基于DynNom动态评分构建脑梗死后并发血管性痴呆的风险预测模型OA
Construction of a model for predicting the risk of vascular dementia after cerebral infarction based on the DynNom dynamic score
目的 基于 DynNom 动态评分构建脑梗死后并发血管性痴呆的风险预测模型.方法 选取 2021 年 1 月至 2023 年 9 月简阳市人民医院收治的 206 例脑梗死患者作为研究对象.根据 VD 发生情况将患者分为VD 组 74 例和非 VD 组 132 例,比较两组患者的一般资料,使用多因素 Logistic 回归分析脑梗死后并发 VD 的危险因素,并建立和验证 DynNom 动态评分模型.结果 两组患者受教育年限、糖尿病、高血压、美国国立卫生研究院卒中量表(NIHSS)评分、格拉斯哥昏迷评分法(GCS)评分比较,差异有统计学意义(t/χ2=3.533、16.240、15.698、4.255、6.569,P<0.05).多因素 Logistic 回归分析显示,受教育年限较短、糖尿病、高血压、高 NIHSS评分和低 GCS 评分是脑梗死后并发 VD 的危险因素(P<0.05).DynNom 动态评分模型验证结果显示,模型的校准曲线趋近于理想曲线,平均绝对误差值为 0.020;Hosmer-Lemeshow 拟合度检验显示模型的预测值与观测值差异无统计学意义(χ2=13.488,P>0.05);ROC 曲线的 AUC 为0.876(95%CI=0.826～0.926);决策曲线分析结果表明,在 0%～100%预测范围内,模型的净获益值>0.结论 脑梗死后并发 VD 的危险因素包括受教育年限较短、糖尿病、高血压、高 NIHSS评分和低 GCS 评分,基于上述因素构建的 DynNom 动态评分模型对脑梗死后 VD 的发生风险具有良好的预测价值.
Objective To construct a risk prediction model for vascular dementia(VD)after cerebral infarction based on DynNom dynamic scoring.Methods A total of 206 patients with cerebral infarction admitted to Jianyang People's Hospital from January 2021 to September 2023 were selected as the study subjects.Based on the occurrence of VD,the patients were divided into a VD group(74 cases)and a non-VD group(132 cases).General data were compared between the two groups.Multivariate Logistic regression analysis was used to identify risk factors for VD after cerebral infarction,and a DynNom dynamic scoring model was established and validated.Results Significant differences were observed between the two groups in terms of years of education,diabetes,hypertension,National Institutes of Health Stroke Scale(NIHSS)score,and Glasgow Coma Scale(GCS)score(t/χ2=3.533,16.240,15.698,4.255,6.569,respectively;P<0.05).Multivariate Logistic regression analysis showed that fewer years of education,diabetes,hypertension,high NIHSS score,and low GCS score were risk factors for VD after cerebral infarction(P<0.05).Validation of the DynNom dynamic scoring model indicated that the calibration curve closely approximated the ideal curve,with an average absolute error value of 0.020.The Hosmer-Lemeshow goodness-of-fit test showed no statistically significant difference between the predicted and observed values(χ2=13.488,P>0.05).The area under the ROC curve(AUC)was 0.876(95%CI=0.826～0.926).Decision curve analysis demonstrated that the model had a net benefit>0 across a prediction range of 0%～100%.Conclusion Risk factors for concurrent VD after cerebral infarction include fewer years of education,diabetes,hypertension,higher NIHSS scores,and lower GCS scores.The DynNom dynamic scoring model constructed based on these factors has good predictive value for the risk of VD after cerebral infarction.
龚柳盛;付钟;傅慧;宋正希
641400 成都,简阳市人民医院神经内科641400 成都,简阳市人民医院神经内科641400 成都,简阳市人民医院神经内科641400 成都,简阳市人民医院神经内科
脑梗死血管性痴呆DynNom动态评分模型
Cerebral infarctionVascular dementiaDynNom dynamic scoring model
《心脑血管病防治》 2026 (4)
8-12,5
成都市医学科研课题(2022261)
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