Objective: This study aims to construct a risk prediction model for post-stroke mild cognitive impairment(PSMCI) patients, to accurately assess the degree of risk of mild cognitive impairment, and to provide a basis for clinical identification of high-risk groups. Methods: Stroke patients admitted to the Department of Neurology, Zhongda Hospital Affiliated to Southeast University, from January 2020 to June 2022 were recruited. 16 indicators such as the Auditory-Verbal Learning Test(AVLT), Logical Memory Test(LMT), Rey-Osterrieth Complex Figure Test(ROCF), Verbal Fluency Test(VFT), Picture-Naming Test, Digit Span Test(DST), Symbol Digit Modalities Test(SDMT), Trail Making Test(TMT), Stroop Color Word Test(SCWT) and Word Fluency Test were used to investigate patients. Principal component analysis method was used to determine the dimensions of risk for PSMCI patients and to develop a risk prediction model. Results: Three dimensions of risk for PSMCI patients were identified, with a cumulative contribution of 59.227% to the total variance:memory function and attention level, memory function and executive ability, and depressive state and activities of daily living. The evaluation formula of cognitive impairment risk was formed based on the percentage of the three dimensions in the original variable. Conclusion: The risk prediction model for PSMCI patients constructed in this study can objectively evaluate and grade the risk of cognitive impairment. This is helpful for the early clinical identification of stroke people with different levels of risk, and the targeted provision of preventive measures and intervention. |
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