Implant dynamics of transcatheter aortic valve implantation in Europe
Bibliographic record
Abstract
Purpose: Transcatheter aortic valve implantation (TAVI) gained Conformité Européenne (CE)-mark approval in 2007, and in subsequent years the number of patients undergoing TAVI in Europe has increased exponentially. Disparate adoption of medical technology is pervasive and results in inequitable patient access. Adoption kinetics of a novel medical technology such as TAVI has not been previously described. We sought to examine the adoption kinetics of TAVI in Western Europe Methods: TAVI adoption was investigated across 11 European nations: Germany, France, Italy, United Kingdom (UK), Spain, The Netherlands, Switzerland, Belgium, Portugal, Denmark and Ireland. Two sources of data were used: (1) lead physicians in each nation provided national registry data; and (2) the European Cardiovascular Monitor system. The penetration of TAVI in each nation was determined as a measure of actual TAVI use relative to potential use. Results: Between January 2007 and December 2011, 34,317 patients underwent TAVI in the 11 study nations. Almost half of all implants were performed in Germany (45.9%). In 2011, the highest annual increase in procedural volume was observed in France (61%) and Germany (49%), while Ireland (-15%) and Portugal (-3%) experienced a decline. We observed a wide variation in the number of TAVI implants per million of population. Germany (88.7) and Portugal (6.1) accounted for the highest and lowest number of TAVI implants per million of population in 2011, respectively. Among the 11 study nations, the mean number of TAVI implants per million was 32.9±24.9. The number of centres performing TAVI increased 9-fold from 37 in 2007 to 342 in 2011. In 2011, Germany (90) and Italy (87) had the highest number of TAVI centres whereas Portugal, Denmark and Ireland (3) had the lowest. Belgium had the highest number of TAVI centres per million (2·1). On average, there were 0·9±0.6 TAVI centres per million. These numbers led to an average of 41±28 TAVI implants per centre in 2011, with estimates in individual countries ranging from 10 in Ireland to 89 in Germany. In 2011, we estimate that there were 28,400 living TAVI recipients and 158,371 potential TAVI candidates in the 11 study nations. Thus, the calculated weighted average TAVI penetration rate was 17.9%. Germany (36.2%) and Portugal had the highest and lowest TAVI penetration rates, respectively. Conclusions: There is substantial variation in the adoption of TAVI and in the annual number of TAVI implants per centre across nations. TAVI remains greatly underutilised with an estimated weighted penetration rate of 17.9%.
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 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 | 0.007 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".