Network impact score is an independent predictor of post-stroke cognitive impairment: a multicenter cohort study in 2341 patients with acute ischemic stroke.

Nick A.  Weaver; Hugo P.  Aben; Hugo J.  Kuijf; Jill Abrigo; Hee-Joon Bae; Mélanie  Barbay; Jonathan G.  Best; Régis Bordet; Francesca M Chappell; Christopher P.L.H.  Chen; Thibaut  Dondaine; Ruben S.  van der Giessen; Olivier  Godefroy; Bibek Gyanwali; Olivia K.L.  Hamilton; Saima Hilal; Irene M.C.  Huenges Wajer; Yeonwook  Kang; L. Jaap  Kappelle; Beom Joon Kim; Sebastian Köhler; Paul L.M.  de Kor; Peter J.  Koudstaal; Gregory  Kuchcinski; Bonnie Y.K.  Lam; Byung-Chul  Lee; Keon-Joo Lee; Jae-Sung Lim; Renaud  Lopes; Stephen Makin; Anne-Marie Mendyk; Vincent C.T.  Mok; Mi Sun  Oh; Robert J. Van Oostenbrugge; Martine  Roussel; Lin  Shi; Julie Staals; Maria Del Carmen  Valdés Hernández; Narayanaswamy Venketasubramanian; Frans R.J.  Verhey; Joanna M. Wardlaw; David J. Werring; Xu  Xin; Kyung-Ho  Yu; Martine J.E.  van Zandvoort; Lei Zhao; Geert Jan  Biessels; J.M. Biesbroek

doi:10.1016/j.nicl.2022.103018

Network impact score is an independent predictor of post-stroke cognitive impairment: a multicenter cohort study in 2341 patients with acute ischemic stroke.

Nick A. Weaver, Hugo P. Aben, Hugo J. Kuijf, Jill Abrigo, Hee-Joon Bae, Mélanie Barbay, Jonathan G. Best, Régis Bordet, Francesca M Chappell, Christopher P.L.H. Chen, Thibaut Dondaine, Ruben S. van der Giessen, Olivier Godefroy, Bibek Gyanwali, Olivia K.L. Hamilton, Saima Hilal, Irene M.C. Huenges Wajer, Yeonwook Kang, L. Jaap Kappelle, Beom Joon KimSebastian Köhler, Paul L.M. de Kor, Peter J. Koudstaal, Gregory Kuchcinski, Bonnie Y.K. Lam, Byung-Chul Lee, Keon-Joo Lee, Jae-Sung Lim, Renaud Lopes, Stephen Makin, Anne-Marie Mendyk, Vincent C.T. Mok, Mi Sun Oh, Robert J. Van Oostenbrugge, Martine Roussel, Lin Shi, Julie Staals, Maria Del Carmen Valdés Hernández, Narayanaswamy Venketasubramanian, Frans R.J. Verhey, Joanna M. Wardlaw, David J. Werring, Xu Xin, Kyung-Ho Yu, Martine J.E. van Zandvoort, Lei Zhao, Geert Jan Biessels, J.M. Biesbroek^* (Corresponding Author)

^*Corresponding author for this work

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Abstract

Background
Post-stroke cognitive impairment (PSCI) is a common consequence of stroke. Accurate prediction of PSCI risk is challenging. The recently developed network impact score, which integrates information on infarct location and size with brain network topology, may improve PSCI risk prediction.

Aims
To determine if the network impact score is an independent predictor of PSCI, and of cognitive recovery or decline.

Methods
We pooled data from patients with acute ischemic stroke from 12 cohorts through the Meta VCI Map consortium. PSCI was defined as impairment in ≥ 1 cognitive domain on neuropsychological examination, or abnormal Montreal Cognitive Assessment. Cognitive recovery was defined as conversion from PSCI < 3 months post-stroke to no PSCI at follow-up, and cognitive decline as conversion from no PSCI to PSCI. The network impact score was related to serial measures of PSCI using Generalized Estimating Equations (GEE) models, and to PSCI stratified according to post-stroke interval (<3, 3–12, 12–24, >24 months) and cognitive recovery or decline using logistic regression. Models were adjusted for age, sex, education, prior stroke, infarct volume, and study site.

