Objective To investigate the influencing factors of brain lesions in Wilson's dis-ease(WD)and to construct a predictive model for brain lesions in WD to facilitate early identifica-tion and intervention.Methods A retrospective analysis was conducted on the clinical and labora-tory data of 198 patients with neurological WD who were treated at the Neurology Institute affili-ated with Anhui University of Chinese Medicine from April 2019 to April 2023.All patients un-derwent cranial magnetic resonance imaging(MRI)and exhibited varying degrees of MRI changes in the brain.LASSO regression and multivariate Logistic regression analysis were used to identify factors influencing the occurrence of brain pathology and to construct a nomogram prediction model.The effectiveness of the predictive model was verified using the receiver operating charac-teristic(ROC)curve,calibration curve,and decision curve analysis(DCA).The model was inter-nally validated using 1000 bootstrap resamples and 10-fold cross-validation.Results The age of WD patients,and MRI findings of pontine and thalamic lesions were identified as independent risk factors for brain pathology.The nomogram demonstrated good discrimination,calibration,and clinical utility.After 1000 bootstrap resamples and 10-fold cross-validation,the model main-tained robust predictive performance.Conclusion The nomogram prediction model developed in this study has good predictive and discriminative capabilities,which can assist clinicians in predic-ting the occurrence of brainstem lesions in WD patients and has potential clinical translational ap-plication value.
Wilson's DiseaseMesencephalonNomogramPredictive Model
NETL
NSTL
万方数据
