Regional variation and temporal trends in transcatheter and surgical aortic valve replacement in Switzerland: A population-based small area analysis
Bibliographic record
Abstract
BACKGROUND: Aortic valve stenosis (AS) is the most common valvular heart disease and if severe, is treated with either transcatheter (TAVR) or surgical aortic valve replacement (SAVR). We assessed temporal trends and regional variation of these interventions in Switzerland and examined potential determinants of geographic variation. METHODS: We conducted a population-based analysis using patient discharge data from all Swiss public and private acute care hospitals from 2013 to 2018. We generated hospital service areas (HSAs) based on patient flows for TAVR. We calculated age-standardized mean procedure rates and variation indices (extremal quotient [EQ] and systematic component of variation [SCV]). Using multilevel regression, we calculated the influence of calendar year and regional demographics, socioeconomic factors (language, insurance status), burden of disease, and number of cardiologists/cardiovascular surgeons on geographic variation. RESULTS: Overall, 8074 TAVR and 11,825 SAVR procedures were performed in 8 HSAs from 2013 to 2018. Whereas the age-/sex-standardized rate of TAVR increased from 12 to 22 procedures/100,000 persons, the SAVR rate decreased from 33 to 24 procedures during this period. After full adjustment, the predicted TAVR and SAVR rates varied from 12 to 22 and 20 to 35 per 100,000 persons across HSAs, respectively. The regional procedure variation was low to moderate over time, with a low overall variation in TAVR (EQ 1.9, SCV 3.9) and SAVR (EQ 1.6, SCV 2.2). In multilevel regression, TAVR rates increased annually by 10% and SAVR rates decreased by 5%. Determinants of higher TAVR rates were older age, male sex, living in a German speaking area, and higher burden of disease. A higher proportion of (semi)private insurance was also associated with higher TAVR and lower SAVR rates. After full adjustment, 10.6% of the variance in TAVR and 18.4% of the variance in SAVR remained unexplained. Most variance in TAVR and SAVR rates was explained by language region and insurance status. CONCLUSION: The geographic variation in TAVR and SAVR rates was low to moderate across Swiss regions and largely explained by differences in regional demographics and socioeconomic factors. The use of TAVR increased at the expense of SAVR over time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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".