Results
We included 2341 patients with 4657 cognitive assessments. PSCI was present in 398/844 patients (47%) <3 months, 709/1640 (43%) at 3–12 months, 243/853 (28%) at 12–24 months, and 208/522 (40%) >24 months. Cognitive recovery occurred in 64/181 (35%) patients and cognitive decline in 26/287 (9%). The network impact score predicted PSCI in the univariable (OR 1.50, 95%CI 1.34–1.68) and multivariable (OR 1.27, 95%CI 1.10–1.46) GEE model, with similar ORs in the logistic regression models for specified post-stroke intervals. The network impact score was not associated with cognitive recovery or decline.

Conclusions
The network impact score is an independent predictor of PSCI. As such, the network impact score may contribute to a more precise and individualized cognitive prognostication in patients with ischemic stroke. Future studies should address if multimodal prediction models, combining the network impact score with demographics, clinical characteristics and other advanced brain imaging biomarkers, will provide accurate individualized prediction of PSCI. A tool for calculating the network impact score is freely available at https://metavcimap.org/features/software-tools/lsm-viewer/.

Original language	English
Article number	103018
Number of pages	7
Journal	NeuroImage: Clinical
Volume	34
Early online date	30 Apr 2022
DOIs	https://doi.org/10.1016/j.nicl.2022.103018
Publication status	Published - 30 Apr 2022

Bibliographical note

Funding
This study was supported by a VIMP grant (project 7330505031) from The Netherlands Organisation for Health Research and Development (ZonMw) to JMB and GJB. The Meta VCI Map consortium is supported by Vici Grant 918.16.616 from ZonMw to GJB. The funding sources had no in role in study design, collection, analysis and interpretation of data, writing of the report, and the decision to submit the article for publication.

Data Availability Statement

Supplementary data
Supplementary data to this article can be found online at https://doi.org/10.1016/j.nicl.2022.103018.

Keywords

Post-stroke cognitive impairment
Brain connectomics
Ischaemic stroke
Dementia
Diffusion-weighted imaging

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Weaver, N. A., Aben, H. P., Kuijf, H. J., Abrigo, J., Bae, H.-J., Barbay, M., Best, J. G., Bordet, R., Chappell, F. M., Chen, C. P. L. H., Dondaine, T., van der Giessen, R. S., Godefroy, O., Gyanwali, B., Hamilton, O. K. L., Hilal, S., Huenges Wajer, I. M. C., Kang, Y., Kappelle, L. J., ... Biesbroek, J. M. (2022). Network impact score is an independent predictor of post-stroke cognitive impairment: a multicenter cohort study in 2341 patients with acute ischemic stroke. NeuroImage: Clinical, 34, Article 103018. https://doi.org/10.1016/j.nicl.2022.103018

Weaver, NA, Aben, HP, Kuijf, HJ, Abrigo, J, Bae, H-J, Barbay, M, Best, JG, Bordet, R, Chappell, FM, Chen, CPLH, Dondaine, T, van der Giessen, RS, Godefroy, O, Gyanwali, B, Hamilton, OKL, Hilal, S, Huenges Wajer, IMC, Kang, Y, Kappelle, LJ, Kim, BJ, Köhler, S, de Kor, PLM, Koudstaal, PJ, Kuchcinski, G, Lam, BYK, Lee, B-C, Lee, K-J, Lim, J-S, Lopes, R, Makin, S, Mendyk, A-M, Mok, VCT, Oh, MS, Van Oostenbrugge, RJ, Roussel, M, Shi, L, Staals, J, Valdés Hernández, MDC, Venketasubramanian, N, Verhey, FRJ, Wardlaw, JM, Werring, DJ, Xin, X, Yu, K-H, van Zandvoort, MJE, Zhao, L, Biessels, GJ & Biesbroek, JM 2022, 'Network impact score is an independent predictor of post-stroke cognitive impairment: a multicenter cohort study in 2341 patients with acute ischemic stroke.', NeuroImage: Clinical, vol. 34, 103018. https://doi.org/10.1016/j.nicl.2022.103018

