Patient Risk Assessment for Transcatheter Aortic Valve Replacement at Veterans Health Administration Hospitals
Bibliographic record
Abstract
OBJECTIVE: To compare patient-level risk assessment at Veterans Affairs (VA) hospitals in patients undergoing transcatheter aortic valve replacement (TAVR) with patients included in the Society for Thoracic Surgeons/American College of Cardiology Transcatheter Valve Therapy (STS/ACC TVT) registry. METHODS: We retrospectively analyzed the outcomes of veterans with severe aortic stenosis (AS) receiving TAVR from 2012-2016 at eight VA hospitals and compared them with TVT registry outcomes from 2012-2015. Patients were identified via administrative data. Univariable and multivariable Cox proportional hazards models were used to examine 30-day and 1-year all-cause mortality, 30-day and 1-year transient ischemic attack/stroke rates, and permanent pacemaker (PPM) implantation rates. RESULTS: During the study period, a total of 726 veterans underwent TAVR including valve-in-valve procedures (n = 50). Patients were predominantly male (98.2%), with mean age of 78.5 ± 9.3 years; 49.1% were at prohibitive risk and 12.1% were at high risk for surgical aortic valve replacement; 30-day and 1-year all-cause mortality rates were 2.5% and 14.7%, respectively; 30-day and 1-year combined TIA/stroke rates were 6.5% and 13.5%, respectively. In the TVT registry, 15.8% and 37.8% of patients were at prohibitive and high risk, respectively; 30-day and 1-year mortality rates were 5.7% and 22.7%, respectively, and stroke rates were 2.1% and 4.0%, respectively. CONCLUSIONS: This report on TAVR risk assessment within the VA system demonstrates that despite a large proportion of patients classified as prohibitive risk, TAVR was associated with favorable 30-day and 1-year all-cause mortality rates when compared with published outcomes from the STS/ACC TVT registry.
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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 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".