personalized medicine for MS

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personalized medicine for MS

Postby frodo » Thu May 31, 2018 8:52 am

Towards personalized therapy for multiple sclerosis: prediction of individual treatment response

https://academic.oup.com/brain/article- ... 26/4061515

Abstract

Timely initiation of effective therapy is crucial for preventing disability in multiple sclerosis; however, treatment response varies greatly among patients.

Comprehensive predictive models of individual treatment response are lacking. Our aims were: (i) to develop predictive algorithms for individual treatment response using demographic, clinical and paraclinical predictors in patients with multiple sclerosis; and (ii) to evaluate accuracy, and internal and external validity of these algorithms.

This study evaluated 27 demographic, clinical and paraclinical predictors of individual response to seven disease-modifying therapies in MSBase, a large global cohort study.

Treatment response was analysed separately for disability progression, disability regression, relapse frequency, conversion to secondary progressive disease, change in the cumulative disease burden, and the probability of treatment discontinuation.

Multivariable survival and generalized linear models were used, together with the principal component analysis to reduce model dimensionality and prevent overparameterization. Accuracy of the individual prediction was tested and its internal validity was evaluated in a separate, non-overlapping cohort. External validity was evaluated in a geographically distinct cohort, the Swedish Multiple Sclerosis Registry. In the training cohort (n = 8513), the most prominent modifiers of treatment response comprised age, disease duration, disease course, previous relapse activity, disability, predominant relapse phenotype and previous therapy.

Importantly, the magnitude and direction of the associations varied among therapies and disease outcomes. Higher probability of disability progression during treatment with injectable therapies was predominantly associated with a greater disability at treatment start and the previous therapy. For fingolimod, natalizumab or mitoxantrone, it was mainly associated with lower pretreatment relapse activity.

The probability of disability regression was predominantly associated with pre-baseline disability, therapy and relapse activity. Relapse incidence was associated with pretreatment relapse activity, age and relapsing disease course, with the strength of these associations varying among therapies.

Accuracy and internal validity (n = 1196) of the resulting predictive models was high (>80%) for relapse incidence during the first year and for disability outcomes, moderate for relapse incidence in Years 2–4 and for the change in the cumulative disease burden, and low for conversion to secondary progressive disease and treatment discontinuation.

External validation showed similar results, demonstrating high external validity for disability and relapse outcomes, moderate external validity for cumulative disease burden and low external validity for conversion to secondary progressive disease and treatment discontinuation.

We conclude that demographic, clinical and paraclinical information helps predict individual response to disease-modifying therapies at the time of their commencement

Also a similar article says:

http://journals.sagepub.com/doi/full/10 ... 8516672017

Conclusion

Understanding the heterogeneity of the MS syndrome involves an active process of ‘deconstruction’ to define the biologically distinct diseases included within it and their interactions with individual, patient-specific factors. Coordinated collection and sharing of data, development of predictive models and their progressive evaluation in the care of individual patients will be an essential part of this. At the same time, a much broader range of data on patients will be needed.

We should look towards a near future in which care for patients is supported by a comprehensive medical profile including not just clinical data but also that from devices and patient-reported outcomes and behavioural, employment and lifestyle data. In some instances – with the consent and involvement of the patient – this may include data reflecting personal expectations or areas of concern drawn from non-traditional sources, such as social media or online search sites.

We believe that ‘personalisation’, if we take its premise as deconstructing the disease heterogeneity to balance benefit and risk optimally in the management of each patient, should be a clinical priority, rather than a clinical ideal. MS care is an area of neurology in which personalised medicine in neurology needs to be championed.
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