Examination of Risk Factors Associated With Falls and Injurious Falls in People With Multiple Sclerosis: An Updated Nationwide Study

Abou, Libak; McCloskey, Chloe; Wernimont, Cory; Fritz, Nora E.; Kratz, Anna L.

doi:10.1016/j.apmr.2023.11.011

Cited by 2 publications

(1 citation statement)

References 45 publications

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“…Accumulating evidence shows that falls are a multifactorial problem influenced by personal, physiological, and environmental factors, highlighting the personalized nature of fall management [2,5]. Physiological factors, including balance impairment, muscle weakness, decreased walking speed, and use of mobility aids, have been associated with falls in pwMS [2,6].…”

Section: Introductionmentioning

confidence: 99%

Discriminative ability of the original and short form of the Activities-specific Balance Confidence scale and its individual items for falls in people with multiple sclerosis

Abasıyanık,

Kahraman,

Baba

et al. 2024

Acta Neurol Belg

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Background Balance confidence is an essential component of fall risk assessment in persons with multiple sclerosis (pwMS). Aims The aims of this cross-sectional study were to 1) investigate the ability of the 16-item Activities-specific Balance Confidence scale (ABC-16), 6-item Activities-specific Balance Confidence scale (ABC-6), and each item of the ABC-16 for distinguishing fallers and 2) determine cutoff scores for these scales to discriminate fallers and non-fallers in pwMS. Methods One hundred and fifty-six participants [fallers/non-fallers: 60 (38.5%)/96 (61.5%), median EDSS: 1.5] were enrolled. Balance confidence was assessed using the ABC-16 and ABC-6. The self-reported number of falls in the past three months was recorded. Descriptive assessments, including walking, balance, and cognition were performed. Logistic regression and receiver operating characteristic analyses were conducted to estimate the sensitivities and specificities of the ABC-16 and ABC-6. Results Both the ABC-16 (AUC: 0.85) and ABC-6 (AUC: 0.84) had the discriminative ability for falls. Each item of the ABC-16 scale was a significantly related to falls [odds ratio (OR) range: 1.38 to 1.89]. Items 8 and 10 had the highest odds ratio (OR: 1.85; 95%CI: 1.47–2.33, OR: 1.89; 95%CI: 1.49–2.40; respectively). We found cutoff scores of ≤ 70 of 100 (sensitivity: 71.67, specificity: 86.46) and ≤ 65/100 (sensitivity: 76.67, specificity: 79.17) in discrimination between fallers and non-fallers for the ABC-16 and ABC-6, respectively. Conclusion Both original and short forms of the ABC scale are an efficient tool for discriminating fallers and non-fallers in pwMS. Although all items are related to falls, outdoor walking activities have the strongest associations with falls than other items.

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Section: Introductionmentioning

confidence: 99%

Discriminative ability of the original and short form of the Activities-specific Balance Confidence scale and its individual items for falls in people with multiple sclerosis

Abasıyanık,

Kahraman,

Baba

et al. 2024

Acta Neurol Belg

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A machine learning approach to determine the risk factors for fall in multiple sclerosis

Özgür,

Koçaslan Toran,

Toygar

et al. 2024

BMC Med Inform Decis Mak

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Background 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. Methods 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. Results 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). Conclusions 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. Supplementary Information The online version contains supplementary material available at 10.1186/s12911-024-02621-0.

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Examination of Risk Factors Associated With Falls and Injurious Falls in People With Multiple Sclerosis: An Updated Nationwide Study

Cited by 2 publications

References 45 publications

Discriminative ability of the original and short form of the Activities-specific Balance Confidence scale and its individual items for falls in people with multiple sclerosis

Discriminative ability of the original and short form of the Activities-specific Balance Confidence scale and its individual items for falls in people with multiple sclerosis

A machine learning approach to determine the risk factors for fall in multiple sclerosis

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