Posts Tagged prediction

[Abstract] Fine motor skills predict performance in the Jebsen Taylor Hand Function Test after stroke

Highlights

Three characteristic factors differentiate fine motor control in patients and controls.

The three factors are grip force scaling, motor coordination and speed of movement.

These factors are predictors of 69% of variance for the Jebsen Taylor Hand Function Test.

Abstract

Objective

To determine factors characterizing the differences in fine motor performance between stroke patients and controls. To confirm the relevance of the factors by analyzing their predictive power with regard to the Jebsen Taylor Hand Function Test (JTHFT), a common clinical test of fine motor control.

Methods

Twenty-two people with slight paresis in an early chronic phase following stroke and twenty-two healthy controls were examined.. Performance on the JTHFT, Nine-Hole Peg Test and 2-point discrimination was evaluated. To analyze object manipulation skills, grip forces and temporal measures were examined during 1) lifting actions with variations of weight and surface 2) cyclic movements 3) predictive/reactive catching tasks. Three other aspects of force control included 4) visuomotor tracking 5) fast force changes and 6) grip strength.

Results

Based on 9 parameters which significantly distinguished fine motor performance in the two groups, we identified three principal components (factors): grip force scaling, motor coordination and speed of movement. The three factors are shown to predict JTHFT scores via linear regression (R2=.687, p<.001).

Conclusions

We revealed a factor structure behind fine motor impairments following stroke and showed that it explains JTHFT results to a large extend.

Significance: This result can serve as a basis for improving diagnostics and enabling more targeted therapy.

Source: Fine motor skills predict performance in the Jebsen Taylor Hand Function Test after stroke – Clinical Neurophysiology

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[ARTICLE] Prediction of Walking and Arm Recovery after Stroke: A Critical Review – Full Text HTML

Abstract

Clinicians often base their predictions of walking and arm recovery on multiple predictors. Multivariate prediction models may assist clinicians to make accurate predictions. Several reviews have been published on the prediction of motor recovery after stroke, but none have critically appraised development and validation studies of models for predicting walking and arm recovery. In this review, we highlight some common methodological limitations of models that have been developed and validated. Notable models include the proportional recovery model and the PREP algorithm. We also identify five other models based on clinical predictors that might be ready for further validation. It has been suggested that neurophysiological and neuroimaging data may be used to predict arm recovery. Current evidence suggests, but does not show conclusively, that the addition of neurophysiological and neuroimaging data to models containing clinical predictors yields clinically important increases in predictive accuracy.

