Abstract
ObjectiveOne 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 methodsWe 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.ResultsThe 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 conclusionThese results suggest that it is possible to derive surrogate biomarkers that can significantly reduce the sample size required to power future stroke clinical trials.
Highlights
Stroke is the leading cause of permanent disability in the United States [1]
We provide methods and modeling details of a longitudinal study involving 208 patients who had suffered severe to moderate acute ischemic stroke and were assessed with four commonly used clinical instruments [21]–NIH stroke scale (NIH), Fugl-Meyer assessment (FM), Motor Power (MP) [22, 23] and modified Rankin scale (MR)–as well as with a Accurate prediction of clinical stroke scales from robotic measurement robotic assay to measure arm movement 7, 14, 21, 30, and 90 days after the stroke onset
We identified two complementary patient populations: 1) those with complete data for days 7 and 90 for all 35 robot-measured kinematic and kinetic (RMK) variables and all four clinical scales (87 patients, 67 from Burke and 20 from Glasgow, hereafter referred to as Accurate prediction of clinical stroke scales from robotic measurement completers); and 2) those who did not meet these criteria (121 patients, 79 from Burke and 42 from Glasgow, referred to as non-completers)
Summary
Stroke is the leading cause of permanent disability in the United States [1]. Many robotic devices, such as the InMotion Arm [3], afford the possibility to promote faster and better rehabilitation, and the potential to track an individual’s progress [4,5,6]. An important potential advantage of robotic devices over “traditional” clinical instruments is that the measurement variability due to the skills and expertise of the rater can be removed from the assessment process. The ability to remove inter- and intra-rater variability as well as conduct the assessments more efficiently would enable faster and less costly clinical trials [10]
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