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Article type: Research Article
Authors: García-Rudolph, Alejandroa; b; c; * | Saurí, Joana; b; c | Cegarra, Blancaa; b; c | Madai, Vince Istvand; e; f | Frey, Dietmard | Kelleher, John D.g | Cisek, Katrynag | Opisso, Eloya; b; c | Tormos, Josep Maríaa; b; c | Bernabeu, Montserrata; b; c
Affiliations: [a] Department of Research and Innovation, Institut Guttmann, Institut Universitari de Neurorehabilitació Adscrit a la UAB, Badalona, Barcelona, Spain | [b] Universitat Autònomade Barcelona, Bellaterra (Cerdanyola del Vallès), Spain | [c] Fundació Institut d’Investigació en Ciències de la Salut Germans Trias i Pujol, Badalona, Barcelona, Spain | [d] CLAIM Charité Lab for AI in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany | [e] QUEST Center for Transforming Biomedical Research, Berlin Institute of Health (BIH), Charité – Universitätsmedizin Berlin, Berlin, Germany | [f] School of Computing and Digital Technology, Faculty of Computing, Engineering and the Built Environment, Birmingham City University, Birmingham, United Kingdom | [g] Information, Communication and Entertainment Research Institute, Technological University Dublin (TU Dublin), Dublin, Ireland
Correspondence: [*] Address for correspondence: Alejandro García-Rudolph, De-partment of Research and Innovation, Institut Guttmann–Hospital de Neurorehabilitació, Cami Can Ruti s/n 08916–Badalona, Barcelona, Spain. Tel.: +34 93 497 77 00–2265; Fax: +34 93 497 77 07; E-mail: [email protected]; ORICD: 0000-0003-0853-8334
Abstract: BACKGROUND:Stroke is a major worldwide cause of serious long-term disability. Most previous studies addressing functional independence included only inpatients with limited follow-up. OBJECTIVE:To identify novel classes of patients having similar temporal patterns in motor functional independence and relate them to baseline clinical features. METHODS:Retrospective observational cohort study, data were obtained for n = 428 adult patients with ischemic stroke admitted to rehabilitation (March 2005–March 2020), including baseline clinical features and follow-ups of motor Functional Independence Measure (mFIM) categorized as poor, fair or good. Growth mixture models (GMMs) were fitted to identify classes of patients with similar mFIM trajectories. RESULTS:GMM identified three classes of trajectories (1,664 mFIM assessments):C1 (11.2 %), 97.9% having poor admission mFIM, at 4.93 years 61.1% still poor, with the largest percentage of hypertension, neglect, dysphagia, diabetes and dyslipidemia of all three classes.C2 (23.1%), 99% had poor admission mFIM, 25% poor discharge mFIM, the largest percentage of aphasia and greatest mFIM gain, at 4.93 years only 6.2% still poor.C3 (65.7%) the youngest, lowest NIHSS, 37.7% poor admission mFIM, 73% good discharge mFIM, only 4.6% poor discharge mFIM, 90% good at 4.93 years. CONCLUSIONS:GMM identified novel motor functional classes characterized by baseline features.
Keywords: Ischemic stroke, functional independence, trajectories, latent class modeling, growth mixture modeling
DOI: 10.3233/NRE-210293
Journal: NeuroRehabilitation, vol. 50, no. 4, pp. 453-465, 2022
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