Digital biomarker‐based individualized prognosis for people at risk of dementia. Issue 1 (19th August 2020)

• Record Type: Journal Article
• Authors: Buegler, Maximilian; Harms, Robbert L.; Balasa, Mircea; Meier, Irene B.; Exarchos, Themis; Rai, Laura; Boyle, Rory; Tort, Adria; Kozori, Maha; Lazarou, Eutuxia; Rampini, Michaela; Cavaliere, Carlo; Vlamos, Panagiotis; Tsolaki, Magda; Babiloni, Claudio; Soricelli, Andrea; Frisoni, Giovanni; Sanchez‐Valle, Raquel; Whelan, Robert; Merlo‐Pich, Emilio; Tarnanas, Ioannis
• Is Part Of: Alzheimer's & dementia. Volume 12:Issue 1(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-08-19
• Subjects: Altoida Neuro Motor Index -- Alzheimer's disease -- artificial intelligence -- augmented reality -- cognitive aging -- digital biomarker -- machine learning -- risk prediction; Alzheimer's disease -- Periodicals; Alzheimer's disease -- Diagnosis -- Periodicals; Dementia -- Periodicals; Dementia -- Diagnosis -- Periodicals; 616.831
• DOI: 10.1002/dad2.12073
• Languages: English
• ISSNs: 2352-8729

Abstract

Abstract: Background: Research investigating treatments and interventions for cognitive decline fail due to difficulties in accurately recognizing behavioral signatures in the presymptomatic stages of the disease. For this validation study, we took our previously constructed digital biomarker‐based prognostic models and focused on generalizability and robustness of the models. Method: We validated prognostic models characterizing subjects using digital biomarkers in a longitudinal, multi‐site, 40‐month prospective study collecting data in memory clinics, general practitioner offices, and home environments. Results: Our models were able to accurately discriminate between healthy subjects and individuals at risk to progress to dementia within 3 years. The model was also able to differentiate between people with or without amyloid neuropathology and classify fast and slow cognitive decliners with a very good diagnostic performance. Conclusion: Digital biomarker prognostic models can be a useful tool to assist large‐scale population screening for the early detection of cognitive impairment and patient monitoring over time.