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

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 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%) 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/

Post stroke mild and major neurocognitive disorders : prevalence and contribution of amyloidopathy

La prévalence des troubles neurocognitifs post-AVC (TNCPA) dans la littérature est hétérogène. Des travaux récents ont proposé une harmonisation des critères et des procédures d'évaluation cognitive post-AVC. De nombreuses études ont évalués les déterminants neuroradiologiques des TNCPA, mais peu ont évaluées le statut amyloïde. Les objectifs principaux de ce travail étaient : (1) de réaliser une méta-analyse avec revue systématique sur la prévalence et les profils des TNCPA (2) puis d'étudier la prévalence et les profils dans une cohorte française utilisant une méthodologie validée (3) d'évaluer la prévalence des dépôts amyloïdes et le devenir cognitif selon le statut amyloïde post-AVC ; (4) de préciser les corrélats neuroradiologiques du statut amyloïde et cognitif post-AVC. La population étudiée était celle de la cohorte GRECogVASC [NCT01339195] et de l'étude ancillaire IDEA 3 [NCT 02813434]. L'étude 1 montrait que plus de la moitié des patients présenterait des TNCPA, dont les deux-tiers étaient des TNC légers et un tiers des TNC majeurs. L'étude 2 démontrait que le critère optimal était le score cognitif global abrégé (c.-à-d. les performances moyennes pour la vitesse d'action, fonctions exécutives et langage). En utilisant ces critères optimisés, la prévalence des TNCPA dans la cohorte GRECogVASC était de 49,5% (IC : 44,6–54,4), dont 39% de TNC légers (IC 95% :34,4–43,9) et 10,4% de TNC majeurs (IC :7,4–13,4). L'étude 3 montrait que les dépôts amyloïdes étaient observés chez 13,5% (IC 95 : 6,39-20,58) des patients. Le statut amyloïde positif était associé à un déficit cognitif plus précoce. L'étude 4 montrait que les facteurs neuroradiologiques associés au statut amyloïde étaient : le nombre d'espaces dilatées (>10) dans les centres semi-ovales et leur présence dans la région cingulaire postérieure. Les microsaignements lobaires, l'hémosidérose et les anomalies de substance blanche temporale étaient également associées au statut amyloïdePrevalence of post stroke neurocognitive disorders (PSNCD) is heterogeneous in the literature. Recent work has proposed the harmonization of diagnostic criteria and cognitive assessment procedures. Neuroradiological determinants of PSNCD have been studied, less other indicators as amyloid deposits. The main objectives of this work were: (1) to perform a meta-analysis with systematic review on prevalence and profiles of PSNCD; (2) to study the prevalence and profile of mild and major NCD in a French cohort, including a validated methodology (3) to assess the prevalence of post-stroke amyloid deposits and cognitive outcome according to the post-stroke amyloid status; (4) to clarify the neuroradiological correlates of amyloid and post-stroke cognitive status. The study population was the GRECogVASC cohort [NCT01339195] and the ancillary study IDEA 3 [NCT 02813434]. Study 1 showed that more than half of the patients presented with post-stroke NCD, of whom two-thirds had mild NCD and one-third had major NCD. Study 2 demonstrated that the optimal criterion was the shortened summary score (i.e., averaged performance for action speed, executive functions, and language). Using this criterion, the mean (95% confidence interval) prevalence of poststroke NCD was 49.5% (44.6–54.4), most of which corresponded to mild NCD (39.1%; 95 CI: 34.4–43.9) rather than dementia (10.4%; 95% CI:7.4–13.4). Study 3 showed that amyloid deposits were observed in 13.5% (95 CI: 6.39-20.58) of patients. A positive amyloid status was also associated with earlier cognitive impairment. Study 4 showed that neuroradiological factors associated with amyloid status were the number of dilated spaces (> 10) in the semi-oval center and their presence in the posterior cingulate region. Lobar microbleeds, hemosiderosis and temporal white matter abnormalities were also associated with the positive amyloid statu

Is VLSM a valid tool for determining the functional anatomy of the brain? Usefulness of additional Bayesian network analysis

