Readmissions After Left Atrial Appendage Closure in Patients With Previous Ischemic Stroke or Transient Ischemic Attack
Bibliographic record
Abstract
Background: We examined the frequency and risk factors associated with readmission after left atrial appendage closure (LAAC) in patients with and without previous ischemic stroke and/or transient ischemic attack (TIA). Methods: Hospitalizations for LAAC were identified from the US National Readmission Database, 2016-2018. The primary outcome was the first unplanned readmission after LAAC, with readmission times stratified into those occurring within 0 to 30 days vs within 31 to 180 days. Patients were stratified based on the history of previous stroke and/or TIA. Results: Of 12,901 discharges after LAAC, 28% had previous stroke and/or TIA, and 8.2% had a readmission within 30 days while 18% had a readmission within 31 to 180 days. The rates of in-hospital complications and readmissions at both periods were not significantly different between individuals with vs without previous stroke and/or TIA. Cardiac causes accounted for 28% of readmissions within 30 days and 32% of those within 31 to 180 days, and congestive failure, bleeding, and infections were the most common readmission diagnoses. New stroke and/or TIA accounted for 4% and 6% of the total noncardiac readmissions within 30 days and 31 to 180 days, respectively, and the incidence was higher among those with previous stroke and/or TIA. Female sex and index hospitalization length of stay (LOS) > 1 day were factors independently associated with readmission within 30 days, whereas LOS, diabetes, renal disease, chronic obstructive pulmonary disease, and anemia were among the factors associated with readmissions within 31 to 180 days. Conclusions: Unplanned rehospitalizations were common after LAAC and had similar frequency for patients with vs without previous ischemic stroke and/or TIA. Female sex and index hospitalization LOS > 1 day were among the strongest factors that were independently associated with readmission within 30 days.
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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 | 0.004 |
| 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 | 0.000 |
| 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".