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Evaluation of Rehabilitation Outcomes in Patients with Chronic Neurological Health Conditions Using a Machine Learning Approach

Authors :
Gabriele Santilli
Massimiliano Mangone
Francesco Agostini
Marco Paoloni
Andrea Bernetti
Anxhelo Diko
Lucrezia Tognolo
Daniele Coraci
Federico Vigevano
Mario Vetrano
Maria Chiara Vulpiani
Pietro Fiore
Francesca Gimigliano
Source :
Journal of Functional Morphology and Kinesiology, Vol 9, Iss 4, p 176 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

Background: Over one billion people worldwide suffer from neurological conditions that cause mobility impairments, often persisting despite rehabilitation. Chronic neurological disease (CND) patients who lack access to continuous rehabilitation face gradual functional decline. The International Classification of Functioning, Disability, and Health (ICF) provides a comprehensive framework for assessing these patients. Objective: This study aims to evaluate the outcomes of a non-hospitalized neuromotor rehabilitation project for CND patients in Italy using the Barthel Index (BI) as the primary outcome measure. The rehabilitation was administered through an Individual Rehabilitation Plan (IRP), tailored by a multidisciplinary team and coordinated by a physiatrist. The IRP involved an initial comprehensive assessment, individualized therapy administered five days a week, and continuous adjustments based on patient progress. The secondary objectives include assessing mental status and sensory and communication functions, and identifying predictive factors for BI improvement using an artificial neural network (ANN). Methods: A retrospective observational study of 128 CND patients undergoing a rehabilitation program between 2018 and 2023 was conducted. Variables included demographic data, clinical assessments (BI, SPMSQ, and SVaMAsc), and ICF codes. Data were analyzed using descriptive statistics, linear regressions, and ANN to identify predictors of BI improvement. Results: Significant improvements in the mean BI score were observed from admission (40.28 ± 29.08) to discharge (42.53 ± 30.02, p < 0.001). Patients with severe mobility issues showed the most difficulty in transfers and walking, as indicated by the ICF E codes. Females, especially older women, experienced more cognitive decline, affecting rehabilitation outcomes. ANN achieved 86.4% accuracy in predicting BI improvement, with key factors including ICF mobility codes and the number of past rehabilitation projects. Conclusions: The ICF mobility codes are strong predictors of BI improvement in CND patients. More rehabilitation sessions and targeted support, especially for elderly women and patients with lower initial BI scores, can enhance outcomes and reduce complications. Continuous rehabilitation is essential for maintaining progress in CND patients.

Details

Language :
English
ISSN :
24115142
Volume :
9
Issue :
4
Database :
Directory of Open Access Journals
Journal :
Journal of Functional Morphology and Kinesiology
Publication Type :
Academic Journal
Accession number :
edsdoj.184f24258d64edb9b4afd2e2d86a983
Document Type :
article
Full Text :
https://doi.org/10.3390/jfmk9040176