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使用机器学习方法评估慢性神经健康状况患者的康复结果

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

作者信息

Santilli Gabriele, Mangone Massimiliano, Agostini Francesco, Paoloni Marco, Bernetti Andrea, Diko Anxhelo, Tognolo Lucrezia, Coraci Daniele, Vigevano Federico, Vetrano Mario, Vulpiani Maria Chiara, Fiore Pietro, Gimigliano Francesca

机构信息

Physical Medicine and Rehabilitation Unit, Sant'Andrea Hospital, Sapienza University of Rome, 00189 Rome, Italy.

Department of Anatomical and Histological Sciences, Legal Medicine and Orthopedics, Sapienza University, 00185 Rome, Italy.

出版信息

J Funct Morphol Kinesiol. 2024 Sep 26;9(4):176. doi: 10.3390/jfmk9040176.

DOI:10.3390/jfmk9040176
PMID:39449470
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11503389/
Abstract

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. 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). 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. Significant improvements in the mean BI score were observed from admission (40.28 ± 29.08) to discharge (42.53 ± 30.02, < 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. 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.

摘要

全球有超过10亿人患有导致行动障碍的神经系统疾病,即便经过康复治疗,这些障碍往往仍会持续存在。无法获得持续康复治疗的慢性神经疾病(CND)患者会面临功能逐渐衰退的问题。《国际功能、残疾和健康分类》(ICF)为评估这些患者提供了一个全面的框架。本研究旨在以巴氏指数(BI)作为主要结局指标,评估意大利一项针对CND患者的非住院神经运动康复项目的效果。康复治疗通过个性化康复计划(IRP)实施,该计划由多学科团队量身定制,并由一名物理治疗师进行协调。IRP包括初始全面评估、每周五天的个性化治疗,以及根据患者进展进行持续调整。次要目标包括评估精神状态、感觉和沟通功能,以及使用人工神经网络(ANN)识别BI改善的预测因素。对2018年至2023年间接受康复项目的128名CND患者进行了一项回顾性观察研究。变量包括人口统计学数据、临床评估(BI、简易精神状态问卷(SPMSQ)和意大利运动评估量表(SVaMAsc))以及ICF编码。使用描述性统计、线性回归和ANN对数据进行分析,以确定BI改善的预测因素。从入院时(40.28±29.08)到出院时(42.53±30.02,<0.001),平均BI评分有显著改善。如ICF E编码所示,严重行动不便的患者在转移和行走方面困难最大。女性,尤其是老年女性,认知衰退更为明显,这影响了康复效果。ANN在预测BI改善方面的准确率达到86.4%,关键因素包括ICF行动编码和过去康复项目的数量。ICF行动编码是CND患者BI改善的有力预测指标。更多的康复疗程和针对性支持,特别是针对老年女性和初始BI评分较低的患者,可以提高康复效果并减少并发症。持续康复对于维持CND患者的康复进展至关重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e10e/11503389/f2fe1233133f/jfmk-09-00176-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e10e/11503389/2d9a555eed44/jfmk-09-00176-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e10e/11503389/7d9d906a0e64/jfmk-09-00176-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e10e/11503389/f2fe1233133f/jfmk-09-00176-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e10e/11503389/2d9a555eed44/jfmk-09-00176-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e10e/11503389/7d9d906a0e64/jfmk-09-00176-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e10e/11503389/f2fe1233133f/jfmk-09-00176-g003.jpg

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