Do frailty measures improve prediction of mortality and morbidity following transcatheter aortic valve implantation? An analysis of the UK TAVI registry
Bibliographic record
Abstract
OBJECTIVES: Previous studies indicate frailty to be associated with poor outcomes following transcatheter aortic valve implantation (TAVI), but there is limited evidence from multicentre registries. The aim was to investigate the independent association of frailty with TAVI outcomes, and the prognostic utility of adding frailty into existing clinical prediction models (CPMs). DESIGN: The UK TAVI registry incorporated three frailty measures since 2013: Canadian Study of Health and Ageing, KATZ and poor mobility. We investigated the associations between these frailty measures with short-term and long-term outcomes, using logistic regression to estimate multivariable adjusted ORs, and Cox proportional hazards models to explore long-term survival. We compared the predictive performance of existing TAVI CPMs before and after updating them to include each frailty measure. SETTING: All patients who underwent a TAVI procedure in England or Wales between 2013 and 2014. PARTICIPANTS: 2624 TAVI procedures were analysed in this study. PRIMARY AND SECONDARY OUTCOMES: The primary endpoints in this study were 30-day mortality and long-term survival. The Valve Academic Research Consortium (VARC)-2 composite early safety endpoint was considered as a secondary outcome. RESULTS: KATZ <6 (OR 2.10, 95% CI 1.39 to 3.15) and poor mobility (OR 2.15, 95% CI 1.41 to 3.28) predicted 30-day mortality after multivariable adjustment. All frailty measures were associated with increased odds of the VARC-2 composite early safety endpoint. We observed a significant increase in the area under the receiver operating characteristic curves by approximately 5% after adding KATZ <6 or poor mobility into the TAVI CPMs. Risk stratification agreement was significantly improved by the addition of each frailty measure, with an increase in intraclass correlation coefficient of between 0.15 and 0.31. CONCLUSION: Frailty was associated with worse outcomes following TAVI, and incorporating frailty metrics significantly improved the predictive performance of existing CPMs. Physician-estimated frailty measures could aid TAVI risk stratification, until more objective scales are routinely collected.
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 | 0.001 |
| 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".