Carbapenem Resistance, Initial Antibiotic Therapy, and Mortality in<i>Klebsiella pneumoniae</i>Bacteremia: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND Mortality associated with infections caused by carbapenem-resistant Enterobacteriaceae (CRE) is higher than mortality due to carbapenem-sensitive pathogens. OBJECTIVE To examine the association between mortality from bacteremia caused by carbapenem-resistant (CRKP) and carbapenem-sensitive Klebsiella pneumoniae (CSKP) and to assess the impact of appropriate initial antibiotic therapy (IAT) on mortality. DESIGN Systematic review and meta-analysis METHODS We searched MEDLINE, EMBASE, CINAHL, and Wiley Cochrane databases through August 31, 2016, for observational studies reporting mortality among adult patients with CRKP and CSKP bacteremia. Search terms were related to Klebsiella, carbapenem-resistance, and infection. Studies including fewer than 10 patients per group were excluded. A random-effects model and meta-regression were used to assess the relationship between carbapenem-resistance, appropriateness of IAT, and mortality. RESULTS Mortality was higher in patients who had CRKP bacteremia than in patients with CSKP bacteremia (15 studies; 1,019 CRKP and 1,148 CSKP patients; unadjusted odds ratio [OR], 2.2; 95% confidence interval [CI], 1.8-2.6; I2=0). Mortality was lower in patients with appropriate IAT than in those without appropriate IAT (7 studies; 658 patients; unadjusted OR, 0.5; 95% CI, 0.3-0.8; I2=36%). CRKP patients (11 studies; 1,326 patients; 8-year period) were consistently less likely to receive appropriate IAT (unadjusted OR, 0.5; 95% CI, 0.3-0.7; I2=43%). Our meta-regression analysis identified a significant association between the difference in appropriate IAT and mortality (OR per 10% difference in IAT, 1.3; 95% CI, 1.0-1.6). CONCLUSIONS Appropriateness of IAT is an important contributor to the observed difference in mortality between patients with CRKP bacteremia and patients with CSKP bacteremia. Infect Control Hosp Epidemiol 2017;38:1319-1328.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".