Infection in Southern Chinese Patients with Systemic Lupus Erythematosus: Spectrum, Drug Resistance, Outcomes, and Risk Factors
Bibliographic record
Abstract
OBJECTIVE: To investigate the spectrum, antibiotic-resistant pattern, risk factors, and outcomes of infection in patients hospitalized with systemic lupus erythematosus (SLE). METHODS: We collected the clinical and microbiological data from hospitalized patients with SLE with infection between June 2005 and June 2015, and then conducted retrospective analyses. RESULTS: Among our sample of 3815 hospitalized patients, 1321 (34.6%) were diagnosed with infection. The majority (78.3%) of infection occurred within 5 years of SLE onset. Bacterial infection was predominant (50.6%), followed by viral infection (36.4%) and fungal infection (12.5%). The lungs (33.7%) and upper respiratory tracts (26.3%) were most commonly affected. Gram-negative bacteria (GNB) were predominant over gram-positive bacteria (178 isolates vs 90 isolates). The most frequently isolated bacteria were Escherichia coli (24.6%), followed by Acinetobacter baumannii (13.4%) and coagulase-negative Staphylococcus (13.4%). Multidrug-resistant (MDR) strains were detected in 26.9% of bacterial isolates. The most common fungus was Candida spp. (99 episodes), followed by Aspergillus (24 episodes) and Cryptococcus neoformans (13 episodes). The overall mortality rate for this cohort was 2.2%; 48 patients died of infection. Factors associated with bacterial and viral infection were higher Systemic Lupus Erythematosus Disease Activity Index, renal involvement, thrombocytopenia, accumulated dose of glucocorticoids (GC), and treatment with cyclophosphamide (CYC). Renal involvement, accumulated dose of GC, and treatment with CYC were associated with fungal infection. CONCLUSION: Infection was the leading cause of mortality in patients hospitalized with SLE. There were some notable features of infection in Chinese patients including early onset, higher proportion of respiratory tract involvement, predominance of GNB with emergence of MDR isolates, and a variety of pathogens.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".