Hospitalization and Mortality Due to Infection Among Children and Adolescents With Systemic Lupus Erythematosus in the United States
Bibliographic record
Abstract
Objective We aimed to determine the frequency and types of infections in hospitalized children with childhood-onset systemic lupus erythematosus (cSLE), and to identify risk factors for intensive care unit (ICU) admission and mortality. Methods We conducted a retrospective study of youth aged 2 to 21 years using International Classification of Diseases (ICD) codes for SLE assigned during admission to a hospital participating in the Pediatric Health Information System, a database of United States children’s hospitals, from 2009 to 2021. Generalized linear mixed effects models were used to identify risk factors for ICU admission and mortality among children hospitalized with infection. Results We identified 8588 children with cSLE and ≥ 1 hospitalization. Among this cohort, there were 26,269 hospitalizations, of which 13% had codes for infections, a proportion that increased over time (P= 0.04). Bacterial pneumonia was the most common hospitalized infection. In-hospital mortality occurred in 0.4% (n = 103) of cSLE hospitalizations for any indication and 2% of hospitalizations for infection (n = 60). The highest mortality rates occurred withPneumocystis jiroveciipneumonia (21%) and other fungal infections (21%). Lupus nephritis (LN) and endstage renal disease (ESRD) were associated with increased odds of ICU admission (odds ratio [OR] 1.47 [95% CI 1.2-1.8] and OR 2.40 [95% CI 1.7-3.4]) among children admitted for serious infection. ESRD was associated with higher mortality (OR 2.34 [95% CI 1.1-4.9]). Conclusion Hospitalizations with ICD codes for infection comprised a small proportion of cSLE admissions but accounted for the majority of mortality. The proportion of hospitalizations for infection increased over time. LN and ESRD were risk factors for poor outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".