Posts Tagged proportional recovery

[Abstract] Breaking Proportional Recovery After Stroke

People with hemiparesis after stroke appear to recover 70% to 80% of the difference between their baseline and the maximum upper extremity Fugl-Meyer (UEFM) score, a phenomenon called proportional recovery (PR). Two recent commentaries explained that PR should be expected because of mathematical coupling between the baseline and change score. Here we ask, If mathematical coupling encourages PR, why do a fraction of stroke patients (the “nonfitters”) not exhibit PR? At the neuroanatomical level of analysis, this question was answered by Byblow et al—nonfitters lack corticospinal tract (CST) integrity at baseline—but here we address the mathematical and behavioral causes. We first derive a new interpretation of the slope of PR: It is the average probability of scoring across remaining scale items at follow-up. PR therefore breaks when enough test items are discretely more difficult for a patient at follow-up, flattening the slope of recovery. For the UEFM, we show that nonfitters are most unlikely to recover the ability to score on the test items related to wrist/hand dexterity, shoulder flexion without bending the elbow, and finger-to-nose movement, supporting the finding that nonfitters lack CST integrity. However, we also show that a subset of nonfitters respond better to robotic movement training in the chronic phase of stroke. These persons are just able to move the arm out of the flexion synergy and pick up small blocks, both markers of CST integrity. Nonfitters therefore raise interesting questions about CST function and the basis for response to intensive movement training.

via Breaking Proportional Recovery After Stroke – Merav R. Senesh, David J. Reinkensmeyer, 2019

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[Editorial] Proportional Recovery in the Spotlight – Neurorehabilitation and Neural Repair

By Randolph J. Nudo

Prediction of who will recover after stroke has been a perennial focus for both researchers and clinicians in the field of neurorehabilitation. The prospects of applying a population-based model to predict outcome in individual patients might ultimately allow more focused approaches to stroke rehabilitation and foster a better distribution of precious health care resources. Aside from anatomical biomarkers, such as the integrity of the corticospinal tract, recent attention has focused on the proportional recovery rule, formally proposed in this journal more than 10 years ago by Prabhakaran et al,1 who described a surprisingly linear relationship between Fugl-Meyer Assessment upper extremity scores obtained within 3 days after stroke and those obtained at 3 months poststroke, illustrating the general principle of spontaneous recovery with a level of predictability not previously appreciated. This relationship appears to hold for most individuals (so-called “fitters” or “recoverers”), but a subset of individuals (so-called “non-fitters” or “non-recoverers”) fall off the linear regression line. First applied to upper limb motor impairment, the proportional recovery rule has been examined in a variety of motor and nonmotor impairments, and results have generally been in agreement with the initial linear relationship. Recent controversy surrounding the proportional recovery rule has been based on statistical factors such as mathematical coupling and nonlinearity of outcome scales, questioning not only the accuracy but also the underlying validity of this predictive population-based model. Two articles in the current issue of Neurorehabilitation and Neural Repair highlight some of the emerging views and suggestions for future research regarding this model. The first article by Senesh and Reinkensmeyer examines the reasons why “non-fitters” do not recover according to the proportional recovery algorithm. They argue that the local slope of the linear regression reflects the difficulty of test item scores related to arm and hand movement at follow-up, consistent with the view that non-fitters lack sufficient corticospinal tract. They suggest that at least some non-fitters may have a heightened response to intensive movement training and should be targeted early after stroke for such rehabilitative training. In the second article by Kundert et al, the statistical validity of the proportional recovery rule is examined in the context of recent criticisms regarding its underlying assumptions. Despite 2 recent articles critical of statistical relationships of baseline impairment scores to follow-up scores, especially when used for patient-level predictions, Kundert et al contend that the systematic non-artifactual relationship between initial impairment and motor recovery provides a valid statistical and biologically meaningful model, and that future studies of proportional recovery should use more sophisticated analysis techniques and rigorous methods to assess validity, including comparisons to alternative models.

