Objective To construct a clinical prediction model for the risk of Alzheimer's disease based on ApoE,combined with risk factors and common clinical indicators.Methods There were 61 cases of Alzheimer's disease patients and 111 cases of fuzzy matching healthy physical examination from Nanjing Hospital of Chinese Medicine data platform from January 2019 to January 2021.Using LASSO regression screening of risk factors,constructing logistic regression forecasting model,10 fold cross verifies the degree of differentiation,validation the calibration of the bootstrap method.The clinical guidance of the prediction model was evaluated by the clinical decision curve,and finally,the clinical prediction model was visualized by nomogram.Results 12 variables were screened out and four risk factors were included,which are age,free triiodothyroxine(FT3),gender and ApoE.The AUC of ROC of the whole sample was 0.879,and the average AUC of ROC after 10 folded and 9 crossed training sets verification was 0.864.Bootstrap method and Hosmer-Lemeshow were used to test the calibration degree.Results χ2 =6.496,P=0.592>0.05.The threshold probability of clinical decision curve ranged from 1%to 88.6%.Conclusion Individualized evaluation of patients using clinical prediction models constructed by age,FT3,gender and ApoE can provide early warning of Alzheimer's disease,carry out early prevention intervention and slow down the development of the disease.
关键词
痴呆/阿尔兹海默病/预测模型/载脂蛋白E
Key words
Dementia/Alzheimer's disease/Prediction model/Apolipoprotein E
维普
万方数据