International audienc

Determinants of Disability at 6 Months after Stroke: The GRECogVASC Study

International audienceBACKGROUND AND PURPOSE: The aim of this study was to determine the contributions of background disorders responsible for participation restriction as indexed by a structured interview for the modified Rankin Scale (mRS-SI). METHODS: A subset of 256 patients was assessed at 6~months after stroke using the National Institutes of Health Stroke Scale (NIHSS), gait score, comprehensive cognitive battery (yielding a global cognitive Z-score), behavioral dysexecutive disorders (DDs), anxiety and depressive symptoms, epilepsy, and headache. Following bivariate analyses, determinants of participation restriction were selected using ordinal regression analysis with partial odds. RESULTS: Poststroke participation restriction (mRS-SI score~>~1) was observed in 59% of the patients. In bivariate analyses, mRS-SI score was associated with prestroke mRS-SI score, 6-month NIHSS score, gait score, global cognitive Z-score, behavioral DDs, and presence of anxiety and depression (all: p~=~0.0001; epilepsy: p~=0.3; headache: p~=~0.7). After logistic regression analysis, NIHSS score was associated with increasing mRS-SI score (p~=~0.00001). Prestroke mRS-SI score (p~=~0.00001), behavioral DDs (p~=~0.0008) and global cognitive Z-score (p~=~0.01) were associated with both mRS-SI~score >~1 and mRS-SI~score >~2. In addition, gait score was associated with mRS-SI~score >~2 (p~=~0.00001). This model classified 85% of mRS-SI scores correctly (p~=~0.001). Structural equation modeling showed the contributions of gait limitation (standardized coefficient [SC]: 0.68; p~=~0.01), prestroke mRS-SI (SC: 0.41; p~=~0.01), severity of neurological impairment (SC: 0.16; p~=~0.01), global cognitive Z-score (SC: -0.14; p~=~0.05), and behavioral DDs (SC: 0.13; p~=~0.01). CONCLUSION: These results provide a statistical model of weights of determinants responsible for poststroke participation restriction and highlight a new independent determinant: behavioral DDs

STROKOG (stroke and cognition consortium): An international consortium to examine the epidemiology, diagnosis, and treatment of neurocognitive disorders in relation to cerebrovascular disease

Peer reviewe

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

© 2022 The Author(s)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/.N

Strategic infarct locations for post-stroke cognitive impairment: a pooled analysis of individual patient data from 12 acute ischaemic stroke cohorts

Background: Post-stroke cognitive impairment (PSCI) occurs in approximately half of people in the first year after stroke. Infarct location is a potential determinant of PSCI, but a comprehensive map of strategic infarct locations predictive of PSCI is unavailable. We aimed to identify infarct locations most strongly predictive of PSCI after acute ischaemic stroke and use this information to develop a prediction model. Methods: In this large-scale multicohort lesion-symptom mapping study, we pooled and harmonised individual patient data from 12 cohorts through the Meta-analyses on Strategic Lesion Locations for Vascular Cognitive Impairment using Lesion-Symptom Mapping (Meta VCI Map) consortium. The identified cohorts (as of Jan 1, 2019) comprised patients with acute symptomatic infarcts on CT or MRI (with available infarct segmentations) and a cognitive assessment up to 15 months after acute ischaemic stroke onset. PSCI was defined as performance lower than the fifth percentile of local normative data, on at least one cognitive domain on a multidomain neuropsychological assessment or on the Montreal Cognitive Assessment. Voxel-based lesion-symptom mapping (VLSM) was used to calculate voxel-wise odds ratios (ORs) for PSCI that were mapped onto a three-dimensional brain template to visualise PSCI risk per location. For the prediction model of PSCI risk, a location impact score on a 5-point scale was derived from the VLSM results on the basis of the mean voxel-wise coefficient (ln[OR]) within each patient's infarct. We did combined internal–external validation by leave-one-cohort-out cross-validation for all 12 cohorts using logistic regression. Predictive performance of a univariable model with only the location impact score was compared with a multivariable model with addition of other clinical PSCI predictors (age, sex, education, time interval between stroke onset and cognitive assessment, history of stroke, and total infarct volume). Testing of visual ratings was done by three clinicians, and accuracy, inter-rater reliability, and intra-rater reliability were assessed with Cohen's weighted kappa. Findings: In our sample of 2950 patients (mean age 66·8 years [SD 11·6]; 1157 [39·2%] women), 1286 (43·6%) had PSCI. We achieved high lesion coverage of the brain in our analyses (86·9%). Infarcts in the left frontotemporal lobes, left thalamus, and right parietal lobe were strongly associated with PSCI (after false discovery rate correction, q<0·01; voxel-wise ORs >20). On cross-validation, the location impact score showed good correspondence, based on visual assessment of goodness of fit, between predicted and observed risk of PSCI across cohorts after adjusting for cohort-specific PSCI occurrence. Cross-validations showed that the location impact score by itself had similar performance to the combined model with other PSCI predictors, while allowing for easy visual assessment. Therefore the univariable model with only the location impact score was selected as the final model. Correspondence between visual ratings and actual location impact score (Cohen's weighted kappa: range 0·88–0·92), inter-rater agreement (0·85–0·87), and intra-rater agreement (for a single rater, 0·95) were all high. Interpretation: To the best of our knowledge, this study provides the first comprehensive map of strategic infarct locations associated with risk of PSCI. A location impact score was derived from this map that robustly predicted PSCI across cohorts. Furthermore, we developed a quick and reliable visual rating scale that might in the future be applied by clinicians to identify individual patients at risk of PSCI. Funding: The Netherlands Organisation for Health Research and Development

