Advanced search
Publication Cover
Biomarkers Volume 23, 2018 - Issue 8
Submit an article Journal homepage
254
Views
4
CrossRef citations to date
0
Altmetric
Original Article

Methods used to assess the performance of biomarkers for the diagnosis of acute kidney injury: a systematic review and meta-analysis

Anaïs CodorniuDepartment of Anesthesiology, Intensive Care and Burn Unit, University Hospital Saint Louis – Lariboisière, Paris, France; ;Biomarkers in CArdio-Neuro-VAScular Diseases (BioCANVAS), Paris, France
,
Léa LemasleDepartment of Anesthesiology, Intensive Care and Burn Unit, University Hospital Saint Louis – Lariboisière, Paris, France; ;Biomarkers in CArdio-Neuro-VAScular Diseases (BioCANVAS), Paris, France
,
Matthieu LegrandDepartment of Anesthesiology, Intensive Care and Burn Unit, University Hospital Saint Louis – Lariboisière, Paris, France; ;Biomarkers in CArdio-Neuro-VAScular Diseases (BioCANVAS), Paris, France
,
Alice BletDepartment of Anesthesiology, Intensive Care and Burn Unit, University Hospital Saint Louis – Lariboisière, Paris, France; ;Biomarkers in CArdio-Neuro-VAScular Diseases (BioCANVAS), Paris, France
,
Alexandre MebazaaDepartment of Anesthesiology, Intensive Care and Burn Unit, University Hospital Saint Louis – Lariboisière, Paris, France; ;Biomarkers in CArdio-Neuro-VAScular Diseases (BioCANVAS), Paris, France
&
Etienne GayatDepartment of Anesthesiology, Intensive Care and Burn Unit, University Hospital Saint Louis – Lariboisière, Paris, France; ;Biomarkers in CArdio-Neuro-VAScular Diseases (BioCANVAS), Paris, FranceCorrespondence[email protected]
Pages 766-772 | Received 20 Jan 2018, Accepted 23 Jun 2018, Published online: 23 Aug 2018

References

  • Ahmad, T., et al., 2014. Charting a roadmap for heart failure biomarker studies. JACC Heart failure, 2 (5), 477–488.
  • Bellomo, R., et al., 2004. Acute renal failure – definition, outcome measures, animal models, fluid therapy and information technology needs: the Second International Consensus Conference of the Acute Dialysis Quality Initiative (ADQI) Group. Critical care, 8 (4), R204–R212.
  • Bossuyt, P.M., et al., 2015. STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. Clinical chemistry, 61 (12), 1446–1452.
  • Coca, S.G., et al., 2009. Long-term risk of mortality and other adverse outcomes after acute kidney injury: a systematic review and meta-analysis. American journal of kidney diseases, 53 (6), 961–973.
  • De Rosa, S., Samoni, S., and Ronco, C., 2016. Creatinine-based definitions: from baseline creatinine to serum creatinine adjustment in intensive care. Critical care, 20 (1), 69.
  • Hilden, J. and Gerds, T.A., 2014. A note on the evaluation of novel biomarkers: do not rely on integrated discrimination improvement and net reclassification index. Statistics in medicine, 33 (19), 3405–3414.
  • Jackson, C.E., et al., 2016. The incremental prognostic and clinical value of multiple novel biomarkers in heart failure. European journal of heart failure, 18 (12), 1491–1498.
  • Kellum, J.A. and Lameire, N., 2013. Diagnosis, evaluation, and management of acute kidney injury: a KDIGO summary (Part 1). Critical care, 17 (1), 204.
  • Kellum, J.A., Bellomo, R., and Ronco, C., 2007. Classification of acute kidney injury using RIFLE: What’s the purpose? Critical care medicine, 35 (8), 1983–1984.
  • Kellum, J.A., et al., 2005. The 3rd international consensus conference of the acute dialysis quality initiative (ADQI). The international journal of artificial organs, 28 (5), 441–444.
  • Kerr, K.F., et al., 2014. Net reclassification indices for evaluating risk prediction instruments: a critical review. Epidemiology (Cambridge mass.), 25 (1), 114–121.
  • Lassus, J., et al., 2013. Incremental value of biomarkers to clinical variables for mortality prediction in acutely decompensated heart failure: the Multinational Observational Cohort on Acute Heart Failure (MOCA) study. International journal of cardiology, 168 (3), 2186–2194.
  • Legrand, M., Januzzi, J.L., and Mebazaa, A., 2013. Critical research on biomarkers: what’s new? Intensive care medicine, 39 (10), 1824–1828.
  • Moher, D., et al., 2009. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS medicine, 6 (7), e1000097.
  • Molitoris, B.A., et al., 2007. Improving outcomes of acute kidney injury: report of an initiative. Nature clinical practice nephrology, 3 (8), 439–442.
  • Rosman, A.S., and Korsten, M.A., 2007. Application of summary receiver operating characteristics (sROC) analysis to diagnostic clinical testing. Advances in medical sciences, 52, 76–82.
  • Siew, E.D., et al., 2012. Estimating baseline kidney function in hospitalized patients with impaired kidney function. Clinical journal of the American society of nephrology, 7 (5), 712–719.
  • Waikar, S.S., et al., 2012. Imperfect gold standards for kidney injury biomarker evaluation. Journal of the American society of nephrology, 23 (1), 13–21.
  • Whiting, P., et al., 2003. The development of QUADAS: a tool for the quality assessment of studies of diagnostic accuracy included in systematic reviews. BMC medical research methodology, 3 (1), 25.
  • Whiting, P.F., et al., 2011. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Annals of internal medicine, 155 (8), 529–536.
  • Yavchitz, A., et al., 2016. A new classification of spin in systematic reviews and meta-analyses was developed and ranked according to the severity. Journal of clinical epidemiology, 75, 56–65.
  • Závada, J., et al., 2010. A comparison of three methods to estimate baseline creatinine for RIFLE classification. Nephrology dialysis transplantation, 25 (12), 3911–3918.
  • Zethelius, B., et al., 2008. Use of multiple biomarkers to improve the prediction of death from cardiovascular causes. The new England journal of medicine, 358 (20), 2107–2116.
  • Zhang, Z., 2015. Biomarkers, diagnosis and management of sepsis-induced acute kidney injury: a narrative review. Heart lung vessels, 7 (1), 64–73.
  • Zhang, Z., 2016. Univariate description and bivariate statistical inference: the first step delving into data. Annals of translational medicine, 4 (5), 91.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Order Reprints Request Corporate Permissions

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

Request Academic Permissions

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.