Multimarker Approach to Improve Risk Stratification of Patients Undergoing Transcatheter Aortic Valve Implantation
Bibliographic record
Abstract
A blood multimarker approach may be useful to enhance risk stratification in patients undergoing TAVI. The objective of this study was to determine the prognostic value of multiple blood biomarkers in transcatheter aortic valve implantation (TAVI) patients. In this prospective study, several blood biomarkers of cardiovascular function, inflammation, and renal function were measured in 362 patients who underwent TAVI. The cohort was divided into 3 groups according to the number of elevated blood biomarkers (ie, ≥median value for the whole cohort) for each patient before the procedure. Survival analyses were conducted to evaluate the association between blood biomarkers and risk of adverse event following TAVI. During a median follow-up of 2.5 (IQR: 1.9-3.2) years, 34 (9.4%) patients were rehospitalized for heart failure, 99 (27%) patients died, and 113 (31.2%) met the composite end point of all-cause mortality or heart failure rehospitalization. Compared to patients with 0 to 3 elevated biomarkers (referent group), those with 4 to 7 and 8 to 9 elevated biomarkers had a higher risk of all-cause mortality (HR: 0.84-2.80], P = 0.16, and HR: 2.81 [95% CI: 1.53-5.15], P < 0.001, respectively) and of the composite end point (HR: 1.65 [95% CI: 0.95-2.84], P = 0.07, and HR: 2.67 [95% CI: 1.52-4.70] P < 0.001, respectively). Moreover, adding the number of elevated blood biomarkers into the clinical multivariable model provided significant incremental predictive value for all-cause mortality (Net Reclassification Index = 0.71, P < 0.001). An increasing number of elevated blood biomarkers is associated with higher risks of adverse clinical outcomes following TAVI. The blood multimarker approach may be helpful to enhance risk stratification in TAVI patients.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".