@article{57483f60b63b479fb38894e23aa2a1ba,

title = "Network impact score is an independent predictor of post-stroke cognitive impairment: a multicenter cohort study in 2341 patients with acute ischemic stroke.",

abstract = "BackgroundPost-stroke cognitive impairment (PSCI) is a common consequence of stroke. Accurate prediction of PSCI risk is challenging. The recently developed network impact score, which integrates information on infarct location and size with brain network topology, may improve PSCI risk prediction.AimsTo determine if the network impact score is an independent predictor of PSCI, and of cognitive recovery or decline.MethodsWe pooled data from patients with acute ischemic stroke from 12 cohorts through the Meta VCI Map consortium. PSCI was defined as impairment in ≥ 1 cognitive domain on neuropsychological examination, or abnormal Montreal Cognitive Assessment. Cognitive recovery was defined as conversion from PSCI < 3 months post-stroke to no PSCI at follow-up, and cognitive decline as conversion from no PSCI to PSCI. The network impact score was related to serial measures of PSCI using Generalized Estimating Equations (GEE) models, and to PSCI stratified according to post-stroke interval (<3, 3–12, 12–24, >24 months) and cognitive recovery or decline using logistic regression. Models were adjusted for age, sex, education, prior stroke, infarct volume, and study site.ResultsWe included 2341 patients with 4657 cognitive assessments. PSCI was present in 398/844 patients (47%) <3 months, 709/1640 (43%) at 3–12 months, 243/853 (28%) at 12–24 months, and 208/522 (40%) >24 months. Cognitive recovery occurred in 64/181 (35%) patients and cognitive decline in 26/287 (9%). The network impact score predicted PSCI in the univariable (OR 1.50, 95%CI 1.34–1.68) and multivariable (OR 1.27, 95%CI 1.10–1.46) GEE model, with similar ORs in the logistic regression models for specified post-stroke intervals. The network impact score was not associated with cognitive recovery or decline.ConclusionsThe network impact score is an independent predictor of PSCI. As such, the network impact score may contribute to a more precise and individualized cognitive prognostication in patients with ischemic stroke. Future studies should address if multimodal prediction models, combining the network impact score with demographics, clinical characteristics and other advanced brain imaging biomarkers, will provide accurate individualized prediction of PSCI. A tool for calculating the network impact score is freely available at https://metavcimap.org/features/software-tools/lsm-viewer/.",

keywords = "Post-stroke cognitive impairment, Brain connectomics, Ischaemic stroke, Dementia, Diffusion-weighted imaging",

author = "Weaver, {Nick A.} and Aben, {Hugo P.} and Kuijf, {Hugo J.} and Jill Abrigo and Hee-Joon Bae and M{\'e}lanie Barbay and Best, {Jonathan G.} and R{\'e}gis Bordet and Chappell, {Francesca M} and Chen, {Christopher P.L.H.} and Thibaut Dondaine and {van der Giessen}, {Ruben S.} and Olivier Godefroy and Bibek Gyanwali and Hamilton, {Olivia K.L.} and Saima Hilal and {Huenges Wajer}, {Irene M.C.} and Yeonwook Kang and Kappelle, {L. Jaap} and Kim, {Beom Joon} and Sebastian K{\"o}hler and {de Kor}, {Paul L.M.} and Koudstaal, {Peter J.} and Gregory Kuchcinski and Lam, {Bonnie Y.K.} and Byung-Chul Lee and Keon-Joo Lee and Jae-Sung Lim and Renaud Lopes and Stephen Makin and Anne-Marie Mendyk and Mok, {Vincent C.T.} and Oh, {Mi Sun} and {Van Oostenbrugge}, {Robert J.} and Martine Roussel and Lin Shi and Julie Staals and {Vald{\'e}s Hern{\'a}ndez}, {Maria Del Carmen} and Narayanaswamy Venketasubramanian and Verhey, {Frans R.J.} and Wardlaw, {Joanna M.} and Werring, {David J.} and Xu Xin and Kyung-Ho Yu and {van Zandvoort}, {Martine J.E.} and Lei Zhao and Biessels, {Geert Jan} and J.M. Biesbroek",

