Patients with Systemic Lupus Erythematosus Have Increased Risk of Short-term Adverse Events after Total Hip Arthroplasty
Bibliographic record
Abstract
OBJECTIVE: Total hip arthroplasty (THA) is performed more frequently in patients with systemic lupus erythematosus (SLE) than in the general population. However, whether patients with SLE have higher complication rates than patients with osteoarthritis (OA) is unknown. This study compares adverse events (AE) in SLE with OA controls. METHODS: Patients in our institution's registry were eligible. SLE was identified by the International Classification of Diseases, 9th ed code. AE were identified by chart review and questionnaire. Patients with SLE were matched with OA controls. Multivariate regression was performed to identify independent predictors of AE. RESULTS: Fifty-eight patients with SLE THA were matched with 116 OA controls. Of the patients with SLE, 47.4% had Charlson-Deyo comorbidity scores (excluding SLE) > 1 versus 13.1% of OA (p < 0.0001). Length of stay was longer for SLE (6.0 days vs 4.7 days, p = 0.0008). Patients with SLE had more falls (10.3% vs 1.7%, p = 0.017), deep vein thrombosis (5.2% vs 0%, p = 0.036), acute renal disease (8.6% vs 0%, p = 0.004), wound infections (6.9% vs 0.9%, p = 0.043), and revision surgeries (5.2% vs 0%, p = 0.036). In a logistic regression controlling for comorbidities, SLE had an increased risk of AE (OR 3.77, 95% CI 1.74-8.16). Comorbidity scores were not significantly associated with AE. Among those with SLE, there were no significant differences in AE in those taking corticosteroids. CONCLUSION: SLE is an independent risk factor for AE after THA. Patients with SLE had higher rates of falls, acute renal disease, infections, and revision surgeries than matched OA controls. Further research is needed to understand the causes of increased AE in patients with SLE.
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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 | 0.000 | 0.003 |
| 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) | 0.003 | 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".