European Society of Cardiology quality indicators for the care and outcomes of adults undergoing transcatheter aortic valve implantation
Bibliographic record
Abstract
AIMS: To develop a suite of quality indicators (QIs) for the evaluation of the care and outcomes for adults undergoing transcatheter aortic valve implantation (TAVI). METHODS AND RESULTS: We followed the European Society of Cardiology (ESC) methodology for the development of QIs. Key domains were identified by constructing a conceptual framework for the delivery of TAVI care. A list of candidate QIs was developed by conducting a systematic review of the literature. A modified Delphi method was then used to select the final set of QIs. Finally, we mapped the QIs to the EuroHeart (European Unified Registries on Heart Care Evaluation and Randomized Trials) data standards for TAVI to ascertain the extent to which the EuroHeart TAVI registry captures information to calculate the QIs. We formed an international group of experts in quality improvement and TAVI, including representatives from the European Association of Percutaneous Cardiovascular Interventions, the European Association of Cardiovascular Imaging, and the Association of Cardiovascular Nursing and Allied Professions. In total, 27 QIs were selected across 8 domains of TAVI care, comprising 22 main (81%) and 5 secondary (19%) QIs. Of these, 19/27 (70%) are now being utilized in the EuroHeart TAVI registry. CONCLUSION: We present the 2023 ESC QIs for TAVI, developed using a standard methodology and in collaboration with ESC Associations. The EuroHeart TAVI registry allows calculation of the majority of the QIs, which may be used for benchmarking care and quality improvement initiatives.
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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 | 0.032 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".