Comorbidity burden in patients undergoing left atrial appendage closure
Bibliographic record
Abstract
Objective To estimate the risk of in-hospital complications after left atrial appendage closure (LAAC) in relationship with comorbidity burden. Methods Cohort-based observational study using the US National Inpatient Sample database, 1 October 2015 to 31 December 2017. The main outcome of interest was the occurrence of in-hospital major adverse events (MAE) defined as the composite of bleeding complications, acute kidney injury, vascular complications, cardiac complications and postprocedural stroke. Comorbidity burden and thromboembolic risk were assessed by the Charlson Comorbidity Index (CCI), Elixhauser Comorbidity Score (ECS) and CHA 2 DS 2 -VASc score. MAE were identified using International Classification of Diseases, Tenth Revision, Clinical Modification codes. The associations of comorbidity with in-hospital MAE were evaluated using logistic regression models. Results A total of 3294 hospitalisations were identified, among these, the mean age was 75.7±8.2 years, 60% were male and 86% whites. The mean CHA 2 DS 2 -VASc score was 4.3±1.5 and 29.5% of the patients had previous stroke or transient ischaemic attack. The mean CCI and ECS were 2.2±1.9 and 9.7±5.8, respectively. The overall composite rate of in-hospital MAE after LAAC was 4.6%. Females and non-whites had about 1.5 higher odds of in-hospital AEs as well participants with higher CCI (adjusted OR (aOR): 1.19, 95% CI: 1.13 to 1.24, p<0.001), ECS (aOR: 1.06, 95% CI: 1.05 to 1.08, p<0.001) and CHA 2 DS 2 -VASc score (aOR: 1.08, 95% CI: 1.02 to 1.15, p=0.01) were significantly associated with in-hospital MAE. Conclusion In this large cohort of LAAC patients, the majority of them had significant comorbidity burden. In-hospital MAE occurred in 4.6% and female patients, non-whites and those with higher burden of comorbidities were at higher risk of in-hospital MAE after LAAC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".