1. Introduction

It would be useful to be able to predict recovery of walking and arm after stroke. Accurate predictions are needed so that clinicians can provide patients with prognoses, set goals, select therapies and plan discharge [1,2,3,4]. For example, if it was possible to predict with some certainty that a particular patient would be unable to walk independently at six months, the clinicians providing that patient with acute and subacute care might work toward a discharge goal of safe transfers. Therapy might involve carer training and equipment prescription rather than intensive gait training. The ability to make accurate predictions could reduce the length of stay in hospitals and enable efficient utilization of stroke care resources [4,5].
Several systematic reviews have identified strong predictors of walking and arm recovery after stroke [2,3,6]. In one systematic review of prognostic studies on walking, clinical variables such as age, severity of paresis and leg power were found to be strong predictors of walking after stroke (based on five studies, each of between 197 and 804 patients) [2]. In another systematic review of prognostic studies on arm recovery, clinical, neurophysiological and neuroimaging data were found to be strong predictors of arm recovery after stroke (based on 58 studies of 9–1197 patients) [3]. These clinical, neurophysiological and neuroimaging data included measures of upper limb impairment, upper limb function, lower limb impairment, motor and somatosensory evoked potentials, and measures obtained with diffusion tensor imaging [3].
In practice, clinicians base their predictions about clinical outcomes on multiple variables [7,8,9]. If multiple predictors are to be used to make prognoses, there needs to be a proper accounting of the independent (incremental) predictive value of each predictor variable. Therefore the most useful information about prognosis is likely to come from multivariate prediction models [7,8,9].
The research which underpins establishment of clinically useful multivariate prediction models involves several steps. First ‘development studies’ are conducted to build the multivariate prediction models [7]. Subsequently the predictive accuracy of the models is tested on new cohorts [7,10]. These studies are known as ‘validation studies’ [7]. It is recommended that prediction models should not be used in clinical practice until both development and validation studies have been conducted [7,10]. Once development and validation studies have been conducted, impact studies may be conducted, although the reality is that few reports of impact studies are published. Impact studies resemble clinical trials; they test the efficacy of use of prediction models on patient outcomes, clinician behaviour and cost-effectiveness of care [7,11]. Recent narrative reviews have provided updates on the prediction of motor recovery after stroke [5,12] but these reviews have not focused on development and validation studies of models for predicting walking and arm recovery.
This review provides a critical review of prediction models of walking and arm recovery after stroke. Studies were identified using the search strategy and inclusion criteria in the Appendix. The review begins in the second section with the definitions and measurements of walking and arm recovery. The third section provides a detailed description of the recommended process for developing and validating a prediction model because this process provides a benchmark against which prediction modelling studies of walking and arm recovery can be evaluated. The fourth section critically appraises development and validation studies of walking and arm recovery with the aim of identifying multivariate models that could potentially be implemented in clinical practice. Much has been written about the role of neurophysiological and neuroimaging data in predicting arm recovery. The fifth section considers whether neurophysiological and neuroimaging data provide additional predictive value over clinical data alone in predicting arm recovery. We conclude with a summary and recommendations for future prediction modelling studies.

Continue —> Brain Sciences | Free Full-Text | Prediction of Walking and Arm Recovery after Stroke: A Critical Review | HTML

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[ARTICLE] Prediction of Walking and Arm Recovery after Stroke: A Critical Review – Full Text HTML

Abstract

Clinicians often base their predictions of walking and arm recovery on multiple predictors. Multivariate prediction models may assist clinicians to make accurate predictions. Several reviews have been published on the prediction of motor recovery after stroke, but none have critically appraised development and validation studies of models for predicting walking and arm recovery.
In this review, we highlight some common methodological limitations of models that have been developed and validated. Notable models include the proportional recovery model and the PREP algorithm. We also identify five other models based on clinical predictors that might be ready for further validation. It has been suggested that neurophysiological and neuroimaging data may be used to predict arm recovery. Current evidence suggests, but does not show conclusively, that the addition of neurophysiological and neuroimaging data to models containing clinical predictors yields clinically important increases in predictive accuracy.

