Risk factors for metachronous periprosthetic joint infection in patients with multiple prosthetic joints: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECT: Although periprosthetic joint infection (PJI) can affect multiple joints simultaneously, most individuals with multiple joint involvement exhibit PJI in only one joint. Data regarding the metachronous PJI management for these patients are limited. This study aimed to explore the risk factors for metachronous PJI in patients with multiple prosthetic joints, thereby guiding and optimizing clinical practice. METHODS: The MEDLINE, Web of Science, Cochrane Library, and EMBASE were searched for all clinical studies of metachronous PJI from inception until May 2024. The clinical studies on risk factors for metachronous PJI in patients with multiple prosthetic joints after experiencing a periprosthetic infection were collected, with two authors independently screening the literatures. Newcastle Ottawa scale was used as a quality assessment tool for the included studies, and the meta-analysis was conducted to evaluate the potential risk factors of metachronous PJI. RESULTS: A total of 1,544 patients with PJI after multiple joint arthroplasties were reported in 9 studies, including 189 with metachronous PJI. The meta-analysis showed that methicillin-resistant staphylococcus aureus (MRSA; OR, 3.43; 95%CI, 1.71-6.88; p = 0.0005), rheumatoid arthritis (RA; OR, 2.38; 95%CI, 1.06-5.38; p = 0.04), history of steroid use (OR, 2.93; 95%CI, 1.58-5.43; p = 0.0007), and previous or ongoing non-periprosthetic infection (OR, 4.47; 95%CI, 1.45-13.82; p = 0.009) were identified as significant risk factors for metachronous PJI in patients with multiple prosthetic joints. However, there was no significant difference between the metachronous PJI group and non-metachronous group in terms of revision, age, diabetes, and gender. CONCLUSION: Patients with MRSA, RA, history of steroid use, previous or ongoing non-periprosthetic infection are at significantly higher risk for metachronous PJI. Further research is needed to optimize management strategies for preventing metachronous PJI in patients with multiple prostheses after a single joint PJI.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.042 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".