Predicting Acute Kidney Injury following Transcatheter Aortic Valve Replacement
Bibliographic record
Abstract
Purpose Acute kidney injury occurs in up to a quarter of patients following transcatheter aortic valve replacement (TAVR) and has been associated with increased short and long-term mortality rates. A variety of patient characteristics predictive of post-TAVR acute kidney injury (AKI) have been identified, however discrepancies among studies exist almost uniformly. We investigated the hypothesis that the change in glomerular filtration rate (ΔGFR) in response to contrast administered during pre-TAVR coronary angiography is predictive of ΔGFR post-TAVR. Methods The study comprised 195 patients who underwent TAVR at a single center between August 2008 and June 2015 and were prospectively included in the CAPITAL TAVR registry. Multiple linear regression analysis was conducted to estimate the effect of independent variables on the change in renal function post-TAVR. Results There was no relationship identified between the ΔGFR post-angiogram and the ΔGFR post-TAVR (r=0.043, P=0.582). Multiple linear regression analysis revealed that a significant amount of the change in renal function post-TAVR can be explained by the patient's baseline creatinine (beta coefficient, -0.310, P<0.001) and the volume of contrast administered during TAVR (beta coefficient, -0.225, P0.002). The presence of an AKI following diagnostic coronary angiogram was not predictive of the change in renal function post-TAVR using the Valve Academic Research Consortium (VARC) definitions: VARC1 (beta coefficient, 0.102, P=0.170) or VARC2 (beta coefficient, 0.124, P=0.099). Conclusions A patient's previous renal response to contrast administered during coronary angiogram is not predictive of their response post-TAVR; instead, as demonstrated previously, baseline renal function and contrast volume administered are two of the most important predictors of post-TAVR AKI.
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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 | 0.001 | 0.005 |
| 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) | 0.001 | 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".