Randolph J. Nudo, PhD
Editor-in-Chief

1. Prabhakaran, S, Zarahn, E, Riley, C, et alInter-individual variability in the capacity for motor recovery after ischemic stroke. Neurorehabil Neural Repair. 2008;22:6471. doi:10.1177/1545968307305302
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via Proportional Recovery in the Spotlight – Randolph J. Nudo, 2019

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[Editorial] Proportional Recovery in the Spotlight – Neurorehabilitation and Neural Repair

Prediction of who will recover after stroke has been a perennial focus for both researchers and clinicians in the field of neurorehabilitation. The prospects of applying a population-based model to predict outcome in individual patients might ultimately allow more focused approaches to stroke rehabilitation and foster a better distribution of precious health care resources. Aside from anatomical biomarkers, such as the integrity of the corticospinal tract, recent attention has focused on the proportional recovery rule, formally proposed in this journal more than 10 years ago by Prabhakaran et al,1 who described a surprisingly linear relationship between Fugl-Meyer Assessment upper extremity scores obtained within 3 days after stroke and those obtained at 3 months poststroke, illustrating the general principle of spontaneous recovery with a level of predictability not previously appreciated. This relationship appears to hold for most individuals (so-called “fitters” or “recoverers”), but a subset of individuals (so-called “non-fitters” or “non-recoverers”) fall off the linear regression line. First applied to upper limb motor impairment, the proportional recovery rule has been examined in a variety of motor and nonmotor impairments, and results have generally been in agreement with the initial linear relationship. Recent controversy surrounding the proportional recovery rule has been based on statistical factors such as mathematical coupling and nonlinearity of outcome scales, questioning not only the accuracy but also the underlying validity of this predictive population-based model. Two articles in the current issue of Neurorehabilitation and Neural Repair highlight some of the emerging views and suggestions for future research regarding this model. The first article by Senesh and Reinkensmeyer examines the reasons why “non-fitters” do not recover according to the proportional recovery algorithm. They argue that the local slope of the linear regression reflects the difficulty of test item scores related to arm and hand movement at follow-up, consistent with the view that non-fitters lack sufficient corticospinal tract. They suggest that at least some non-fitters may have a heightened response to intensive movement training and should be targeted early after stroke for such rehabilitative training. In the second article by Kundert et al, the statistical validity of the proportional recovery rule is examined in the context of recent criticisms regarding its underlying assumptions. Despite 2 recent articles critical of statistical relationships of baseline impairment scores to follow-up scores, especially when used for patient-level predictions, Kundert et al contend that the systematic non-artifactual relationship between initial impairment and motor recovery provides a valid statistical and biologically meaningful model, and that future studies of proportional recovery should use more sophisticated analysis techniques and rigorous methods to assess validity, including comparisons to alternative models.

Randolph J. Nudo, PhD
Editor-in-Chief

1. Prabhakaran, S, Zarahn, E, Riley, C, et alInter-individual variability in the capacity for motor recovery after ischemic stroke. Neurorehabil Neural Repair. 2008;22:6471. doi:10.1177/1545968307305302
Google Scholar | SAGE Journals | ISI

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[ARTICLE] What the Proportional Recovery Rule Is (and Is Not): Methodological and Statistical Considerations – Full Text

In 2008, it was proposed that the magnitude of recovery from nonsevere upper limb motor impairment over the first 3 to 6 months after stroke, measured with the Fugl-Meyer Assessment (FMA), is approximately 0.7 times the initial impairment (“proportional recovery”). In contrast to patients with nonsevere hemiparesis, about 30% of patients with an initial severe paresis do not show such recovery (“nonrecoverers”). Hence it was suggested that the proportional recovery rule (PRR) was a manifestation of a spontaneous mechanism that is present in all patients with mild-to-moderate paresis but only in some with severe paresis. Since the introduction of the PRR, it has subsequently been applied to other motor and nonmotor impairments. This more general investigation of the PRR has led to inconsistencies in its formulation and application, making it difficult to draw conclusions across studies and precipitating some cogent criticism. Here, we conduct a detailed comparison of the different studies reporting proportional recovery and, where appropriate, critique statistical methodology. On balance, we conclude that existing data in aggregate are largely consistent with the PRR as a population-level model for upper limb motor recovery; recent reports of its demise are exaggerated, as these excessively focus on the less conclusive issue of individual subject-level predictions. Moving forward, we suggest that methodological caution and new analytical approaches will be needed to confirm (or refute) a systematic character to spontaneous recovery from motor and other poststroke impairments, which can be captured by a mathematical rule either at the population or at the subject level.

It has been appreciated since Hippocrates that the strongest predictor of final motor impairment after stroke is initial impairment (Aphorisms of Hippocrates, Section 2: 42). A prominent poststroke motor impairment in humans is the intrusion of unwanted synergies, with synergy referring to a systematic pattern of either joint co-articulation or muscle co-activation. The Fugl-Meyer Assessment (FMA) was explicitly developed to track progression of recovery from such synergies. A seminal study tracking the recovery of patients using the upper extremity subscale of the Fugl-Meyer Assessment (FMA-UE) demonstrated that more severely affected patients saw greater recovery in this outcome, on average, than more mildly affected patients in the immediate poststroke recovery period1; however, the average final score of the FMA-UE among the severly affected still trailed behind the mildly affected. The authors of this study stated, “The most dramatic recovery in motor function occurred over the first 30 days, regardless of the initial severity of the stroke.” On the basis of this study and other considerations, Krakauer et al2 sought to investigate the nature of this FMA-UE change early after stroke; work that led to the formulation of the proportional recovery rule (PRR).2 The PRR states that patients recover approximately 70% of their maximal potential reduction in impairment as measured by the FMA.2

