Literature DB >> 31268414

PREP2 Algorithm Predictions Are Correct at 2 Years Poststroke for Most Patients.

Marie-Claire Smith1, Suzanne J Ackerley1, P Alan Barber1,2, Winston D Byblow1, Cathy M Stinear1.   

Abstract

Background. The PREP2 algorithm combines clinical and neurophysiological measures to predict upper-limb (UL) motor outcomes 3 months poststroke, using 4 prediction categories based on Action Research Arm Test (ARAT) scores. The algorithm was accurate at 3 months for 75% of participants in a previous validation study. Objective. This study aimed to evaluate whether PREP2 predictions made at baseline are correct 2 years poststroke. We also assessed whether patients' UL performance remained stable, improved, or worsened between 3 months and 2 years after stroke. Methods. This is a follow-up study of 192 participants recruited and assessed in the original PREP2 validation study. Participants who completed assessments 3 months poststroke (n = 157) were invited to complete follow-up assessments at 2 years poststroke for the present study. UL outcomes were assessed with the ARAT, upper extremity Fugl-Meyer Scale, and Motor Activity Log. Results. A total of 86 participants completed 2-year follow-up assessments in this study. PREP2 predictions made at baseline were correct for 69/86 (80%) participants 2 years poststroke, and PREP2 UL outcome category was stable between 3 months and 2 years poststroke for 71/86 (83%). There was no difference in age, stroke severity, or comorbidities among patients whose category remained stable, improved, or deteriorated. Conclusions. PREP2 algorithm predictions made within days of stroke are correct at both 3 months and 2 years poststroke for most patients. Further investigation may be useful to identify which patients are likely to improve, remain stable, or deteriorate between 3 months and 2 years.

Entities:  

Keywords:  biomarkers; motor; outcome; prognosis; rehabilitation; stroke; upper limb

Mesh:

Year:  2019        PMID: 31268414     DOI: 10.1177/1545968319860481

Source DB:  PubMed          Journal:  Neurorehabil Neural Repair        ISSN: 1545-9683            Impact factor:   3.919


  7 in total

1.  Similarity of hand muscle synergies elicited by transcranial magnetic stimulation and those found during voluntary movement.

Authors:  Mathew Yarossi; Dana H Brooks; Deniz Erdoğmuş; Eugene Tunik
Journal:  J Neurophysiol       Date:  2022-08-24       Impact factor: 2.974

Review 2.  Transcranial magnetic stimulation implementation on stroke prognosis.

Authors:  Stella Karatzetzou; Dimitrios Tsiptsios; Aikaterini Terzoudi; Nikolaos Aggeloussis; Konstantinos Vadikolias
Journal:  Neurol Sci       Date:  2021-11-30       Impact factor: 3.830

Review 3.  To stimulate or not to stimulate? A rapid systematic review of repetitive sensory stimulation for the upper-limb following stroke.

Authors:  Rachel C Stockley; Kerry Hanna; Louise Connell
Journal:  Arch Physiother       Date:  2020-11-30

4.  Machine Learning Methods Predict Individual Upper-Limb Motor Impairment Following Therapy in Chronic Stroke.

Authors:  Ceren Tozlu; Dylan Edwards; Aaron Boes; Douglas Labar; K Zoe Tsagaris; Joshua Silverstein; Heather Pepper Lane; Mert R Sabuncu; Charles Liu; Amy Kuceyeski
Journal:  Neurorehabil Neural Repair       Date:  2020-03-20       Impact factor: 3.919

5.  Does Measurement of Corticospinal Tract Involvement Add Value to Clinical Behavioral Biomarkers in Predicting Motor Recovery after Stroke?

Authors:  Jong Youb Lim; Mi-Kyoung Oh; Jihong Park; Nam-Jong Paik
Journal:  Neural Plast       Date:  2020-11-27       Impact factor: 3.599

6.  Utility of Transcranial Magnetic Stimulation and Diffusion Tensor Imaging for Prediction of Upper-Limb Motor Recovery in Acute Ischemic Stroke Patients.

Authors:  Pradeep Kumar; Manya Prasad; Animesh Das; Deepti Vibha; Ajay Garg; Vinay Goyal; Achal K Srivastava
Journal:  Ann Indian Acad Neurol       Date:  2021-09-27       Impact factor: 1.383

Review 7.  Challenges and Opportunities for the Future of Brain-Computer Interface in Neurorehabilitation.

Authors:  Colin Simon; David A E Bolton; Niamh C Kennedy; Surjo R Soekadar; Kathy L Ruddy
Journal:  Front Neurosci       Date:  2021-07-02       Impact factor: 4.677

  7 in total

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