Computer-assisted prediction of clinical progression in the earliest stages of AD

Hanneke F.M. Rhodius-Meester (Corresponding Author), 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 LötjönenWiesje M. van der Flier

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)

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
Number of pages11
JournalAlzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring
Volume10
DOIs
Publication statusPublished - Jan 2018
MoE publication typeNot Eligible

Fingerprint

Routine Diagnostic Tests
Cerebrospinal Fluid
Dementia
Magnetic Resonance Spectroscopy
Cognitive Dysfunction

Keywords

  • Alzheimer's disease
  • Clinical decision support system
  • Diagnostic test assessment
  • Prognosis
  • Subjective cognitive decline

Cite this

Rhodius-Meester, H. F. M., Liedes, H., Koikkalainen, J., Wolfsgruber, S., Coll-Padros, N., Kornhuber, J., ... van der Flier, W. M. (2018). Computer-assisted prediction of clinical progression in the earliest stages of AD. Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring, 10, 726-736. https://doi.org/10.1016/j.dadm.2018.09.001
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. / Computer-assisted prediction of clinical progression in the earliest stages of AD. In: Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring. 2018 ; Vol. 10. pp. 726-736.
@article{5533f738d4e64fcc90d79f022b588e67,
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.",
keywords = "Alzheimer's disease, Clinical decision support system, Diagnostic test assessment, Prognosis, Subjective cognitive decline",
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",
month = "1",
doi = "10.1016/j.dadm.2018.09.001",
language = "English",
volume = "10",
pages = "726--736",
journal = "Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring",
issn = "2352-8729",
publisher = "Elsevier",

}

Rhodius-Meester, HFM, Liedes, H, Koikkalainen, J, Wolfsgruber, S, Coll-Padros, N, Kornhuber, J, Peters, O, Jessen, F, Kleineidam, L, Molinuevo, JL, Rami, L, Teunissen, CE, Barkhof, F, Sikkes, SAM, Wesselman, LMP, Slot, RER, Verfaillie, SCJ, Scheltens, P, Tijms, BM, Lötjönen, J & van der Flier, WM 2018, 'Computer-assisted prediction of clinical progression in the earliest stages of AD', Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring, vol. 10, pp. 726-736. https://doi.org/10.1016/j.dadm.2018.09.001

Computer-assisted prediction of clinical progression in the earliest stages of AD. / Rhodius-Meester, Hanneke F.M. (Corresponding Author); 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, 01.2018, p. 726-736.

Research output: Contribution to journalArticleScientificpeer-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 - 2018/1

Y1 - 2018/1

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

UR - http://www.scopus.com/inward/record.url?scp=85056565151&partnerID=8YFLogxK

U2 - 10.1016/j.dadm.2018.09.001

DO - 10.1016/j.dadm.2018.09.001

M3 - Article

AN - SCOPUS:85056565151

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

SN - 2352-8729

ER -