Since it was introduced, the PRR has been applied in a broad range of studies that involve recovery from stroke, both for FMA-UE and for other outcomes. Claims related to the PRR have been made for upper and lower limb impairment measured by the FMA,310 aphasia measured with the Western Aphasia Battery (WAB),11 the resting motor threshold (RMT) of the extensor carpi radialis,6 and visuospatial neglect measured with the Letter Cancellation Test (LCT),12 among others. Applications of the PRR typically distinguish between two distinct subgroups of patients, referred to as “recoverers” and “nonrecoverers”: the former subgroup is composed of patients who recover a significant amount of lost function, and the latter is composed of those who do not. The PRR is thought to usefully characterize the recovery process among recoverers only. Although the methods by which the PRR was applied and evaluated have differed substantially across publications, many authors have argued that their findings are evidence for a PRR that accurately describes an underlying biological process that arises across neurolocical domains. Recently, however, the PRR has been the subject of criticism related to the validity of the statistical methods underlying its implementation and to the degree to which data are consistent with claims in support of the PRR.13,14 Much of the critique on the PRR articulated by these articles was focused on specific statements associated with the PRR followed by a general dismissal of all findings.

Our goal in this work is to provide a critical reexamination of the literature pertaining to the PRR. We focus first on the interpretation and implementation of PRR as a statistical model, and on data-driven concerns about the use of the PRR in studies of recovery. We then reexamine data reported in the literature and the extent to which past studies provide evidence for the PRR with these considerations in mind. Our hope is that this will serve as an instructive overview of issues that can arise in the application of the PRR to studies of recovery, aiming to improve future investigations into the PRR. Although our primary purpose is not to provide direct response to recent critiques,13,14 we are mindful of the concerns raised and address these directly in the Discussion section.

The breadth of work on the PRR introduces a commensurate range of methodological concerns one might consider. We attempt to be complete in our discussion but prefer to focus on overarching concerns regarding the statistical validity of the PRR instead of point-by-point inspections of the existing literature. Two themes we will revisit while pursuing the main goals of this paper are the identification of recoverers and the distinction between describing biological mechanisms and making patient-level predictions. The manner in which nonrecoverers are identified is a point of legitimate concern, as some statistical approaches can artifactually create evidence for the PRR. The PRR was originally intended to describe biological mechanisms at the population level, although implicitly it is expected that the PRR may be useful for predicting recovery of individual patients. Both of these are related to recent concerns regarding the PRR.

The next section provides an overview of the statistical formulation of the PRR and introduces three simulated datasets to illustrate scenarios over which the PRR shows varying degrees of validity. Subsequent sections conduct a selective review of the literature, reevaluating specific articles in the light of the three scenarios, comment on recent criticisms of the PRR, and end with our current view on the veracity of the PRR.

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Continue —>  What the Proportional Recovery Rule Is (and Is Not): Methodological and Statistical Considerations – Robinson Kundert, Jeff Goldsmith, Janne M. Veerbeek, John W. Krakauer, Andreas R. Luft,

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[Abstract+References] Does Stroke Rehabilitation Really Matter? Part A: Proportional Stroke Recovery in the Rat

Abstract

Background. In human upper-limb stroke, initial level of functional impairment or corticospinal tract injury can accurately predict the degree of poststroke recovery, independent of rehabilitation practices. This proportional recovery rule implies that current rehabilitation practices may play little or no role in brain repair, with recovery largely a result of spontaneous biological recovery processes.

Objective. The present study sought to determine if similar biomarkers predict recovery of poststroke function in rats, indicating that an endogenous biological recovery process might be preserved across mammalian species.

Methods. Using a cohort of 593 male Sprague-Dawley rats, we predicted poststroke change in pellet retrieval in the Montoya staircase-reaching task based on initial impairment alone. Stratification of the sample into “fitters” and “nonfitters” of the proportional recovery rule using hierarchical cluster analysis allowed identification of distinguishing characteristics of these subgroups.

Results. Approximately 30% of subjects were identified as fitters of the rule. These rats showed recovery in proportion to their initial level of impairment of 66% (95% CI = 62%-70%). This interval overlaps with those of multiple human clinical trials. A number of variables, including less severe infarct volumes and initial poststroke impairments distinguished fitters of the rule from nonfitters.

