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.
Original languageEnglish
Pages (from-to)726-736
JournalAlzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring
Volume10
DOIs
Publication statusPublished - 2018

Cite this

@article{39ac05fc68d647e29a1c9aa1bbc3b727,
title = "Computer-assisted prediction of clinical progression in the earliest stages of AD",
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.",
author = "Rhodius-Meester, {Hanneke F. M.} and Hilkka Liedes and Juha Koikkalainen and Steffen Wolfsgruber and Nina Coll-Padros and Johannes Kornhuber and Oliver Peters and Frank Jessen and Luca Kleineidam and Molinuevo, {Jos{\'e} Luis} and Lorena Rami and Teunissen, {Charlotte E.} and Frederik Barkhof and Sikkes, {Sietske A. M.} and Wesselman, {Linda M. P.} and Slot, {Rosalinde E. R.} and Verfaillie, {Sander C. J.} and Philip Scheltens and Tijms, {Betty M.} and Jyrki L{\"o}tj{\"o}nen and {van der Flier}, {Wiesje M.}",
year = "2018",
doi = "10.1016/j.dadm.2018.09.001",
language = "English",
volume = "10",
pages = "726--736",
journal = "Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring",
issn = "2352-8729",
publisher = "Elsevier BV",

}

Computer-assisted prediction of clinical progression in the earliest stages of AD. / Rhodius-Meester, Hanneke F. M.; Liedes, Hilkka; Koikkalainen, Juha; Wolfsgruber, Steffen; Coll-Padros, Nina; Kornhuber, Johannes; Peters, Oliver; Jessen, Frank; Kleineidam, Luca; Molinuevo, José Luis; Rami, Lorena; Teunissen, Charlotte E.; Barkhof, Frederik; Sikkes, Sietske A. M.; Wesselman, Linda M. P.; Slot, Rosalinde E. R.; Verfaillie, Sander C. J.; Scheltens, Philip; Tijms, Betty M.; Lötjönen, Jyrki; van der Flier, Wiesje M.

In: Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring, Vol. 10, 2018, p. 726-736.

Research output: Contribution to journalArticleAcademicpeer-review

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

Y1 - 2018

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.

UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85056565151&origin=inward

U2 - 10.1016/j.dadm.2018.09.001

DO - 10.1016/j.dadm.2018.09.001

M3 - Article

VL - 10

SP - 726

EP - 736

JO - Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring

JF - Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring

SN - 2352-8729

ER -