AUTHOR=Arrieta Enrique , Baz Pablo , García-Ribas Guillermo TITLE=FORTCARE-MCI study protocol: evaluation of Fortasyn Connect in the management of mild cognitive impairment in primary care JOURNAL=Frontiers in Neurology VOLUME=15 YEAR=2024 URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2024.1434210 DOI=10.3389/fneur.2024.1434210 ISSN=1664-2295 ABSTRACT=Background

Neuropsychiatric symptoms are prevalent in patients with mild cognitive impairment (MCI) and are predictive of the conversion to dementia. Fortasyn Connect, a medical food, has shown efficacy in managing cognitive and behavioral symptoms associated with MCI. Early diagnosis and intervention in primary care are essential for managing MCI. However, real-world prospective studies assessing Fortasyn Connect in MCI are still limited.

Methods

This observational, multicenter, prospective study will enroll 150 patients recently diagnosed with MCI by primary care physicians across several regions in Spain. Participants will be followed-up over a 12-month period, with assessments at baseline, 6 months, and 12 months, as per clinical practice. The study aims to evaluate the impact of Fortasyn Connect on neuropsychiatric symptoms, cognition, and health-related quality of life (HRQoL) using validated neuropsychological tests and machine learning (ML) techniques. The primary outcome measure will be changes in neuropsychiatric symptoms using the Neuropsychiatric Inventory Questionnaire (NPI-Q) at 6 months. Secondary outcome measures include further changes in the NPI-Q at 12 months, and changes in cognition (Fototest, and clock-drawing test) and HRQoL (EQ-5D-5L) at 6 and 12 months. Exploratory outcomes will assess speech using an artificial intelligence (AI)-enhanced ML tool, with a correlation analysis of these findings with traditional neuropsychological test results.

Conclusion

This study will provide evidence of the effectiveness of Fortasyn Connect in a real-world setting, exploring its potential to stabilize or improve neuropsychiatric symptoms, cognition, and HRQoL in MCI patients. Results will also contribute to the understanding of AI and ML in identifying early biomarkers of cognitive decline, supporting the timely management of MCI.