Dataset Information

Are we armed with the right data? Pooled individual data review of biomarkers in people with severe upper limb impairment after stroke.

ABSTRACT: To build an understanding of the neurobiology underpinning arm recovery in people with severe arm impairment due to stroke, we conducted a pooled individual data systematic review to: 1) characterize brain biomarkers; 2) determine relationship(s) between biomarkers and motor outcome; and 3) establish relationship(s) between biomarkers and motor recovery. Three electronic databases were searched up to October 2, 2015. Eligible studies included adults with severe arm impairment after stroke. Descriptive statistics were calculated to characterize brain biomarkers, and pooling of individual patient data was performed using mixed-effects linear regression to examine relationships between brain biomarkers and motor outcome and recovery. Thirty-eight articles including individual data from 372 people with severe arm impairment were analysed. The majority of individuals were in the chronic (> 6 months) phase post stroke (51%) and had a subcortical stroke (49%). The presence of a motor evoked potential (indexed by transcranial magnetic stimulation) was the only biomarker related to better motor outcome (p = 0.02). There was no relationship between motor outcome and stroke volume (cm³), location (cortical, subcortical, mixed) or side (left vs. right), and corticospinal tract asymmetry index (extracted from diffusion weighted imaging). Only one study had longitudinal data, thus no data pooling was possible to address change over time (preventing our third objective). Based on the available evidence, motor evoked potentials at rest were the only biomarker that predicted motor outcome in individuals with severe arm impairment following stroke. Given that few biomarkers emerged, this review highlights the need to move beyond currently known biomarkers and identify new indices with sufficient variability and sensitivity to guide recovery models in individuals with severe motor impairments following stroke.

Prospero

CRD42015026107.

SUBMITTER: Hayward KS

PROVIDER: S-EPMC5198729 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Are we armed with the right data? Pooled individual data review of biomarkers in people with severe upper limb impairment after stroke.

Hayward Kathryn S KS Schmidt Julia J Lohse Keith R KR Peters Sue S Peters Sue S Bernhardt Julie J Lannin Natasha A NA Boyd Lara A LA

NeuroImage. Clinical 20160924

To build an understanding of the neurobiology underpinning arm recovery in people with severe arm impairment due to stroke, we conducted a pooled individual data systematic review to: 1) characterize brain biomarkers; 2) determine relationship(s) between biomarkers and motor outcome; and 3) establish relationship(s) between biomarkers and motor recovery. Three electronic databases were searched up to October 2, 2015. Eligible studies included adults with severe arm impairment after stroke. Descr ...[more]

PMID: 28053857

Similar Datasets

Project description:BackgroundEffective rehabilitative therapies are needed for patients with long-term deficits after stroke.MethodsIn this multicenter, randomized, controlled trial involving 127 patients with moderate-to-severe upper-limb impairment 6 months or more after a stroke, we randomly assigned 49 patients to receive intensive robot-assisted therapy, 50 to receive intensive comparison therapy, and 28 to receive usual care. Therapy consisted of 36 1-hour sessions over a period of 12 weeks. The primary outcome was a change in motor function, as measured on the Fugl-Meyer Assessment of Sensorimotor Recovery after Stroke, at 12 weeks. Secondary outcomes were scores on the Wolf Motor Function Test and the Stroke Impact Scale. Secondary analyses assessed the treatment effect at 36 weeks.ResultsAt 12 weeks, the mean Fugl-Meyer score for patients receiving robot-assisted therapy was better than that for patients receiving usual care (difference, 2.17 points; 95% confidence interval [CI], -0.23 to 4.58) and worse than that for patients receiving intensive comparison therapy (difference, -0.14 points; 95% CI, -2.94 to 2.65), but the differences were not significant. The results on the Stroke Impact Scale were significantly better for patients receiving robot-assisted therapy than for those receiving usual care (difference, 7.64 points; 95% CI, 2.03 to 13.24). No other treatment comparisons were significant at 12 weeks. Secondary analyses showed that at 36 weeks, robot-assisted therapy significantly improved the Fugl-Meyer score (difference, 2.88 points; 95% CI, 0.57 to 5.18) and the time on the Wolf Motor Function Test (difference, -8.10 seconds; 95% CI, -13.61 to -2.60) as compared with usual care but not with intensive therapy. No serious adverse events were reported.ConclusionsIn patients with long-term upper-limb deficits after stroke, robot-assisted therapy did not significantly improve motor function at 12 weeks, as compared with usual care or intensive therapy. In secondary analyses, robot-assisted therapy improved outcomes over 36 weeks as compared with usual care but not with intensive therapy. (ClinicalTrials.gov number, NCT00372411.)

Project description:IntroductionStroke-induced upper limb disabilities can be characterized by both motor impairments and activity limitations, commonly assessed using Fugl-Meyer Motor Assessment for Upper Extremity (FMMA-UE) and Action Research Arm Test (ARAT), respectively. The relationship between the two assessments during recovery is largely unstudied. Expectedly they diverge over time when recovery of impairment (restitution) plateaus, but compensation-driven improvements still occur. The objective of this study is to evaluate the alignment between FMMA-UE and ARAT in defining upper limb functional recovery categories by ARAT scores. We aimed to establish cut-off scores for both measures from the acute/early subacute, subacute and chronic stages of stroke recovery.MethodsSecondary analysis of four prospective cohort studies (acute/early subacute: n = 133, subacute: n = 113, chronic: n = 92) stages post-stroke. Receiver operating characteristic curves calculated the area under the curve (AUC) to establish optimal FMMA-UE cut-offs based on predefined ARAT thresholds distinguishing five activity levels from no activity to full activity. Weighted kappa was used to determine agreement between the two assessments. We used minimally clinically important difference (MCID) and minimal detectable change (MDC95) for comparison.ResultsFMMA-UE and ARAT scores showed no relevant divergence across all recovery stages. Results indicated similar cut-off scores in all recovery stages with variability below MCID and MDC95 levels. Cut-off scores demonstrated robust AUC values from 0.77 to 0.86 at every recovery stage. Only in highly functional patients at the chronic stage, we found a reduced specificity of 0.55. At all other times sensitivity ranged between 0.68 and 0.99 and specificity between 0.71 and 0.99. Weighted kappa at the acute/early subacute, subacute and chronic stages was 0.76, 0.83, and 0.81, respectively.DiscussionOur research shows a strong alignment between FMMA-UE and ARAT cut-off scores throughout stroke recovery, except among the subgroup of highly recovered patients at the chronic stage. Discrepancies in specificity potentially stem from fine motor deficits affecting dexterity outcomes that are not captured by FMMA-UE. Additionally, the high congruence of both measures suggests they are not suited to distinguish between restitution and compensation. Calling for more comprehensive assessment methods to better understand upper limb functionality in rehabilitation.

Dataset Information

Are we armed with the right data? Pooled individual data review of biomarkers in people with severe upper limb impairment after stroke.

Prospero

Publications

Are we armed with the right data? Pooled individual data review of biomarkers in people with severe upper limb impairment after stroke.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets