Accurate prediction of clinical stroke scales and improved biomarkers of motor impairment from robotic measurements.

Autor: Agrafiotis DK; Janssen Research & Development, Titusville, New Jersey, United States of America.; Novartis Institutes for BioMedical Research, Cambridge, Massachusetts, United States of America., Yang E; Janssen Research & Development, Titusville, New Jersey, United States of America.; Novartis Institutes for BioMedical Research, Cambridge, Massachusetts, United States of America., Littman GS; GSL Statistical Consulting, Ardmore, Pennsylvania, United States of America., Byttebier G; Bioconstat Bvba, Gent, Belgium., Dipietro L; Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America., DiBernardo A; Janssen Research & Development, Titusville, New Jersey, United States of America., Chavez JC; Biogen-Idec, Cambridge, Massachusetts, United States of America., Rykman A; Burke Medical Research Institute, White Plains, New York, United States of America., McArthur K; Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, United Kingdom., Hajjar K; Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, United Kingdom.; Department of Neurology, University of Duisburg-Essen, Essen, Germany., Lees KR; Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, United Kingdom., Volpe BT; Feinstein Institute for Medical Research, Manhasset, New York, United States of America., Krams M; Janssen Research & Development, Titusville, New Jersey, United States of America., Krebs HI; Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2021 Jan 29; Vol. 16 (1), pp. e0245874. Date of Electronic Publication: 2021 Jan 29 (Print Publication: 2021).
DOI: 10.1371/journal.pone.0245874
Abstrakt: Objective: One of the greatest challenges in clinical trial design is dealing with the subjectivity and variability introduced by human raters when measuring clinical end-points. We hypothesized that robotic measures that capture the kinematics of human movements collected longitudinally in patients after stroke would bear a significant relationship to the ordinal clinical scales and potentially lead to the development of more sensitive motor biomarkers that could improve the efficiency and cost of clinical trials.
Materials and Methods: We used clinical scales and a robotic assay to measure arm movement in 208 patients 7, 14, 21, 30 and 90 days after acute ischemic stroke at two separate clinical sites. The robots are low impedance and low friction interactive devices that precisely measure speed, position and force, so that even a hemiparetic patient can generate a complete measurement profile. These profiles were used to develop predictive models of the clinical assessments employing a combination of artificial ant colonies and neural network ensembles.
Results: The resulting models replicated commonly used clinical scales to a cross-validated R2 of 0.73, 0.75, 0.63 and 0.60 for the Fugl-Meyer, Motor Power, NIH stroke and modified Rankin scales, respectively. Moreover, when suitably scaled and combined, the robotic measures demonstrated a significant increase in effect size from day 7 to 90 over historical data (1.47 versus 0.67).
Discussion and Conclusion: These results suggest that it is possible to derive surrogate biomarkers that can significantly reduce the sample size required to power future stroke clinical trials.
Competing Interests: The authors have read the journal’s policy and the authors of this manuscript have the following competing interests: AD and MK are employed by Janssen Research & Development, DA and EY were employed by Janssen Research & Development when this work was conducted and are currently employed by Novartis Institutes for BioMedical Research, GL is employed by GSL Statistical Consulting, GB is employed by Bioconstat Bvba, and JC is employed by Biogen-Idec. Wyeth provided grant funding for the study to HIK. This does not alter our adherence to PLOS ONE policies on sharing data and materials. HIK was also the founder of Interactive Motion Technologies and Chairman of the Board from 1998 to 2016. He sold Interactive Motion Technologies on April 2016 to Bionik Laboratories, where he served as Chief Science Officer and Board Member until July 2017. HIK holds equity positions in Interactive Motion Technologies, Watertown, MA, USA, the company that manufactures this type of technology under license from MIT. HIK was the founder of 4Motion Robotics. The authors would like to declare the following patents/patent applications associated with this research: HIK is a co-inventor in patents held by the Massachusetts Institute of Technology for the robotic technology used in this work (US Patent 7,618,381, US Patent 5,466,213, and US Patent App. 11/154,197).
Databáze: MEDLINE
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