Hospitalizations in patients with systemic lupus erythematosus: updated analyses from 2006 to 2011
Bibliographic record
Abstract
Health resource use is believed to be significant in patients with systemic lupus erythematosus (SLE), but there is a lack of data especially in Canadian patients regarding the reasons why persons with SLE require hospitalization and the rates of hospitalization compared with the general population. Our objective was to provide recent estimates for hospitalization rates and reasons for admission, in a clinical SLE cohort. We evaluated data from patients with SLE followed at the McGill University Health Center Lupus Clinic. Information on disease activity, drug exposure, health outcomes, and hospitalizations by self-report were collected from annual research visits. The hospitalization rates of the SLE patients were generated. We compared this with the Canadian general population by calculating the standardized incidence ratio (SIR), which represents the ratio of the number of events observed in the SLE cohort to the number of events that would be expected based on the age-specific and sex-specific Canadian general population hospitalization rates. Over the interval studied, 350 patients (325 female, 25 male) provided 1,261 person-years of follow-up. There were 163 reported admissions with an incidence of 12.8 hospitalizations per 100 person-years (12.4 in females, 19.5 in males). SLE-related causes (for example, flares) accounted for the highest proportion of hospitalization (22.7%), followed by infections (20.2%), surgery (14.7%), childbirth (11.7%) and cardiovascular reasons (11.0%). The overall SIR was 1.73 (95% CI = 1.48 to 2.02). Stratified by sex, the SIR was 2.87 (95% CI = 1.67 to 4.60) for males and 1.39 (95% CI = 1.18 to 1.64) for females. However, stratifying further by age, female SLE patients aged >65 actually underwent fewer hospitalizations than expected, based on age/sex-specific general population rates (SIR = 0.35; 95% CI = 0.17 to 0.64). In male SLE patients over 65, there were no hospitalizations (compared with 1.48 expected events), and the confidence interval (95% CI = 0.0 to 2.49) around the SIR was very imprecise in this demographic, due to the relatively low number of older males in our cohort. We documented high rates of hospitalization in our SLE patients, particularly for male patients. Hospitalizations were often due to SLE-related reasons and infections. Female SLE patients over the age of 65 were shown to have a much lower hospitalization rate compared with the general population, which may be due to a survivorship bias. Further work on the variables affecting hospitalizations in SLE patients is in progress.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 | 0.002 |
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; both teacher heads agree on what is shown here.
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".