The era of precision medicine has arrived and conveys tremendous potential, particularly for stroke neurology. The diagnosis of stroke, its underlying aetiology, theranostic strategies, recurrence risk and path to recovery are populated by a series of highly individualised questions. Moreover, the phenotypic complexity of a clinical diagnosis of stroke makes a simple genetic risk assessment only partially informative on an individual basis. The guiding principles of precision medicine in stroke underscore the need to identify, value, organise and analyse the multitude of variables obtained from each individual to generate a precise approach to optimise cerebrovascular health. Existing data may be leveraged with novel technologies, informatics and practical clinical paradigms to apply these principles in stroke and realise the promise of precision medicine. Importantly, precision medicine in stroke will only be realised once efforts to collect, value and synthesise the wealth of data collected in clinical trials and routine care starts. Stroke theranostics, the ultimate vision of synchronising tailored therapeutic strategies based on specific diagnostic data, demand cerebrovascular expertise on big data approaches to clinically relevant paradigms. This review considers such challenges and delineates the principles on a roadmap for rational application of precision medicine to stroke and cerebrovascular health.
- CEREBRAL BLOOD FLOW
- IMAGE ANALYSIS
Statistics from Altmetric.com
Contributors JDH drafted original content, compiled, reviewed, and integrated the other sections. DSL organized the effort, delegated specific topical content, drafted and finalized the manuscript. All other authors drafted original content for the composition of this paper.
Funding JDH is supported by the UCLA Cardiovascular Theme Discovery Award (CVTDA-0001-2016), Partners in Discovery Pilot Stroke Research Award, NIH NS083740, and the USA Department of Veterans Affairs Greater Los Angeles Healthcare System. NSR is in supported in part by NIH-NINDS R01 NS082285 & NS086905. DSL is supported by NIH-NINDS K24 NS072272.
Competing interests None declared.
Provenance and peer review Commissioned; externally peer reviewed.