Meng-Yuan Ding1, Yi Xu1, Ying-Zhe Wang1, Pei-Xi Li1, Yi-Ting Mao1, Jin-Tai Yu1, Mei Cui1, Qiang Dong1,2. 1. Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China. 2. Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
Abstract
BACKGROUND: Post-stroke cognitive impairment (PSCI) significantly affects stroke survivors' quality of life and rehabilitation. A risk model identifying cognitive decline at admission would help to improve early detection and management of post-stroke patients. OBJECTIVE: To develop a new clinical risk score for ischemic stroke survivors in predicting 6-12 months PSCI. METHODS: We prospectively enrolled 179 patients diagnosed with acute ischemic stroke within a 7-day onset. Data were analyzed based on baseline demographics, clinical risk factors, and radiological parameters. Logistic regression and area under the receiver operating curve (AUROC) were used to evaluate model efficiency. RESULTS: One hundred forty-five subjects completed a 6-12-month follow-up visit, and 77 patients (53.1%) were diagnosed with PSCI. Age (β= 0.065, OR = 1.067, 95% CI = 1.016-1.120), years of education (β= -0.346, OR = 0.707, 95% CI = 0.607-0.824), periventricular hyperintensity grading (β= 1.253, OR = 3.501, 95% CI = 1.652-7.417), diabetes mellitus (β= 1.762, OR = 5.825, 95% CI = 2.068-16.412), and the number of acute nonlacunar infarcts (β= 0.569, OR = 1.766, 95% CI = 1.243-2.510) were independently associated with 6-12 month PSCI, constituting a model with optimal predictive efficiency (AUC = 0.884, 95% CI = 0.832-0.935). CONCLUSIONS: The optimized risk model was effective in screening stroke survivors at high risk of developing 6-12 months PSCI in a simple and pragmatic way. It could be a potential tool to identify patients with a high risk of PSCI at an early stage in clinical practice after further independent external cohort validation.
BACKGROUND: Post-stroke cognitive impairment (PSCI) significantly affects stroke survivors' quality of life and rehabilitation. A risk model identifying cognitive decline at admission would help to improve early detection and management of post-strokepatients. OBJECTIVE: To develop a new clinical risk score for ischemic stroke survivors in predicting 6-12 months PSCI. METHODS: We prospectively enrolled 179 patients diagnosed with acute ischemic stroke within a 7-day onset. Data were analyzed based on baseline demographics, clinical risk factors, and radiological parameters. Logistic regression and area under the receiver operating curve (AUROC) were used to evaluate model efficiency. RESULTS: One hundred forty-five subjects completed a 6-12-month follow-up visit, and 77 patients (53.1%) were diagnosed with PSCI. Age (β= 0.065, OR = 1.067, 95% CI = 1.016-1.120), years of education (β= -0.346, OR = 0.707, 95% CI = 0.607-0.824), periventricular hyperintensity grading (β= 1.253, OR = 3.501, 95% CI = 1.652-7.417), diabetes mellitus (β= 1.762, OR = 5.825, 95% CI = 2.068-16.412), and the number of acute nonlacunar infarcts (β= 0.569, OR = 1.766, 95% CI = 1.243-2.510) were independently associated with 6-12 month PSCI, constituting a model with optimal predictive efficiency (AUC = 0.884, 95% CI = 0.832-0.935). CONCLUSIONS: The optimized risk model was effective in screening stroke survivors at high risk of developing 6-12 months PSCI in a simple and pragmatic way. It could be a potential tool to identify patients with a high risk of PSCI at an early stage in clinical practice after further independent external cohort validation.
Authors: Terence J Quinn; Edo Richard; Yvonne Teuschl; Thomas Gattringer; Melanie Hafdi; John T O'Brien; Niamh Merriman; Celine Gillebert; Hanne Huyglier; Ana Verdelho; Reinhold Schmidt; Emma Ghaziani; Hysse Forchammer; Sarah T Pendlebury; Rose Bruffaerts; Milija Mijajlovic; Bogna A Drozdowska; Emily Ball; Hugh S Markus Journal: Eur Stroke J Date: 2021-10-08