Bedside risk score for prediction of acute kidney injury after transcatheter aortic valve replacement
Bibliographic record
Abstract
Background: Acute kidney injury (AKI) is a common post-transcatheter aortic valve replacement (TAVR) complication associated with a poor prognosis. We sought to create a risk calculator using information that would be available during the work-up period. Methods: Data were obtained from a multicentre TAVR registry (n=1993) with cases from 1 January 2012 to 31 December 2015. We used logistic regression to create a risk calculator to predict AKI as defined by the Valve Academic Research Consortium Guidelines. We internally validated our risk calculator using bootstrapping, and evaluated model discrimination and calibration. Results: A simple risk score was derived with six variables, including New York Heart Association functional classification class 4, non-femoral access site, valve-in-valve procedure, haemoglobin, creatinine clearance and weight in kilograms. The score was able to predict the absolute risk of AKI from 1% to 72%. The model showed good discrimination with c-statistic 0.713, with good agreement between predicted and observed AKI rates across quintiles of risk. Conclusions: This is the first risk calculator to assess post-TAVR risk of AKI. We found that information known pre-procedurally can be used to predict AKI. This may allow for more informed decision-making as well as identifying high-risk 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".