Predicting Recovery Potential for Individual Stroke Patients Increases Rehabilitation Efficiency

Stinear, Cathy M.; Byblow, Winston D.; Ackerley, Suzanne J.; Barber, P. Alan; Smith, Marie-Claire

doi:10.1161/strokeaha.116.015790

Cited by 152 publications

(160 citation statements)

References 33 publications

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“…While PREP2 requires TMS for a smaller proportion of patients, this does not eliminate barriers to using this technique in a clinical setting. The major barrier is the cost of the TMS equipment but this might be offset by a reduced average length of stay when algorithm predictions are used in clinical practice 5. MEP status is a simple TMS measure, which can be obtained in approximately 20 min 5.…”

Section: Discussionmentioning

confidence: 99%

“…The major barrier is the cost of the TMS equipment but this might be offset by a reduced average length of stay when algorithm predictions are used in clinical practice 5. MEP status is a simple TMS measure, which can be obtained in approximately 20 min 5. Few patients (2%) have contraindications to TMS such as a history of epilepsy 5.…”

Section: Discussionmentioning

confidence: 99%

“…A retrospective analysis of data from two previous studies of 207 patients [103 females (50%); median age 72 (range 18–98) years] was carried out 5, 15. Patient clinical characteristics are summarized in Table 1.…”

Section: Methodsmentioning

confidence: 99%

“…The algorithm is unique in its sequential nature, which begins with a simple clinical test and then uses biomarkers as required to resolve uncertainty. The algorithm has been validated in a sample of 192 patients including those with previous stroke 5. Using PREP in clinical practice increased therapist confidence, modified therapy content, and was associated with a 1 week reduction in length of stay, with no detrimental effects on patient outcomes 5.…”

Section: Introductionmentioning

confidence: 99%

“…The algorithm has been validated in a sample of 192 patients including those with previous stroke 5. Using PREP in clinical practice increased therapist confidence, modified therapy content, and was associated with a 1 week reduction in length of stay, with no detrimental effects on patient outcomes 5. While using PREP can increase rehabilitation efficiency, not all clinical settings have access to transcranial magnetic stimulation and the ability to derive quantitative biomarkers from diffusion‐weighted MRI.…”

Section: Introductionmentioning

confidence: 99%

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PREP2: A biomarker‐based algorithm for predicting upper limb function after stroke

Stinear

Byblow

Ackerley

et al. 2017

Ann Clin Transl Neurol

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ObjectiveRecovery of motor function is important for regaining independence after stroke, but difficult to predict for individual patients. Our aim was to develop an efficient, accurate, and accessible algorithm for use in clinical settings. Clinical, neurophysiological, and neuroimaging biomarkers of corticospinal integrity obtained within days of stroke were combined to predict likely upper limb motor outcomes 3 months after stroke.MethodsData from 207 patients recruited within 3 days of stroke [103 females (50%), median age 72 (range 18–98) years] were included in a Classification and Regression Tree analysis to predict upper limb function 3 months poststroke.ResultsThe analysis produced an algorithm that sequentially combined a measure of upper limb impairment; age; the presence or absence of upper limb motor evoked potentials elicited with transcranial magnetic stimulation; and stroke lesion load obtained from MRI or stroke severity assessed with the NIHSS score. The algorithm makes correct predictions for 75% of patients. A key biomarker obtained with transcranial magnetic stimulation is required for one third of patients. This biomarker combined with NIHSS score can be used in place of more costly magnetic resonance imaging, with no loss of prediction accuracy.InterpretationThe new algorithm is more accurate, efficient, and accessible than its predecessors, which may support its use in clinical practice. While further work is needed to potentially incorporate sensory and cognitive factors, the algorithm can be used within days of stroke to provide accurate predictions of upper limb functional outcomes at 3 months after stroke. www.presto.auckland.ac.nz

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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PREP2: A biomarker‐based algorithm for predicting upper limb function after stroke

Stinear

Byblow

Ackerley

et al. 2017

Ann Clin Transl Neurol

Self Cite

254

321

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Neuroimaging biomarkers for predicting stroke outcomes: A systematic review

