Dataset Information

Development and validation of a risk score (CHANGE) for cognitive impairment after ischemic stroke.

ABSTRACT: Post-stroke cognitive impairment (PSCI) warrants early detection and management. We sought to develop a risk score for screening patients at bedside for risk of delayed PSCI. Ischemic stroke survivors with PSCI and no cognitive impairments (NCI) 3-6 months post-stroke were studied to identify candidate variables predictive of PSCI. These variables were used to develop a risk score using regression models. The score, and the best identified clinical cutoff point, underwent development, stability testing, and internal and external validation in three independent cohorts from Singapore and Hong Kong. Across 1,088 subjects, the risk score, dubbed CHANGE, had areas under the receiver operating characteristics curve (AUROC) from 0.74 to 0.82 in detecting significant risk for PSCI, and had predicted values following actual prevalence. In validation data 3-6 and 12-18 months post-stroke, subjects with low, medium, and high scores had PSCI prevalence of 7-23%, 25-58%, and 67-82%. CHANGE was effective in screening ischemic stroke survivors for significant risk of developing PSCI up to 18 months post-stroke. CHANGE used readily available and reliable clinical data, and may be useful in identifying at-risk patients for PSCI.

SUBMITTER: Chander RJ

PROVIDER: S-EPMC5622067 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Development and validation of a risk score (CHANGE) for cognitive impairment after ischemic stroke.

Chander Russell J RJ Lam Bonnie Y K BYK Lin Xuling X Ng Aloysius Y T AYT Wong Adrian P L APL Mok Vincent C T VCT Kandiah Nagaendran N

Scientific reports 20170929 1

Post-stroke cognitive impairment (PSCI) warrants early detection and management. We sought to develop a risk score for screening patients at bedside for risk of delayed PSCI. Ischemic stroke survivors with PSCI and no cognitive impairments (NCI) 3-6 months post-stroke were studied to identify candidate variables predictive of PSCI. These variables were used to develop a risk score using regression models. The score, and the best identified clinical cutoff point, underwent development, stability ...[more]

PMID: 28963553

Similar Datasets

Project description:BackgroundThere is currently no validated risk prediction model for recurrent events among patients with acute ischemic stroke (AIS) and atrial fibrillation (AF). Considering that the application of conventional risk scores has contextual limitations, new strategies are needed to develop such a model. Here, we set out to develop and validate a comprehensive risk prediction model for stroke recurrence in AIS patients with AF.MethodsAIS patients with AF were collected from multicenter registries in South Korea and Japan. A developmental dataset was constructed with 5648 registered cases from both countries for the period 2011‒2014. An external validation dataset was also created, consisting of Korean AIS subjects with AF registered between 2015 and 2018. Event outcomes were collected during 1 year after the index stroke. A multivariable prediction model was developed using the Fine-Gray subdistribution hazard model with non-stroke mortality as a competing risk. The model incorporated 21 clinical variables and was further validated, calibrated, and revised using the external validation dataset.ResultsThe developmental dataset consisted of 4483 Korean and 1165 Japanese patients (mean age, 74.3 ± 10.2 years; male 53%); 338 patients (6%) had recurrent stroke and 903 (16%) died. The clinical profiles of the external validation set (n = 3668) were comparable to those of the developmental dataset. The c-statistics of the final model was 0.68 (95% confidence interval, 0.66 ‒0.71). The developed prediction model did not show better discriminative ability for predicting stroke recurrence than the conventional risk prediction tools (CHADS2, CHA2DS2-VASc, and ATRIA).ConclusionsNeither conventional risk stratification tools nor our newly developed comprehensive prediction model using available clinical factors seemed to be suitable for identifying patients at high risk of recurrent ischemic stroke among AIS patients with AF in this modern direct oral anticoagulant era. Detailed individual information, including imaging, may be warranted to build a more robust and precise risk prediction model for stroke survivors with AF.

Project description: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/.

Dataset Information

Development and validation of a risk score (CHANGE) for cognitive impairment after ischemic stroke.

Publications

Development and validation of a risk score (CHANGE) for cognitive impairment after ischemic stroke.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets