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Risk Prediction of Cognitive Decline after Stroke

Research output: Contribution to journalArticlepeer-review

Original languageEnglish
Article number105849
Pages (from-to)105849
JournalJournal of Stroke and Cerebrovascular Diseases
Volume30
Issue number8
DOIs
Published1 Aug 2021

Bibliographical note

Funding Information: The study forms part of a wider PhD thesis. We thank patients, their families, and the fieldworkers who have collected data for the South London Stroke Register since 1995. This work was supported by funding from the National Institute for Health Research (NIHR) Applied Research Collaboration (ARC) South London at King's College Hospital National Health Service Foundation Trust and the Royal College of Physicians, as well as the support from the NIHR Biomedical Research Centre based at Guy's and St Thomas’ National Health Service Foundation Trust and King's College London. The views expressed are those of the authors and not necessarily those of the Kings College London, NHS, the NIHR or the Department of Health. Publisher Copyright: © 2021 Elsevier Inc. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

King's Authors

Abstract

Background and purpose: Cognitive decline is one of the major outcomes after stroke. We have developed and evaluated a risk predictive tool of post-stroke cognitive decline and assessed its clinical utility. Methods: In this population-based cohort, 4,783 patients with first-ever stroke from the South London Stroke Register (1995-2010) were included in developing the model. Cognitive impairment was measured using the Mini Mental State Examination (cut off 24/30) and the Abbreviated Mental Test (cut off 8/10) at 3-months and yearly thereafter. A penalised mixed-effects linear model was developed and temporal-validated in a new cohort consisted of 1,718 stroke register participants recruited from (2011-2018). Prediction errors on discrimination and calibration were assessed. The clinical utility of the model was evaluated using prognostic accuracy measurements and decision curve analysis. Results: The overall predictive model showed good accuracy, with root mean squared error of 0.12 and R2 of 73%. Good prognostic accuracy for predicting severe cognitive decline was observed AUC: (88%, 95% CI [85-90]), (89.6%, 95% CI [86-92]), (87%, 95% CI [85-91]) at 3 months, one and 5 years respectively. Average predicted recovery patterns were analysed by age, stroke subtype, Glasgow-coma scale, and left-stroke and showed variability. Decision: curve analysis showed an increased clinical benefit, particularly at threshold probabilities of above 15% for predictive risk of cognitive impairment. Conclusions: The derived prognostic model seems to accurately screen the risk of post-stroke cognitive decline. Such prediction could support the development of more tailored management evaluations and identify groups for further study and future trials.

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