This study takes an evidence-based approach to making a personalised predictive tool for MSers. The aim is to be able to predict your chances of converting from CIS (clinically isolated syndrome) to CDMS (clinically-definite MS), RRMS (relapsing-remitting MS) to SPMS (secondary progressive MS) and your predicted level of disability at 10 and 20 years from diagnosis.
The author states that similar tools have previously been created but that they are not as user-friendly. Such predictive tools are commonplace in the management of cardiovascular disease, high cholestrol and hypertension and usually utilise large observational or prospective study data.
The programme inputs prognostic data from your MRI scans, OCB status, BREMS score, age at diagnosis/sex etc and measures this against natural history studies to provide a personalised prediction of your disease trajectory. The most useful aspect is that you can get separate predictions based on whether you start treatment or whether you opt to take a wait-and-see approach. This will undoubtedly help with decision making in the clinic.
If this system is successful, I hope it evolves to give you statistics based on each specific treatment as this will help even more with the decision making process. The authors acknowledge that predictions of disability do not take into account cognitive impairment which is obviously an important limitation.
All in all, this is a good example of personalised medicine in a field where this can sometimes be lacking. What I mean by this is that MS is different in every patient and therefore the evidence that is out there isn't always relevant to you as an individual. Approaches that try to take a more personalised approach are the future of medicine.
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.