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    老年糖尿病合并高血压患者认知衰弱的影响因素及风险预测模型构建

    Influencing factors of cognitive decline in elderly diabetic patients with hypertension and construction of a prediction model

      摘要
    • 摘要: 目的 探究老年糖尿病合并高血压患者认知衰弱的影响因素并构建风险预测模型。方法 纳入2023年5月—2024年4月江南大学附属医院收治的198例老年糖尿病合并高血压患者,按是否发生认知衰弱分为认知衰弱组(41例)与非认知衰弱组(157例)。比较2组临床资料,采用二元logistic回归分析影响因素,构建预测模型,并通过受试者工作特征(ROC)曲线评估预测价值。结果 2组在年龄、睡眠情况、用脑情况、营养状况、抑郁状态方面差异有统计学意义(P<0.05)。年龄>69岁、失眠、无用脑活动、营养不良、抑郁均为认知衰弱的独立危险因素(P<0.05)。年龄、睡眠情况、用脑情况、营养状况、抑郁状态、预测模型对老年糖尿病合并高血压患者认知衰弱均有预测价值。AUC分别为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)、0.942(95%CI:0.909~0.974)。 对应的敏感度分别为0.796、0.771、0.732、0.815、0.739、0.828,特异度分别为0.707、0.756、0.659、0.634、0.780、0.927。Bootstrap法内部验证显示预测曲线与理想线基本吻合,在0.01~0.90阈值概率范围净收益率高于无效线,模型预测能力较好。结论 年龄、睡眠、用脑、营养及抑郁状态均是老年糖尿病合并高血压患者认知衰弱的独立危险因素。基于该5项因素构建的风险预测模型具有良好的预测能力和实用价值,可为临床风险评估与干预提供依据。

       

      Abstract: 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 independent risk factors for cognitive decline in elderly diabetic patients with hypertension. The risk prediction model constructed based on these five factors demonstrates good predictive ability and utility, providing a basis for clinical risk assessment and intervention.

       

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