Subacute Subclinical Brain Infarctions after Transcatheter Aortic Valve Implantation Negatively Impact Cognitive Function in Long-Term Follow-Up
Bibliographic record
Abstract
AIMS: To date every post-procedural cerebrovascular embolic event (CVE) is dreaded for its potential to accelerate cognitive decline after transcatheter aortic valve implantation (TAVI). This study differentiates the impact of acute (procedural) and post-acute cerebrovascular embolic events (CVEs) on cognitive performance. METHODS: Magnetic resonance imaging (MRI) before, early and late after TAVI was performed to quantify embolic burden. Quantification of diffusion- and T1-weighted lesions, as well as white-matter and total brain volumes, as well as cognitive function testing (MMSE) were assessed in 28 patients with a medium follow-up period of 34 months. RESULTS: Procedural diffusion-weighted lesions were observed in 17 patients (61%), but demonstrated locoregional remnants only in a minority of patients in long-term follow-up (6.5%). Acute CVEs did not impact the trajectory of late silent brain infarctions (SBI), white-matter hyperintensities, and cerebral atrophy. Functionally, early CVEs did not affect cognitive function. In contrast, patients with "new" SBIs after TAVI had a trend to cognitive deterioration in long-term follow-up ("new"SBI: MMSE -1.4 / no "new"SBI: MMSE +1.5, p = 0.067). Interestingly, only a fraction of these "new" SBIs evolved from procedural CVEs (22.2%). CONCLUSIONS: Aquired SBIs after TAVI, but not DW-CVE per se are associated with functional impairment long-term after TAVI. In the context of subacute thrombosis seen in TAVI prostheses, these findings set the stage for tailored stroke prevention and comprehensive surrogate endpoint definitions in neuroprotective trials.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".