TY - JOUR
T1 - Importance of Prospective Registries and Clinical Research Networks in the Evolution of Spinal Cord Injury Care
AU - Kelly-Hedrik, Margot
AU - Abd-El-Barr, Muhammad M.
AU - Aarabi, Bizhan
AU - Curt, Armin
AU - Howley, Susan P.
AU - Harrop, James S.
AU - Kirshblum, Steven
AU - Neal, Christopher J.
AU - Noonan, Vanessa
AU - Park, Christine
AU - Ugiliweneza, Beatrice
AU - Tator, Charles
AU - Toups, Elizabeth G.
AU - Fehlings, Michael G.
AU - Williamson, Theresa
AU - Guest, James D.
N1 - Funding Information:
The SCIMS was founded in 1970 to create a network of rehabilitation centers across the United States providing care for patients with SCI. The lack of existing care programs linking acute and rehabilitative care was specifically viewed as suboptimal. The SCIMS Program was conceptually influenced by the successes in the United Kingdom at Stoke Mandeville and at the Royal Perth Hospital in Australia. Thus, the program aimed to develop a comprehensive care system linking acute and rehabilitative care and to stimulate research on the long-term outcomes of SCI as described in the Federal Register. To achieve this second aim, the program founded the National SCIMS Database in 1975 to aggregate prospectively acquired data across the network's sites. The funding for the SCIMS was initially under the Rehabilitation Services Administration (RSA), then the National Institute of Handicapped Research (NIHR), and later, the National Institute on Disability, Independent Living, and Rehabilitation Research (NIDILRR). The SCIMS also provides competitive funding to the current member centers for independent and collaborative research studies supported by NIDILRR. The collected data continually update the database hosted at the National Spinal Cord Injury Statistical Center (NSCISC) at the University of Alabama at Birmingham. The goals of the database are to explore the demographics of patients with SCI, track outcomes, identify trends across time, and facilitate research.
Funding Information:
The NACTN has been supported by the U.S. Department of Defense and the Christopher & Dana Reeve Foundation.
Funding Information:
This article is based upon work supported by the U.S. Army Medical Research Acquisition Activity under grant numbers W81XWH-07-1-0361, W81XWH-10-2-0042, and W81XWH-13-2-0040, contract number W81XWH-16-C-0031, and the Christopher & Dana Reeve Foundation.
Publisher Copyright:
© Copyright 2023, Mary Ann Liebert, Inc., publishers 2023.
PY - 2023/9/1
Y1 - 2023/9/1
N2 - Only 100 years ago, traumatic spinal cord injury (SCI) was commonly lethal. Today, most people who sustain SCI survive with continual efforts to improve their quality of life and neurological outcomes. SCI epidemiology is changing as preventative interventions reduce injuries in younger individuals, and there is an increased incidence of incomplete injuries in aging populations. Early treatment has become more intensive with decompressive surgery and proactive interventions to improve spinal cord perfusion. Accurate data, including specialized outcome measures, are crucial to understanding the impact of epidemiological and treatment trends. Dedicated SCI clinical research and data networks and registries have been established in the United States, Canada, Europe, and several other countries. We review four registry networks: the North American Clinical Trials Network (NACTN) SCI Registry, the National Spinal Cord Injury Model Systems (SCIMS) Database, the Rick Hansen SCI Registry (RHSCIR), and the European Multi-Center Study about Spinal Cord Injury (EMSCI). We compare the registries' focuses, data platforms, advanced analytics use, and impacts. We also describe how registries' data can be combined with electronic health records (EHRs) or shared using federated analysis to protect registrants' identities. These registries have identified changes in epidemiology, recovery patterns, complication incidence, and the impact of practice changes such as early decompression. They've also revealed latent disease-modifying factors, helped develop clinical trial stratification models, and served as matched control groups in clinical trials. Advancing SCI clinical science for personalized medicine requires advanced analytical techniques, including machine learning, counterfactual analysis, and the creation of digital twins. Registries and other data sources help drive innovation in SCI clinical science.
AB - Only 100 years ago, traumatic spinal cord injury (SCI) was commonly lethal. Today, most people who sustain SCI survive with continual efforts to improve their quality of life and neurological outcomes. SCI epidemiology is changing as preventative interventions reduce injuries in younger individuals, and there is an increased incidence of incomplete injuries in aging populations. Early treatment has become more intensive with decompressive surgery and proactive interventions to improve spinal cord perfusion. Accurate data, including specialized outcome measures, are crucial to understanding the impact of epidemiological and treatment trends. Dedicated SCI clinical research and data networks and registries have been established in the United States, Canada, Europe, and several other countries. We review four registry networks: the North American Clinical Trials Network (NACTN) SCI Registry, the National Spinal Cord Injury Model Systems (SCIMS) Database, the Rick Hansen SCI Registry (RHSCIR), and the European Multi-Center Study about Spinal Cord Injury (EMSCI). We compare the registries' focuses, data platforms, advanced analytics use, and impacts. We also describe how registries' data can be combined with electronic health records (EHRs) or shared using federated analysis to protect registrants' identities. These registries have identified changes in epidemiology, recovery patterns, complication incidence, and the impact of practice changes such as early decompression. They've also revealed latent disease-modifying factors, helped develop clinical trial stratification models, and served as matched control groups in clinical trials. Advancing SCI clinical science for personalized medicine requires advanced analytical techniques, including machine learning, counterfactual analysis, and the creation of digital twins. Registries and other data sources help drive innovation in SCI clinical science.
KW - counterfactual
KW - digital twin
KW - personalized medicine
KW - registry
KW - spinal cord injury
UR - http://www.scopus.com/inward/record.url?scp=85168011025&partnerID=8YFLogxK
U2 - 10.1089/neu.2022.0450
DO - 10.1089/neu.2022.0450
M3 - Article
C2 - 36576020
AN - SCOPUS:85168011025
SN - 0897-7151
VL - 40
SP - 1834
EP - 1848
JO - Journal of Neurotrauma
JF - Journal of Neurotrauma
IS - 17-18
ER -