A Physician-Completed Digital Tool for Evaluating Disease Progression (Multiple Sclerosis Progression Discussion Tool): Validation Study

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Abstract

Background

Defining the transition from relapsing-remitting multiple sclerosis (RRMS) to secondary progressive multiple sclerosis (SPMS) can be challenging and delayed. A digital tool (MSProDiscuss) was developed to facilitate physician-patient discussion in evaluating early, subtle signs of multiple sclerosis (MS) disease progression representing this transition.

Objective

This study aimed to determine cut-off values and corresponding sensitivity and specificity for predefined scoring algorithms, with or without including Expanded Disability Status Scale (EDSS) scores, to differentiate between RRMS and SPMS patients and to evaluate psychometric properties.

Methods

Experienced neurologists completed the tool for patients with confirmed RRMS or SPMS and those suspected to be transitioning to SPMS. In addition to age and EDSS score, each patient’s current disease status (disease activity, symptoms, and its impacts on daily life) was collected while completing the draft tool. Receiver operating characteristic (ROC) curves determined optimal cut-off values (sensitivity and specificity) for the classification of RRMS and SPMS.

Results

Twenty neurologists completed the draft tool for 198 patients. Mean scores for patients with RRMS (n=89), transitioning to SPMS (n=47), and SPMS (n=62) were 38.1 (SD 12.5), 55.2 (SD 11.1), and 69.6 (SD 12.0), respectively ( P<.001, each between-groups comparison). Area under the ROC curve (AUC) including and excluding EDSS were for RRMS (including) AUC 0.91, 95% CI 0.87-0.95, RRMS (excluding) AUC 0.88, 95% CI 0.84-0.93, SPMS (including) AUC 0.91, 95% CI 0.86-0.95, and SPMS (excluding) AUC 0.86, 95% CI 0.81-0.91. In the algorithm with EDSS, the optimal cut-off values were ≤51.6 for RRMS patients (sensitivity=0.83; specificity=0.82) and ≥58.9 for SPMS patients (sensitivity=0.82; specificity=0.84). The optimal cut-offs without EDSS were ≤46.3 and ≥57.8 and resulted in similar high sensitivity and specificity (0.76-0.86). The draft tool showed excellent interrater reliability (intraclass correlation coefficient=.95).

Conclusions

The MSProDiscuss tool differentiated RRMS patients from SPMS patients with high sensitivity and specificity. In clinical practice, it may be a useful tool to evaluate early, subtle signs of MS disease progression indicating the evolution of RRMS to SPMS. MSProDiscuss will help assess the current level of progression in an individual patient and facilitate a more informed physician-patient discussion.

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Most cited references 16

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Silent progression in disease activity–free relapsing multiple sclerosis

Bruce A C Cree, Jill Hollenbach, Riley Bove … (2019)

Objective Rates of worsening and evolution to secondary progressive multiple sclerosis (MS) may be substantially lower in actively treated patients compared to natural history studies from the pretreatment era. Nonetheless, in our recently reported prospective cohort, more than half of patients with relapsing MS accumulated significant new disability by the 10th year of follow‐up. Notably, “no evidence of disease activity” at 2 years did not predict long‐term stability. Here, we determined to what extent clinical relapses and radiographic evidence of disease activity contribute to long‐term disability accumulation. Methods Disability progression was defined as an increase in Expanded Disability Status Scale (EDSS) of 1.5, 1.0, or 0.5 (or greater) from baseline EDSS = 0, 1.0–5.0, and 5.5 or higher, respectively, assessed from baseline to year 5 (±1 year) and sustained to year 10 (±1 year). Longitudinal analysis of relative brain volume loss used a linear mixed model with sex, age, disease duration, and HLA‐DRB1*15:01 as covariates. Results Relapses were associated with a transient increase in disability over 1‐year intervals (p = 0.012) but not with confirmed disability progression (p = 0.551). Relative brain volume declined at a greater rate among individuals with disability progression compared to those who remained stable (p < 0.05). Interpretation Long‐term worsening is common in relapsing MS patients, is largely independent of relapse activity, and is associated with accelerated brain atrophy. We propose the term silent progression to describe the insidious disability that accrues in many patients who satisfy traditional criteria for relapsing–remitting MS. Ann Neurol 2019;85:653–666

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To assess factors affecting the rate of conversion to secondary progressive (SP) multiple sclerosis (MS) and its subsequent evolution. Among 806 patients with relapsing remitting (RR) onset MS from the London Ontario database, we used Kaplan-Meier, Cox regression and multiple logistic regression analyses to investigate the effect of baseline clinical and demographic features on (1) the probability of, and the time to, SP disease, (2) the time to bedbound status (Disability Status Scale (DSS 8)) from onset of progression. The risk of entering the SP phase increased proportionally with disease duration (OR=1.07 for each additional year; p 30 HR=0.52 (p<0.001), 0.65 (p<0.001), respectively) and high early relapse frequency (1-2 attacks vs ≥3 HR=0.63 (p<0.001), 0.75 (p=0.04), respectively) predicted significantly higher risk of SP MS and shorter latency to progression. Times to DSS 8 from onset of progression were significantly shorter among those with high early relapse frequency (≥3 attacks), and among those presenting with cerebellar and brainstem symptoms. The onset of SP MS is the dominant determinant of long-term prognosis, and its prevention is the most important target measure for treatment. Baseline clinical features of early relapse frequency and age at onset can be used to select groups at higher risk of developing severe disability based on the probability of their disease becoming progressive within a defined time period.

