Abstract 15440: The Incidence and Outcomes of Acute Kidney Injury After TAVR
Bibliographic record
Abstract
Introduction: Acute kidney injury (AKI) is a known complication after cardiovascular procedures and is associated with increased all-cause-mortality. Hypothesis: We aimed to assess the incidence, predictors, and long-term outcomes of AKI after TAVR. Methods: We evaluated inoperable patients with severe aortic stenosis who received TAVR in an academic institution from 04/2008 to 06/2015. AKI was defined according to the consensus report of the Valve Academic Research Consortium. Predictors of AKI were evaluated using multivariable logistic regression modeling. Results: Among 457 patients who underwent TAVR, 78 (17%) developed AKI. There were no differences in age, gender, BMI, hypertension, dyslipidemia, smoking status, or medications use among patients with and without AKI. However, patients with AKI were more likely to have diabetes mellitus (DM), chronic kidney disease (CKD) and prior history of stroke than patients without AKI (Figure). Patients with AKI had also lower baseline hemoglobin (AKI: 11 g/dL vs. No AKI: 12, p=0.004) and higher baseline creatinine (1.45 vs. 1.1, p=<0.001). Procedural characteristics that were associated with AKI included: hypotension, IABP/LV assist device use, bleeding, and vascular complications. Patients with AKI had higher all-cause-mortality (14% vs. 2%, p <0.001), higher median hospital length of stay (12 vs. 7 days, p <0.001) and a higher incidence of new dialysis requirement (12% vs. 1%, p <0.001), and dialysis at one month (8% vs. 1%, p =0.001). Multivariate logistsic regression analysis identified DM, CKD, and IABP or LV assist device use as independent predictors of AKI. Conclusion: Our results suggest that AKI is relatively common post TAVR and it is associated with increased mortality, length of stay, and dialysis requirement. Patients with underlying CKD and DM who require IABP or LV assist devices for hemodynamic support are at the highest risk of 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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".