Gaviria,

Eltayeb Hamid

2024

Health Science Reports

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Background and AimsStroke is a prominent cause of long‐term adult impairment globally and a significant global health issue. Only 14% of stroke survivors achieve full recovery, while 25% to 50% require varying degrees of support, and over half become dependent. The aftermath of a stroke brings profound changes to an individual's life, with early choices significantly impacting their quality of life. This review aims to establish the efficacy of neuroimaging data in predicting long‐term outcomes and recovery rates following a stroke.MethodsA scientific literature search was conducted using the Centre of Reviews and Dissemination (CRD) criteria and PRISMA guidelines for a combined meta‐narrative and systematic quantitative review. The methodology involved a structured search in databases like PubMed and The Cochrane Library, following inclusion and exclusion criteria to identify relevant studies on neuroimaging biomarkers for stroke outcome prediction. Data collection utilized the Microsoft Edge Zotero plugin, with quality appraisal conducted via the CASP checklist. Studies published from 2010 to 2024, including observational, randomized control trials, case reports, and clinical trials. Non‐English and incomplete studies were excluded, resulting in the identification of 11 pertinent articles. Data extraction emphasized study methodologies, stroke conditions, clinical parameters, and biomarkers, aiming to provide a thorough literature overview and evaluate the significance of neuroimaging biomarkers in predicting stroke recovery outcomes.ResultsThe results of this systematic review indicate that integrating advanced neuroimaging methods with highly successful reperfusion therapies following a stroke facilitates the diagnosis of the condition and assists in improving neurological impairments resulting from stroke. These measures reduce the possibility of death and improve the treatment provided to stroke patients.ConclusionThese findings highlight the crucial role of neuroimaging in advancing our understanding of post‐stroke outcomes and improving patient care.

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Tractometry‐Based Estimation of Corticospinal Tract Injury to Assess Initial Impairment and Predict Functional Outcomes in Ischemic Stroke Patients

Yan

Zhang

et al. 2021

Magnetic Resonance Imaging

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Background: Corticospinal tract (CST) injury has been shown to exert a major influence on functional recovery after ischemic stroke. Purpose: To evaluate the prognostic value of CST injury estimated using a recent developed tractometry-based method. Study Type: Prospective. Population: Forty-eight patients with CST damage induced by stroke lesion who underwent brain magnetic resonance imaging within 7 days from onset. Sequence: Diffusion-weighted imaging (b = 1000 seconds/mm 2 ) and diffusion kurtosis imaging (DKI) spin-echo echoplanar sequence with three b-values (0, 1250, and 2500 seconds/mm 2 ) at 3.0 T. Assessment: A recently developed approach that combines tract segmentation and orientation mapping was used for CST-specific tractography and tractometry. CST injury was estimated using the proposed method with diffusion metrics extracted from DKI sequence and with the first principal component (PC1) of the metrics. We also calculated the weighted lesion load (wLL) for comparison. Clinical evaluation included the National Institutes of Health Stroke Score in the acute phase and the modified Rankin scale at 3 months post-stroke. The correlations between CST injury and initial motor impairment, as well as the prognostic values of CST injury for functional outcomes were evaluated. Statistical Tests: Pearson correlation and logistic regression. Area under the receiver operating characteristic curve. P < 0.05 was considered statistically significant. Results: CST injury calculated with diffusion metrics except fractional anisotropy all showed significant correlations with initial motor impairment. PC1 achieved the largest correlation coefficient (R = 0.65) compared with wLL and other diffusion metrics. In addition to wLL, DKI_AK, AFD_total, and PC1 maximum all showed predictive values for functional outcomes. Data Conclusion: Structural injury to CST is important for the assessment of the extent of injury and the prediction of functional outcome. The method proposed in our study could provide an imaging indicator to quantify the CST injury after ischemic stroke. Level of Evidence: 2 Technical Efficacy: Stage 1

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Predicting Recovery Potential for Individual Stroke Patients Increases Rehabilitation Efficiency

Abstract: URL: http://anzctr.org.au. Unique identifier: ACTRN12611000755932.

Cited by 152 publications

References 33 publications

PREP2: A biomarker‐based algorithm for predicting upper limb function after stroke

PREP2: A biomarker‐based algorithm for predicting upper limb function after stroke

Neuroimaging biomarkers for predicting stroke outcomes: A systematic review

Tractometry‐Based Estimation of Corticospinal Tract Injury to Assess Initial Impairment and Predict Functional Outcomes in Ischemic Stroke Patients

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