Prognostic models for predicting clinical disease progression, worsening and activity in people with multiple sclerosis

Reeve, Kelly; On, Begum Irmak; Havla, Joachim; Burns, Jacob; Gosteli-Peter, Martina A; Alabsawi, Albraa; Alayash, Zoheir; Götschi, Andrea; Seibold, Heidi; Mansmann, Ulrich; Held, Ulrike

doi:10.1002/14651858.cd013606.pub2

Cited by 4 publications

(2 citation statements)

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“…Only 2 of 75 models were shown to be validated in external cohorts multiple times, which, however, is formally the most stringent quality standard of prognostic research. Also, the studies reviewed differed in terms of clinical characteristics of the patient cohorts, definition of predictors, and outcome parameters, which made comparability difficult [ 17 ]. Overcoming this, ProVal-MS is a validation study for the DIFUTURE-MS-TDS emerging from a combined analysis of existing data in a separately conducted retrospective cohort study, thus validating a predictive model in a different prospective cohort using clinical routine data.…”

Section: Methodsmentioning

confidence: 99%

Prospective study validating a multidimensional treatment decision score predicting the 24-month outcome in untreated patients with clinically isolated syndrome and early relapsing–remitting multiple sclerosis, the ProVal-MS study

Bayas,

Mansmann,

Ön

et al. 2024

Neurol. Res. Pract.

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Introduction In Multiple Sclerosis (MS), patients´ characteristics and (bio)markers that reliably predict the individual disease prognosis at disease onset are lacking. Cohort studies allow a close follow-up of MS histories and a thorough phenotyping of patients. Therefore, a multicenter cohort study was initiated to implement a wide spectrum of data and (bio)markers in newly diagnosed patients. Methods ProVal-MS (Prospective study to validate a multidimensional decision score that predicts treatment outcome at 24 months in untreated patients with clinically isolated syndrome or early Relapsing–Remitting-MS) is a prospective cohort study in patients with clinically isolated syndrome (CIS) or Relapsing–Remitting (RR)-MS (McDonald 2017 criteria), diagnosed within the last two years, conducted at five academic centers in Southern Germany. The collection of clinical, laboratory, imaging, and paraclinical data as well as biosamples is harmonized across centers. The primary goal is to validate (discrimination and calibration) the previously published DIFUTURE MS-Treatment Decision score (MS-TDS). The score supports clinical decision-making regarding the options of early (within 6 months after study baseline) platform medication (Interferon beta, glatiramer acetate, dimethyl/diroximel fumarate, teriflunomide), or no immediate treatment (> 6 months after baseline) of patients with early RR-MS and CIS by predicting the probability of new or enlarging lesions in cerebral magnetic resonance images (MRIs) between 6 and 24 months. Further objectives are refining the MS-TDS score and providing data to identify new markers reflecting disease course and severity. The project also provides a technical evaluation of the ProVal-MS cohort within the IT-infrastructure of the DIFUTURE consortium (Data Integration for Future Medicine) and assesses the efficacy of the data sharing techniques developed. Perspective Clinical cohorts provide the infrastructure to discover and to validate relevant disease-specific findings. A successful validation of the MS-TDS will add a new clinical decision tool to the armamentarium of practicing MS neurologists from which newly diagnosed MS patients may take advantage. Trial registration ProVal-MS has been registered in the German Clinical Trials Register, `Deutsches Register Klinischer Studien` (DRKS)—ID: DRKS00014034, date of registration: 21 December 2018; https://drks.de/search/en/trial/DRKS00014034

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Section: Methodsmentioning

confidence: 99%

Prospective study validating a multidimensional treatment decision score predicting the 24-month outcome in untreated patients with clinically isolated syndrome and early relapsing–remitting multiple sclerosis, the ProVal-MS study

Bayas,

Mansmann,

Ön

et al. 2024

Neurol. Res. Pract.

