https://doi.org/10.55788/e123f61c
Prof. Waldemar noted important challenges and unmet needs in neurodegenerative disorders [1]. In general, they have a low diagnostic rate (mostly years after onset of the first symptoms), they progress slowly, there is no cure for any of the dementia disorders, and there is an (expected) steep increase in the number of people with dementia, pressurising healthcare systems. A patient called Anna served as an example of the current situation: she is a retired 70-year-old schoolteacher who experiences memory problems, and is subsequently diagnosed with mild cognitive impairment (MCI) due to Alzheimer's disease (AD) in a memory clinic.
Prof. Waldemar explained that evidence for (modifiable) risk factors signalled with the help of big data may enable implementation of personalised prevention. She mentioned an abundance of data sources, such as molecular and imaging biomarkers, genomic registries, secondary use of research data, electronic health data and health records, as well as health data derived from self-monitoring, such as wearables and smartphone apps. Opportunities in clinical care include automated reading of brain imaging, digital communication with patients and caregivers, decision support systems, digital biomarkers, etc. (see Box). This is how data-driven care could help people like Anna in years to come, stated Prof. Waldemar. For example, imagine that Anna subscribes to a health program at the age of 50 that provides her with an AD “risk profile”. She receives a personalised prevention program, and consents to tracking of digital biomarkers for early-phase AD, such as speech analysis, geographical mobility, and annual cognitive testing. At the age of 63, the program warns Anna that changes have been observed. She performs additional tests at home, and is interviewed by her digital doctor. The program recommends her to see her GP, who refers her to a memory clinic. Decisions to refer her to brain imaging and other procedures are based on a clinical decision support model (CDSS). Anna then receives a diagnosis of prodromal AD, 2 years prior to symptom onset and 7 years prior to the diagnosis she received in the previous scenario. At this early stage, she may benefit from new disease-modifying treatments.
Box: Examples of ongoing programs are PROMINENT and COGNO-SPEAK. PROMINENT is a recently launched pan-European initiative to create a digital platform for the diagnosis and treatment of neurodegenerative diseases, and to assist clinicians in the optimal application of novel disease-modifying treatments of AD. COGNO-SPEAK is a new AI tool where patients speak to a digital doctor. It helps clinicians assess the early signs of dementia and AD more quickly and efficiently.
Prof. Waldemar further identified challenges surrounding data-driven care that neurologists themselves can take responsibility for:
• Risk of over- and misdiagnosis, creating more stigma and pressure on healthcare systems.
• Validation in relevant samples.
• Updates of underlying scientific evidence, as new knowledge appears.
• Inequality of access to healthcare that digital health may create (though it may also contribute to inclusiveness) for people with dementia.
In conclusion, Prof. Waldemar said that data-driven care has the potential to contribute to cost-effective and improved evidence-based healthcare; to contribute to precision in prevention, diagnosis, and treatment in the early phase; to discover new drug targets; and to revolutionise the physician-patient relationship and the role of the neurologist.
1. Waldemar G. Data driven care for patients with neurodegenerative disorders. EAN 2023 Annual Meeting, 1–4 July, Budapest, Hungary.
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