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ROC curves in clinical chemistry: uses, misuses, and possible solutions.

M Lieber, Frank H Wians, N Obuchowski (2004)

ROC curves have become the standard for describing and comparing the accuracy of diagnostic tests. Not surprisingly, ROC curves are used often by clinical chemists. Our aims were to observe how the accuracy of clinical laboratory diagnostic tests is assessed, compared, and reported in the literature; to identify common problems with the use of ROC curves; and to offer some possible solutions. We reviewed every original work using ROC curves and published in Clinical Chemistry in 2001 or 2002. For each article we recorded phase of the research, prospective or retrospective design, sample size, presence/absence of confidence intervals (CIs), nature of the statistical analysis, and major analysis problems. Of 58 articles, 31% were phase I (exploratory), 50% were phase II (challenge), and 19% were phase III (advanced) studies. The studies increased in sample size from phase I to III and showed a progression in the use of prospective designs. Most phase I studies were powered to assess diagnostic tests with ROC areas >/=0.70. Thirty-eight percent of studies failed to include CIs for diagnostic test accuracy or the CIs were constructed inappropriately. Thirty-three percent of studies provided insufficient analysis for comparing diagnostic tests. Other problems included dichotomization of the gold standard scale and inappropriate analysis of the equivalence of two diagnostic tests. We identify available software and make some suggestions for sample size determination, testing for equivalence in diagnostic accuracy, and alternatives to a dichotomous classification of a continuous-scale gold standard. More methodologic research is needed in areas specific to clinical chemistry.

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Author and article information

Contributors

Tjalf Ziemssen:

ORCID: https://orcid.org/0000-0001-8799-8202

Center of Clinical NeuroscienceNeurological University Clinic Carl Gustav CarusTU DresdenFetscherstraße 74DresdenGermany49 351 458 4465ziemssen@web.de

Journal

Journal ID (nlm-ta): J Med Internet Res

Journal ID (iso-abbrev): J. Med. Internet Res

Journal ID (publisher-id): JMIR

Title: Journal of Medical Internet Research

Publisher: JMIR Publications (Toronto, Canada )

ISSN (Print): 1439-4456

ISSN (Electronic): 1438-8871

Publication date Collection: February 2020

Publication date (Electronic): 12 February 2020

Volume: 22

Issue: 2

Electronic Location Identifier: e16932

Affiliations

[1 ] Center of Clinical Neuroscience Neurological University Clinic Carl Gustav Carus TU Dresden Dresden Germany

[2 ] Novartis Pharma AG Basel Switzerland

[3 ] Adelphi Values Macclesfield United Kingdom

[4 ] Ottawa Health Research Institute University of Ottawa Ottawa, ON Canada

Author notes

Corresponding Author: Tjalf Ziemssen Ziemssen@ 123456web.de

Author information

Tjalf Ziemssen https://orcid.org/0000-0001-8799-8202

Daniela Piani-Meier https://orcid.org/0000-0003-0890-9422

Bryan Bennett https://orcid.org/0000-0002-5295-5576

Chloe Johnson https://orcid.org/0000-0002-2278-2176

Katie Tinsley https://orcid.org/0000-0001-9446-9886

Andrew Trigg https://orcid.org/0000-0002-1613-8042

Thomas Hach https://orcid.org/0000-0002-2229-1545

Frank Dahlke https://orcid.org/0000-0003-3333-6291

Davorka Tomic https://orcid.org/0000-0002-1484-1850

Chloe Tolley https://orcid.org/0000-0003-3846-9018

Mark S Freedman https://orcid.org/0000-0003-1255-9701

Article

Publisher ID: v22i2e16932

DOI: 10.2196/16932

PMC ID: 7055760

PubMed ID: 32049062

SO-VID: 10e9ed7f-f477-455b-98f9-e7c83d5f44a3

Copyright © ©Tjalf Ziemssen, Daniela Piani-Meier, Bryan Bennett, Chloe Johnson, Katie Tinsley, Andrew Trigg, Thomas Hach, Frank Dahlke, Davorka Tomic, Chloe Tolley, Mark S Freedman. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 12.02.2020.

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

History

Date received : 7 November 2019

Date revision requested : 5 December 2019

Date revision received : 19 December 2019

Date accepted : 19 December 2019

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