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Predicting Upper Limb Motor Impairment Recovery after Stroke: A Mixture Model
OBJECTIVE: Spontaneous recovery is an important determinant of upper extremity recovery after stroke and has been described by the 70% proportional recovery rule for the Fugl–Meyer motor upper extremity (FM‐UE) scale. However, this rule is criticized for overestimating the predictability of FM‐UE re...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley & Sons, Inc.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7065018/ https://www.ncbi.nlm.nih.gov/pubmed/31925838 http://dx.doi.org/10.1002/ana.25679 |
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author | van der Vliet, Rick Selles, Ruud W. Andrinopoulou, Eleni‐Rosalina Nijland, Rinske Ribbers, Gerard M. Frens, Maarten A. Meskers, Carel Kwakkel, Gert |
author_facet | van der Vliet, Rick Selles, Ruud W. Andrinopoulou, Eleni‐Rosalina Nijland, Rinske Ribbers, Gerard M. Frens, Maarten A. Meskers, Carel Kwakkel, Gert |
author_sort | van der Vliet, Rick |
collection | PubMed |
description | OBJECTIVE: Spontaneous recovery is an important determinant of upper extremity recovery after stroke and has been described by the 70% proportional recovery rule for the Fugl–Meyer motor upper extremity (FM‐UE) scale. However, this rule is criticized for overestimating the predictability of FM‐UE recovery. Our objectives were to develop a longitudinal mixture model of FM‐UE recovery, identify FM‐UE recovery subgroups, and internally validate the model predictions. METHODS: We developed an exponential recovery function with the following parameters: subgroup assignment probability, proportional recovery coefficient r(k), time constant in weeks τ(k), and distribution of the initial FM‐UE scores. We fitted the model to FM‐UE measurements of 412 first‐ever ischemic stroke patients and cross‐validated endpoint predictions and FM‐UE recovery cluster assignment. RESULTS: The model distinguished 5 subgroups with different recovery parameters (r(1) = 0.09, τ(1) = 5.3, r(2) = 0.46, τ(2) = 10.1, r(3) = 0.86, τ(3) = 9.8, r(4) = 0.89, τ(4) = 2.7, r(5) = 0.93, τ(5) = 1.2). Endpoint FM‐UE was predicted with a median absolute error of 4.8 (interquartile range [IQR] = 1.3–12.8) at 1 week poststroke and 4.2 (IQR = 1.3–9.8) at 2 weeks. Overall accuracy of assignment to the poor (subgroup 1), moderate (subgroups 2 and 3), and good (subgroups 4 and 5) FM‐UE recovery clusters was 0.79 (95% equal‐tailed interval [ETI] = 0.78–0.80) at 1 week poststroke and 0.81 (95% ETI = 0.80–0.82) at 2 weeks. INTERPRETATION: FM‐UE recovery reflects different subgroups, each with its own recovery profile. Cross‐validation indicates that FM‐UE endpoints and FM‐UE recovery clusters can be well predicted. Results will contribute to the understanding of upper limb recovery patterns in the first 6 months after stroke. ANN NEUROL 2020;87:383–393 Ann Neurol 2020;87:383–393 |
format | Online Article Text |
id | pubmed-7065018 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | John Wiley & Sons, Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-70650182020-03-16 Predicting Upper Limb Motor Impairment Recovery after Stroke: A Mixture Model van der Vliet, Rick Selles, Ruud W. Andrinopoulou, Eleni‐Rosalina Nijland, Rinske Ribbers, Gerard M. Frens, Maarten A. Meskers, Carel Kwakkel, Gert Ann Neurol Research Articles OBJECTIVE: Spontaneous recovery is an important determinant of upper extremity recovery after stroke and has been described by the 70% proportional recovery rule for the Fugl–Meyer motor upper extremity (FM‐UE) scale. However, this rule is criticized for overestimating the predictability of FM‐UE recovery. Our objectives were to develop a longitudinal mixture model of FM‐UE recovery, identify FM‐UE recovery subgroups, and internally validate the model predictions. METHODS: We developed an exponential recovery function with the following parameters: subgroup assignment probability, proportional recovery coefficient r(k), time constant in weeks τ(k), and distribution of the initial FM‐UE scores. We fitted the model to FM‐UE measurements of 412 first‐ever ischemic stroke patients and cross‐validated endpoint predictions and FM‐UE recovery cluster assignment. RESULTS: The model distinguished 5 subgroups with different recovery parameters (r(1) = 0.09, τ(1) = 5.3, r(2) = 0.46, τ(2) = 10.1, r(3) = 0.86, τ(3) = 9.8, r(4) = 0.89, τ(4) = 2.7, r(5) = 0.93, τ(5) = 1.2). Endpoint FM‐UE was predicted with a median absolute error of 4.8 (interquartile range [IQR] = 1.3–12.8) at 1 week poststroke and 4.2 (IQR = 1.3–9.8) at 2 weeks. Overall accuracy of assignment to the poor (subgroup 1), moderate (subgroups 2 and 3), and good (subgroups 4 and 5) FM‐UE recovery clusters was 0.79 (95% equal‐tailed interval [ETI] = 0.78–0.80) at 1 week poststroke and 0.81 (95% ETI = 0.80–0.82) at 2 weeks. INTERPRETATION: FM‐UE recovery reflects different subgroups, each with its own recovery profile. Cross‐validation indicates that FM‐UE endpoints and FM‐UE recovery clusters can be well predicted. Results will contribute to the understanding of upper limb recovery patterns in the first 6 months after stroke. ANN NEUROL 2020;87:383–393 Ann Neurol 2020;87:383–393 John Wiley & Sons, Inc. 2020-01-25 2020-03 /pmc/articles/PMC7065018/ /pubmed/31925838 http://dx.doi.org/10.1002/ana.25679 Text en © 2020 The Authors. Annals of Neurology published by Wiley Periodicals, Inc. on behalf of American Neurological Association. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. |
spellingShingle | Research Articles van der Vliet, Rick Selles, Ruud W. Andrinopoulou, Eleni‐Rosalina Nijland, Rinske Ribbers, Gerard M. Frens, Maarten A. Meskers, Carel Kwakkel, Gert Predicting Upper Limb Motor Impairment Recovery after Stroke: A Mixture Model |
title | Predicting Upper Limb Motor Impairment Recovery after Stroke: A Mixture Model |
title_full | Predicting Upper Limb Motor Impairment Recovery after Stroke: A Mixture Model |
title_fullStr | Predicting Upper Limb Motor Impairment Recovery after Stroke: A Mixture Model |
title_full_unstemmed | Predicting Upper Limb Motor Impairment Recovery after Stroke: A Mixture Model |
title_short | Predicting Upper Limb Motor Impairment Recovery after Stroke: A Mixture Model |
title_sort | predicting upper limb motor impairment recovery after stroke: a mixture model |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7065018/ https://www.ncbi.nlm.nih.gov/pubmed/31925838 http://dx.doi.org/10.1002/ana.25679 |
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