Purpose To construct a risk factor prediction model for reversible cognitive frailty(RCF)by combining MRI quantitative indexes and clinical information of hippocampus.Materials and Methods Forty-one patients with RCF and 40 elderly people without RCF served as a control group were retrospectively included in this study.All subjects underwent quantitative susceptibility mapping(QSM)and three-dimensional arterial spin labeling scans to extract the cerebral iron content and blood flow indexes in bilateral hippocampus by standardized methods.Meanwhile,clinical information was collected to construct baseline tables.First,least absolute shrinkage and selection operator regression and single Logistic regression were used to screen variables,then multivariate Logistic regression analysis was used.The selected independent predictors were used to construct the model and draw Nomo chart.Finally,the internal validation of the model was carried out by strengthening Bootstrap method for 500 times of repeated sampling.The differentiation,calibration and decision curve were used to evaluate model.Results There were significant differences in cerebral blood flow value,QSM value,social activity and sleep quality between RCF and non-RCF groups(χ2=5.13,4.27,9.13,15.53,all P<0.05).The independent risk factors screened by multivariate Logistic regression analysis were four variables:sleep quality,social activities,QSM and cerebral blood flow.The model had a good differentiation in predicting the risk of RCF,with area under curve was 0.927(95%CI 0.856-0.978).The calibration curve showed that the model predicting the risk of RCF occurrence was highly consistent with the actual situation(χ2=52.20,P=4.14).The decision curve showed that the model had clinical applicability.Conclusion The multi-scale clinical prediction model of RCF based on MRI quantitative data combined with clinical information has good differentiation,calibration and clinical applicability,which can provide certain help for the screening of risk factors for RCF.
NETL
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