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Introduction <p>Biomarkers have been identified to predict, diagnose and prognosticate acute kidney injury (AKI) but existing studies are heterogenous and contradictory.</p> Objective <p>To compare diagnostic performance of AKI biomarkers, evaluate the quality of AKI biomarker studies and to develop standards for reporting studies of diagnostic test accuracy (DTA) of AKI biomarkers.</p> Methods <p>A systematic literature review was conducted to identify studies focusing on the diagnostic performance of AKI biomarkers published before February 2025. Retrieved DTA studies were assessed for methodological quality and completeness using the QUADAS-2 and Standards for Reporting Diagnostic Accuracy (STARD) 2015 checklists. An international 17 member expert panel was convened to agree consensus standards for AKI biomarker studies (STARDaki) via a modified Delphi process.</p> Results <p>122 DTA studies for AKI biomarkers were identified, but 15 were insufficiently reported. Of the remaining 107 studies, only 19 reported on diagnosis of AKI within 48&#xa0;h of sampling. Of these studies, only 16 were considered high-quality based on the QUADAS-2 criteria. The compliance level with the STARD checklist was too low to permit meta-analysis. The expert panel agreed criteria for patient selection, reference standards, and reporting of test–retest reliability to supplement the STARD guidance for AKI biomarker studies.</p> Conclusion <p>Most studies examining AKI biomarker performance fail to conform to the STARD standards for reporting, leading to poor diagnostic accuracy estimates and reduced clinical applicability and generalizability. An expert panel proposed STARDaki criteria to advance the development and clinical use of AKI biomarkers&#xa0;(<a href="https://www.stardaki.icu">www.stardaki.icu</a>).</p> Visual abstract <p></p>

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STARDaki: a consensus-based STARD extension for standardized reporting of diagnostic accuracy in acute kidney injury

  • 期刊论文

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  • 发表日期：

    2026年7月28日

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  • Haichuan Yu,
  • Yiming Li,
  • Gaozhi P. Mo,
  • Zhe Luo,
  • Alexander Zarbock,
  • Dana Fuhrman,
  • Yan Kang,
  • Dechang Chen,
  • Thomas Rimmelé,
  • John Prowle,
  • Patrick Murray,
  • Nattachai Srisawat,
  • Daniel De Backer,
  • Vedran Premuzic,
  • Rajit K. Basu,
  • Claudio Ronco,
  • Marlies Ostermann,
  • Kianoush Kashani,
  • John A. Kellum,
  • Zhiyong Peng

摘要

Introduction

Biomarkers have been identified to predict, diagnose and prognosticate acute kidney injury (AKI) but existing studies are heterogenous and contradictory.

Objective

To compare diagnostic performance of AKI biomarkers, evaluate the quality of AKI biomarker studies and to develop standards for reporting studies of diagnostic test accuracy (DTA) of AKI biomarkers.

Methods

A systematic literature review was conducted to identify studies focusing on the diagnostic performance of AKI biomarkers published before February 2025. Retrieved DTA studies were assessed for methodological quality and completeness using the QUADAS-2 and Standards for Reporting Diagnostic Accuracy (STARD) 2015 checklists. An international 17 member expert panel was convened to agree consensus standards for AKI biomarker studies (STARDaki) via a modified Delphi process.

Results

122 DTA studies for AKI biomarkers were identified, but 15 were insufficiently reported. Of the remaining 107 studies, only 19 reported on diagnosis of AKI within 48 h of sampling. Of these studies, only 16 were considered high-quality based on the QUADAS-2 criteria. The compliance level with the STARD checklist was too low to permit meta-analysis. The expert panel agreed criteria for patient selection, reference standards, and reporting of test–retest reliability to supplement the STARD guidance for AKI biomarker studies.

Conclusion

Most studies examining AKI biomarker performance fail to conform to the STARD standards for reporting, leading to poor diagnostic accuracy estimates and reduced clinical applicability and generalizability. An expert panel proposed STARDaki criteria to advance the development and clinical use of AKI biomarkers (www.stardaki.icu).

Visual abstract