note = "Funding This study was supported by a VIMP grant (project 7330505031) from The Netherlands Organisation for Health Research and Development (ZonMw) to JMB and GJB. The Meta VCI Map consortium is supported by Vici Grant 918.16.616 from ZonMw to GJB. The funding sources had no in role in study design, collection, analysis and interpretation of data, writing of the report, and the decision to submit the article for publication.",

year = "2022",

month = apr,

day = "30",

doi = "10.1016/j.nicl.2022.103018",

language = "English",

volume = "34",

journal = "NeuroImage: Clinical",

issn = "2213-1582",

publisher = "Elsevier BV",

}

TY - JOUR

T1 - Network impact score is an independent predictor of post-stroke cognitive impairment

T2 - a multicenter cohort study in 2341 patients with acute ischemic stroke.

AU - Weaver, Nick A.

AU - Aben, Hugo P.

AU - Kuijf, Hugo J.

AU - Abrigo, Jill

AU - Bae, Hee-Joon

AU - Barbay, Mélanie

AU - Best, Jonathan G.

AU - Bordet, Régis

AU - Chappell, Francesca M

AU - Chen, Christopher P.L.H.

AU - Dondaine, Thibaut

AU - van der Giessen, Ruben S.

AU - Godefroy, Olivier

AU - Gyanwali, Bibek

AU - Hamilton, Olivia K.L.

AU - Hilal, Saima

AU - Huenges Wajer, Irene M.C.

AU - Kang, Yeonwook

AU - Kappelle, L. Jaap

AU - Kim, Beom Joon

AU - Köhler, Sebastian

AU - de Kor, Paul L.M.

AU - Koudstaal, Peter J.

AU - Kuchcinski, Gregory

AU - Lam, Bonnie Y.K.

AU - Lee, Byung-Chul

AU - Lee, Keon-Joo

AU - Lim, Jae-Sung

AU - Lopes, Renaud

AU - Makin, Stephen

AU - Mendyk, Anne-Marie

AU - Mok, Vincent C.T.

AU - Oh, Mi Sun

AU - Van Oostenbrugge, Robert J.

AU - Roussel, Martine

AU - Shi, Lin

AU - Staals, Julie

AU - Valdés Hernández, Maria Del Carmen

AU - Venketasubramanian, Narayanaswamy

AU - Verhey, Frans R.J.

AU - Wardlaw, Joanna M.

AU - Werring, David J.

AU - Xin, Xu

AU - Yu, Kyung-Ho

AU - van Zandvoort, Martine J.E.

AU - Zhao, Lei

AU - Biessels, Geert Jan

AU - Biesbroek, J.M.

N1 - Funding This study was supported by a VIMP grant (project 7330505031) from The Netherlands Organisation for Health Research and Development (ZonMw) to JMB and GJB. The Meta VCI Map consortium is supported by Vici Grant 918.16.616 from ZonMw to GJB. The funding sources had no in role in study design, collection, analysis and interpretation of data, writing of the report, and the decision to submit the article for publication.

