The CMET is founded on the key idea that future clinical diagnoses are likely to benefit from quantitative indices (physiomarkers) constructed by use of data-based predictive dynamic models of the subject-specific physiological processes underlying a disease.
An example is the use of model-based indices of cerebral hemodynamics (flow autoregulation and vasomotor reactivity) for improved diagnosis of Alzheimer’s disease or other neurodegenerative diseases with vascular components (such as vascular dementia, mild cognitive impairment etc). The data-based model describes the dynamic nonlinear relationship between arterial blood pressure, cerebral blood flow and end-tidal CO2 (all variables measured as time-series data non-invasively, safely and inexpensively in a clinical context). The model is constructed by use of our pioneering methodologies that have been developed over the years within the BMSR. The diagnostic efficacy of the particular physiomarker of cerebral vasomotor reactivity is being examined in our current work and has been shown to yield excellent diagnostic results for early-stage Alzheimer’s. Confirmation of this capability in a large population of patients will provide the means for improved diagnosis and treatment monitoring – translating into better care for millions, greater patient comfort and huge annual savings for our health care system.