From September 25th to 28th, the 92nd Congress of the German Society for Neurology took place in Stuttgart. The congress covers the entire spectrum of scientific and clinical neurology, but of course MS – as a common neurological disease – always plays an important role. This year too, the MS Competence Network and the Federal Association organized a large symposium. The topics discussed there indicate what is currently particularly moving the MS scene and which future topics will be of importance.
The contributions included topics such as the therapy of childhood MS. It is now well known that MS has different pathophysiological manifestations in different life phases. Childhood MS is characterized by a high degree of inflammation. Accordingly, it can be very well treated with anti-inflammatory therapies. This is shown by the results of the first prospective study on the treatment of childhood MS with fingolimod. Prof. Jutta Gärtner from Göttingen also pointed out in her contribution that fingolimod has a different side effect profile when used in children, leading to more frequent occurrence of seizures.
Prof. Martin Weber, also from the University of Göttingen, lectured on new insights into the therapy of progressive MS – thus about the late phase of the disease, which is mainly found in older and elderly MS patients. For the first time, studies with ocrelizumab and siponimod have shown that this phase of the disease can also benefit from anti-inflammatory therapy. Professor Weber pointed out, however, that the use is justified especially in patients who still show signs of active inflammation. He therefore advocated the use of a newer MS classification that primarily distinguishes between active and inactive progression. However, it also became clear that there is still a high need for new possibilities and concepts with regard to the prevention of progression.
An important concept, namely the promotion of remyelination, was the subject of the lecture by Prof. Martin Stangel from the Hanover Medical School. Prof. Stangel explained that remyelination is a very effective strategy for neuroprotection. Unfortunately, however, initial studies on promoting remyelination are rather unsatisfactory. In particular, the antibody opicinumab (anti-Lingo) did not meet expectations in clinical studies. Prof. Stangel also pointed out how difficult it is to detect therapeutic success at all with common monitoring methods.
Dr. Mike Wattjes, also from the Hanover Medical School, dealt with the most important monitoring instrument, the MRI. The main content of the lecture were the magnetic resonance differences between the inflammatory relapsing phase of the disease and the later secondary chronically progressive phase. It is certainly worth highlighting the statement that the administration of gadolinium contrast agent for follow-up controls does not provide any significant gain in knowledge compared to active T2 lesions and is therefore often dispensable.
In the subsequent symposium of the DMSG Federal Association, “big data/smart data” was the dominant topic. In addition to clinical studies – the current status of which was reported by Prof. Ludwig Kappos from Basel – so-called “real-world” data play an important role when it comes to prognosis, therapy and other influencing factors. These “real-world” data are mainly obtained in Germany through the German MS register – here Prof. Peter Flachenecker from Bad Wildbad gave an overview of the current status. As far as the data basis is concerned, there is still a lot of room for improvement in Germany, because the more patients that can be included, the better the quality of the findings from the registry studies.
In addition to these conventional data collections, the topic of artificial intelligence in data evaluation will occupy us in the future. This year’s research funding from the DMSG has been awarded to Prof. Kerstin Ritter from Berlin, among others. In this project, it should be possible to predict the individual course of disease of patients by analyzing MRI data sets. Computer algorithms from the field of machine learning (“Deep Learning”) are used for this. We are very much looking forward to the outcome of this project. But the contribution impressively showed how much digitalization will change medicine in the coming years.