The Incidence of Prosthetic Hip and Knee Joint Infections in Ontario and Risk Factors for Treatment Failure
Bibliographic record
Abstract
Deep periprosthetic hip and knee joint infections (PJI) are a feared complication of primary arthroplasty and are associated with substantial morbidity. Linked health administrative databases are the mainstay of studying PJIs in Ontario while a detailed retrospective cohort is needed to understand risk factors predictive of treatment failure. The aims of this thesis were to evaluate PJI detection algorithms in administrative databases, describe the incidence and trends of PJIs in Ontario and describe characteristics associated with treatment failure.In the first study, we describe the performance characteristics of algorithms of diagnosis and procedure codes for detecting PJIs, demonstrating that the combination of a PJI diagnosis code along with a procedure code for an arthroplasty in conjunction with the code for a peripherally inserted central catheter had a sensitivity of 0.92 (95% CI 0.88-0.94) and positive predictive value of 0.78 (95% CI 0.74-0.82). This PJI detection algorithm was used to identify the incidence of PJIs in a large cohort using administrative databases. Of the 504,322 primary hip and knee joint replacements performed from 2003-2016, there were 7331 PJIs, with a cumulative incidence at 1-year of 0.97% (95% CI 0.92-1.01%) for hips and 0.76% (95% CI 0.73-0.79%) for knees. To assess for characteristics associated with PJI treatment failure, a retrospective cohort of 533 individuals with a PJI where prosthesis removal occurred was created. By two years 25% had experienced failure with liver disease (adjusted hazard ratio (aHR) 3.12, 95% CI 2.09-4.66), 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 one-stage procedure (aHR 1.72, 95% CI 1.28-2.32), and infection with Gram-negative bacilli (aHR 1.35, 95% CI 1.04-1.76) associated with failure. The majority of recurrent PJIs (53%, 56/105) were caused by a different bacterial species. We designed an algorithm that maximizes the identification of PJIs in administrative databases, which was used to demonstrate that PJIs are increasing over time. Failing to cure a PJI is common and most risk factors are not easily modifiable, which should spur the development of novel treatment paradigms. PJIs cause considerable morbidity and attention needs to be directed toward understanding why these infections occur.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".