Causes and Predictors of Early Hospital Readmission in Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: We investigated characteristics of adult patients with systemic lupus erythematosus (SLE) readmitted to the hospital within 30 days of discharge, in an attempt to identify the causes of early readmission. METHODS: We performed a retrospective case-control study examining all inpatient electronic health records of patients with SLE at Cedars-Sinai Medical Center over a 2.5-year period (2012-2014). Patients were included if they had an International Classification of Diseases, 9th ed diagnosis of SLE and were readmitted within 30 days of their initial hospitalization. Patients with SLE not readmitted during this time period were used as a control group. Demographic and clinical variables for each patient were collected, and we used the Charlson Comorbidity Index to characterize comorbidities. The Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index (SDI) was used to assess the chronic damage of SLE. Stepwise multivariable logistic regression analysis was used to predict factors associated with readmission. RESULTS: In total, 570 hospitalizations representing 455 unique patients met our inclusion and exclusion criteria. Of these, 154 patients (34%) underwent readmission within 30 days of their initial hospitalization. Patients in the early readmission group were more likely to have government-sponsored Medicaid insurance and were significantly associated with an increased SDI (OR 1.27, 95% CI 1.1-1.48), lower serum hemoglobin (OR 0.82, 95% CI 0.72-0.93), and lower serum albumin (OR 0.66, 95% CI 0.47-0.91). CONCLUSION: One-third of hospitalized patients with SLE were readmitted within 30 days at our institution. We identified characteristics of this at-risk population at time of discharge with high specificity, in hopes of reducing this costly outcome.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".