A Physician-Completed Digital Tool for Evaluating Disease Progression (Multiple Sclerosis Progression Discussion Tool): Validation Study.
digital
multiple sclerosis
progression
relapsing-remitting multiple sclerosis
secondary progressive multiple sclerosis
transition
validation
Journal
Journal of medical Internet research
ISSN: 1438-8871
Titre abrégé: J Med Internet Res
Pays: Canada
ID NLM: 100959882
Informations de publication
Date de publication:
12 02 2020
12 02 2020
Historique:
received:
07
11
2019
accepted:
19
12
2019
revised:
19
12
2019
entrez:
13
2
2020
pubmed:
13
2
2020
medline:
21
10
2020
Statut:
epublish
Résumé
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. 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. 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. 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). 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.
Sections du résumé
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.
Identifiants
pubmed: 32049062
pii: v22i2e16932
doi: 10.2196/16932
pmc: PMC7055760
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e16932Informations de 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.
Références
BMC Neurol. 2016 Aug 08;16:129
pubmed: 27502119
Mult Scler. 2019 Jul;25(8):1102-1112
pubmed: 29911467
Cancer. 1950 Jan;3(1):32-5
pubmed: 15405679
Mult Scler. 2014 Oct;20(12):1654-7
pubmed: 24493475
Neurology. 2014 Sep 9;83(11):1022-4
pubmed: 25200713
Ann Neurol. 2011 Feb;69(2):292-302
pubmed: 21387374
Neurology. 2014 Jul 15;83(3):278-86
pubmed: 24871874
J Neurol. 2019 Jul 30;:
pubmed: 31363847
Lancet Neurol. 2015 Feb;14(2):183-93
pubmed: 25772897
Mult Scler. 2008 Apr;14(3):314-24
pubmed: 18208898
Brain. 2016 Sep;139(Pt 9):2395-405
pubmed: 27401521
J Neurol Neurosurg Psychiatry. 2014 Jan;85(1):67-75
pubmed: 23486991
Rev Prat. 2006 Jun 30;56(12):1313-20
pubmed: 16948219
Mult Scler Relat Disord. 2016 Sep;9 Suppl 1:S5-S48
pubmed: 27640924
Brain. 2006 Mar;129(Pt 3):584-94
pubmed: 16401620
PLoS One. 2014 Dec 04;9(12):e114468
pubmed: 25474472
Mult Scler Relat Disord. 2019 Nov 18;38:101861
pubmed: 31865132
Nat Rev Neurol. 2012 Nov 5;8(11):647-56
pubmed: 23007702
J Neurol Neurosurg Psychiatry. 2010 Sep;81(9):1039-43
pubmed: 20639385
Trends Neurosci. 2016 May;39(5):325-339
pubmed: 26987259
Mult Scler Relat Disord. 2014 Sep;3(5):584-92
pubmed: 26265270
Neurology. 1983 Nov;33(11):1444-52
pubmed: 6685237
Ann Neurol. 2019 May;85(5):653-666
pubmed: 30851128
Clin Chem. 2004 Jul;50(7):1118-25
pubmed: 15142978