Racial, ethnic and socioeconomic disparities in patients undergoing left atrial appendage closure
Bibliographic record
Abstract
OBJECTIVE: This manuscript aims to explore the impact of race/ethnicity and socioeconomic status on in-hospital complication rates after left atrial appendage closure (LAAC). METHODS: The US National Inpatient Sample was used to identify hospitalisations for LAAC between 1 October 2015 to 31 December 2018. These patients were stratified by race/ethnicity and quartiles of median neighbourhood income. The primary outcome was the occurrence of in-hospital major adverse events, defined as a composite of postprocedural bleeding, cardiac and vascular complications, acute kidney injury and ischaemic stroke. RESULTS: Of 6478 unweighted hospitalisations for LAAC, 58% were male and patients of black, Hispanic and 'other' race/ethnicity each comprised approximately 5% of the cohort. Adjusted by the older Americans population, the estimated number of LAAC procedures was 69.2/100 000 for white individuals, as compared with 29.5/100 000 for blacks, 47.2/100 000 for Hispanics and 40.7/100 000 for individuals of 'other' race/ethnicity. Black patients were ~5 years younger but had a higher comorbidity burden. The primary outcome occurred in 5% of patients and differed significantly between racial/ethnic groups (p<0.001) but not across neighbourhood income quartiles (p=0.88). After multilevel modelling, the overall rate of in-hospital major adverse events was higher in black patients as compared with whites (OR: 1.60, 95% CI 1.22 to 2.10, p<0.001); however, the incidence of acute kidney injury was higher in Hispanics (OR: 2.19, 95% CI 1.52 to 3.17, p<0.001). No significant differences were found in adjusted overall in-hospital complication rates between income quartiles. CONCLUSION: In this study assessing racial/ethnic disparities in patients undergoing LAAC, minorities are under-represented, specifically patients of black race/ethnicity. Compared with whites, black patients had higher comorbidity burden and higher rates of in-hospital complications. Lower socioeconomic status was not associated with complication rates.
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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".