PY - 2022/4/30

Y1 - 2022/4/30

N2 - BackgroundPost-stroke cognitive impairment (PSCI) is a common consequence of stroke. Accurate prediction of PSCI risk is challenging. The recently developed network impact score, which integrates information on infarct location and size with brain network topology, may improve PSCI risk prediction.AimsTo determine if the network impact score is an independent predictor of PSCI, and of cognitive recovery or decline.MethodsWe pooled data from patients with acute ischemic stroke from 12 cohorts through the Meta VCI Map consortium. PSCI was defined as impairment in ≥ 1 cognitive domain on neuropsychological examination, or abnormal Montreal Cognitive Assessment. Cognitive recovery was defined as conversion from PSCI < 3 months post-stroke to no PSCI at follow-up, and cognitive decline as conversion from no PSCI to PSCI. The network impact score was related to serial measures of PSCI using Generalized Estimating Equations (GEE) models, and to PSCI stratified according to post-stroke interval (<3, 3–12, 12–24, >24 months) and cognitive recovery or decline using logistic regression. Models were adjusted for age, sex, education, prior stroke, infarct volume, and study site.ResultsWe included 2341 patients with 4657 cognitive assessments. PSCI was present in 398/844 patients (47%) <3 months, 709/1640 (43%) at 3–12 months, 243/853 (28%) at 12–24 months, and 208/522 (40%) >24 months. Cognitive recovery occurred in 64/181 (35%) patients and cognitive decline in 26/287 (9%). The network impact score predicted PSCI in the univariable (OR 1.50, 95%CI 1.34–1.68) and multivariable (OR 1.27, 95%CI 1.10–1.46) GEE model, with similar ORs in the logistic regression models for specified post-stroke intervals. The network impact score was not associated with cognitive recovery or decline.ConclusionsThe network impact score is an independent predictor of PSCI. As such, the network impact score may contribute to a more precise and individualized cognitive prognostication in patients with ischemic stroke. Future studies should address if multimodal prediction models, combining the network impact score with demographics, clinical characteristics and other advanced brain imaging biomarkers, will provide accurate individualized prediction of PSCI. A tool for calculating the network impact score is freely available at https://metavcimap.org/features/software-tools/lsm-viewer/.

AB - BackgroundPost-stroke cognitive impairment (PSCI) is a common consequence of stroke. Accurate prediction of PSCI risk is challenging. The recently developed network impact score, which integrates information on infarct location and size with brain network topology, may improve PSCI risk prediction.AimsTo determine if the network impact score is an independent predictor of PSCI, and of cognitive recovery or decline.MethodsWe pooled data from patients with acute ischemic stroke from 12 cohorts through the Meta VCI Map consortium. PSCI was defined as impairment in ≥ 1 cognitive domain on neuropsychological examination, or abnormal Montreal Cognitive Assessment. Cognitive recovery was defined as conversion from PSCI < 3 months post-stroke to no PSCI at follow-up, and cognitive decline as conversion from no PSCI to PSCI. The network impact score was related to serial measures of PSCI using Generalized Estimating Equations (GEE) models, and to PSCI stratified according to post-stroke interval (<3, 3–12, 12–24, >24 months) and cognitive recovery or decline using logistic regression. Models were adjusted for age, sex, education, prior stroke, infarct volume, and study site.ResultsWe included 2341 patients with 4657 cognitive assessments. PSCI was present in 398/844 patients (47%) <3 months, 709/1640 (43%) at 3–12 months, 243/853 (28%) at 12–24 months, and 208/522 (40%) >24 months. Cognitive recovery occurred in 64/181 (35%) patients and cognitive decline in 26/287 (9%). The network impact score predicted PSCI in the univariable (OR 1.50, 95%CI 1.34–1.68) and multivariable (OR 1.27, 95%CI 1.10–1.46) GEE model, with similar ORs in the logistic regression models for specified post-stroke intervals. The network impact score was not associated with cognitive recovery or decline.ConclusionsThe network impact score is an independent predictor of PSCI. As such, the network impact score may contribute to a more precise and individualized cognitive prognostication in patients with ischemic stroke. Future studies should address if multimodal prediction models, combining the network impact score with demographics, clinical characteristics and other advanced brain imaging biomarkers, will provide accurate individualized prediction of PSCI. A tool for calculating the network impact score is freely available at https://metavcimap.org/features/software-tools/lsm-viewer/.

KW - Post-stroke cognitive impairment

KW - Brain connectomics

KW - Ischaemic stroke

KW - Dementia

KW - Diffusion-weighted imaging

U2 - 10.1016/j.nicl.2022.103018

DO - 10.1016/j.nicl.2022.103018

M3 - Article

SN - 2213-1582

VL - 34

JO - NeuroImage: Clinical

JF - NeuroImage: Clinical

M1 - 103018

ER -

Network impact score is an independent predictor of post-stroke cognitive impairment: a multicenter cohort study in 2341 patients with acute ischemic stroke.

Abstract

Bibliographical note

Data Availability Statement

Keywords

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