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      Acute kidney injury in the era of big data: the 15 th Consensus Conference of the Acute Dialysis Quality Initiative (ADQI)

      editorial
      , , , , for the ADQI 15 Consensus Group
      Canadian Journal of Kidney Health and Disease
      BioMed Central

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          Abstract

          The world is immersed in “ big data”. Big data has brought about radical innovations in the methods used to capture, transfer, store and analyze the vast quantities of data generated every minute of every day. At the same time; however, it has also become far easier and relatively inexpensive to do so. Rapidly transforming, integrating and applying this large volume and variety of data are what underlie the future of big data. The application of big data and predictive analytics in healthcare holds great promise to drive innovation, reduce cost and improve patient outcomes, health services operations and value. Acute kidney injury (AKI) may be an ideal syndrome from which various dimensions and applications built within the context of big data may influence the structure of services delivery, care processes and outcomes for patients. The use of innovative forms of “information technology” was originally identified by the Acute Dialysis Quality Initiative (ADQI) in 2002 as a core concept in need of attention to improve the care and outcomes for patients with AKI. For this 15 th ADQI consensus meeting held on September 6–8, 2015 in Banff, Canada, five topics focused on AKI and acute renal replacement therapy were developed where extensive applications for use of big data were recognized and/or foreseen. In this series of articles in the Canadian Journal of Kidney Health and Disease, we describe the output from these discussions.

          ABRÉGÉ

          Le monde nage actuellement dans une mer de données informatiques. L’apparition de mégadonnées a entraîné des changements majeurs dans la façon de saisir, de transférer, de stocker et d’analyser la multitude de données générée chaque minute de chaque jour. Parallèlement, il est aussi plus facile de gérer ces informations et de le faire à un coût relativement moindre. La capacité de transformer, d’intégrer et d’appliquer rapidement la variété et le volume considérable de données est ce sur quoi repose le futur des mégadonnées. Le traitement des mégadonnées ainsi que leur analyse prévisionnelle dans le système de santé se veut très prometteur pour favoriser l’innovation, réduire les coûts, apporter des changements favorables au fonctionnement et à la portée des services ainsi que pour améliorer le pronostic des patients. Il semble que l’insuffisance rénale aiguë (IRA) soit un syndrome idéal à partir duquel les différents aspects et applications mis en place pour gérer les mégadonnées pourraient influencer les modèles existants de prestation de services et d’offre de soins et, par extension, l’évolution de l’état de santé des patients. L’utilisation de formes novatrices pour assimiler les technologies et l’information a d’abord été identifiée en 2002 par l’ Acute Dialysis Quality Initiative (ADQI) en tant que concept nécessitant une attention particulière et qui pourrait améliorer les soins et le pronostic des patients atteints d’IRA. Lors de la 15e réunion annuelle de concertation de l’ADQI qui s’est tenue du 6 au 8 septembre 2015 à Banff, au Canada, on a mis l’accent sur cinq thèmes reliés à l’IRA et aux thérapies de remplacement rénal. Ils ont été développés dans des cadres où l’on reconnaissait ou prévoyait l’application étendue des mégadonnées. Dans une série d’articles qui paraîtra dans le Canadian Journal of Kidney Health and Disease, nous ferons ressortir les conclusions de ces discussions.

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          Most cited references13

          • Record: found
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          The randomized registry trial--the next disruptive technology in clinical research?

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            Cost and Mortality Associated With Postoperative Acute Kidney Injury.

            To determine the incremental hospital cost and mortality associated with the development of postoperative acute kidney injury (AKI) and with other associated postoperative complications.
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              • Article: not found

              Validity of International Classification of Diseases, Ninth Revision, Clinical Modification Codes for Acute Renal Failure.

              Administrative and claims databases may be useful for the study of acute renal failure (ARF) and ARF that requires dialysis (ARF-D), but the validity of the corresponding diagnosis and procedure codes is unknown. The performance characteristics of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes for ARF were assessed against serum creatinine-based definitions of ARF in 97,705 adult discharges from three Boston hospitals in 2004. For ARF-D, ICD-9-CM codes were compared with review of medical records in 150 patients with ARF-D and 150 control patients. As compared with a diagnostic standard of a 100% change in serum creatinine, ICD-9-CM codes for ARF had a sensitivity of 35.4%, specificity of 97.7%, positive predictive value of 47.9%, and negative predictive value of 96.1%. As compared with review of medical records, ICD-9-CM codes for ARF-D had positive predictive value of 94.0% and negative predictive value of 90.0%. It is concluded that administrative databases may be a powerful tool for the study of ARF, although the low sensitivity of ARF codes is an important caveat. The excellent performance characteristics of ICD-9-CM codes for ARF-D suggest that administrative data sets may be particularly well suited for research endeavors that involve patients with ARF-D.
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                Author and article information

                Contributors
                780.407.6755 , bagshaw@ualberta.ca
                Stuart.Goldstein@cchmc.org
                cronco@goldnet.it
                kellumja@ccm.upmc.edu
                Journal
                Can J Kidney Health Dis
                Can J Kidney Health Dis
                Canadian Journal of Kidney Health and Disease
                BioMed Central (London )
                2054-3581
                26 February 2016
                26 February 2016
                2016
                : 3
                : 5
                Affiliations
                [ ]Division of Critical Care Medicine, Faculty of Medicine and Dentistry, University of Alberta, 2-124E, Clinical Sciences Building, 8440-112 ST NW, Edmonton, T6G 2B7 Canada
                [ ]Center for Acute Care Nephrology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH USA
                [ ]Department of Nephrology, Dialysis and Transplantation, International Renal Research Institute of Vicenza, San Bortolo Hospital, Vicenza, Italy
                [ ]Center for Critical Care Nephrology, Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA USA
                Article
                103
                10.1186/s40697-016-0103-z
                4768324
                26925244
                c3756091-e9f3-443d-935a-bc5e0c77141d
                © Bagshaw et al. 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 15 December 2015
                : 17 January 2016
                Categories
                Editorial
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                © The Author(s) 2016

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