Mobile digital gait analysis captures effects of botulinum toxin in hereditary spastic paraplegia.

European journal of neurology 2024 Vol.31(8) p. e16367

Ibrahim AA, Ollenschläger M, Klebe S, Schüle R, Jeschonneck N, Kellner M, Loris E, Greinwalder T, Eskofier BM, Winkler J, Gaßner H, Regensburger M

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Abstract

[BACKGROUND AND PURPOSE] Hereditary spastic paraplegias (HSPs) comprise a group of inherited neurodegenerative disorders characterized by progressive spasticity and weakness. Botulinum toxin has been approved for lower limb spasticity following stroke and cerebral palsy, but its effects in HSPs remain underexplored. We aimed to characterize the effects of botulinum toxin on clinical, gait, and patient-reported outcomes in HSP patients and explore the potential of mobile digital gait analysis to monitor treatment effects and predict treatment response.

[METHODS] We conducted a prospective, observational, multicenter study involving ambulatory HSP patients treated with botulinum toxin tailored to individual goals. Comparing data at baseline, after 1 month, and after 3 months, treatment response was assessed using clinical parameters, goal attainment scaling, and mobile digital gait analysis. Machine learning algorithms were used for predicting individual goal attainment based on baseline parameters.

[RESULTS] A total of 56 patients were enrolled. Despite the heterogeneity of treatment goals and targeted muscles, botulinum toxin led to a significant improvement in specific clinical parameters and an improvement in specific gait characteristics, peaking at the 1-month and declining by the 3-month follow-up. Significant correlations were identified between gait parameters and clinical scores. With a mean balanced accuracy of 66%, machine learning algorithms identified important denominators to predict treatment response.

[CONCLUSIONS] Our study provides evidence supporting the beneficial effects of botulinum toxin in HSP when applied according to individual treatment goals. The use of mobile digital gait analysis and machine learning represents a novel approach for monitoring treatment effects and predicting treatment response.

추출된 의학 개체 (NER)

유형영어 표현한국어 / 풀이UMLS CUI출처등장
시술 botulinum toxin 보툴리눔독소 주사 dict 6
해부 lower limb scispacy 1
해부 cerebral scispacy 1
해부 muscles scispacy 1
약물 [BACKGROUND AND PURPOSE] Hereditary scispacy 1
약물 [RESULTS] A scispacy 1
약물 botulinum scispacy 1
약물 [CONCLUSIONS] scispacy 1
질환 hereditary spastic paraplegia C0037773
Spastic Paraplegia, Hereditary
scispacy 1
질환 Hereditary spastic paraplegias C0037773
Spastic Paraplegia, Hereditary
scispacy 1
질환 inherited neurodegenerative disorders scispacy 1
질환 spasticity C0026838
Muscle Spasticity
scispacy 1
질환 stroke C0038454
Cerebrovascular accident
scispacy 1
질환 cerebral palsy C0007789
Cerebral Palsy
scispacy 1
질환 HSP C0018850
Heat shock proteins
scispacy 1
질환 Machine learning C0376284
Machine Learning
scispacy 1
질환 HSP patients scispacy 1
기타 HSPs → Hereditary spastic paraplegias scispacy 1
기타 patients scispacy 1
기타 HSP scispacy 1

MeSH Terms

Humans; Male; Female; Spastic Paraplegia, Hereditary; Adult; Middle Aged; Gait Analysis; Prospective Studies; Neuromuscular Agents; Treatment Outcome; Botulinum Toxins, Type A; Young Adult; Aged; Botulinum Toxins

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