Meta-Analysis of Prevalence and Risk Factors for Cognitive Decline and Improvement After Transcatheter Aortic Valve Implantation
Bibliographic record
Abstract
Changes to cognition, both decline and improvement, are commonly reported after transcatheter aortic valve implantation (TAVI). However, previous systematic reviews and meta-analyses have missed these subgroups by assessing whole-group-averages for cognitive outcomes. We sought to pool estimates to identify the prevalence of cognitive decline and improvement after TAVI, as well as associated factors for these outcomes. A systematic review identified 15 articles appropriate for meta-analysis. When robust cognitive change definitions were employed, the pooled prevalence of incident cognitive impairment up to 1-, 1 to 6-, and ≥6-months post-TAVI was 7%, 14%, and 12%, respectively. For cognitive improvement, the prevalence from 1 to 6 months and ≥6 months after TAVI was estimated to be 19% and 11%, respectively. Two factors were associated with these cognitive outcomes: (1) using a cerebral embolic protection device was associated with decreased prevalence of cognitive decline up to 1-week post-TAVI; (2) baseline cognitive impairment had a large association with post-TAVI cognitive improvement. In conclusion, cognitive decline and cognitive improvement are experienced by approximately 7% to 19% of patients after TAVI, respectively. Those with the lowest cognitive performance pre-TAVI appear to have the most to gain in terms of cognitive improvement post-TAVI. Identifying further predictive factors for cognitive decline and improvement post-TAVI will facilitate a personalized-medicine approach for cognitive care and prognosis.
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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 | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.059 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".