401. Predictors of Treatment Failure for Hip and Knee Prosthetic Joint Infections in the Setting of Prosthesis Removal: A Multi-Center Retrospective Cohort
Bibliographic record
Abstract
Abstract Background Prosthetic hip and knee joint infections (PJIs) are challenging to eradicate despite prosthesis removal and long courses of antibiotics. We aimed to describe the risk factors for PJI treatment failure in a multicenter retrospective cohort. Methods A retrospective cohort of individuals who underwent prosthetic joint removal for a PJI at one of five hospitals in Toronto, Ontario, Canada from 2010–2014. Individuals eligible for the cohort were obtained by searching operative listings and PJIs were defined according to the criteria of the Musculoskeletal Infection Society. Treatment failure was defined as recurrent PJI, amputation, death or chronic antibiotic suppression. Potential risk factors for treatment failure were abstracted by chart review and assessed using a Cox proportional hazards model. Results 533 PJIs were analyzed over a median follow-up duration of 1102 days with 21 surgeons performing more than 5 revision arthroplasties for a PJI. Two-stage procedures were performed in 81% (430/533) and the most common organism was coagulase negative staphylococci (32%). Treatment failure occurred in 28% (150/533) over 1443 patient-years and was caused by a different bacterial species in 53% (56/105). On multivariate analysis the characteristics associated with PJI treatment failure included liver disease (adjusted hazard ratio (aHR) 3.12, 95% confidence interval (95% CI) 2.09–4.66), the presence of a sinus tract (aHR 1.53, 94% CI (1.12–2.10), preceding debridement with prosthesis retention (aHR 1.68, 95% CI 1.13–2.51), a one-stage procedure (aHR 1.72, 95% CI (1.28–2.32), and infection due to Gram-negative bacilli (aHR 1.35, 95% CI 1.04–1.76). Conclusion PJI treatment failure remains high despite prosthesis removal and the patient risk factors identified are non-modifiable. Novel treatment paradigms are urgently needed along with efforts to reduce orthopedic surgical site infections. Disclosures All authors: No reported disclosures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".