Harmonization of Databases: A Step for Advancing the Knowledge About Spinal Cord Injury

Short Title:
Model System:
SCI
Reference Type:
Journal Article
Accession No.:
Journal:
Archives of Physical Medicine and Rehabilitation
Year, Volume, Issue, Page(s):
2016, vol. 97, issue 10, pp 1805–1818
Publication Website:
Abstract:
The objectives of this article are to (1) provide an overview of existing spinal cord injury (SCI) clinical research databases—their purposes, characteristics, and accessibility to users; and (2) present a vision for future collaborations required for cross-cutting research in SCI. This vision highlights the need for validated and relevant data for longitudinal clinical trials and observational and epidemiologic SCI-related studies. Three existing SCI clinical research databases/registries are reviewed and summarized with regard to current formats, collection methods, and uses, including major strengths and weaknesses. Efforts to provide a uniform approach to data collection are also reviewed. The databases reviewed offer different approaches to capture important clinical information on SCI. They vary on size, purpose, data points, inclusion of standard outcomes, and technical requirements. Each presents with a set of limitations including lack of population data and lack of a common platform for data comparisons and exchanges. It is clear that numerous issues need to be considered when planning to establish common ways of collecting data through data sets or patient registries, ranging from a carefully crafted implementation plan that lists purposes, cost, resources required, and policies to guide such development to establishing a framework for dissemination of data and findings. For the present, taking advantage of the vast but different data already collected over many decades may require a variety of statistical skills and epidemiologic techniques. Ultimately, our ability to speak the same language with regard to variables and assessment tools will facilitate international collaborations and enhance comparability, data pooling, and the ability to generalize findings to a broader population.
Author(s):
Susan Charlifue, PhD'Correspondence information about the author PhD Susan CharlifueEmail the author PhD Susan Charlifue, Denise Tate, PhD, Fin Biering-Sorensen, MD, PhD, Stephen Burns, MD, Yuying Chen, MD, PhD, Sophia Chun, PhD, Lyn B. Jakeman, PhD, Robert G. Kowalski, MBBCh, MS, Vanessa K. Noonan, PT, PhD, Philip Ullrich, PhD
Author Address(es):
Participating Centers:

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