Mortality prediction after transcatheter treatment of failed bioprosthetic aortic valves utilizing various international scoring systems: Insights from the Valve‐in‐Valve International Data (VIVID)
Bibliographic record
Abstract
BACKGROUND: Transcatheter Aortic Valve Implantation (TAVI) is commonly used to deploy new bioprosthetic valves inside degenerated surgically implanted aortic valves in high risk patients. The three scoring systems used to assess risk of postprocedural mortality are: Logistic EuroSCORE (LES), EuroSCORE II (ES II), and Society of Thoracic Surgeons (STS). OBJECTIVE: The purpose of this study is to analyze the accuracy of LES, ES II, and STS in estimating all-cause mortality after transcatheter aortic valve-in-valve (ViV) implantations, which was not assessed before. METHODS: Using the Valve-in-Valve International Data (VIVID) registry, a total of 1,550 patients from 110 centers were included. The study compared the observed 30-day overall mortality vs. the respective predicted mortalities calculated by risk scores. The accuracy of prediction models was assessed based on calibration and discrimination. RESULTS: Observed mortality at 30 days was 5.3%, while average expected mortalities by LES, ES II and STS were 29.49 (± 17.2), 14.59 (± 8.6), and 9.61 (± 8.51), respectively. All three risk scores overestimated 30-day mortality with ratios of 0.176 (95% CI 0.138-0.214), 0.342 (95% CI 0.264-0.419), and 0.536 (95% CI 0.421-0.651), respectively. 30-day mortality ROC curves demonstrated that ES II had the largest AUC at 0.722, followed by STS at 0.704, and LES at 0.698. CONCLUSIONS: All three scores overestimated mortality at 30 days with ES II showing the highest predictability compared to LES and STS; and therefore, should be recommended for ViV procedures. There is a need for a dedicated scoring system for patients undergoing ViV interventions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".