Microbiological Trends and Antimicrobial Resistance in Peritoneal Dialysis-Related Peritonitis, 2005 to 2014
Bibliographic record
Abstract
Background Information related to the microbiology of peritonitis is critical to the optimal management of patients receiving peritoneal dialysis (PD). The goal was to characterize the microbiological etiology and antimicrobial susceptibilities of PD-related peritonitis (PDRP) from 2005 to 2014, inclusive. Methods The distribution of organisms in culture-positive PDRP was described for new episodes and relapse infections, and further detailed for monomicrobial and polymicrobial peritonitis. Annual and overall rates of PDRP were also characterized. Antimicrobial susceptibility rates were calculated for the most common and significant organisms. Results We identified 539 episodes of PDRP including 501 new and 38 relapse infections. New episodes of peritonitis were associated with a single organism in 85% of cases, and 44% of those involved staphylococci. Polymicrobial PDRP was more likely to involve gram-negative organisms, observed in 58% versus 24% of monomicrobial infections. Antimicrobial resistance was relatively stable from 2005 to 2014. Methicillin resistance was present in 57% of Staphylococcus epidermidis and 20% of other coagulase-negative staphylococci. Methicillin-resistant Staphylococcus aureus (MRSA) accounted for only 11% of S. aureus peritonitis compared with 2% in our previous study of PDRP from 1991 to 1998. Ciprofloxacin resistance in Escherichia coli increased from 3% in our previous study to 24% in 2005 – 2014. Conclusions This study characterizes important differences in the distribution of organisms in new episodes of PDRP and relapse infections, as well as monomicrobial versus polymicrobial peritonitis. It also shows relatively stable rates of antimicrobial resistance from 2005 to 2014, but some increases compared with our previous study.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".