Quality of life after transcatheter aortic valve implantation: Perspectives from a Canadian data base
Bibliographic record
Abstract
Abstract Funding Acknowledgements Type of funding sources: None. Background We examined changes in quality of life (QOL) of patients after transcatheter aortic valve implantation (TAVI). Methods We conducted an observational cohort study of consecutive patients who had TAVI between 2016-2019 in British Columbia, Canada. QOL was measured at baseline, 30-day and 1-year using the Kansas City Cardiomyopathy Questionnaire (KCCQ-OS). We used linear regression modelling to examine factors associated with 30-day changes in QOL, logistic regression modelling to identify predictors of having a poor outcome, and Cox regression modelling to ascertain risk estimates of the effect of QOL on 1-year mortality. Results The cohort included 1,706 patients [742 women (43.5%)]; median [interquartile range, IQR] age 83 (77,86). Median (IQR) baseline KCCQ-OS was 45 (28.2,67), indicating severe impairment. Patients alive at 1-year (91.3%) reported a mean improvement of 24.1 (95% CI, 22.7-25.6) points in the KCCQ-OS at 30-day, which was sustained at 1-year (25.3; 95% CI, 23.8,26.8). Older age, lower baseline health status, lower aortic valve gradient, lower hemoglobin, atrial fibrillation and non-transfemoral access were associated with worse 30-day QOL. At 1-year, 65% of patients had a favourable outcome; additional risk factors for 1-year mortality (8.7%) were male sex, NYHA Class IV, severe pulmonary and renal disease, diabetes, and in-patient status. Conclusions TAVI is associated with significant early improvement in QOL which is sustained at 1 year in a "real world" registry. The inclusion of QOL can support treatment decision and the patient-centred evaluation of TAVI
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.018 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".