1. Introduction

It would be useful to be able to predict recovery of walking and arm after stroke. Accurate predictions are needed so that clinicians can provide patients with prognoses, set goals, select therapies and plan discharge [1,2,3,4]. For example, if it was possible to predict with some certainty that a particular patient would be unable to walk independently at six months, the clinicians providing that patient with acute and subacute care might work toward a discharge goal of safe transfers. Therapy might involve carer training and equipment prescription rather than intensive gait training. The ability to make accurate predictions could reduce the length of stay in hospitals and enable efficient utilization of stroke care resources [4,5].
Several systematic reviews have identified strong predictors of walking and arm recovery after stroke [2,3,6]. In one systematic review of prognostic studies on walking, clinical variables such as age, severity of paresis and leg power were found to be strong predictors of walking after stroke (based on five studies, each of between 197 and 804 patients) [2]. In another systematic review of prognostic studies on arm recovery, clinical, neurophysiological and neuroimaging data were found to be strong predictors of arm recovery after stroke (based on 58 studies of 9–1197 patients) [3]. These clinical, neurophysiological and neuroimaging data included measures of upper limb impairment, upper limb function, lower limb impairment, motor and somatosensory evoked potentials, and measures obtained with diffusion tensor imaging [3].
In practice, clinicians base their predictions about clinical outcomes on multiple variables [7,8,9]. If multiple predictors are to be used to make prognoses, there needs to be a proper accounting of the independent (incremental) predictive value of each predictor variable. Therefore the most useful information about prognosis is likely to come from multivariate prediction models [7,8,9].
The research which underpins establishment of clinically useful multivariate prediction models involves several steps. First ‘development studies’ are conducted to build the multivariate prediction models [7]. Subsequently the predictive accuracy of the models is tested on new cohorts [7,10]. These studies are known as ‘validation studies’ [7]. It is recommended that prediction models should not be used in clinical practice until both development and validation studies have been conducted [7,10]. Once development and validation studies have been conducted, impact studies may be conducted, although the reality is that few reports of impact studies are published. Impact studies resemble clinical trials; they test the efficacy of use of prediction models on patient outcomes, clinician behaviour and cost-effectiveness of care [7,11]. Recent narrative reviews have provided updates on the prediction of motor recovery after stroke [5,12] but these reviews have not focused on development and validation studies of models for predicting walking and arm recovery.
This review provides a critical review of prediction models of walking and arm recovery after stroke. Studies were identified using the search strategy and inclusion criteria in the Appendix. The review begins in the second section with the definitions and measurements of walking and arm recovery. The third section provides a detailed description of the recommended process for developing and validating a prediction model because this process provides a benchmark against which prediction modelling studies of walking and arm recovery can be evaluated. The fourth section critically appraises development and validation studies of walking and arm recovery with the aim of identifying multivariate models that could potentially be implemented in clinical practice. Much has been written about the role of neurophysiological and neuroimaging data in predicting arm recovery. The fifth section considers whether neurophysiological and neuroimaging data provide additional predictive value over clinical data alone in predicting arm recovery. We conclude with a summary and recommendations for future prediction modelling studies.

Continue —> Brain Sciences | Free Full-Text | Prediction of Walking and Arm Recovery after Stroke: A Critical Review | HTML

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[ARTICLE] Prediction of Upper Limb Motor Recovery after Subacute Ischemic Stroke Using Diffusion Tensor Imaging: A Systematic Review and Meta-Analysis – Full Text

Abstract

Early evaluation of the pyramidal tract using Diffusion Tensor Imaging (DTI) is a prerequisite to decide the optimal treatment or to assess appropriate rehabilitation. The early predictive value of DTI for assessing motor and functional recovery in ischemic stroke (IS) has yielded contradictory results. The purpose is to systematically review and summarize the current available literature on the value of Fractional Anisotropy (FA) parameter of the DTI in predicting upper limb motor recovery after sub-acute IS. MEDLINE, PubMed, EMBASE, Google Scholar and Cochrane CENTRAL searches were conducted from January 1, 1950, to July 31, 2015, which was supplemented with relevant articles identified in the references. Correlation between FA and upper limb motor recovery measure was done. Heterogeneity was examined using Higgins I-squared, Tau-squared. Summary of correlation coefficient was determined using Random Effects model. Out of 166 citations, only eleven studies met the criteria for inclusion in the systematic review and six studies were included in the meta-analysis. A random effects model revealed that DTI parameter FA is a significant predictor for upper limb motor recovery after sub-acute IS [Correlation Coefficient=0.82; 95% Confidence Interval-0.66 to 0.90, P value<0.001]. Moderate heterogeneity was observed (Tau-squared=0.12, I-squared=62.14). The studies reported so far on correlation between DTI and upper limb motor recovery are few with small sample sizes. This meta-analysis suggests strong correlation between DTI parameter FA and upper limb motor recovery. Well-designed prospective trials embedded with larger sample size are required to establish these findings.

