Risk Factors for Drug-resistant Bloodstream Infections in Patients with Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: To identify risk factors for developing drug-resistant bacterial infections in patients with systemic lupus erythematosus (SLE). METHODS: A retrospective, case-control study was performed. Patients fulfilled American College of Rheumatology criteria for SLE and had an episode of bloodstream infection between 2001 and 2012. Cases were defined as those with bloodstream infection caused by drug-resistant bacteria (Pseudomonas aeruginosa, methicillin-resistant Staphylococcus aureus, or extended-spectrum-β-lactalamase-producing Escherichia coli); while controls had susceptible strains of S. aureus or E. coli. Differences between groups were analyzed by Student t test or Mann-Whitney U test. Association between variables was assessed by OR (CI 95%). Multivariate analysis was performed by binary logistic regression model. RESULTS: Forty-four patients were included in each group. Variables associated with drug-resistant bloodstream infection were history of central nervous system activity; hematological activity, immunosuppressive treatment and prednisone dose at the time of the infection; and low C3 levels, antibiotic use, or hospitalization in the previous 3 months. In multivariate analysis, variables that remained significant were low C3 previous to infection (OR 3.12, CI 95% 1.91-8.22), previous hospitalization (OR 2.22, CI 95% 1.42-4.10), and prednisone dose at the time of infection (OR 1.10, CI 95% 1.04-1.22). CONCLUSION: Low C3 levels, recent hospitalization, and prednisone dose at time of infection are independent risk factors for acquiring drug-resistant bacteria in patients with SLE. Although the present data do not fully support a change in initial treatment-decision strategies, this information could lead to prospective studies designed to address this issue, which could determine the best approach in clinical practice.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".