Predictors of Outcome following Bacterial Peritonitis in Peritoneal Dialysis
Bibliographic record
Abstract
OBJECTIVE: No studies have been done to examine factors that predict the outcome of bacterial peritonitis during peritoneal dialysis (PD), beyond the contribution of the organism causing the peritonitis, concurrent exit-site or tunnel infection, and abdominal catastrophes. DESIGN: In this study we examined several clinical and laboratory parameters that might predict the outcome of an episode of bacterial peritonitis. Between March 1995 and July 2000, we identified 399 episodes of bacterial peritonitis in 191 patients on dialysis. RESULTS: There were 260 episodes of gram-positive peritonitis, 99 episodes of gram-negative peritonitis, and 40 episodes of polymicrobial peritonitis. Gram-positive peritonitis had a significantly higher resolution rate than either polymicrobial peritonitis or gram-negative peritonitis. Staphylococcus aureus episodes had poorer resolution than other gram-positive infections. Nonpseudomonal peritonitis had a better outcome than Pseudomonas aeruginosa episodes. Among all the gram-negative infections, Serratia marcescens had the worst outcome. Episodes associated with a purulent exit site had poor outcome only on univariate analysis. For those peritonitis episodes in which the PD fluid cell count was > 100/microL for more than 5 days, the nonresolution rate was 45.6%, compared to a 4.2% nonresolution rate when the cell count returned to 100/microL or less in less than 5 days. Those patients that had a successful outcome had been on continuous ambulatory PD for a significantly shorter period of time than those patients that had nonresolution. The nonresolution rate for those patients that had been on PD for more than 2.4 years was 24.4%, compared to 16.5% for those that had been on PD for less than 2.4 years (p = 0.05). CONCLUSION: The duration of PD and the number of days the PD effluent cell count remained > 100/microL were the only factors that independently predicted the outcome of an episode of peritonitis. Caucasians seem to have a higher nonresolution (failure) rate compared to Blacks. Other variables, such as the number of peritonitis episodes before the episode in question, vancomycin-based initial empiric treatment, serum albumin level, total lymphocyte count and initial dialysate white blood cell count, age, sex, diabetes, previous renal transplantation, and the use of steroids did not affect the outcome of peritonitis.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".