Clinical Outcomes of Cefepime Dosing in Patients With <i>Pseudomonas aeruginosa</i> Infections Undergoing Renal Replacement Therapies
Bibliographic record
Abstract
OBJECTIVE: Data regarding the efficacy of cefepime in treating patients with Pseudomonas aeruginosa infections undergoing renal replacement therapy (RRT) are limited. Therefore, we conducted a study to investigate the outcomes of recommended cefepime dosing in this population. METHODS: A retrospective descriptive cohort study was conducted between May 2015 and December 2022 on patients diagnosed with active sepsis secondary to P. aeruginosa infection, requiring cefepime treatment and undergoing RRT. The primary endpoint was 30-day all-cause mortality, with secondary endpoints including clinical cure, microbiologic cure, infection recurrence rate, and incidence of adverse events. RESULTS: . The most common diagnoses were pneumonia (58.3%), followed by bacteremia (18.9%). The median minimum inhibitory concentration (MIC) of cefepime for P. aeruginosa was 2 mcg/mL. The median dose of cefepime was 2000 mg every 12 h in those undergoing continuous venovenous hemofiltration and 1000 mg every 24 h in those undergoing intermittent hemodialysis, with a median duration of therapy of 8 days. The median Charlson Comorbidity index for survivors was 9 (8-11), while it was 7.5 (5.25-9.75) for non-survivors (p = 0.754). The 30-day survival rate was achieved in 96 patients (72.7%), the clinical cure was achieved in 58.3% of subjects, and a microbiologic cure rate of 31%. The 30-day reinfection rate was 9.1%, with no documented adverse events associated with the doses used. Multivariable logistic regression analysis identified the use of vasopressors (Odds Ratio 4.6, 95% CI 1.3-17.1) and white blood cell count at the end of therapy (Odds Ratio 1.1, 95% CI 1.01-1.4) as the main predictors of all-cause mortality in this population. CONCLUSION: Our results suggest that the utilized doses of cefepime in patients with P. aeruginosa infections undergoing RRT were effective. Larger study is needed to confirm our findings.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".