Introduction

Stroke is a major leading cause of death and disability worldwide especially in the elderly population [1]. Upper limb motor weakness is one of the most frequent complications after stroke with over 50% of stroke patients experiencing residual motor deficit [2]. Despite advances in treatment of acute ischemic stroke (IS) and post-stroke rehabilitation, the dependency rate after stroke still reaches 20%-30% [3]. Prognostication of upper limb motor outcomes after stroke is an important for specific rehabilitation strategies and final motor outcomes but, considered a difficult task. Many studies have tried to predict motor outcome in hemiparetic stroke patients using clinical findings [4,5], electrophysiological methods [6,7], and functional neuroimaging [8,9]. However, these studies have an inherent weakness that they were unable to visualize the corticospinal tract (CST), the most important structure for motor control, especially for fine motor control of the hand in humans [10].
Diffusion tensor imaging (DTI) is an advance non-invasive magnetic resonance imaging technique used to characterize the orientation properties of diffusion process of water molecules. DTI has a unique advantage in visualization and estimation of CST which is the most important neural tract for mainly upper limb motor function [11]. DTI permits the imaging of axonal pathways of the living brain and provides information about tissue microstructure by measuring fractional anisotropy (FA) [12]. FA is an index of the diffusion characteristics of water molecules preferentially directed along the axis of major axonal pathways. FA of the entire tract, acquired early after stroke, reflect acute and permanent damage to pyramidal tracts to determine clinical motor deficit and outcome. A tissue is considered to be fully isotropic when its FA is equal to 0, and fully anisotropic when its FA is equal to 1 [13].
Over the past two decades, numerous cross-sectional DTI studies have examined the relationships between age and the degree of anisotropy FA in white matter tracts [14]. Cross-sectional studies have demonstrated that older adults display lower FA values and higher mean diffusivity and radial diffusivity values compared with younger adults [15,16], with age correlations relatively weak during adulthood and stronger in senescence [17,18]. Currently, the most widely used invariant measure of anisotropy is FA described originally by Basser and Pierpaoli [12]. In the parametric data obtained from DTI, taking advantage of the much larger FA values of highly directional white matter structures, FA images are used to distinguish white matter and non-white matter tissues [19]. Studies that have examined small homogeneous samples of subcortical stroke patients have found that large asymmetries in FA are associated with poorer motor recovery [20,21]. Findings from recent studies have demonstrated the predictive value of DTI for motor outcome after stroke [22,23], however, it is not yet used routinely to make a prognosis but there have been some interesting recent developments in this area. Therefore, the purpose of this review is to establish the predictive value of DTI for upper limb motor recovery in IS patients.

Continue —> Prediction of Upper Limb Motor Recovery after Subacute Ischemic Stroke Using Diffusion Tensor Imaging: A Systematic Review and Meta-Analysis

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[ARTICLE] Predicting Daily Use of the Affected Upper Extremity 1 Year after Stroke

Background

The ultimate goal of upper extremity (UE) stroke rehabilitation is for the individual with stroke to return using their arms and hands during daily activities in their own environment. No studies have monitored arm use as individuals with stroke transition from rehabilitation to the home setting. This longitudinal study compared the functional ability and daily use of the affected UE of individuals with stroke between discharge to home and 12 months after stroke and predicted the UE daily use 12 months after stroke.

Methods

Participants were assessed on discharge to home from rehabilitation and at 12 months after stroke. UE daily use was measured by wrist accelerometers and self-report by the Motor Activity Log (MAL). Multivariate logistic regression models were used to predict UE daily use 12 months after stroke.

Results

The UE functional ability improved significantly from discharge to 12 months after stroke. The amount of self-report UE daily use significantly improved (z = −2.9, P = .004), but accelerometer activity counts did not (z = −0.15, P = .88), and the daily use of the nonaffected UE was 3 times more than the affected UE. After controlling for age and accelerometer daily use on discharge, UE variables of movement, function, dexterity, and strength accounted for an additional 10.9%-13.6% of the variance for accelerometer readings. After controlling for gender and MAL daily use on discharge, UE variables accounted for an additional 7%-12% of the variance for the MAL.

Conclusions

UE daily use 12 months after stroke is very limited despite the motor and functional improvement. Enhanced motor and functional ability at discharge predicts more UE daily use at 12 months after stroke. Interventions that monitor and encourage these individuals to use their UE are required to ensure that functional gains translate to daily use.

via Predicting Daily Use of the Affected Upper Extremity 1 Year after Stroke.

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