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

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.

Robotic measurement of arm movements after stroke establishes biomarkers of motor recovery.

BACKGROUND AND PURPOSE: Because robotic devices record the kinematics and kinetics of human movements with high resolution, we hypothesized that robotic measures collected longitudinally in patients after stroke would bear a significant relationship to standard clinical outcome measures and, therefore, might provide superior biomarkers. METHODS: In patients with moderate-to-severe acute ischemic stroke, we used clinical scales and robotic devices to measure arm movement 7, 14, 21, 30, and 90 days after the event at 2 clinical sites. The robots are interactive devices that measure speed, position, and force so that calculated kinematic and kinetic parameters could be compared with clinical assessments. RESULTS: Among 208 patients, robotic measures predicted well the clinical measures (cross-validated R(2) of modified Rankin scale=0.60; National Institutes of Health Stroke Scale=0.63; Fugl-Meyer=0.73; Motor Power=0.75). When suitably scaled and combined by an artificial neural network, the robotic measures demonstrated greater sensitivity in measuring the recovery of patients from day 7 to day 90 (increased standardized effect=1.47). CONCLUSIONS: These results demonstrate that robotic measures of motor performance will more than adequately capture outcome, and the altered effect size will reduce the required sample size. Reducing sample size will likely improve study efficiency.

A Bayesian dose-finding trial with adaptive dose expansion to flexibly assess efficacy and safety of an investigational drug.

BACKGROUND: Adaptive dose-ranging trials are more efficient than traditional approaches and may be designed to explicitly address the goals and decisions inherent in learn-phase drug development. We report the design, implementation, and outcome of an innovative Bayesian, response-adaptive, dose-ranging trial of an investigational drug in patients with diabetes, incorporating a dose expansion approach to flexibly address both efficacy and safety. PURPOSE: The design was developed to assess whether one or more doses of an investigational drug demonstrated superior efficacy to an active control while maintaining an acceptable safety profile. METHODS: The trial used a two-stage design, in which patients were initially allocated equally to placebo, investigational drug at a low and a medium dose, and an active control. Movement to the second stage was contingent upon evidence of efficacy (measured by change in fasting blood glucose) to add a very low dose of the investigational drug and of safety (measured by weight gain) to add a high dose of the investigational drug. The design incorporated a longitudinal model to maximize use of incomplete data, predictive probabilities to guide the decisions to terminate the trial for futility or move on to Stage 2, and a dose-response model in Stage 2 to borrow information across adjacent doses. Extensive simulations were used to fine tune trial parameters, to define operating characteristics, and to determine the required sample sizes. A data monitoring committee was provided with frequent reports to aid in trial oversight. RESULTS: In Stage 1, as trial data accrued, the predictive probability that either the low or medium dose of the investigational drug was superior to the active control fell to low values. Stage 1 termination was recommended after 199 subjects were randomized, out of a maximum trial size of 500 subjects, and the final sample size was 218. Thus the trial did not progress to Stage 2. LIMITATIONS: Because of the relatively narrow dose range to be assessed, and the inability to utilize the highest dose at the beginning of the trial, a fully responsive-adaptive design incorporating dose-response modeling was not considered a viable option. This limited the efficiency gains possible with a full set of adaptive design elements. CONCLUSIONS: The two-stage dose-expansion design functioned as designed, recommending early termination based on a low probability that the tested doses had efficacy greater than the active control.