TY - JOUR
T1 - Computer-assisted prediction of clinical progression in the earliest stages of AD
AU - Rhodius-Meester, Hanneke F. M.
AU - Liedes, Hilkka
AU - Koikkalainen, Juha
AU - Wolfsgruber, Steffen
AU - Coll-Padros, Nina
AU - Kornhuber, Johannes
AU - Peters, Oliver
AU - Jessen, Frank
AU - Kleineidam, Luca
AU - Molinuevo, José Luis
AU - Rami, Lorena
AU - Teunissen, Charlotte E.
AU - Barkhof, Frederik
AU - Sikkes, Sietske A. M.
AU - Wesselman, Linda M. P.
AU - Slot, Rosalinde E. R.
AU - Verfaillie, Sander C. J.
AU - Scheltens, Philip
AU - Tijms, Betty M.
AU - Lötjönen, Jyrki
AU - van der Flier, Wiesje M.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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.
AB - 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.
KW - Alzheimer's disease
KW - Clinical decision support system
KW - Diagnostic test assessment
KW - Prognosis
KW - Subjective cognitive decline
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U2 - https://doi.org/10.1016/j.dadm.2018.09.001
DO - https://doi.org/10.1016/j.dadm.2018.09.001
M3 - Article
C2 - 30619929
SN - 2352-8729
VL - 10
SP - 726
EP - 736
JO - Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring
JF - Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring
ER -
