MSProDiscuss™ Clinical Decision Support Tool for Identifying Multiple Sclerosis Progression.
Tjalf ZiemssenJo VandercappellenValeria Jordan MondragonGavin GiovannoniPublished in: Journal of clinical medicine (2022)
This article describes the rationale for the development of the MSProDiscuss™ clinical decision support (CDS) tool, its development, and insights into how it can help neurologists improve care for patients with multiple sclerosis (MS). MS is a progressive disease characterized by heterogeneous symptoms and variable disease course. There is growing consensus that MS exists on a continuum, with overlap between relapsing-remitting and secondary progressive phenotypes. Evidence demonstrates that neuroaxonal loss occurs from the outset, that progression can occur independent of relapse activity, and that continuous underlying pathological processes may not be reflected by inflammatory activity indicative of the patient's immune response. Early intervention can benefit patients, and there is a need for a tool that assists physicians in rapidly identifying subtle signs of MS progression. MSProDiscuss, developed with physicians and patients, facilitates a structured approach to patient consultations. It analyzes multidimensional data via an algorithm to estimate the likelihood of progression (the MSProDiscuss score), the contribution of various symptoms, and the impact of symptoms on daily living, enabling a more personalized approach to treatment and disease management. Data from CDS tools such as MSProDiscuss offer new insights into disease course and facilitate informed decision-making and a holistic approach to MS patient care.
Keyphrases
- multiple sclerosis
- clinical decision support
- ms ms
- end stage renal disease
- electronic health record
- immune response
- chronic kidney disease
- newly diagnosed
- ejection fraction
- white matter
- primary care
- mass spectrometry
- decision making
- prognostic factors
- healthcare
- peritoneal dialysis
- palliative care
- case report
- clinical trial
- machine learning
- systemic lupus erythematosus
- patient reported
- deep learning
- quality improvement
- liquid chromatography tandem mass spectrometry
- chronic pain
- artificial intelligence
- smoking cessation
- data analysis
- neural network
- high resolution mass spectrometry