Assessment of risk factors for early-onset deep surgical site infection following primary total hip arthroplasty for osteoarthritis
Bibliographic record
Abstract
Abstract. The aim of this study was to determine the incidence, annual trend, and perioperative outcomes and identify risk factors of early-onset (≤90 d) deep surgical site infection (SSI) following primary total hip arthroplasty (THA) for osteoarthritis. We performed a retrospective study using prospectively collected patient-level data from January 2013 to March 2020. The diagnosis of deep SSI was based on the published Centre for Disease Control/National Healthcare Safety Network (CDC/NHSN) definition. The Mann–Kendall trend test was used to detect monotonic trends. Secondary outcomes were 90 d mortality and 90 d readmission. A total of 22 685 patients underwent primary THA for osteoarthritis. A total of 46 patients had a confirmed deep SSI within 90 d of surgery representing a cumulative incidence of 0.2 %. The annual infection rate decreased over the 7-year study period (p=0.026). Risk analysis was performed on 15 466 patients. Risk factors associated with early-onset deep SSI included a BMI > 30 kg m−2 (odds ratio (OR) 3.42 [95 % CI 1.75–7.20]; p<0.001), chronic renal disease (OR, 3.52 [95 % CI 1.17–8.59]; p=0.011), and cardiac illness (OR, 2.47 [1.30–4.69]; p=0.005), as classified by the Canadian Institute for Health Information. Early-onset deep SSI was not associated with 90 d mortality (p=0.167) but was associated with an increased chance of 90 d readmission (p<0.001). This study establishes a reliable baseline infection rate for early-onset deep SSI after THA for osteoarthritis through the use of a robust methodological process. Several risk factors for early-onset deep SSI are potentially modifiable, and therefore targeted preoperative interventions of patients with these risk factors is encouraged.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".