Personalized prediction of rehabilitation outcomes in multiple sclerosis: a proof-of-concept using clinical data, digital health metrics, and machine learning

Christoph M Kanzler,Roger Gassert,Olivier Lambercy,Ilse Lamers,Peter Feys

doi:10.1007/s11517-021-02467-y

Christoph M Kanzler, Roger Gassert + Show 3 more

Open Access

https://doi.org/10.1007/s11517-021-02467-y

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Abstract

Predicting upper limb neurorehabilitation outcomes in persons with multiple sclerosis (pwMS) is essential to optimize therapy allocation. Previous research identified population-level predictors through linear models and clinical data. This work explores the feasibility of predicting individual neurorehabilitation outcomes using machine learning, clinical data, and digital health metrics. Machine learning models were trained on clinical data and digital health metrics recorded pre-intervention in 11 pwMS. The dependent variables indicated whether pwMS considerably improved across the intervention, as defined by the Action Research Arm Test (ARAT), Box and Block Test (BBT), or Nine Hole Peg Test (NHPT). Improvements in ARAT or BBT could be accurately predicted (88% and 83% accuracy) using only patient master data. Improvements in NHPT could be predicted with moderate accuracy (73%) and required knowledge about sensorimotor impairments. Assessing these with digital health metrics over clinical scales increased accuracy by 10%. Non-linear models improved accuracy for the BBT (+ 9%), but not for the ARAT (-1%) and NHPT (-2%). This work demonstrates the feasibility of predicting upper limb neurorehabilitation outcomes in pwMS, which justifies the development of more representative prediction models in the future. Digital health metrics improved the prediction of changes in hand control, thereby underlining their advanced sensitivity.Graphical This work explores the feasibility of predicting individual neurorehabilitation outcomes using machine learning, clinical data, and digital health metrics.

Highlights

Multiple sclerosis (MS) is a chronic neurodegenerative disorder with 2.2 million prevalent cases worldwide [1]
For this purpose, predicting whether a patient is susceptible to positively respond to a specific neurorehabilitation intervention is of primary interest to researchers and clinicians, as it can help to set more realistic therapy goals, optimize therapy time, and reduce costs related to unsuccessful interventions [9,10,11,12]
The objective of this work was to explore the feasibility of predicting the response of individual persons with multiple sclerosis (pwMS) to specific upper limb neurorehabilitation interventions by applying machine learning to clinical data and digital health metrics recorded pre-intervention

Summary

Introduction

Multiple sclerosis (MS) is a chronic neurodegenerative disorder with 2.2 million prevalent cases worldwide [1]. One of the active ingredients to ensure successful neurorehabilitation is a careful adaptation of the therapy regimen to the characteristics and deficits of an individual (i.e., personalized therapy) [5, 6, 8] For this purpose, predicting whether a patient is susceptible to positively respond to a specific neurorehabilitation intervention is of primary interest to researchers and clinicians, as it can help to set more realistic therapy goals, optimize therapy time, and reduce costs related to unsuccessful interventions [9,10,11,12]. Most of the approaches focused on establishing correlations between clinical variables at admission and discharge on a population level This allowed the identification of, for example, typical routinely collected data (e.g., chronicity) and the severity of initial sensorimotor impairments as factors determining the efficacy of neurorehabilitation [5, 13,14,15]. Identifying trends on a population level has limited relevance to inform daily clinical decision-making

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