Management Challenges of Prosthetic Joint Infections at Sunnybrook Health Sciences Centre
Bibliographic record
Abstract
Background. Prosthetic joint infections (PJI) are becoming more common; however, there is a lack of high-grade evidence to guide therapy of these complex infections. We sought to assess current practice heterogeneity at Sunnybrook Health Sciences Centre (SHSC) and Holland Orthopedic and Arthritic Centre (HOAC) and assess surgical and antibiotic strategies associated with clinical outcomes. Methods. We retrospectively reviewed patients with PJI who had revision surgery between 1 January 2000 and 31 December 2010 at SHSC/ HOAC. PJI were identified using the Ontario Joint Replacement Record System. Infection was defined by gross intraoperative evidence, positive intraoperative culture, and/or sinus tract prior to operation. The primary outcome was treatment failure at one year after revision surgery based on further surgery, ongoing infection and/or continuous suppressive antibiotics. Results. Of the 139 patients with PJI, 85 (61%) involved knees and 50 (36%) hips. For the initial procedure, 29 (21%) underwent I&D, 35 (25%) one-stage, and 75 (54%) two-stage revisions. Of 81 (58%) culture positive cases, CNST (47;34%) and Staphylococcus aureus (18;13%) were the most common pathogens The overall treatment success rate was 78% (95% CI: 70-84). There was no difference in outcome between one-stage (94%; 95% CI: 81-98) and two-stage revisions (84%; 95% CI: 74-91). Only 41% (95%; CI: 26-59) of I&D procedures were successful. Multivariate logistic regression analysis revealed I&D associated with high failure rates (odds ratio = 102.4; 95% CI: 8.6-1225.3), with a trend towards lower failure rates with one versus two-stage revision (odds ratio = 0.028, 95% CI: 0.001-1.500). Other predictors of treatment failure included history of prior revisions, and infection with Peptostreptococcus spp. Among two-stage revisions, we did not detect a significant reduction in treatment failure associated with antibiotic-free period prior to the second procedure (OR 2.6, 95% CI 0.23-29.8). Conclusion. Our findings question the value of customary approaches to prosthetic joint infection management such as the use of two stage procedures and antibiotic-free periods prior to definitive revision. Randomized controlled trials are needed to inform best practice for treatment of these complex infections. Disclosures. All authors: No reported disclosures.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".