Stratified predictions of upper limb motor outcomes after stroke

Chang-Hyun Park; Min-Su Kim

doi:10.3389/fneur.2023.1323529

Stratified predictions of upper limb motor outcomes after stroke

Front Neurol. 2024 Jan 4:14:1323529. doi: 10.3389/fneur.2023.1323529. eCollection 2023.

Authors

Chang-Hyun Park¹, Min-Su Kim^{2

3}

Affiliations

¹ Division of Artificial Intelligence and Software, College of Engineering, Ewha Womans University, Seoul, Republic of Korea.
² Department of Physical Medicine and Rehabilitation, Soonchunhyang University Cheonan Hospital, Cheonan, Republic of Korea.
³ Department of Regenerative Medicine, College of Medicine, Soonchunhyang University, Cheonan, Republic of Korea.

Abstract

Introduction: Longitudinal observations of upper limb motor recovery after stroke have suggested that certain subgroups may exhibit distinct recovery patterns. Here we sought to examine whether the predictive ability for post-stroke upper limb motor outcomes could be enhanced by applying conventional stratification strategies.

Method: For 60 individuals who suffered the first stroke, upper limb motor impairment was assessed with the upper extremity Fugl-Meyer assessment (UE-FMA) at 2 weeks as a baseline and then 3 months post-stroke. Brain structural damage at baseline was assessed by MRI data-derived markers ranging from traditional lesion size to the lesion load and to the disconnectome. Linear regression models for predicting upper limb motor outcomes (UE-FMA score at 3 months post-stroke) based on baseline upper limb motor impairment (UE-FMA score at 2 weeks post-stroke), brain structural damage, and their combinations were generated, and those with the best predictive performance were determined for individual subgroups stratified according to initial impairment (severe and non-severe), lesion location (cortical and non-cortical), and neurophysiological status (motor evoked potential-positive and motor evoked potential-negative).

Results: The best predictions were made by baseline upper limb motor impairment alone for subgroups with less functional impairment (non-severe) or less structural involvement (non-cortical), but by the combination of baseline upper limb motor impairment and brain structural damage for the other subgroups. The predictive models tailored for subgroups determined according to initial impairment and neurophysiological status yielded a smaller overall error than that for the whole group in upper limb motor outcome predictions.

Discussion: The predictive ability for upper limb motor outcomes could be enhanced beyond the one-size-fits-all model for all individuals with stroke by applying specific stratification strategies, with stratification according to initial impairment being the most promising. We expect that predictive models tailored for individual subgroups could lead closer to the personalized prognosis of upper limb motor outcomes after stroke.

Keywords: brain structural damage; predictive model; proportional recovery; upper limb motor impairment; upper limb motor outcome.

Grants and funding

The authors declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the Institute of Information and communications Technology Planning and Evaluation grant [RS-2022-00155966 to CP], the National Research Foundation of Korea grant funded by the Korea government [RS-2023-00240457 to CP], and the Soonchunhyang University Research Fund (2023 to M-SK).