Literature DB >> 26088508

Therapeutic Area Data Standards for Autosomal Dominant Polycystic Kidney Disease: A Report From the Polycystic Kidney Disease Outcomes Consortium (PKDOC).

Ronald D Perrone1, Jon Neville2, Arlene B Chapman3, Berenice Y Gitomer4, Dana C Miskulin5, Vicente E Torres6, Frank S Czerwiec7, Eslie Dennis2, Bron Kisler8, Steve Kopko8, Holly B Krasa7, Elizabeth LeRoy2, Juliana Castedo9, Robert W Schrier4, Steve Broadbent2.   

Abstract

Data standards provide a structure for consistent understanding and exchange of data and enable the integration of data across studies for integrated analysis. There is no data standard applicable to kidney disease. We describe the process for development of the first-ever Clinical Data Interchange Standards Consortium (CDISC) data standard for autosomal dominant polycystic kidney disease (ADPKD) by the Polycystic Kidney Disease Outcomes Consortium (PKDOC). Definition of common data elements and creation of ADPKD-specific data standards from case report forms used in long-term ADPKD registries, an observational cohort (Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease [CRISP] 1 and 2), and a randomized clinical trial (Halt Progression of Polycystic Kidney Disease [HALT-PKD]) are described in detail. This data standard underwent extensive review, including a global public comment period, and is now available online as the first PKD-specific data standard (www.cdisc.org/therapeutic). Submission of clinical trial data that use standard data structures and terminology will be required for new electronic submissions to the US Food and Drug Administration for all disease areas by the end of 2016. This data standard will allow for the mapping and pooling of available data into a common data set in addition to providing a foundation for future studies, data sharing, and long-term registries in ADPKD. This data set will also be used to support the regulatory qualification of total kidney volume as a prognostic biomarker for use in clinical trials. The availability of consensus data standards for ADPKD has the potential to facilitate clinical trial initiation and increase sharing and aggregation of data across observational studies and among completed clinical trials, thereby improving our understanding of disease progression and treatment.
Copyright © 2015 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Autosomal dominant polycystic kidney disease (ADPKD); Clinical Data Interchange Standards Consortium (CDISC); Polycystic Kidney Disease Outcomes Consortium (PKDOC); consensus data standards; controlled terminology; data pooling; disease progression biomarker; standard data structure; total kidney volume (TKV)

Mesh:

Year:  2015        PMID: 26088508     DOI: 10.1053/j.ajkd.2015.04.044

Source DB:  PubMed          Journal:  Am J Kidney Dis        ISSN: 0272-6386            Impact factor:   8.860


  10 in total

Review 1.  ADPKD-what the radiologist should know.

Authors:  Pritika Gaur; Wladyslaw Gedroyc; Peter Hill
Journal:  Br J Radiol       Date:  2019-04-30       Impact factor: 3.039

Review 2.  A Practical Guide for Treatment of Rapidly Progressive ADPKD with Tolvaptan.

Authors:  Fouad T Chebib; Ronald D Perrone; Arlene B Chapman; Neera K Dahl; Peter C Harris; Michal Mrug; Reem A Mustafa; Anjay Rastogi; Terry Watnick; Alan S L Yu; Vicente E Torres
Journal:  J Am Soc Nephrol       Date:  2018-09-18       Impact factor: 10.121

3.  Total Kidney Volume Is a Prognostic Biomarker of Renal Function Decline and Progression to End-Stage Renal Disease in Patients With Autosomal Dominant Polycystic Kidney Disease.

Authors:  Ronald D Perrone; Mohamad-Samer Mouksassi; Klaus Romero; Frank S Czerwiec; Arlene B Chapman; Berenice Y Gitomer; Vicente E Torres; Dana C Miskulin; Steve Broadbent; Jean F Marier
Journal:  Kidney Int Rep       Date:  2017-01-16

4.  A Drug Development Tool for Trial Enrichment in Patients With Autosomal Dominant Polycystic Kidney Disease.

Authors:  Ronald D Perrone; Mohamad-Samer Mouksassi; Klaus Romero; Frank S Czerwiec; Arlene B Chapman; Berenice Y Gitomer; Vicente E Torres; Dana C Miskulin; Steve Broadbent; Jean F Marier
Journal:  Kidney Int Rep       Date:  2017-02-21

5.  Application of a Dynamic Map for Learning, Communicating, Navigating, and Improving Therapeutic Development.

Authors:  John A Wagner; Andrew M Dahlem; Lynn D Hudson; Sharon F Terry; Russ B Altman; C Taylor Gilliland; Christopher DeFeo; Christopher P Austin
Journal:  Clin Transl Sci       Date:  2017-12-22       Impact factor: 4.689

6.  Practical approaches to the management of autosomal dominant polycystic kidney disease patients in the era of tolvaptan.

Authors:  Roman-Ulrich Müller; Christian S Haas; John A Sayer
Journal:  Clin Kidney J       Date:  2017-07-27

7.  Data Sharing in Neurosurgery and Neurology Journals.

Authors:  Jeremiah N Johnson; Keith A Hanson; Caleb A Jones; Ramesh Grandhi; Jaime Guerrero; Jesse S Rodriguez
Journal:  Cureus       Date:  2018-05-23

8.  The challenges in data integration - heterogeneity and complexity in clinical trials and patient registries of Systemic Lupus Erythematosus.

Authors:  Helen Le Sueur; Ian N Bruce; Nophar Geifman
Journal:  BMC Med Res Methodol       Date:  2020-06-24       Impact factor: 4.615

9.  Standardized Data Structures in Rare Diseases: CDISC User Guides for Duchenne Muscular Dystrophy and Huntington's Disease.

Authors:  Ariana P Mullin; Diane Corey; Emily C Turner; Richard Liwski; Daniel Olson; Jackson Burton; Sudhir Sivakumaran; Lynn D Hudson; Klaus Romero; Diane T Stephenson; Jane Larkindale
Journal:  Clin Transl Sci       Date:  2020-08-25       Impact factor: 4.689

10.  Sharing raw data from clinical trials: what progress since we first asked "Whose data set is it anyway?".

Authors:  Andrew J Vickers
Journal:  Trials       Date:  2016-05-04       Impact factor: 2.279

  10 in total

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