Convenience sampling was adopted to enroll elderly hypertensive inpatients from the cardiovascular medicine department of a tertiary hospital in Shaanxi between November 2023 and June 2024; the incidence of mild cognitive impairment (MCI) was assessed via the Mini-Mental State Examination (MMSE), and relevant data were collected through multiple scales, with Logistic regression used to screen independent risk factors for MCI. A nomogram model was constructed and internally validated by the Bootstrap method, and its predictive performance was evaluated using indicators including the area under the curve (AUC), calibration curve and Brier score. Among the 368 enrolled patients, the incidence of MCI was 38.86%, with age, nutritional status, education level, hypertension duration and depression identified as independent influencing factors; the constructed nomogram model yielded an AUC of 0.868, a specificity of 0.840, a sensitivity of 0.785 and a Brier score of 0.138 after internal validation, while the decision tree model had an AUC of 0.860, a specificity of 0.911, a sensitivity of 0.720 and a Brier score of 0.131, both demonstrating favorable predictive performance, which indicates that age, nutritional status, education level, hypertension duration and depression exert impacts on MCI in elderly hypertensive inpatients, and the established nomogram and decision tree models possess satisfactory predictive efficacy.

Xu Jilin, Wang Xiaobo, Sun Baomei, Han Shufang, Tang Lili. Comparison of Nomogram and Decision Tree Models for Risk Prediction of Mild Cognitive Impairment in Elderly Hospitalized Hypertensive. International Journal of Frontiers in Medicine (2026), Vol. 8, Issue 2: 25-34. https://doi.org/10.25236/IJFM.2026.080203.

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