Rates and reasons for hospital readmission after acute ischemic stroke in a US population-based cohort
Bibliographic record
Abstract
Hospital readmissions following stroke are costly and lead to worsened patient outcomes. We examined readmissions rates, diagnoses at readmission, and risk factors associated with readmission following acute ischemic stroke (AIS) in a large United States (US) administrative database. Using the 2019 Nationwide Readmissions Database, we identified adults discharged with AIS (ICD-10-CM I63*) as the principal diagnosis. Survival analysis with Weibull accelerated failure time regression was used to examine variables associated with hospital readmission. In 2019, 273,811 of 285,451 AIS patients survived their initial hospitalization. Of these, 60,831 (22.2%) were readmitted within 2019. Based on Kaplan Meyer analysis, readmission rates were 9.7% within 30 days and 30.5% at 1 year following initial discharge. The most common causes of readmissions were stroke and post stroke sequalae (25.4% of 30-day readmissions, 15.0% of readmissions between 30-364 days), followed by sepsis (10.3% of 30-day readmissions, 9.4% of readmissions between 30-364 days), and acute renal failure (3.2% of 30-day readmissions, 3.0% of readmissions between 30-364 days). After adjusting for multiple patient and hospital-level characteristics, patients at increased risk of readmission were older (71.6 vs. 69.8 years, p<0.001) and had longer initial lengths of stay (7.6 vs. 6.2 day, p<0.001). They more often had modifiable comorbidities, including vascular risk factors (hypertension, diabetes, atrial fibrillation), depression, epilepsy, and drug abuse. Social determinants associated with increased readmission included living in an urban (vs. rural) setting, living in zip-codes with the lowest median income, and having Medicare insurance. All factors were significant at p<0.001. Unplanned hospital readmissions following AIS were high, with the most common reasons for readmission being recurrent stroke and post stroke sequalae, followed by sepsis and acute renal failure. These findings suggest that efforts to reduce readmissions should focus on optimizing secondary stroke and infection prevention, particularly among older socially disadvantaged patients.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".