Intraoperative Renal Resistive Index as an Acute Kidney Injury Biomarker: Development and Validation of an Automated Analysis Algorithm.

Journal Article (Journal Article)

OBJECTIVE: Intraoperative Doppler-determined renal resistive index (RRI) is a promising early acute kidney injury (AKI) biomarker. As RRI continues to be studied, its clinical usefulness and robustness in research settings will be linked to the ease, efficiency, and precision with which it can be interpreted. Therefore, the authors assessed the usefulness of computer vision technology as an approach to developing an automated RRI-estimating algorithm with equivalent reliability and reproducibility to human experts. DESIGN: Retrospective. SETTING: Single-center, university hospital. PARTICIPANTS: Adult cardiac surgery patients from 7/1/2013 to 7/10/2014 with intraoperative transesophageal echocardiography-determined renal blood flow measurements. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Renal Doppler waveforms were obtained retrospectively and assessed by blinded human expert raters. Images (430) were divided evenly into development and validation cohorts. An algorithm for automated RRI analysis was built using computer vision techniques and tuned for alignment with experts using bootstrap resampling in the development cohort. This algorithm then was applied to the validation cohort for an unbiased assessment of agreement with human experts. Waveform analysis time per image averaged 0.144 seconds. Agreement was excellent by intraclass correlation coefficient (0.939; 95% confidence interval [CI] 0.921 to 0.953) and in Bland-Altman analysis (mean difference [human-algorithm] -0.0015; 95% CI -0.0054 to 0.0024), without evidence of systematic bias. CONCLUSION: The authors confirmed the value of computer vision technology to develop an algorithm for RRI estimation from automatically processed intraoperative renal Doppler waveforms. This simple-to-use and efficient tool further adds to the clinical and research value of RRI, already the "earliest" among several early AKI biomarkers being assessed.

Full Text

Duke Authors

Cited Authors

  • Andrew, BY; Andrew, EY; Cherry, AD; Hauck, JN; Nicoara, A; Pieper, CF; Stafford-Smith, M

Published Date

  • October 2018

Published In

Volume / Issue

  • 32 / 5

Start / End Page

  • 2203 - 2209

PubMed ID

  • 29753670

Pubmed Central ID

  • PMC6153038

Electronic International Standard Serial Number (EISSN)

  • 1532-8422

Digital Object Identifier (DOI)

  • 10.1053/j.jvca.2018.04.014


  • eng

Conference Location

  • United States