Predictors of mortality in patients with carbapenem-resistant Klebsiella pneumoniae infection: a meta-analysis and a systematic review
Bibliographic record
Abstract
BACKGROUND: Cases of carbapenem-resistant Klebsiella pneumoniae infection have been increasing. Patients with carbapenem-resistant Klebsiella pneumoniae infection have a poor prognosis and a high mortality rate. Identification of potential risk factors associated with carbapenem-resistant Klebsiella pneumoniae infection-related mortality may help improve patient outcomes. METHODS: Embase, PubMed, and the Cochrane Library databases were searched to identify articles describing predictors of mortality in patients with carbapenem-resistant Klebsiella pneumoniae infection. The quality of articles was assessed with the Newcastle-Ottawa Scale score (NOS). Review Manager was used for statistical analyses. RESULTS: Twenty-seven observational studies were included in the analysis. Factors associated with higher mortality were septic shock [odds ratio (OR): 4.41, 95% CI: 3.17-6.15], congestive heart failure (OR: 2.65, 95% CI: 1.71-4.13), chronic obstructive pulmonary disease (COPD; OR: 2.43, 95% CI: 1.87-3.15), chronic kidney disease (CKD; OR: 1.78, 95% CI: 1.43-2.22), diabetes mellitus (OR: 1.41, 95% CI: 1.16-1.72), mechanical ventilation (OR: 1.65, 95% CI: 1.25-2.18), and inappropriate empirical antimicrobial treatment (OR: 1.25, 95% CI: 1.03-1.52). The average Acute Physiology and Chronic Health Evaluation (APACHE) II score at the time of diagnosis of carbapenem-resistant Klebsiella pneumoniae infection was considerably higher in patients who did not survive than in those who survived (weighted mean difference: 5.86, 95% CI: 2.46-9.26). DISCUSSION: Patient condition, timing appropriate antimicrobial treatment, and disease severity according to the APACHE II score are the most important risk factors for death in patients with carbapenem-resistant Klebsiella pneumoniae infection. Our finding may help predict patients' outcomes and improve management for them. REGISTRATION NUMBER: 20210417EuEGX/INPLASY2020100037.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".