Combining Arterial and Venous Intrarenal Doppler Assessment for the Prediction of AKI After Cardiac Surgery
Bibliographic record
Abstract
Background: Acute kidney injury (AKI) is common after cardiac surgery and often hemodynamically mediated. The roles of ultrasound measures of intrarenal perfusion to predict AKI are yet to be determined. The objective of this study was to determine if point-of-care ultrasound Doppler measures of intrarenal arterial and venous flow predict AKI after cardiac surgery. Methods: We conducted a secondary analysis of a prospective cohort study of adult patients undergoing cardiac surgery in whom ultrasound assessments were performed at ICU admission after surgery. AKI was defined by the KDIGO creatinine criteria. Intrarenal arterial markers included renal resistive index (RRI) and velocity-time integral normalized to peak systolic velocity (VTI/PSV), while venous markers included intrarenal venous flow (IRVF) categories and renal venous stasis index (RVSI). The area under the receiving operating characteristic (AUROC) curves were used to determine the predictive characteristics for post-operative AKI. The performance of individual markers were compared to a combined RRI and RVSI logistic regression model using the net classification index (NRI) and AUROC were compared with the DeLong test. Results: We included 131 patients in total, with 47 patients (35.9%) developing postoperative AKI. All studied ultrasound markers showed moderate discrimination for the subsequent development of AKI (Table 1). More complex measurements (VTI/PSV and RVSI) were not superior to simpler indices (RRI and IRVF). In a multivariable model, both RRI (aOR:1.70, CI:1.09-2.66, p=0.02) and RVSI (aOR:0.85, CI:0.04-0.98, p=0.048) remained associated with AKI. The predicted probabilities from the model were slightly better than each index taken individually according to the NRI. However, the AUROC were not significantly different (Table 1). Conclusions: Intrarenal arterial and venous Doppler indices moderately predict the development of post-operative AKI in cardiac surgery patients. However, combining arterial and venous Doppler indices only marginally improves prediction. - Echographic Parameters and AKI After Cardiac Surgery Echographic Parameter Postoperative Day 0 DeLong Test/Net Reclassification Index AUROC 95% CI P Value RRI 0.64 0.55-0.74 0.006 p=0.38 / NRI: 0.32 (CI: -0.06; 0.69) VTI/PSV 0.67 0.57-0.77 0.001 IRVF Category 0.64 0.53-0.74 0.009 p=0.19 / NRI: -0.52 (CI: -0.82; 0.20) RVSI 0.60 0.50-0.71 0.045 Predicted probability of model: RRI + RVSI 0.69 0.59-0.78 <0.001 Vs RRI: p=0.50 / NRI: 0.50 (CI: 0.17; 0.84) Vs RVSI: p=0.38 / NRI: 0.38 (CI: 0.04; 0.70)
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".