Predictors of Treatment Failure for Hip and Knee Prosthetic Joint Infections in the Setting of 1- and 2-Stage Exchange Arthroplasty: A Multicenter Retrospective Cohort
Bibliographic record
Abstract
Abstract Background Prosthetic hip and knee joint infections (PJIs) are challenging to eradicate despite prosthesis removal and antibiotic therapy. There is a need to understand risk factors for PJI treatment failure in the setting of prosthesis removal. Methods A retrospective cohort of individuals who underwent prosthesis removal for a PJI at 5 hospitals in Toronto, Canada, from 2010 to 2014 was created. 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 A total of 533 individuals with prosthesis removal were followed for a median (interquartile range) of 814 (235–1530) days. A 1-stage exchange was performed in 19% (103/533), whereas a 2-stage procedure was completed in 88% (377/430). Treatment failure occurred in 24.8% (132/533) at 2 years; 53% (56/105) of recurrent PJIs were caused by a different bacterial species. At 4 years, treatment failure occurred in 36% of 1-stage and 32% of 2-stage procedures (P = .06). Characteristics associated with treatment failure included liver disease (adjusted hazard ratio [aHR], 3.12; 95% confidence interval [CI], 2.09–4.66), the presence of a sinus tract (aHR, 1.53; 95% CI, 1.12–2.10), preceding debridement with prosthesis retention (aHR, 1.68; 95% CI, 1.13–2.51), a 1-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). Conclusions Failure of PJI therapy is common, and risk factors are not easily modified. Improvements in treatment paradigms are needed, along with efforts to reduce orthopedic surgical site infections.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".