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

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 der Flier

doi:10.1016/j.dadm.2018.09.001

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 journal › Article › Scientific › peer-review

9 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 language	English
Pages (from-to)	726-736
Number of pages	11
Journal	Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring
Volume	10
DOIs	https://doi.org/10.1016/j.dadm.2018.09.001
Publication status	Published - Jan 2018
MoE publication type	A1 Journal article-refereed

Keywords

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.dadm.2018.09.001

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Rhodius-Meester, H. F. M., Liedes, H., Koikkalainen, J., Wolfsgruber, S., Coll-Padros, N., Kornhuber, J., Peters, O., Jessen, F., Kleineidam, L., Molinuevo, J. L., Rami, L., Teunissen, C. E., Barkhof, F., Sikkes, S. A. M., Wesselman, L. M. P., Slot, R. E. R., Verfaillie, S. C. J., Scheltens, P., Tijms, B. M., ... 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

@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.}",

note = "Funding Information: Research of the VUmc Alzheimer Center is part of the neurodegeneration research program of the Amsterdam Neuroscience. The VUmc Alzheimer Center is supported by Alzheimer Nederland and Stichting VUmc Fonds . The clinical database structure was developed with funding from Stichting Dioraphte . For the development of the PredictAD tool, the VTT Technical Research Centre of Finland has received funding from European Union's Seventh Framework Programme for research, technological development, and demonstration under grant agreements 601055 (VPH-DARE@IT), 224328 (PredictAD), and 611005 (PredictND). The Euro-SCD project has been funded by the EU Joint Program–Neurodegenerative Disease Research ( JPND_PS_FP-689-019 ). DCN has been funded by a grant from the German Federal Ministry of Education and Research (BMBF): Kompetenznetz Demenzen ( 01GI0420 ). Hanneke FM Rhodius-Meester is appointed on PredictND, a grant from the European Seventh Framework Program project PredictND under grant agreement 611005. Frederik Barkhof is supported by the NIHR UCLH Biomedical Research Center. Sietske AM Sikkes is supported by an Off Road grant ( ZonMw #451001010 ). Wiesje M. van der Flier is a recipient of a research grant from Gieskes-Strijbis Fonds . Betty M. Tijms receives grant support from ZonMw ( #73305056 and #733050824 ). ",

year = "2018",

month = jan,

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

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.

N1 - Funding Information: Research of the VUmc Alzheimer Center is part of the neurodegeneration research program of the Amsterdam Neuroscience. The VUmc Alzheimer Center is supported by Alzheimer Nederland and Stichting VUmc Fonds . The clinical database structure was developed with funding from Stichting Dioraphte . For the development of the PredictAD tool, the VTT Technical Research Centre of Finland has received funding from European Union's Seventh Framework Programme for research, technological development, and demonstration under grant agreements 601055 (VPH-DARE@IT), 224328 (PredictAD), and 611005 (PredictND). The Euro-SCD project has been funded by the EU Joint Program–Neurodegenerative Disease Research ( JPND_PS_FP-689-019 ). DCN has been funded by a grant from the German Federal Ministry of Education and Research (BMBF): Kompetenznetz Demenzen ( 01GI0420 ). Hanneke FM Rhodius-Meester is appointed on PredictND, a grant from the European Seventh Framework Program project PredictND under grant agreement 611005. Frederik Barkhof is supported by the NIHR UCLH Biomedical Research Center. Sietske AM Sikkes is supported by an Off Road grant ( ZonMw #451001010 ). Wiesje M. van der Flier is a recipient of a research grant from Gieskes-Strijbis Fonds . Betty M. Tijms receives grant support from ZonMw ( #73305056 and #733050824 ).

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

C2 - 30619929

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 -

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

Abstract

Keywords

UN SDGs

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