Abstract 12052: Multimarker Approach for Risk Stratification in Patients Undergoing Transcatheter Aortic Valve Implantation
Bibliographic record
Abstract
Introduction: Transcatheter aortic valve implantation (TAVI) is an alternative to surgical aortic valve replacement for the treatment of severe aortic stenosis (AS). However, TAVI may be futile in a substantial proportion of patients. The development of a blood multimarker approach encompassing cardiac damage, inflammation, cancer, and renal function could be useful to accurately identify patients who may benefit from TAVI versus those who may not. Hypothesis: the objective of this study was to examine the association between blood biomarkers and all-cause mortality following TAVI. Methods: Three hundred and sixty-three patients who underwent TAVI at our institution were prospectively recruited. Clinical and echocardiographic data were collected. Nine biomarkers were measured from blood samples collected before TAVI. The cohort was divided in 3 groups according to the number of elevated biomarkers (i.e ≥median of the cohort) per patients. Multivariable survival analysis was performed to evaluate the association between biomarkers and all-cause mortality. The incremental prognostic value of the biomarkers model to predict all-cause mortality was assessed using the net reclassification index (NRI). Results: In comparison with patients with 0-3 elevated biomarkers, those with 4-7 and 8-9 were older, had more comorbidities and higher rate of NYHA ≥III. They also had longer hospital stay following TAVI. During a median follow-up of 2.5 (1.9-3.2) years, 99 (27%) patients died. Using multivariable Cox analysis and patients with 0-3 elevated biomarkers as the referent group, those with 4-7 and 8-9 elevated biomarkers had a higher risk of all-cause mortality (HR [95%CI]: 1.83 [1.01-3.38], p=0.05, and HR [95%CI]: 4.09 [2.16-7.76], p<0.001, respectively). The addition of the number of elevated blood biomarkers provided important incremental prognostic value in comparison with the clinical model (NRI=0.71, p<0.001). Conclusions: An increasing number of elevated biomarkers was associated with higher risk of mortality after adjustment for baseline differences. Moreover, the biomarker model provided important incremental prognostic value beyond the clinical variables and could thus enhance risk stratification of patients undergoing TAVI.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".