The Analysis of Related Factors of Cognitive Impairment after the Acute Ischemic Stroke and Construction of Nomogram Model

Yanmei JI; Wenjun LI; Qingyun LI; Ni GUO; Ni MENG; Dan ZHOU; Qiuyu LI; Xingfang JIN

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Yanmei JI, Wenjun LI, Qingyun LI, Ni GUO, Ni MENG, Dan ZHOU, Qiuyu LI, Xingfang JIN. The Analysis of Related Factors of Cognitive Impairment after the Acute Ischemic Stroke and Construction of Nomogram Model[J]. Journal of Kunming Medical University.

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The Analysis of Related Factors of Cognitive Impairment after the Acute Ischemic Stroke and Construction of Nomogram Model

1.
Dept. of Cardiovascular Surgery
2.
Dept. of Neurology
3.
Dept. of Intensive Care Unit，Yan’an Hospital Affiliated to Kunming Medical University，Kunming Yunnan 650051，China

Received Date: 2023-09-30
Available Online: 2024-04-29

Abstract

Abstract

Objective To explore the related factors of cognitive impairment after the acute ischemic stroke and develop a clinical nomogram model. Methods 175 patients with the acute ischemic stroke were selected as the study objects, and the cognitive function was assessed using the simple mental State Scale and the Montreal Cognitive Assessment Scale after the admission. There were 81 cases in post-stroke cognitive impairment （PSCI） group and 94 cases in post-stroke no cognitive impairment （PSNCI） group. The baseline data, peripheral blood and brain MRI results of the two groups were collected and the univariate and multivariate analysis were used to explore the influencing factors of the cognitive impairment after the acute ischemic stroke, and the prediction model was constructed based on the nomogram and evaluated. Results Multivariate regression analysis showed that several factors, including impaired daily activity, high levels of HCY, larger cerebral infarction volume, and cerebral atrophy, were independent risk factors for early PSCI. On the other hand, education and hemoglobin were identified as the protective factors against PSCI. A nomogram prediction model was created from this data. The ROC curve analysis predicted an area under the curve of 0.830 （95%CI: 0.77-0.89）. The calibration curve indicated that the model had the good differentiation and prediction probability, with bias correction tending towards the ideal curve and consistent incidence in actual outcomes. The clinical decision curve showed that the model could provide a better net benefit for clinical use, making it a valuable tool for healthcare professionals. Conclusion The development of PSCI may be overlooked in its early stages. A clinical predictive model that considers multiple factors can aid in the early detection of PSCI and identification of high-risk individuals, which is crucial for the effective prevention and treatment.
- Stroke,
- Cognitive impairment,
- Influencing factors,
- Nomogram,
- Predictive model

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References(39)

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