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Intraoperative Renal Resistive Index as an Acute Kidney Injury Biomarker: Development and Validation of an Automated Analysis Algorithm.
Andrew, Benjamin Y; Andrew, Elias Y; Cherry, Anne D; Hauck, Jennifer N; Nicoara, Alina; Pieper, Carl F; Stafford-Smith, Mark.
Afiliação
  • Andrew BY; Department of Anesthesiology, Duke University Medical Center, Durham, NC; Clinical Research Training Program, Duke University School of Medicine, Durham, NC.
  • Andrew EY; Department of Electrical and Computer Engineering, School of Engineering and Applied Sciences, The George Washington University, Washington, DC.
  • Cherry AD; Department of Anesthesiology, Duke University Medical Center, Durham, NC.
  • Hauck JN; Department of Anesthesiology, Duke University Medical Center, Durham, NC.
  • Nicoara A; Department of Anesthesiology, Duke University Medical Center, Durham, NC.
  • Pieper CF; Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC.
  • Stafford-Smith M; Department of Anesthesiology, Duke University Medical Center, Durham, NC. Electronic address: mark.staffordsmit@dm.duke.edu.
J Cardiothorac Vasc Anesth ; 32(5): 2203-2209, 2018 10.
Article em En | MEDLINE | ID: mdl-29753670
ABSTRACT

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.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Complicações Pós-Operatórias / Circulação Renal / Monitorização Intraoperatória / Injúria Renal Aguda / Procedimentos Cirúrgicos Cardíacos Tipo de estudo: Diagnostic_studies / Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Aged / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Complicações Pós-Operatórias / Circulação Renal / Monitorização Intraoperatória / Injúria Renal Aguda / Procedimentos Cirúrgicos Cardíacos Tipo de estudo: Diagnostic_studies / Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Aged / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2018 Tipo de documento: Article