Outcomes in severe aortic stenosis patients with echocardiogram characteristics of cardiac amyloid and new onset left bundle branch block after transcatheter aortic valve insertion
Bibliographic record
Abstract
Transcatheter aortic valve implantation (TAVI) has become a cornerstone therapy for patients with severe aortic stenosis (AS), yet heterogeneity in outcomes remains, with prognosis influenced by both myocardial function and conduction system complications. Occult cardiac amyloid (CA) is present in up to one in seven TAVI patients, echocardiographic parameters linked to CA have been associated with poor outcomes and may provide prognostic insight for the broader severe AS population. At the same time, cardiac conduction disturbances—particularly new left bundle branch block (LBBB)—represent one of the most frequent and clinically challenging post-procedural complications. This thesis evaluates prognostic markers derived from transthoracic echocardiography (TTE) and characterizes conduction disease management in order to identify strategies for improved patient selection, monitoring, and post-TAVI care. In the first study, 34 patients with severe AS and preserved ejection fraction undergoing TAVI were retrospectively analyzed. Echocardiographic parameters previously associated with CA and adverse outcomes were assessed in relation to survival, hospital utilization, and quality of life. The cohort had an average age of 85 years and was 38% female. A stroke volume index (SVi) <35 mL/m² was present in 21% of patients and emerged as the strongest predictor of mortality at two years (RR 3.08, 95% CI 1.11–8.55). Left ventricular S’ (LV S’) <6 cm/s, observed in 70%, was most predictive of health resource use, correlating with increased hospital days both before (20 vs. 4 days, p=0.01) and after TAVI (30 vs. 8 days, p=0.005). These findings suggest that echocardiographic measures of impaired systolic function beyond LVEF provide independent prognostic information in TAVI patients. Routine incorporation of SVi and LV S’ into pre-procedural assessment may allow for better risk stratification, targeted surveillance, and potentially earlier intervention, although larger studies are needed to confirm this. The second study assessed the incidence, management, and outcomes of new LBBB following TAVI. A retrospective review of 355 patients without baseline conduction disease at the Mazankowski Alberta Heart Institute (2010–2020) revealed that 27% developed new-onset LBBB, with 54% of these being persistent at discharge (NOP-LBBB). The average age was 83 years and 42% were female. Of patients with new LBBB, 22.4% underwent permanent pacemaker implantation before discharge, with 60% of these implants being prophylactic, reflecting concern for progression to high-grade AV block. However, neither QRS duration >150 ms nor PR interval >240 ms was associated with prophylactic device implantation (X² p=0.053; p=0.067). At follow-up, average pacing burdens were modest (12.6% at first follow-up, 21% at 12 months), and 41.6% of prophylactically paced patients were paced <1%, suggesting limited clinical need. Moreover, a significant temporal increase in prophylactic pacemaker use for NOP-LBBB was observed between early (2010–2015) and contemporary (2019–2020) practice (X² adjusted p=0.04). These findings highlight practice variability and the lack of robust markers for identifying which patients with NOP-LBBB truly require device implantation. In settings without ready access to prolonged outpatient monitoring or electrophysiological testing, conservative strategies may lead to overtreatment. Ongoing randomized trials are needed to establish standardized management pathways. Together, these two studies underscore complementary challenges in TAVI care: risk stratification of patients with severe AS and the management of conduction complications post-procedure. Echocardiographic markers such as SVi and LV S’ appear to provide independent prognostic value, supporting their use in pre-procedural assessment. Concurrently, the high incidence but variable clinical significance of new LBBB emphasizes the importance of refining post-TAVI monitoring and device implantation strategies. Future research should aim to develop algorithms to optimize outcomes, reduce unnecessary interventions, and personalize care in this growing patient population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".