Personalised medicine for multiple sclerosis care

Gafson, Arie; Craner, Matt; Matthews, Paul M.

doi:10.1177/1352458516672017

Cited by 54 publications

(35 citation statements)

References 48 publications

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“…Involving MS patients in the decision-making process is crucial for selecting the treatment that best suits the patient’s profile and preferences [7]. Therefore, treatment decisions in MS should be conjointly made by the neurologist and the patient, and should be based not only on the best available evidence but also taking into account the patient’s values and preferences [26, 27]. …”

Section: Discussionmentioning

confidence: 99%

Psychometric properties of the SDM-Q-9 questionnaire for shared decision-making in multiple sclerosis: item response theory modelling and confirmatory factor analysis

Ballesteros

Moral

Brieva

et al. 2017

Health Qual Life Outcomes

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BackgroundShared decision-making is a cornerstone of patient-centred care. The 9-item Shared Decision-Making Questionnaire (SDM-Q-9) is a brief self-assessment tool for measuring patients’ perceived level of involvement in decision-making related to their own treatment and care. Information related to the psychometric properties of the SDM-Q-9 for multiple sclerosis (MS) patients is limited. The objective of this study was to assess the performance of the items composing the SDM-Q-9 and its dimensional structure in patients with relapsing-remitting MS.MethodsA non-interventional, cross-sectional study in adult patients with relapsing-remitting MS was conducted in 17 MS units throughout Spain. A nonparametric item response theory (IRT) analysis was used to assess the latent construct and dimensional structure underlying the observed responses. A parametric IRT model, General Partial Credit Model, was fitted to obtain estimates of the relationship between the latent construct and item characteristics. The unidimensionality of the SDM-Q-9 instrument was assessed by confirmatory factor analysis.ResultsA total of 221 patients were studied (mean age = 42.1 ± 9.9 years, 68.3% female). Median Expanded Disability Status Scale score was 2.5 ± 1.5. Most patients reported taking part in each step of the decision-making process. Internal reliability of the instrument was high (Cronbach’s α = 0.91) and the overall scale scalability score was 0.57, indicative of a strong scale. All items, except for the item 1, showed scalability indices higher than 0.30. Four items (items 6 through to 9) conveyed more than half of the SDM-Q-9 overall information (67.3%). The SDM-Q-9 was a good fit for a unidimensional latent structure (comparative fit index = 0.98, root-mean-square error of approximation = 0.07). All freely estimated parameters were statistically significant (P < 0.001). All items presented standardized parameter estimates with salient loadings (>0.40) with the exception of item 1 which presented the lowest loading (0.26). Items 6 through to 8 were the most relevant items for shared decision-making.ConclusionsThe SDM-Q-9 presents appropriate psychometric properties and is therefore useful for assessing different aspects of shared decision-making in patients with multiple sclerosis.

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Section: Discussionmentioning

confidence: 99%

Psychometric properties of the SDM-Q-9 questionnaire for shared decision-making in multiple sclerosis: item response theory modelling and confirmatory factor analysis

Ballesteros

Moral

Brieva

et al. 2017

Health Qual Life Outcomes

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“…It may be helpful for HCPs to use more neutral terms; for example, 'progression' can have negative connotations that may be avoided by the use of alternatives such as 'evolution' or 'spectrum', or referring to varying levels of ability rather than increased disability. Discussions also need to be personalised with regard to timing, patient circumstances, and cultural considerations in order to provide tailored, personalised care [20,24,46].…”

Section: Optimising Conversations About Disease Progressionmentioning

confidence: 99%

Knowledge Is Power, but Is Ignorance Bliss? Optimising Conversations About Disease Progression in Multiple Sclerosis

et al. 2019

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Communication about multiple sclerosis (MS) disease progression between healthcare professionals (HCPs) and people with MS (PwMS) has historically been considered difficult, and attention to improving it has been neglected. However, a growing number of studies have shown that this is a key area to get right, since negative experiences can affect patient satisfaction, treatment adherence, and clinical outcomes. This article reports on a symposium at the European Charcot Foundation, 2018, led by a panel of leading clinicians and patient experts from MS in the 21st Century, who debated the benefits, drawbacks, and challenges of communicating about disease progression, for both HCPs and PwMS, and potential ways to optimise these discussions. PwMS' preferences and priorities regarding conversations about disease progression vary widely. While the majority want to have these conversations, some will be reluctant and/or emotionally unready. Communication therefore needs to be personalised, and HCPs should always be prepared to have such conversations in an appropriate and sensitive manner. Clinical information can be opaque for PwMS, so HCPs also need to use language that is clear, easily understandable, and patient-friendly. MS in the 21st Century is in the process of developing several resources and programmes to help improve disease progression communication between HCPs and PwMS.

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“…Indeed, several studies show that a multi-parametric approach may improve our prognostic ability in MS [53,54]. It involves the development of predictive models involving the integration of clinical and biological data with an understanding of the impact of disease on the lives of individual patients [55]. Besides the inclusion of extra biomarkers, another step of great practical importance is to move towards multicenter design studies.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Analysis of Motor Evoked Potential Time Series to Predict Disability Progression in Multiple Sclerosis

Yperman

Becker

Valkenborg

et al. 2019

Preprint

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Background: Evoked potentials (EPs) are a measure of the conductivity of the central nervous system. They are used to monitor disease progression of multiple sclerosis patients. Previous studies only extracted a few variables from the EPs, which are often further condensed into a single variable: the EP score. We perform a machine learning analysis of motor EP that uses the whole time series, instead of a few variables, to predict disability progression after two years. Obtaining realistic performance estimates of this task has been difficult because of small data set sizes. We recently extracted a dataset of EPs from the Rehabiliation & MS Center in Overpelt, Belgium. Our data set is large enough to obtain, for the first time, a performance estimate on an independent test set containing different patients. Methods:We extracted a large number of time series features from the motor EPs with the highly comparative time series analysis software package. Mutual information with the target and the Boruta method are used to find features which contain information not included in the features studied in the literature. We use random forests (RF) and logistic regression (LR) classifiers to predict disability progression after two years. Statistical significance of the performance increase when adding extra features is checked with the DeLong hypothesis test.Results: Including extra time series features in motor EPs leads to a statistically significant improvement compared to using only the known features, although the effect is limited in magnitude (∆AUC = 0.02 for RF and ∆AUC = 0.05 for LR). RF with extra time series features obtains the best performance (AUC = 0.75 ± 0.07), which is good considering the limited number of biomarkers in the model. RF (a nonlinear classifier) outperforms LR (a linear classifier).Conclusions: Using machine learning methods on EPs shows promising predictive performance. Using additional EP time series features beyond those already in use leads to a modest increase in performance. Larger datasets, preferably multi-center, are needed for further research. Given a large enough dataset, these models may be used to support clinicians in their decision making process regarding future treatment.

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Personalised medicine for multiple sclerosis care

Cited by 54 publications

References 48 publications

Psychometric properties of the SDM-Q-9 questionnaire for shared decision-making in multiple sclerosis: item response theory modelling and confirmatory factor analysis

Psychometric properties of the SDM-Q-9 questionnaire for shared decision-making in multiple sclerosis: item response theory modelling and confirmatory factor analysis

Knowledge Is Power, but Is Ignorance Bliss? Optimising Conversations About Disease Progression in Multiple Sclerosis

Machine Learning Analysis of Motor Evoked Potential Time Series to Predict Disability Progression in Multiple Sclerosis

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