Evaluation of Rehabilitation Outcomes in Patients with Chronic Neurological Health Conditions Using a Machine Learning Approach
<b>Background:</b> 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...
Saved in:
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-09-01
|
| Series: | Journal of Functional Morphology and Kinesiology |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2411-5142/9/4/176 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846104250426327040 |
|---|---|
| author | 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 |
| author_facet | 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 |
| author_sort | Gabriele Santilli |
| collection | DOAJ |
| description | <b>Background:</b> 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. <b>Objective:</b> 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). <b>Methods:</b> 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. <b>Results:</b> Significant improvements in the mean BI score were observed from admission (40.28 ± 29.08) to discharge (42.53 ± 30.02, <i>p</i> < 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. <b>Conclusions:</b> 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. |
| format | Article |
| id | doaj-art-e184f24258d64edb9b4afd2e2d86a983 |
| institution | Kabale University |
| issn | 2411-5142 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Functional Morphology and Kinesiology |
| spelling | doaj-art-e184f24258d64edb9b4afd2e2d86a9832024-12-27T14:32:08ZengMDPI AGJournal of Functional Morphology and Kinesiology2411-51422024-09-019417610.3390/jfmk9040176Evaluation of Rehabilitation Outcomes in Patients with Chronic Neurological Health Conditions Using a Machine Learning ApproachGabriele Santilli0Massimiliano Mangone1Francesco Agostini2Marco Paoloni3Andrea Bernetti4Anxhelo Diko5Lucrezia Tognolo6Daniele Coraci7Federico Vigevano8Mario Vetrano9Maria Chiara Vulpiani10Pietro Fiore11Francesca Gimigliano12Physical Medicine and Rehabilitation Unit, Sant’Andrea Hospital, Sapienza University of Rome, 00189 Rome, ItalyDepartment of Anatomical and Histological Sciences, Legal Medicine and Orthopedics, Sapienza University, 00185 Rome, ItalyDepartment of Anatomical and Histological Sciences, Legal Medicine and Orthopedics, Sapienza University, 00185 Rome, ItalyDepartment of Anatomical and Histological Sciences, Legal Medicine and Orthopedics, Sapienza University, 00185 Rome, ItalyDepartment of Biological and Environmental Science and Technologies, University of Salento, 73100 Lecce, ItalyDepartment of Anatomical and Histological Sciences, Legal Medicine and Orthopedics, Sapienza University, 00185 Rome, ItalyDepartment of Neuroscience, Section of Rehabilitation, University of Padua, 35122 Padua, ItalyDepartment of Neuroscience, Section of Rehabilitation, University of Padua, 35122 Padua, ItalyNeurorehabilitation Department, IRCCS San Raffaele, 00163 Rome, ItalyPhysical Medicine and Rehabilitation Unit, Sant’Andrea Hospital, Sapienza University of Rome, 00189 Rome, ItalyPhysical Medicine and Rehabilitation Unit, Sant’Andrea Hospital, Sapienza University of Rome, 00189 Rome, ItalyNeurorehabilitation Unit, Istituti Clinici Scientifici Maugeri IRCCS, 70124 Bari, ItalyDepartment of Physical and Mental Health and Preventive Medicine, University of Campania “Luigi Vanvitelli”, 80100 Naples, Italy<b>Background:</b> 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. <b>Objective:</b> 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). <b>Methods:</b> 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. <b>Results:</b> Significant improvements in the mean BI score were observed from admission (40.28 ± 29.08) to discharge (42.53 ± 30.02, <i>p</i> < 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. <b>Conclusions:</b> 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.https://www.mdpi.com/2411-5142/9/4/176neurological conditionsrehabilitationBarthel indexartificial neural networkinternational classification of functioningmobility |
| spellingShingle | 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 Evaluation of Rehabilitation Outcomes in Patients with Chronic Neurological Health Conditions Using a Machine Learning Approach Journal of Functional Morphology and Kinesiology neurological conditions rehabilitation Barthel index artificial neural network international classification of functioning mobility |
| title | Evaluation of Rehabilitation Outcomes in Patients with Chronic Neurological Health Conditions Using a Machine Learning Approach |
| title_full | Evaluation of Rehabilitation Outcomes in Patients with Chronic Neurological Health Conditions Using a Machine Learning Approach |
| title_fullStr | Evaluation of Rehabilitation Outcomes in Patients with Chronic Neurological Health Conditions Using a Machine Learning Approach |
| title_full_unstemmed | Evaluation of Rehabilitation Outcomes in Patients with Chronic Neurological Health Conditions Using a Machine Learning Approach |
| title_short | Evaluation of Rehabilitation Outcomes in Patients with Chronic Neurological Health Conditions Using a Machine Learning Approach |
| title_sort | evaluation of rehabilitation outcomes in patients with chronic neurological health conditions using a machine learning approach |
| topic | neurological conditions rehabilitation Barthel index artificial neural network international classification of functioning mobility |
| url | https://www.mdpi.com/2411-5142/9/4/176 |
| work_keys_str_mv | AT gabrielesantilli evaluationofrehabilitationoutcomesinpatientswithchronicneurologicalhealthconditionsusingamachinelearningapproach AT massimilianomangone evaluationofrehabilitationoutcomesinpatientswithchronicneurologicalhealthconditionsusingamachinelearningapproach AT francescoagostini evaluationofrehabilitationoutcomesinpatientswithchronicneurologicalhealthconditionsusingamachinelearningapproach AT marcopaoloni evaluationofrehabilitationoutcomesinpatientswithchronicneurologicalhealthconditionsusingamachinelearningapproach AT andreabernetti evaluationofrehabilitationoutcomesinpatientswithchronicneurologicalhealthconditionsusingamachinelearningapproach AT anxhelodiko evaluationofrehabilitationoutcomesinpatientswithchronicneurologicalhealthconditionsusingamachinelearningapproach AT lucreziatognolo evaluationofrehabilitationoutcomesinpatientswithchronicneurologicalhealthconditionsusingamachinelearningapproach AT danielecoraci evaluationofrehabilitationoutcomesinpatientswithchronicneurologicalhealthconditionsusingamachinelearningapproach AT federicovigevano evaluationofrehabilitationoutcomesinpatientswithchronicneurologicalhealthconditionsusingamachinelearningapproach AT mariovetrano evaluationofrehabilitationoutcomesinpatientswithchronicneurologicalhealthconditionsusingamachinelearningapproach AT mariachiaravulpiani evaluationofrehabilitationoutcomesinpatientswithchronicneurologicalhealthconditionsusingamachinelearningapproach AT pietrofiore evaluationofrehabilitationoutcomesinpatientswithchronicneurologicalhealthconditionsusingamachinelearningapproach AT francescagimigliano evaluationofrehabilitationoutcomesinpatientswithchronicneurologicalhealthconditionsusingamachinelearningapproach |