Comparison of Predictors and Mortality Between Bloodstream Infections Caused by ESBL-Producing <i>Escherichia coli</i> and ESBL-Producing <i>Klebsiella pneumoniae</i>
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".