P6029Impact of elixhauser comorbidity score on the outcomes of transcatheter aortic valve replacement
Bibliographic record
Abstract
Background: Patients undergoing transcatheter aortic valve replacement (TAVR) are often multi-morbid. Whilst the impact of individual comorbid conditions on clinical outcomes following TAVR have been previously assessed, the impact of more global measures of comorbidity burden remains unknown in this cohort. Objective: To explore the relationship between comorbidity burden and mortality, peri-procedural complications, length of stay and healthcare costs in individuals undergoing TAVR in the National Inpatient Sample (NIS). Methods: TAVR procedures were identified between 2011 and 2014 and comorbidities were defined by Elixhauser classification system (EC) consisting of 30 comorbidity measures. Endpoints included in-hospital mortality, peri-procedural complications, length of stay and cost. Patients were classified based on their Elixhouser score (ECS) into five categories (ECS I<0, ECS II=0, ECS III=1–5, ECS IV=6–13, ECS V ≥14). Results: 40,604 TAVR patients were identified and the mean age of patients was 81.2±8.5 years (mean, SD). Patients with the greatest comorbid burden (ECS category V) accounted for more than 40% of the cohort and experienced more than a two-fold increase in hospital mortality [odds ratio (OR): 2.47, 95% confidence interval (CI):1.30–4.69], increased risk of acute kidney injury (OR: 6.72, 95% CI: 4.38–10.32), major bleeding (OR: 2.15, 95% CI: 1.62–2.85) and Stroke and TIA (OR: 2.05, 95% CI: 1.10–3.80). It was also associated with a mean 4.24-day increased length of stay (95% CI: 3.72 - 4.76) and a further $55,000 mean healthcare costs (95% CI: 42,000$-68,000$) compared to patients with lower comorbidities undergoing TAVR after adjusting for confounding factors.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".