Advanced chronic kidney disease in patients undergoing transcatheter aortic valve implantation: insights on clinical outcomes and prognostic markers from a large cohort of patients
Bibliographic record
Abstract
AIM: The aim of this study was to determine the effects of advanced chronic kidney disease (CKD) on early and late outcomes after transcatheter aortic valve implantation (TAVI), and to evaluate the predictive factors of poorer outcomes in such patients. METHODS AND RESULTS: This was a multicentre study including a total of 2075 consecutive patients who had undergone TAVI. Patients were grouped according the estimated glomerular filtration rate as follows: CKD stage 1-2 (≥60 mL/min/1.73 m(2); n = 950), stage 3 (30-59 mL/min/1.73 m(2); n = 924), stage 4 (15-29 mL/min/1.73 m(2); n = 134) and stage 5 (<15 mL/min/1.73 m² or dialysis; n = 67). Clinical outcomes were evaluated at 30-days and at follow-up (median of 15 [6-29] months) and defined according to the VARC criteria. Advanced CKD (stage 4-5) was an independent predictor of 30-day major/life-threatening bleeding (P = 0.001) and mortality (P = 0.027), and late overall, cardiovascular and non-cardiovascular mortality (P < 0.01 for all). Pre-existing atrial fibrillation (HR: 2.29, 95% CI: 1.47-3.58, P = 0.001) and dialysis therapy (HR: 1.86, 95% CI: 1.17-2.97, P = 0.009) were the predictors of mortality in advanced CKD patients, with a mortality rate as high as 71% at 1-year follow-up in those patients with these 2 factors. Advanced CKD patients who had survived at 1-year follow-up exhibited both a significant improvement in NYHA class (P < 0.001) and no deterioration in valve hemodynamics (P = NS for changes in mean gradient and valve area over time). CONCLUSIONS: Advanced CKD was associated with a higher rate of early and late mortality and bleeding events following TAVI, with AF and dialysis therapy determining a higher risk in these patients. The mortality rate of patients with both factors was unacceptably high and this should be taken into account in the clinical decision-making process in this challenging group of patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".