Association Between Periprosthetic Joint Infection and Mortality Following Primary Total Hip Arthroplasty
Bibliographic record
Abstract
BACKGROUND: Periprosthetic joint infection (PJI) remains a dreaded and unpredictable complication after total hip arthroplasty (THA). In addition to causing substantial morbidity, PJI may contribute to long-term mortality risk. Our objective was to determine the long-term mortality risk associated with PJI following THA. METHODS: This population-based, retrospective cohort study included adult patients (≥18 years old) in Ontario, Canada, who underwent their first primary elective THA for arthritis between April 1, 2002, and March 31, 2021. The primary outcome was death within 10 years after the index THA. Mortality was compared between propensity-score-matched groups (PJI within 1 year after surgery versus no PJI within 1 year after surgery) with use of survival analyses. Patients who died within 1 year after surgery were excluded to avoid immortal time bias. RESULTS: A total of 175,432 patients (95,883 [54.7%] women) with a mean age (and standard deviation) of 67 ± 11.4 years underwent primary THA during the study period. Of these, 868 patients (0.49%) underwent surgery for a PJI of the replaced joint within 1 year after the index procedure. After matching, patients with a PJI within the first year had a significantly higher 10-year mortality rate than their counterparts (11.4% [94 of 827 patients] versus 2.2% [18 of 827 patients]; absolute risk difference, 9.19% [95% confidence interval (CI), 6.81% to 11.6%]; hazard ratio, 5.49 [95% CI, 3.32 to 9.09]). CONCLUSIONS: PJI within 1 year after surgery is associated with over a fivefold increased risk of mortality within 10 years. The findings of this study underscore the importance of prioritizing efforts related to the prevention, diagnosis, and treatment of PJIs. LEVEL OF EVIDENCE: Prognostic Level III . See Instructions for Authors for a complete description of levels of evidence.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".