Outcomes of Elderly Patients Undergoing Left Atrial Appendage Closure
Bibliographic record
Abstract
Background Elderly patients have a higher burden of comorbidities that influence clinical outcomes. We aimed to compare in‐hospital outcomes in patients ≥80 years old to younger patients, and to determine the factors associated with increased risk of major adverse events (MAE) after left atrial appendage closure. Methods and Results The National Inpatient Sample was used to identify discharges after left atrial appendage closure between October 2015 and December 2018. The primary outcome was in‐hospital MAE defined as the composite of postprocedural bleeding, vascular and cardiac complications, acute kidney injury, stroke, and death. A total of 6779 hospitalizations were identified, of which, 2371 (35%) were ≥80 years old and 4408 (65%) were <80 years old. Patients ≥80 years old experienced a higher rate of MAE compared with those aged <80 years old (6.0% versus 4.6%, P =0.01), and this difference was driven by a numerically higher rate of cardiac complications (2.4% versus 1.8%, P =0.09) and death (0.3% versus 0.1%, P =0.05) among individuals ≥80 years old. In patients ≥80 years old, higher odds of in‐hospital MAE were observed in women (1.61‐fold), and those with preprocedural congestive heart failure (≈2‐fold), diabetes (≈1.5‐fold), renal disease (≈2.6‐fold), anemia (≈2.7‐fold), and dementia (≈5‐fold). In patients <80 years old, a higher risk of in‐hospital MAE was encountered among women (≈1.4‐fold) and those with diabetes (≈1.3‐fold), renal disease (≈2.6‐fold), anemia (≈2‐fold), and dyslipidemia (≈1.2‐fold). Conclusions Patients ≥80 years old had higher rates of in‐hospital MAE compared with patients aged <80 years old. Female sex and the presence of heart failure, diabetes, renal disease, and anemia were factors associated with in‐hospital MAE among both groups.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".