cancer patients and pose a major obstacle to the success of cancer care. The global rise of antimicrobial resistance threatens to make these obstacles even greater and hinder continuing progress in cancer care. To prevent and handle such infections, a better understanding of the risk factors are needed. This internally funded systematic review (PROSPERO registration: CRD42021282769) aims to review what risk factors for resistant infections and deaths due to resistant infections have been investigated, and with what methodological approaches. Methods We employed two broad searches of antimicrobial resistance in cancer patients, using terms associated with antimicrobial resistant infections, in MEDLINE and Embase through Ovid, in addition to Cinahl through EBSCOhost and Web of Science Core Collection. Primary, observational studies in English from January 2015 to November 2021 on human cancer patients that explicitly modelled infection or mortality associated with antimicrobial resistance in a multivariable model were included. We extracted data on the study populations and their malignancies, risk factors, the microbial aetiology, methods for variable selection, and assessed the risk of bias using the NHLBI Study Quality Assessment Tools. Results Two searches yielded a total of 27151 unique records, of which 144 studies were included after screening and reading. Of the outcomes studied, mortality was the most common (68/144, 47%). Forty-five percent (65/144) of the studies focused on haemato-oncological patients, and 27% (39/144) studied several bacteria or fungi. Studies included a median of 200 patients and 46 events. One-hundred-and-three (72%) studies used a p-value-based variable selection. Studies included a median of seven variables in the final (and largest) model, which yielded a median of 6.9 events per variable. An in-depth example of vancomycin-resistant enterococci was reported. Conclusions We found the current research on this topic to be heterogeneous, in both the methodological and epidemiological approach. Methodological choices resulting in very diverse models made it difficult or even impossible to draw statistical inferences and summarise what risk factors were of clinical relevance. The development and adherence to more standardised protocols that build on existing literature is urgent.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Study Characteristicsmentioning

confidence: 99%

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Clinical outcomes of antimicrobial resistance in cancer patients: A systematic review of multivariable models

Danielsen

Franconeri

Page

et al. 2022

Preprint

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“…ESBL-E hydrolysis of carbapenem antibiotics is low, so carbapenem antibiotics are often used as the first choice in clinical treatment of ESBL-E infections. However, the abuse of carbapenems may result in the selection of carbapenem resistant Enterobacteriaceae, which will ultimately make it more difficult to treat this kind of bacteria [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

A clinical prediction tool for extended-spectrum β-lactamase-producing Enterobacteriaceae urinary tract infection

et al. 2022

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Background Prevalence of extended-spectrum beta-lactamase-producing-Enterobacteriaceae (ESBL-E) has risen in patients with urinary tract infections. The objective of this study was to determine explore the risk factors of ESBL-E infection in hospitalized patients and establish a predictive model. Methods This retrospective study included all patients with an Enterobacteriaceae-positive urine sample at the first affiliated hospital of Jinan university from January 2018 to December 2019. Antimicrobial susceptibility patterns of ESBL-E were analyzed, and multivariate analysis of related factors was performed. From these, a nomogram was established to predict the possibility of ESBL-E infection. Simultaneously, susceptibility testing of a broad array of carbapenem antibiotics was performed on ESBL-E cultures to explore possible alternative treatment options. Results Of the total 874 patients with urinary tract infections (UTIs), 272 (31.1%) were ESBL-E positive. In the predictive analysis, five variables were identified as independent risk factors for ESBL-E infection: male gender (OR = 1.607, 95% CI 1.066–2.416), older age (OR = 4.100, 95% CI 1.678–12.343), a hospital stay in preceding 3 months (OR = 1.872, 95% CI 1.141–3.067), invasive urological procedure (OR = 1.810, 95% CI 1.197–2.729), and antibiotic use within the previous 3 months (OR = 1.833, 95% CI 1.055–3.188). In multivariate analysis, the data set was divided into a training set of 611 patients and a validation set of 263 patients The model developed to predict ESBL-E infection was effective, with the AuROC of 0.650 (95% CI 0.577–0.725). Among the antibiotics tested, several showed very high effectiveness against ESBL-E: amikacin (85.7%), carbapenems (83.8%), tigecycline (97.1%) and polymyxin (98.2%). Conclusions The nomogram is useful for estimating a UTI patient’s likelihood of infection with ESBL-E. It could improve clinical decision making and enable more efficient empirical treatment. Empirical treatment may be informed by the results of the antibiotic susceptibility testing.

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“…ESBL-PE hydrolysis of carbapenem antibiotics is low, so carbapenem antibiotics are often used as the rst choice in clinical treatment of ESBL-PE infections. However, the abuse of carbapenems may result in the selection of carbapenem resistant Enterobacteriaceae, which will ultimately make it more di cult to treat this kind of bacteria [3,4].…”

Section: Introductionmentioning

confidence: 99%

A Clinical Prediction Tool for Extended-Spectrum β-Lactamase-Producing Enterobacteriaceae Urinary Tract Infection

Liu

Qiu

Chen

et al. 2021

Preprint

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Background To explore the risk factors of extended-spectrum β-lactamase-producing Enterobacteriaceae (ESBL-PE) infection through urine samples of hospitalized patients and establish a predictive model to improve treatment outcomes.MethodsThis retrospective study included all patients with an Enterobacteriaceae-positive urine sample at the first affiliated hospital of Jinan university from January 2018 to December 2019. Antimicrobial susceptibility patterns of ESBL-PE were analyzed, and multivariate analysis of related factors was performed. From these, a nomogram was established to predict the possibility of ESBL-PE infection. Simultaneously, susceptibility testing of a broad array of carbapenem antibiotics was performed on ESBL-PE cultures to explore possible alternative treatment options.ResultsOf the total 874 patients with urinary tract infections (UTIs), 272 (31.1%) were ESBL-PE positive. In the predictive analysis, five variables were identified as independent risk factors for ESBL-PE infection: male gender (OR=1.607, 95% CI 1.066-2.416), older age (OR=4.100, 95% CI 1.678-12.343), a hospital stay in preceding 3 months (OR=1.872, 95% CI 1.141-3.067), invasive urological procedure (OR=1.810, 95% CI 1.197-2.729), and antibiotic use within the previous 3 months (OR 0.546, 95% CI 0.314-0.948). In multivariate analysis, the data set was divided into a training set of 611 patients and a validation set of 263 patients The model developed to predict ESBL-PE infection was effective, with the AuROC of 0.650 (95% CI 0.577-0.725). Among the antibiotics tested, several showed very high effectiveness against ESBL-PE: amikacin (85.7%), carbapenems (83.8%), tigecycline (97.1%) and polymyxin (98.2%). ConclusionsThe nomogram is useful for estimating a bacteremic patient’s likelihood of infection with ESBL-PE. It could improve clinical decision making and enable more efficient empirical treatment. Empirical treatment may be informed by the results of the antibiotic susceptibility testing.

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Development and validation of a scoring system for predicting cancer patients at risk of extended-spectrum b-lactamase-producing Enterobacteriaceae infections

Cited by 6 publications

References 33 publications

Clinical outcomes of antimicrobial resistance in cancer patients: A systematic review of multivariable models

Clinical outcomes of antimicrobial resistance in cancer patients: A systematic review of multivariable models

A clinical prediction tool for extended-spectrum β-lactamase-producing Enterobacteriaceae urinary tract infection

A Clinical Prediction Tool for Extended-Spectrum β-Lactamase-Producing Enterobacteriaceae Urinary Tract Infection

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