학술논문
Computer‐assisted prediction of clinical progression in the earliest stages of AD
Document Type
article
Author
Hanneke F.M. Rhodius‐Meester; Hilkka Liedes; Juha Koikkalainen; Steffen Wolfsgruber; Nina Coll‐Padros; Johannes Kornhuber; Oliver Peters; Frank Jessen; Luca Kleineidam; José Luis Molinuevo; Lorena Rami; Charlotte E. Teunissen; Frederik Barkhof; Sietske A.M. Sikkes; Linda M.P. Wesselman; Rosalinde E.R. Slot; Sander C.J. Verfaillie; Philip Scheltens; Betty M. Tijms; Jyrki Lötjönen; Wiesje M. van derFlier
Source
Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, Vol 10, Iss 1, Pp 726-736 (2018)
Subject
Language
English
ISSN
2352-8729
Abstract
Abstract Introduction Individuals with subjective cognitive decline (SCD) are at increased risk for clinical progression. We studied how combining different diagnostic tests can help to identify individuals who are likely to show clinical progression. Methods We included 674 patients with SCD (46% female, 64 ± 9 years, Mini–Mental State Examination 28 ± 2) from three memory clinic cohorts. A multivariate model based on the Disease State Index classifier incorporated the available baseline tests to predict progression to MCI or dementia over time. We developed and internally validated the model in one cohort and externally validated it in the other cohorts. Results After 2.9 ± 2.0 years, 151(22%) patients showed clinical progression. Overall performance of the classifier when combining cognitive tests, magnetic resonance imagining, and cerebrospinal fluid showed a balanced accuracy of 74.0 ± 5.5, with high negative predictive value (93.3 ± 2.8). Discussion We found that a combination of diagnostic tests helps to identify individuals at risk of progression. The classifier had particularly good accuracy in identifying patients who remained stable.