Influencing factors of cognitive decline in elderly diabetic patients with hypertension and construction of a prediction model
摘要
Objective To explore the influencing factors of cognitive decline in elderly diabetic patients with hypertension and to construct a risk prediction model. Methods A total of 198 elderly diabetic patients with hypertension, who were admitted to the Affiliated Hospital of Jiangnan University from May 2023 to April 2024, were enrolled. According to the experience of cognitive decline, the patients were divided into two groups: a cognitive decline group (n=41) and a non-cognitive decline group (n=157). Both groups were compared for clinical data. Binary logistic regression analysis was performed to identify influencing factors and a prediction model was constructed. A receiver operating characteristic (ROC) curve was plotted to evaluate the predictive performance of the constructed model. Results Both groups showed significant differences in terms of age, sleep conditions, cognitive activity, nutritional status, and depression (P<0.05). Age >69 years, insomnia, lack of cognitive activity, malnutrition, and depression were independent risk factors for cognitive decline (P<0.05). Age, sleep conditions, cognitive activity, nutritional status, depression, and the prediction model all had predictive value for cognitive decline in elderly diabetic patients with hypertension. The areas under the curve (AUC) were 0.752 (95%CI: 0.663-0.841), 0.763 (95%CI: 0.678-0.848), 0.696 (95%CI: 0.602-0.789), 0.725 (95%CI: 0.631-0.819), 0.760 (95%CI: 0.676-0.843), and 0.942 (95%CI: 0.909-0.974), respectively. The corresponding sensitivities were 0.796, 0.771, 0.732, 0.815, 0.739, and 0.828, and the specificities were 0.707, 0.756, 0.659, 0.634, 0.780, and 0.927. Internal validation through the bootstrap method showed that the prediction curve closely approximated the ideal line, with a net benefit rate higher than the null line in the 0.01-0.90 threshold probability range, indicating good predictive ability. Conclusions Age, sleep, cognitive activity, nutritional status, and depression are i