Predicting diagnostic conversion from mild cognitive impairment to Alzheimer's disease: A Bayesian hierarchical model approach using ADNI patient data
Year: 2025
Type: article
Abstract: Background There is still need for a better understanding of which specific follow-up medical assessments might offer greater predictive value for diagnostic conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD). Objective To examine the longitudinal predictive importance of follow-up medical assessments to detect diagnostic conversion from MCI to AD. Methods A sample of 572 participants from the ADNI database with valid data at baseline medical visit were included. Bayesian hierarchical models were employed to investigate longitudinal predictors of diagnostic conversion in a 36-month medical follow-up cohort, for measures of cognitive function, psychopathological symptoms, and demographical data. An additional 48-month medical follow-up cohort was considered to investigate the predictive importance of cerebrospinal fluid biomarkers (Aβ 42 /Aβ 40 ratio) for diagnostic conversion. Results Mini-mental State Examination (MMSE) (β = −2.6; 95% HDI: [−3.6–−1.5]) and Clinical Dementia Rating scale Sum of Boxes (CDR-SB) (β = 5.6; 95% HDI: [4.3–7.0]) can predict diagnostic conversion from MCI to AD over a 36-month medical follow-up, with CDR-SB showing the greatest predictive importance in all Bayesian models. Higher scores on CDR-SB were associated with increased risk for a diagnosis conversion, approximately 30% greater probability at 24-month follow-up, and > 50% greater probability at 36-month follow-up. Conclusions The CDR-SB provides a reliable cognitive assessment to detect diagnostic conversion from MCI to AD over a period of 36 months, which is key to help clinicians screening for early diagnosis of AD using affordable non-invasive procedures.
Source: Journal of Alzheimer s Disease
Institutions: University of Essex, University of Aveiro, University of Coimbra
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4412507006