Predictors of Cognitive Impairment After Stroke: A Prospective Stroke Cohort Study

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.

To develop a new clinical risk score for ischemic stroke survivors in predicting 6-12 months PSCI.

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.

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).

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.