Anticipating Tomorrow: Tailoring Parkinson's Symptomatic Therapy Using Predictors of Outcome - Postuma, Ronald B.; Weintraub, Daniel; Simuni, Tanya; Rodríguez‐Violante, Mayela; Leentjens, Albert F.G.; Hu, Michele T.; Espay, Alberto J.; Erro, Roberto; Dujardin, Kathy; Bohnen, Nicolaas I.; Berg, Daniela; Mestre, Tiago A.; et al. (Movement disorders clinical practice (Hoboken, N.J.), 2024)
Background Although research into Parkinson's disease (PD) subtypes and outcome predictions has continued to advance, recommendations for using outcome prediction to guide current treatment decisions remain sparse. Objectives To provide expert opinion‐based recommendations for individually tailored PD symptomatic treatment based on knowledge of risk prediction and subtypes. Methods Using a modified Delphi approach, members of the Movement Disorders Society (MDS) Task Force on PD subtypes generated a series of general recommendations around the question: “Using what you know about genetic/biological/clinical subtypes (or any individual‐level predictors of outcome), what advice would you give for selecting symptomatic treatments for an individual patient now, based on what their subtype or individual characteristics predict about their future disease course?” After four iterations and revisions, those recommendations with over 75% endorsement were adopted. Results A total of 19 recommendations were endorsed by a group of 13 panelists. The recommendations primarily centered around two themes: (1) incorporating future risk of cognitive impairment into current treatment plans; and (2) identifying future symptom clusters that might be forestalled with a single medication. Conclusions These recommendations provide clinicians with a framework for integrating future outcomes into patient‐specific treatment choices. They are not prescriptive guidelines, but adaptable suggestions, which should be tailored to each individual. They are to be considered as a first step of a process that will continue to evolve as additional stakeholders provide new insights and as new information becomes available. As individualized risk prediction advances, the path to better tailored treatment regimens will become clearer.