by Keyword: Major clinical study
McGill, Kris, Sackley, Catherine, Godwin, Jon, Gavaghan, David, Ali, Myzoon, Ballester, Belen Rubio, Brady, Marian C, Brady, M.C, Ali, M, Ashburn, A, Barer, D, Barzel, A, Bernhardt, J, Bowen, A, Drummond, A, Edmans, J, English, C, Gladman, J, Godecke, E, Hiekkala, S, Hoffman, T, Kalra, L, Kuys, S, Langhorne, P, Laska, A.C, Lees, K, Logan, P, Machner, B, Mead, G, Morris, J, Pandyan, A, Pollock, A, Pomeroy, V, Rodgers, H, Sackley, C, Shaw, L, Stott, D.J, Sunnerhagen, K.S, Tyson, S, van Vliet, P, Walker, M, Whiteley, W, (2022). Using the Barthel Index and modified Rankin Scale as Outcome Measures for Stroke Rehabilitation Trials; A Comparison of Minimum Sample Size Requirements Journal Of Stroke & Cerebrovascular Diseases 31, 106229
Underpowered trials risk inaccurate results. Recruitment to stroke rehabilitation randomised controlled trials (RCTs) is often a challenge. Statistical simulations offer an important opportunity to explore the adequacy of sample sizes in the context of specific outcome measures. We aimed to examine and compare the adequacy of stroke rehabilitation RCT sample sizes using the Barthel Index (BI) or modified Rankin Scale (mRS) as primary outcomes.We conducted computer simulations using typical experimental event rates (EER) and control event rates (CER) based on individual participant data (IPD) from stroke rehabilitation RCTs. Event rates are the proportion of participants who experienced clinically relevant improvements in the RCT experimental and control groups. We examined minimum sample size requirements and estimated the number of participants required to achieve a number needed to treat within clinically acceptable boundaries for the BI and mRS.We secured 2350 IPD (18 RCTs). For a 90% chance of statistical accuracy on the BI a rehabilitation RCT would require 273 participants per randomised group. Accurate interpretation of effect sizes would require 1000s of participants per group. Simulations for the mRS were not possible as a clinically relevant improvement was not detected when using this outcome measure.Stroke rehabilitation RCTs with large sample sizes are required for accurate interpretation of effect sizes based on the BI. The mRS lacked sensitivity to detect change and thus may be unsuitable as a primary outcome in stroke rehabilitation trials.Copyright © 2021 Elsevier Inc. All rights reserved.
JTD Keywords:  , barthel index, design, increasing value, modified rankin scale, randomised controlled trials, recruitment, reducing waste, reliability, sample size calculations, simulations, stroke rehabilitation, Adult, Article, Barthel index, Calculation, Computer simulation, Controlled study, Effect size, Female, Human, Human experiment, Major clinical study, Male, Modified rankin scale, Numbers needed to treat, Outcome assessment, Randomised controlled trials, Randomized controlled trial, Randomized controlled-trials, Rankin scale, Recruitment, Rehabilitation, Sample size, Sample size calculations, Simulations, Stroke rehabilitation
Ballester, BR, Antenucci, F, Maier, M, Coolen, ACC, Verschure, PFMJ, (2021). Estimating upper-extremity function from kinematics in stroke patients following goal-oriented computer-based training Journal Of Neuroengineering And Rehabilitation 18,
Introduction: After a stroke, a wide range of deficits can occur with varying onset latencies. As a result, assessing impairment and recovery are enormous challenges in neurorehabilitation. Although several clinical scales are generally accepted, they are time-consuming, show high inter-rater variability, have low ecological validity, and are vulnerable to biases introduced by compensatory movements and action modifications. Alternative methods need to be developed for efficient and objective assessment. In this study, we explore the potential of computer-based body tracking systems and classification tools to estimate the motor impairment of the more affected arm in stroke patients. Methods: We present a method for estimating clinical scores from movement parameters that are extracted from kinematic data recorded during unsupervised computer-based rehabilitation sessions. We identify a number of kinematic descriptors that characterise the patients' hemiparesis (e.g., movement smoothness, work area), we implement a double-noise model and perform a multivariate regression using clinical data from 98 stroke patients who completed a total of 191 sessions with RGS. Results: Our results reveal a new digital biomarker of arm function, the Total Goal-Directed Movement (TGDM), which relates to the patients work area during the execution of goal-oriented reaching movements. The model's performance to estimate FM-UE scores reaches an accuracy of R-2: 0.38 with an error (sigma: 12.8). Next, we evaluate its reliability (r = 0.89 for test-retest), longitudinal external validity (95% true positive rate), sensitivity, and generalisation to other tasks that involve planar reaching movements (R-2: 0.39). The model achieves comparable accuracy also for the Chedoke Arm and Hand Activity Inventory (R-2: 0.40) and Barthel Index (R-2: 0.35). Conclusions: Our results highlight the clinical value of kinematic data collected during unsupervised goal-oriented motor training with the RGS combined with data science techniques, and provide new insight into factors underlying recovery and its biomarkers.
JTD Keywords: interactive feedback, motion classification, motion sensing, multivariate regression, posture monitoring, rehabilitation, stroke, Adult, Aged, Analytic method, Arm movement, Article, Barthel index, Brain hemorrhage, Cerebrovascular accident, Chedoke arm and hand activity inventory, Clinical protocol, Cognitive defect, Computer analysis, Controlled study, Convergent validity, Correlation coefficient, Disease severity, External validity, Female, Fugl meyer assessment for the upper extremity, Functional assessment, Functional status assessment, General health status assessment, Hemiparesis, Human, Interactive feedback, Ischemic stroke, Kinematics, Major clinical study, Male, Mini mental state examination, Motion classification, Motion sensing, Motor analog scale, Movement, Multivariate regression, Muscle function, Posture monitoring, Probability, Recovery, Rehabilitation, Reliability, Retrospective study, Stroke, Stroke patient, Test retest reliability, Therapy, Total goal directed movement, Upper extremities, Upper limb, Upper-limb, Wolf motor function test