Temporal Changes in Mortality After Transcatheter and Surgical Aortic Valve Replacement: Retrospective Analysis of US Medicare Patients (2012–2019)
Bibliographic record
Abstract
Background The treatment of aortic stenosis is evolving rapidly. Pace of change in the care of patients undergoing transcatheter aortic valve replacement (TAVR) and surgical aortic valve replacement (SAVR) differs. We sought to determine differences in temporal changes in 30-day mortality, 30-day readmission, and length of stay after TAVR and SAVR. Methods and Results We conducted a retrospective cohort study of patients treated in the United States between 2012 and 2019 using data from the Medicare Data Set Analytic File 100% Fee for Service database. We included consecutive patients enrolled in Medicare Parts A and B and aged ≥65 years who had SAVR or transfemoral TAVR. We defined 3 study cohorts, including all SAVR, isolated SAVR (without concomitant procedures), and elective isolated SAVR and TAVR. The primary end point was 30-day mortality; secondary end points were 30-day readmission and length of stay. Statistical models controlled for patient demographics, frailty measured by the Hospital Frailty Risk Score, and comorbidities measured by the Elixhauser Comorbidity Index (ECI). Cox proportional hazard models were developed with TAVR versus SAVR as the main covariates with a 2-way interaction term with index year. We repeated these analyses restricted to full aortic valve replacement hospitals offering both SAVR and TAVR. The main study cohort included 245 269 patients with SAVR and 188 580 patients with TAVR, with mean±SD ages 74.3±6.0 years and 80.7±6.9 years, respectively, and 36.5% and 46.2% female patients, respectively. Patients with TAVR had higher ECI scores (6.4±3.6 versus 4.4±3) and were more frail (55.4% versus 33.5%). Total aortic valve replacement volumes increased 61% during the 7-year span; TAVR volumes surpassed SAVR in 2017. The magnitude of mortality benefit associated with TAVR increased until 2016 in the main cohort (2012: hazard ratio [HR], 0.76 [95% CI, 0.67-0.86]; 2016: HR, 0.39 [95% CI, 0.36-0.43]); although TAVR continued to have lower mortality rates from 2017 to 2019, the magnitude of benefit over SAVR was attenuated. A similar pattern was seen with readmission, with a lower risk of readmission from 2012 to 2016 for patients with TAVR (2012: HR, 0.68 [95% CI, 0.63-0.73]; 2016: HR, 0.43 [95% CI, 0.41-0.45]) followed by a lesser difference from 2017 to 2019. Year over year, TAVR was associated with increasingly shorter lengths of stay compared with SAVR (2012: HR, 1.91 [95% CI, 1.84-1.98]; 2019: HR, 5.34 [95% CI, 5.22-5.45]). These results were consistent in full aortic valve replacement hospitals. Conclusions The rate of improvement in TAVR outpaced SAVR until 2016, with the recent presence of U-shaped phenomena suggesting a narrowing gap between outcomes. Future longitudinal research is needed to determine the long-term implications of lowering risk profiles across treatment options to guide case selection and clinical care.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".