Abstract 18377: The Relationship Between Heart Failure Readmission and Mortality in Patients Receiving Transcatheter Aortic Valve Implantation
Bibliographic record
Abstract
Introduction: Symptomatic severe aortic stenosis (AS) is associated with significant morbidity and mortality. Transcatheter aortic valve implantation (TAVI) is an option for select high-risk patients with AS. We examined the 30-day and 1-year heart failure (HF) and non-HF hospitalization rates and their associated mortality rates. Hypothesis: HF hospitalization post-TAVI is associated with higher mortality rates at 30-day and 1-year when compared to non-HF hospitalization. Methods: We conducted a retrospective analysis of 535 TAVI patients between August 6 th , 2010 and March 31 st , 2014 at St. Paul’s Hospital, in Vancouver, BC. We used data from the CSBC TAVI registry, and linked this to the CIHI DAD and Vital Statistics for hospitalization and mortality. Results: Within 1-year, 279 (52%) patients were hospitalized, of which 108 (20%) had a HF hospitalization (HFH), 171 (32%) had a non-HFH, and 256 (47%) were not hospitalized. There were no differences in age, sex, presence of severe COPD, and history of PCI, CABG and AVR. The pre-TAVI mean LVEF, AVA and gradients were not different between groups. The HFH cohort had more patients with baseline eGFR Conclusions: Readmission rates post-TAVI is high, with 52% of patients requiring hospitalization within one year; with over one-third of these readmissions due to HF. Mortality rates were higher in patients with HFH at 30-days and 1-year when compared with patients with non-HFH.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".