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Record W4397044824 · doi:10.1681/asn.20233411s1761a

Combining Arterial and Venous Intrarenal Doppler Assessment for the Prediction of AKI After Cardiac Surgery

2023· article· en· W4397044824 on OpenAlexaff
C. Giles, André Denault, William Beaubien‐Souligny

Bibliographic record

VenueJournal of the American Society of Nephrology · 2023
Typearticle
Languageen
FieldMedicine
TopicHemodynamic Monitoring and Therapy
Canadian institutionsUniversité de MontréalMcMaster University
Fundersnot available
KeywordsMedicineRadiologyCardiologyCardiac surgeryInternal medicineSurgery

Abstract

fetched live from OpenAlex

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)

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.726
Threshold uncertainty score0.166

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.296
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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