Development of a Prognostic Model for Poststroke Dementia Using Multiple International Cohorts
Year: 2026
Type: article
Abstract: BACKGROUND AND OBJECTIVES: Dementia risk prediction models developed for the general population perform poorly in stroke cohorts. Existing stroke-specific models are few and limited by short prediction horizons or reliance on neuroimaging. The aim of this study was to develop a clinically practical model for predicting 5-year dementia risk after stroke using commonly available variables and individual participant data from the Stroke and Cognition Consortium (STROKOG). METHODS: Data were pooled from 12 studies across 10 countries. Dementia was diagnosed mainly by expert panel consensus and algorithmic classification. Fine-Gray subdistribution hazard models estimated dementia probability, accounting for death as a competing event. Candidate predictors included routinely collected baseline clinical and stroke-related variables, selected through backward stepwise elimination. Model performance was evaluated using discrimination (C-index) and calibration for prediction up to 5 years after stroke. Internal-external cross-validation (IECV) assessed generalizability across studies, regions, and study periods. RESULTS: A total of 2,663 participants (mean age 67.0 years [SD 11.1]; 40% female) were followed for a median of 2.0 years (IQR 1.0-5.0), during which 655 developed dementia (8.7 per 100 person-years). The final model included age, sex, education, history of previous stroke, diabetes, stroke severity, 2 interactions (age × sex; age × stroke severity), and study-level variables including national current health expenditure. An Excel-based risk calculator is available in the Supplement (eAppendix 1). The model demonstrated strong discrimination (C-index: 0.81; 95% CI 0.75-0.87) and excellent calibration in the full data set used for development. In IECV, discrimination was acceptable across individual studies (pooled C-index: 0.70 [0.67-0.73]) and higher in recent (post-2010; 0.79 [0.76-0.82]) and European (0.74 [0.71-0.78]) cohorts. Risks were slightly overestimated in Asian cohorts. Case numbers were too small for reliable assessment in other regions. DISCUSSION: We developed and internally-externally validated a 5-year dementia risk model for stroke survivors using routinely available clinical variables. The model showed strong performance in the full development data set and generalized well to recent and European cohorts, although external validation in diverse populations is needed. This tool can help identify high-risk individuals for targeted cognitive monitoring and follow-up. By informing clinical decision making and resource planning, it offers a practical means to improve long-term outcomes.
Source: Neurology
Authors: Jessica W. Lo, John D. Crawford, David W. Desmond, Olivier Godefroy, Martine Roussel, Régis Bordet, Thibaut Dondaine, Anne-Marie Mendyk, Hee-Joon Bae, Jae-Sung Lim, Akin Ojagbemi, Toyin Bello, Christopher P.L.H. Chen, Eddie J. Chong, Narayanaswamy Venketasubramanian, Aleksandra Klimkowicz-Mrowiec, Latchezar Traykov, Shima Mehrabian, Chih-Ping Chung, Nai-Fang Chi, Gary Kui Kai Lau, Dillys Xiaodi Liu, Heidi Welberry, Henry Brodaty, Perminder S. Sachdev, Rufus Akinyemi, Philip Bath, Amy Brodtmann, Charlotte Cordonnier, Abdel Douiri, Martin Dichgans, Fatemeh Geranmayeh, Michael Hoffmann, Masafumi Ihara, Raj Kalaria, Nagaendran Kandiah, Hanna Jokinen, Frini Karayanidis, Sebestian Köhler, Paul de Kort, Byung-Chul Lee, Thomas Linden, Hugh Markus, Vincent Mok, Javier Marta-Moreno, Michael O'Sullivan, Behnam Sabayan, Velandai Srikanth, Joanna Wardlaw, Qun Xu, Kyung-Ho Yu
Institutions: UNSW Sydney, Centre for Healthy Brain Ageing, Centre Hospitalier Universitaire Amiens-Picardie, Université de Picardie Jules Verne, Inserm, Université de Lille, Centre Hospitalier Universitaire de Lille, Lille Neurosciences & Cognition, Seoul National University, Seoul National University Bundang Hospital, Asan Medical Center, University of Ulsan, University of Ibadan, National University of Singapore, Jagiellonian University, Alexandrovska Hospital, Medical University of Sofia, Taipei Veterans General Hospital, University of Hong Kong, Centre for Big Data Research in Health
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7123361963