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【Objective】 To construct and validate a risk prediction model for cognitive impairment after stroke based on demographic, clinical, and neuroimaging characteristics. 【Methods】 Through the medical record system, we collected all data of the patients. We finished cognitive function testing three months after the indexed stroke. The Mini-Mental State Examination Scale score≤26 was defined as cognitive dysfunction. Optimal subset regression analysis was used to screen variables, Logistic regression analysis was used to construct a predictive model for cognitive impairment, and C-index, calibration chart and clinical decision curve analyses were used to evaluate the discrimination, consistency, and clinical availability of the model. And nomograms were used to express the performance of the model. 【Results】 Seven variables were selected: cognitive function before stroke, age, years of education, National Institutes of Health Stroke Scale score at admission, history of ischemic heart disease, the number of old lacunar infarct lesions, and medial temporal lobe atrophy scale. The prediction model had a C-index of 0.845 (95% CI: 0.805-0.885). The clinical decision curve showed that the model had a positive net benefit when the threshold probability was 9.0%-90.0%. 【Conclusion】 The predictive model of cognitive impairment in stroke patients has good predictive efficiency and provides an effective assessment tool for screening high-risk cases of cognitive impairment in patients with stroke of various subtypes.
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Objectives Brain atrophy plays a key role in post-stroke dementia. The current study aims to explore risk factors for brain atrophy in different regions in order to find the ultimate therapeutic strategy. Methods Consecutive stroke and/or transient ischemic attack (TIA) patients were recruited from July 2012 to June . The clinical features, neuro?imaging findings and risk factors were collected during hospitalization. Logistic regression analysis showed that, except for age, female gender (Odds ratio, OR=2.447, P=0.007) and the number of silent lacuna infarcts (OR=1.414, P=0.027) were independent risk factors for frontal lobe atrophy. Ischemic stroke history (OR=2.224, P=0.024) was the independent risk factor for parietal lobe atrophy. All of extra-/intracranial larger artery diseases (OR=2.584, P=0.015) and white mat?ter severity score (OR=1.112, P=0.007) as well as the number of silent lacuna infarcts (OR=1.158,P=0.042) were inde?pendent risk factors for medial temporal lobe atrophy. Moreover, diabetes (OR=2.109, P=0.001),atrial fibrillation (OR=1.934, P=0.015) and white matter severity score (OR=1.098, P=0.002) were independent risk factors for global brain atro? phy. Conclusion Risk factors for brain atrophy included diabetes,atrial fibrillation, silent lacuna infarcts and white mat?ter changes. We should pay more attention to those patients with above risk factors in order to slow down the progression of brain atrophy and also prevent them from dementia by early interventions.