ArticleJournal of cardiothoracic and vascular anesthesia2018
Intraoperative Renal Resistive Index as an Acute Kidney Injury Biomarker: Development and Validation of an Automated Analysis Algorithm.
Article in Journal of cardiothoracic and vascular anesthesia, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Biochemical implications of robotic surgery: a new frontier in the operating room.Journal of robotic surgery · 2024Review
- Management of Intraoperative Mechanical Ventilation to Prevent Postoperative Complications after General Anesthesia: A Narrative Review.Journal of clinical medicine · 2021Review
- Intraoperative renal resistive index threshold as an acute kidney injury biomarker.Journal of clinical anesthesia · 2020Article
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Authors and funding
7 authors.
Funding
Abstract
objectiveIntraoperative 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.
designRetrospective.
settingSingle-center, university hospital.
participantsAdult cardiac surgery patients from 7/1/2013 to 7/10/2014 with intraoperative transesophageal echocardiography-determined renal blood flow measurements.
interventionsNone. MEASUREMENTS AND MAIN
resultsRenal 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.
conclusionThe 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.
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