Added value of 18F-florbetaben amyloid PET in the diagnostic workup of most complex patients with dementia in France: A naturalistic study

International audienceIntroductionAlthough some studies have previously addressed the clinical impact of amyloid positron emission tomography (PET), none has specifically addressed its selective and hierarchical implementation in relation to cerebrospinal fluid analysis in a naturalistic setting.MethodsThis multicenter study was performed at French tertiary memory clinics in patients presenting with most complex clinical situations (i.e., early-onset, atypical clinical profiles, suspected mixed etiological conditions, unexpected rate of progression), for whom cerebrospinal fluid analysis was indicated but either not feasible or considered as noncontributory (ClinicalTrials.gov: NCT02681172).ResultsTwo hundred five patients were enrolled with evaluable florbetaben PET scans; 64.4% of scans were amyloid positive. PET results led to changed diagnosis and improved confidence in 66.8% and 81.5% of patients, respectively, and altered management in 80.0% of cases.DiscussionHigh-level improvement of diagnostic certainty and management is provided by selective and hierarchical implementation of florbetaben PET into current standard practices for the most complex dementia cases

Penetrance estimation of Alzheimer disease in SORL1 loss-of-function variant carriers using a family-based strategy and stratification by APOE genotypes

International audienceAbstract Background Alzheimer disease (AD) is a common complex disorder with a high genetic component. Loss-of-function (LoF) SORL1 variants are one of the strongest AD genetic risk factors. Estimating their age-related penetrance is essential before putative use for genetic counseling or preventive trials. However, relative rarity and co-occurrence with the main AD risk factor, APOE -ε4, make such estimations difficult. Methods We proposed to estimate the age-related penetrance of SORL1 -LoF variants through a survival framework by estimating the conditional instantaneous risk combining (i) a baseline for non-carriers of SORL1- LoF variants, stratified by APOE-ε4 , derived from the Rotterdam study ( N = 12,255), and (ii) an age-dependent proportional hazard effect for SORL1- LoF variants estimated from 27 extended pedigrees (including 307 relatives ≥ 40 years old, 45 of them having genotyping information) recruited from the French reference center for young Alzheimer patients. We embedded this model into an expectation-maximization algorithm to accommodate for missing genotypes. To correct for ascertainment bias, proband phenotypes were omitted. Then, we assessed if our penetrance curves were concordant with age distributions of APOE -ε4-stratified SORL1- LoF variant carriers detected among sequencing data of 13,007 cases and 10,182 controls from European and American case-control study consortia. Results SORL1- LoF variants penetrance curves reached 100% (95% confidence interval [99–100%]) by age 70 among APOE -ε4ε4 carriers only, compared with 56% [40–72%] and 37% [26–51%] in ε4 heterozygous carriers and ε4 non-carriers, respectively. These estimates were fully consistent with observed age distributions of SORL1- LoF variant carriers in case-control study data. Conclusions We conclude that SORL1- LoF variants should be interpreted in light of APOE genotypes for future clinical applications