Conclusions. These findings suggest that proportional recovery is a cross-species phenomenon that can be used to uncover biological mechanisms contributing to stroke recovery.

1. Prabhakaran, S, Zarahn, E, Riley, C. Inter-individual variability in the capacity for motor recovery after ischemic stroke. Neurorehabil Neural Repair. 2008;22:6471Google ScholarLink
2. Winters, C, van Wegen, EEH, Daffertshofer, A, Kwakkel, G. Generalizability of the proportional recovery model for the upper extremity after an ischemic stroke. Neurorehabil Neural Repair. 2015;29:614622Google ScholarLinkISI
3. Byblow, WD, Stinear, CM, Barber, PA, Petoe, MA, Ackerley, SJ. Proportional recovery after stroke depends on corticomotor integrity. Ann Neurol. 2015;78:848859Google ScholarCrossrefMedline
4. Feng, W, Wang, J, Chhatbar, PY. Corticospinal tract lesion load: an imaging biomarker for stroke motor outcomes. Ann Neurol. 2015;78:860870Google ScholarCrossrefMedline
5. Stinear, CM, Byblow, WD, Ackerley, SJ, Smith, MC, Borges, VM, Barber, PA. Proportional motor recovery after stroke: implications for trial design. Stroke. 2017;48:795798Google ScholarCrossrefMedline
6. Smith, MC, Byblow, WD, Barber, PA, Stinear, CM. Proportional recovery from lower limb motor impairment after stroke. Stroke. 2017;48:14001403Google ScholarCrossrefMedline
7. Winters, C, van Wegen, EEH, Daffertshofer, A, Kwakkel, G. Generalizability of the maximum proportional recovery rule to visuospatial neglect early poststroke. Neurorehabil Neural Repair. 2017;31:334342Google ScholarLink
8. Lazar, RM, Minzer, B, Antoniello, D, Festa, JR, Krakauer, JW, Marshall, RS. Improvement in aphasia scores after stroke is well predicted by initial severity. Stroke. 2010;41:14851488Google ScholarCrossrefMedline
9. Krakauer, JW, Marshall, RS. The proportional recovery rule for stroke revisited. Ann Neurol. 2015;78:845847Google ScholarCrossrefMedline
10. Gladstone, DJ, Danells, CJ, Black, SE. The Fugl-Meyer assessment of motor recovery after stroke: a critical review of its measurement properties. Neurorehabil Neural Repair. 2002;16:232240Google ScholarLink
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via Does Stroke Rehabilitation Really Matter? Part A: Proportional Stroke Recovery in the RatNeurorehabilitation and Neural Repair – Matthew Strider Jeffers, Sudhir Karthikeyan, Dale Corbett, 2018

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[Abstract+References] Longitudinal Structural and Functional Differences Between Proportional and Poor Motor Recovery After Stroke

Background. Evolution of motor function during the first months after stroke is stereotypically bifurcated, consisting of either recovery to about 70% of maximum possible improvement (“proportional recovery, PROP”) or in little to no improvement (“poor recovery, POOR”). There is currently no evidence that any rehabilitation treatment will prevent POOR and favor PROP. Objective. To perform a longitudinal and multimodal assessment of functional and structural changes in brain organization associated with PROP. Methods. Fugl-Meyer Assessments of the upper extremity and high-density electroencephalography (EEG) were obtained from 63 patients, diffusion tensor imaging from 46 patients, at 2 and 4 weeks (T0) and at 3 months (T1) after stroke onset. Results. We confirmed the presence of 2 distinct recovery patterns (PROP and POOR) in our sample. At T0, PROP patients had greater integrity of the corticospinal tract (CST) and greater EEG functional connectivity (FC) between the affected hemisphere and rest of the brain, in particular between the ventral premotor and the primary motor cortex. POOR patients suffered from degradation of corticocortical and corticofugal fiber tracts in the affected hemisphere between T0 and T1, which was not observed in PROP patients. Better initial CST integrity correlated with greater initial global FC, which was in turn associated with less white matter degradation between T0 and T1. Conclusions. These findings suggest links between initial CST integrity, systems-level cortical network plasticity, reduction of white matter atrophy, and clinical motor recovery after stroke. This identifies candidate treatment targets.

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via Longitudinal Structural and Functional Differences Between Proportional and Poor Motor Recovery After StrokeNeurorehabilitation and Neural Repair – Adrian G. Guggisberg, Pierre Nicolo, Leonardo G. Cohen, Armin Schnider, Ethan R. Buch, 2017

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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.

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