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Record W2795654288 · doi:10.1017/ice.2018.63

Comparison of Predictors and Mortality Between Bloodstream Infections Caused by ESBL-Producing <i>Escherichia coli</i> and ESBL-Producing <i>Klebsiella pneumoniae</i>

2018· article· en· W2795654288 on OpenAlexaff
Oded Scheuerman, Vered Schechner, Yehuda Carmeli, Belén Gutiérrez‐Gutiérrez, Esther Calbo, Benito Almirante, Pier-Luigy Viale, Antonio Oliver, Patricia Ruíz-Garbajosa, Oriol Gasch, Mónica Gozalo, Johann Pitout, Murat Akova, Carmen Peña, José Molina, Alicia Hernández‐Torres, Mario Venditti, Núria Prim, Julia Origüen, Germán Bou, Evelina Tacconelli, M. Tumbarello, Axel Hamprecht, Ilias Karaiskos, Cristina de la Calle, Federico Pérez, Mitchell J. Schwaber, Joaquín Bermejo, Warren Lowman, Po‐Ren Hsueh, Carolina Navarro-San Francisco, Robert A. Bonomo, David L. Paterson, Álvaro Pascual, Jesús Rodríguez‐Baño

Bibliographic record

VenueInfection Control and Hospital Epidemiology · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsCalgary Laboratory ServicesUniversity of Calgary
Fundersnot available
KeywordsBacteremiaMedicineKlebsiella pneumoniaeInternal medicineOdds ratioIntensive care unitBloodstream infectionMicrobiologyAntibioticsEscherichia coliBiology

Abstract

fetched live from OpenAlex

OBJECTIVETo compare the epidemiology, clinical characteristics, and mortality of patients with bloodstream infections (BSI) caused by extended-spectrum β-lactamase (ESBL)-producing Escherichia coli (ESBL-EC) versus ESBL-producing Klebsiella pneumoniae (ESBL-KP) and to examine the differences in clinical characteristics and outcome between BSIs caused by isolates with CTX-M versus other ESBL genotypesMETHODSAs part of the INCREMENT project, 33 tertiary hospitals in 12 countries retrospectively collected data on adult patients diagnosed with ESBL-EC BSI or ESBL-KP BSI between 2004 and 2013. Risk factors for ESBL-EC versus ESBL-KP BSI and for 30-day mortality were examined by bivariate analysis followed by multivariable logistic regression.RESULTSThe study included 909 patients: 687 with ESBL-EC BSI and 222 with ESBL-KP BSI. ESBL genotype by polymerase chain reaction amplification of 286 isolates was available. ESBL-KP BSI was associated with intensive care unit admission, cardiovascular and neurological comorbidities, length of stay to bacteremia >14 days from admission, and a nonurinary source. Overall, 30-day mortality was significantly higher in patients with ESBL-KP BSI than ESBL-EC BSI (33.7% vs 17.4%; odds ratio, 1.64; P=.016). CTX-M was the most prevalent ESBL subtype identified (218 of 286 polymerase chain reaction-tested isolates, 76%). No differences in clinical characteristics or in mortality between CTX-M and non-CTX-M ESBLs were detected.CONCLUSIONSClinical characteristics and risk of mortality differ significantly between ESBL-EC and ESBL-KP BSI. Therefore, all ESBL-producing Enterobacteriaceae should not be considered a homogeneous group. No differences in outcomes between genotypes were detected.CLINICAL TRIALS IDENTIFIERClinicalTrials.gov. Identifier: NCT01764490.Infect Control Hosp Epidemiol 2018;39:660-667.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.306
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations65
Published2018
Admission routes1
Has abstractyes

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