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Updated: Jan 18, 2026
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Construction of a risk predictive model for cognitive frailty in middle-aged and older patients with ischemic stroke:
Shuqi Shi1, Haiya Sun2, Yan He3
1Jinan Maternity And Child Care Hospital Affiliated to Shandong First Medical University, Shandong, China.
Background:
Cognitive frailty is associated with increased morbidity and mortality across a range of medical conditions, and may affect stroke disease trajectory and outcome. This study aimed to establish a predictive model for cognitive frailty in middle-aged and elderly ischemic stroke patients.
Methods:
This study collected from 505 patients. A nomogram prediction model was constructed based on multivariate logistic regression analysis. The performance of the nomogram was evaluated by discrimination, calibration and decision curve analysis.
Results:
A nomogram prediction model was constructed based on age, marital status, residence, Diabetes, Recurrent stroke, Depression, Carotid Plaque. The AUC of the model was 0.819. Calibration curve analysis indicated a good fitted, and the clinical decision curve analysis demonstrated a high net benefit.
Conclusion:
This study established a nomogram with good performance to provide assessment tools to identify the risk of cognitive frailty in middle-aged and elderly ischemic stroke patients.
