Department of Biostatistics and Medical Informatics, Ege University Faculty of Medicine, Izmir, Türkiye.
Ege University Faculty of Medicine, EgeSAM-Translational Pulmonary Research Center, Bornova, İzmir, Türkiye.
BMC Med Inform Decis Mak. 2024 Jul 30;24(1):215. doi: 10.1186/s12911-024-02621-0.
Falls in multiple sclerosis can result in numerous problems, including injuries and functional loss. Therefore, determining the factors contributing to falls in people with Multiple Sclerosis (PwMS) is crucial. This study aims to investigate the contributing factors to falls in multiple sclerosis using a machine learning approach.
This cross-sectional study was conducted with 253 PwMS admitted to the outpatient clinic of a university hospital between February and August 2023. A sociodemographic data collection form, Fall Efficacy Scale (FES-I), Berg Balance Scale (BBS), Fatigue Severity Scale (FSS), Expanded Disability Status Scale (EDSS), Multiple Sclerosis Impact Scale (MSIS-29), and Timed 25 Foot Walk Test (T25-FW) were used for data collection. Gradient-boosting algorithms were employed to predict the important variables for falls in PwMS. The XGBoost algorithm emerged as the best performed model in this study.
Most of the participants (70.0%) were female, with a mean age of 40.44 ± 10.88 years. Among the participants, 40.7% reported a fall history in the last year. The area under the curve value of the model was 0.713. Risk factors of falls in PwMS included MSIS-29 (0.424), EDSS (0.406), marital status (0.297), education level (0.240), disease duration (0.185), age (0.130), family type (0.119), smoking (0.031), income level (0.031), and regular exercise habit (0.026).
In this study, smoking and regular exercise were the modifiable factors contributing to falls in PwMS. We recommend that clinicians facilitate the modification of these factors in PwMS. Age and disease duration were non-modifiable factors. These should be considered as risk increasing factors and used to identify PwMS at risk. Interventions aimed at reducing MSIS-29 and EDSS scores will help to prevent falls in PwMS. Education of individuals to increase knowledge and awareness is recommended. Financial support policies for those with low income will help to reduce the risk of falls.
多发性硬化症患者的跌倒可能会导致诸多问题，包括受伤和功能丧失。因此，确定导致多发性硬化症患者（PwMS）跌倒的因素至关重要。本研究旨在使用机器学习方法探究多发性硬化症患者跌倒的相关因素。
本横断面研究纳入了 2023 年 2 月至 8 月期间在某大学医院门诊就诊的 253 例 PwMS。使用一般资料收集表、跌倒效能量表（FES-I）、伯格平衡量表（BBS）、疲劳严重程度量表（FSS）、扩展残疾状况量表（EDSS）、多发性硬化影响量表（MSIS-29）和 25 英尺步行计时测试（T25-FW）进行数据收集。采用梯度提升算法预测 PwMS 跌倒的重要变量。在本研究中，XGBoost 算法表现最佳。
大多数参与者（70.0%）为女性，平均年龄为 40.44±10.88 岁。参与者中有 40.7%在过去一年中报告有跌倒史。模型的曲线下面积值为 0.713。PwMS 跌倒的危险因素包括 MSIS-29（0.424）、EDSS（0.406）、婚姻状况（0.297）、教育程度（0.240）、病程（0.185）、年龄（0.130）、家庭类型（0.119）、吸烟（0.031）、收入水平（0.031）和定期运动习惯（0.026）。
在本研究中，吸烟和定期运动是导致 PwMS 跌倒的可改变因素。我们建议临床医生促进 PwMS 改变这些因素。年龄和病程是不可改变的因素。这些因素应被视为增加风险的因素，并用于识别有跌倒风险的 PwMS。旨在降低 MSIS-29 和 EDSS 评分的干预措施将有助于预防 PwMS 跌倒。建议对个体进行教育以增加知识和意识。为低收入者提供经济支持政策将有助于降低跌倒风险。
