Open AccessAccurate prediction of clinical stroke scales and improved biomarkers of motor impairment from robotic measurements
Author(s) -
Dimitris Agrafiotis,
Eric Yang,
Gary S. Littman,
Geert Byttebier,
Laura Dipietro,
Allitia DiBernardo,
Juan C. Chávez,
Avrielle Rykman,
Kate McArthur,
Karim Hajjar,
Kennedy R. Lees,
Bruce T. Volpe,
Michael Krams,
Hermano Igo Krebs
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0245874
Subject(s) - physical medicine and rehabilitation , clinical trial , stroke (engine) , modified rankin scale , sample size determination , medicine , computer science , physical therapy , artificial intelligence , ischemic stroke , statistics , mathematics , pathology , cardiology , mechanical engineering , ischemia , engineering
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 R 2 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.