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“…Relapsing-remitting multiple sclerosis (RRMS) is the most frequent form of multiple sclerosis (MS) and is still one of the most common causes of neurological disability in young adults worldwide (3,4). Clinical research on new therapeutic approaches is very active as well as the development of prognostic and predictive models (5,6). These topics are also related to the issue of individual-level surrogacy (ILS).…”

Section: Introductionmentioning

confidence: 99%

Individual level surrogacy of MRI T2 lesion information for future disease severity: a methodological discussion and application to recent MS Phase II and III trials

Buchka,

Joachim,

Begüm

et al. 2024

Preprint

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Background: Individual-level surrogacy (ILS) describes settings where individual-patient information on a biomarker captures information on future patient-relevant clinical outcomes. For persons with relapsing-remitting multiple sclerosis (PwRRMS), whether the number of new or newly enlarged hyperintense lesions in T2 magnetic resonance images (MRI) or the T2 lesions’ volume can act as ILS for disability progression and disease activity is of interest. However, ILS of new T2 lesions for individual PwRRMS has not yet been demonstrated, although new T2 lesions already serve as a primary outcome parameter for pivotal trials. Methods: Our synthesis used individual-patient data from ten randomized controlled trials (n = 5673 PwRRMS). Longitudinal data on either the number or volume of new T2 lesions were analyzed as potential ILS for disability progression measured by the expanded disability status scale (EDSS) and disease activity shown by clinical relapses. ILS metrics were calculated: R2A) for the Meta-analytic (MA) and LRF (likelihood reduction factor) for the information theoretic (IT) approach. A simulation study elucidates the approaches’ potential to detect ILS and provides the basis to interpret the results derived from the trial data. Results: The simulation study demonstrates good performance for the IT approach. MA algorithms work well when applied to continuous data. When applied to counting data, the results may numerically be unstable. However, transformations of the counting data allow successful calculations with methods for Gaussian data. Analyses following IT methodology provide in all trial-based settings reliable results on ILS and identified two studies with weak ILS for the combination of T2 Volume and EDSS (LRF = 0.21, CI95%: 0.16 – 0.26; LRF = 0.28, CI95%: 0.23 – 0.34). Conclusion: The simulation study showed robust and sensitive results for IT and MA when not applied to counting data outcomes-based ILS strategies. The high potential of these algorithms to detect ILS strengthens the interpretation of our findings. Within most arms of ten pivotal MS trials, ILS measures were close to zero. Evidence on the surrogacy of T2 lesion measures and disease progression and activity as a general principle could not be generated. More research using proper designs and methodology is needed.

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Scoping review of clinical decision support systems for multiple sclerosis management: Leveraging information technology and massive health data

Demuth,

Ed‐Driouch,

Dumas

et al. 2024

Euro J of Neurology

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Background and purposeMultiple sclerosis (MS) is a complex autoimmune disease of the central nervous system, with numerous therapeutic options, but a lack of biomarkers to support a mechanistic approach to precision medicine. A computational approach to precision medicine could proceed from clinical decision support systems (CDSSs). They are digital tools aiming to empower physicians through the clinical applications of information technology and massive data. However, the process of their clinical development is still maturing; we aimed to review it in the field of MS.MethodsFor this scoping review, we screened systematically the PubMed database. We identified 24 articles reporting 14 CDSS projects and compared their technical and software development aspects.ResultsThe projects position themselves in various contexts of usage with various algorithmic approaches: expert systems, CDSSs based on similar patients' data visualization, and model‐based CDSSs implementing mathematical predictive models. So far, no project has completed its clinical development up to certification for clinical use with global release. Some CDSSs have been replaced at subsequent project iterations. The most advanced projects did not necessarily report every step of clinical development in a dedicated article (proof of concept, offline validation, refined prototype, live clinical evaluation, comparative prospective evaluation). They seek different software distribution options to integrate into health care: internal usage, “peer‐to‐peer,” and marketing distribution.ConclusionsThis review illustrates the potential of clinical applications of information technology and massive data to support MS management and helps clarify the roadmap for future projects as a multidisciplinary and multistep process.

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Prognostic models for predicting clinical disease progression, worsening and activity in people with multiple sclerosis

Cited by 4 publications

References 203 publications

Prospective study validating a multidimensional treatment decision score predicting the 24-month outcome in untreated patients with clinically isolated syndrome and early relapsing–remitting multiple sclerosis, the ProVal-MS study

Prospective study validating a multidimensional treatment decision score predicting the 24-month outcome in untreated patients with clinically isolated syndrome and early relapsing–remitting multiple sclerosis, the ProVal-MS study

Individual level surrogacy of MRI T2 lesion information for future disease severity: a methodological discussion and application to recent MS Phase II and III trials

Scoping review of clinical decision support systems for multiple sclerosis management: Leveraging information technology and massive health data

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