Ahmedy, Fatimah and Tuah, Nooralisa Mohd and Hashim, Natiara Mohamad and Shah, Syahiskandar Sybil and Ahmedy, Ismail and Tan, Soo Fun (2021) Revisiting spasticity after stroke: Clustering clinical characteristics for identifying at-risk individuals. Journal of Multidisciplinary Healthcare, 14. pp. 2391-2396. ISSN 1178-2390, DOI https://doi.org/10.2147/JMDH.S320543.
Full text not available from this repository.Abstract
Purpose: To collectively identify the clinical characteristics determining the risk of developing spasticity after stroke. Patients and Methods: A cross-sectional study was conducted at a single rehabilitation outpatient clinic from June to December 2019. Inclusion criteria were stroke duration of over four weeks, aged 18 years and above. Exclusion criteria were presence of concurrent conditions other than stroke that could also lead to spasticity. Recruited patients were divided into ``Spasticity'' and ``No spasticity'' groups. Univariate analysis was deployed to identify significant predictive spasticity factors between the two groups followed by a two-step clustering approach for determining group of characteristics that collectively contributes to the risk of developing spasticity in the ``Spasticity'' group. Results: A total of 216 post-stroke participants were recruited. The duration after stroke (p < 0.001) and the absence of hemisensory loss (p = 0.042) were two significant factors in the ``Spasticity'' group revealed by the univariate analysis. From a total of 98 participants with spasticity, the largest cluster of individuals (40 patients, 40.8%) was those within less than 20 months after stroke with moderate stroke and absence of hemisensory loss, while the smallest cluster was those within less than 20 months after severe stroke and absence of hemisensory loss (21 patients, 21.4%). Conclusion: Analyzing collectively the significant factors of developing spasticity may have the potential to be more clinically relevant in a heterogeneous post-stroke population that may assist in the spasticity management and treatment.
| Item Type: | Article |
|---|---|
| Funders: | Science and Technology Development Fund (STDF) Ministry of Higher Education & Scientific Research (MHESR) |
| Uncontrolled Keywords: | Spasticity; Stroke rehabilitation; Clinical characteristics; Clustering analysis |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > R Medicine (General) |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Computer System & Technology |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 16 Aug 2022 08:27 |
| Last Modified: | 16 Aug 2022 08:27 |
| URI: | http://eprints.um.edu.my/id/eprint/28542 |
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