A diagnostic and predictive model for vascular cognitive impairment in elderly patients with acute partial anterior circulation infarction
10.3760/cma.j.issn.0254-9026.2020.09.005
- VernacularTitle:急性部分前循环梗死老年患者血管性认知功能障碍的诊断预测模型
- Author:
Lian MENG
1
;
Lian QIN
;
Zhenhua MO
;
Baogong LIAO
;
Junde QIN
;
Bin WEI
;
Fei LU
;
Hongqiao CHEN
;
Jiang LEI
;
Jinyu HUANG
Author Information
1. 广西科技大学第一附属医院神经内科，柳州 545002
- From:
Chinese Journal of Geriatrics
2020;39(9):1011-1015
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate risk factors for vascular cognitive impairment(VCI)in elderly patients 12-18 months after the onset of acute partial anterior circulation infarction(PACI), and to establish a diagnostic and predictive model.Methods:This was a prospective study. Demographic characteristics, vascular risk factors and laboratory data of 148 patients with acute PACI were collected, and patients were followed up for 12-18 months.The Montreal Cognitive Assessment Scale(MoCA)was used to evaluate patients' cognitive function.Logistic stepwise regression was used to screen risk factors for VCI.We established a diagnostic and predictive model.The area under the receiver operating(ROC)curve(AUC)was used to evaluate the efficiency of the model.Results:A total of 126 subjects completed the 12-18 month follow-up.Multivariate logistic regression analysis found that high homocysteine(Hcy)( OR=1.082, 95% CI: 1.002-1.167), high glycated hemoglobin(HbA1c)( OR=1.653, 95% CI: 1.052-2.598), high National Institutes of Health Stroke Scale(NIHSS)score( OR=1.291, 95% CI: 1.098-1.518), high hypersensitive C-reactive protein(hs-CRP)( OR=1.026, 95% CI: 1.005-1.047)and low education level( OR=2.485, 95% CI: 1.231-5.018)were independent risk factors for VCI in patients 12-18 months after PACI( P<0.05). The AUC of the diagnostic and predictive model was 0.828(95% CI: 0.755-0.902). Conclusions:High Hcy, NIHSS score, hs-CRP and low education level are independent risk factors for VCI in patients 12-18 months after PACI.The diagnostic and predictive model can help to screen patients at high-risk for VCI, so that timely clinical recognition, diagnosis and treatment can be made after acute PACI.
