One-Year Outcomes for Patients Undergoing Transcatheter Aortic Valve Replacement: The Gulf TAVR Registry
Bibliographic record
Abstract
BACKGROUND: The use of transcatheter aortic valve replacement (TAVR) is steadily increasing with TAVR procedures offered to patients across the entire spectrum of surgical risks. The Gulf TAVR registry captures the demographics of patients undergoing TAVR in the Gulf region, comorbidities that drive outcomes, procedural success, complications, and one-year outcomes of death or rehospitalization. METHODS: This is a retrospective cohort study for adult patients aged at least 18 years undergoing TAVR at eight centers in the Gulf region. The primary outcome was a composite of death or re-hospitalization at one-year. Secondary outcomes included the individual components of the composite, stroke, and myocardial infarction (MI). We used multivariable Cox regression to determine factors associated with the composite endpoint. RESULTS: A total of 795 patients (56% male) were included in the final analysis with a mean age of 74.6 (standard deviation (SD) 8.9) years, Society of Thoracic Surgeons Score (STS) Score 4.9 (4.2), ejection fraction of 53% (12.7%). Transfemoral approach was employed in over 95% (762/795). The primary outcomes rate was 12.8% (95% confidence interval [CI]: 10.6-15.4); secondary endpoints were death 5.4% (95% CI 4.0-7.2); stroke 0.8% (95% CI 0.3, 1.7), MI 0.8% (95% CI 0.4-1.9), rehospitalization: 9.3% (95% CI 7.5-11.5) of whom 71.6% were related to cardiovascular causes. 77% of the cardiovascular admissions were attributable to heart failure or the need for pacemaker implantation. Stage IV or V chronic kidney disease was significantly associated with the primary composite endpoint (Hazard Ratio: 2.49, [95% CI: 1.31, 4.73], p = 0.005). Although not significant, paravalvular leak and severe left ventricular dysfunction showed a 2-fold and 3-fold increased risk for the composite endpoint, respectively. CONCLUSIONS: The Gulf TAVR registry is the first of its kind in the region. It profiles an elderly population with a high procedural success rate and a low rate of complications. One-year outcomes were primarily driven by repeat hospitalization for heart failure and pacemaker implantation indicating a need to optimize heart failure management and improve algorithms for the detection of conduction abnormalities.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".