» Articles » PMID: 26265269

A Web-based Decision Support Tool for Prognosis Simulation in Multiple Sclerosis

Overview
Specialty Neurology
Date 2015 Aug 13
PMID 26265269
Citations 5
Authors
Affiliations
Soon will be listed here.
Abstract

A multiplicity of natural history studies of multiple sclerosis provides valuable knowledge of the disease progression but individualized prognosis remains elusive. A few decision support tools that assist the clinician in such task have emerged but have not received proper attention from clinicians and patients. The objective of the current work is to implement a web-based tool, conveying decision relevant prognostic scientific evidence, which will help clinicians discuss prognosis with individual patients. Data were extracted from a set of reference studies, especially those dealing with the natural history of multiple sclerosis. The web-based decision support tool for individualized prognosis simulation was implemented with NetLogo, a program environment suited for the development of complex adaptive systems. Its prototype has been launched online; it enables clinicians to predict both the likelihood of CIS to CDMS conversion, and the long-term prognosis of disability level and SPMS conversion, as well as assess and monitor the effects of treatment. More robust decision support tools, which convey scientific evidence and satisfy the needs of clinical practice by helping clinicians discuss prognosis expectations with individual patients, are required. The web-based simulation model herein introduced proposes to be a step forward toward this purpose.

Citing Articles

Predictive model for converting optic neuritis to multiple sclerosis; decision tree in focus.

Rasouli S, Dakkali M, Ghazvini A, Azarbad R, Asani M, Mirzaasgari Z PLoS One. 2024; 19(12):e0309702.

PMID: 39621693 PMC: 11611084. DOI: 10.1371/journal.pone.0309702.


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

Demuth S, Ed-Driouch C, Dumas C, Laplaud D, Edan G, Vince N Eur J Neurol. 2024; 32(1):e16363.

PMID: 38860844 PMC: 11618115. DOI: 10.1111/ene.16363.


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

Reeve K, Irmak On B, Havla J, Burns J, Gosteli-Peter M, Alabsawi A Cochrane Database Syst Rev. 2023; 9:CD013606.

PMID: 37681561 PMC: 10486189. DOI: 10.1002/14651858.CD013606.pub2.


Development of Registry Data to Create Interactive Doctor-Patient Platforms for Personalized Patient Care, Taking the Example of the DESTINY System.

Bergmann A, Stangel M, Weih M, van Hovell P, Braune S, Kochling M Front Digit Health. 2021; 3:633427.

PMID: 34713104 PMC: 8521878. DOI: 10.3389/fdgth.2021.633427.


Model-Driven Decision Making in Multiple Sclerosis Research: Existing Works and Latest Trends.

Alshamrani R, Althbiti A, Alshamrani Y, Alkomah F, Ma X Patterns (N Y). 2020; 1(8):100121.

PMID: 33294867 PMC: 7691382. DOI: 10.1016/j.patter.2020.100121.