MA Wenqi 1XUE Li 1LI Han 1XIE Anmu1
作者信息
- 1. Department of Neurology,The Affiliated Hospital of Qingdao University,Qingdao 266003,China
- 折叠
摘要
Abstract
Objective To establish a gait-based clinical diagnostic model for Parkinson's disease(PD),and to investigate its clinical diagnostic value.Methods A total of 112 patients with PD who attended Department of Neurology in our hospital from December 2022 to July 2023 were enrolled as PD group,and 84 healthy individuals,matched for age and sex,during the same period of time were enrolled as healthy control(HC)group.According to the time of recruitment,the subjects in the PD group and the HC group were divided into a training set(the subjects enrolled in the first five months,with 87 subjects in the PD group and 70 in the HC group)and a validation set(the subjects enrolled in the last two months,with 25 subjects in the PD group and 14 in the HC group).A wearable quantitative assessment system for motor and gait was used to collect gait parameters from all subjects,and the scores of PD-related scales,including Mini-Mental State Examination(MMSE)and Montreal Cognitive Assessment(Mo-CA),were also collected from all subjects.A univariate analysis was used to compare the above indicators between the PD group and the HC group in the training set,and the indicators with a significant difference were included in the LASSO regression analysis and the multivariate stepwise logistic regression analysis to identify the influencing factors for PD.The above influencing factors were used to establish a diagnostic model for PD,which was presented through a nomogram,and the receiver operating characte-ristic(ROC)curve,the calibration curve,and the clinical decision curve analysis(DCA)were used to evaluate the model in the training set and the validation set.Results The univariate analysis showed that there were significant differences in 28 gait indi-cators,MMSE score,and MoCA score between the PD and HC groups in the training set(t=-10.385-3.505,Z=-8.038--2.042,P＜0.05).The LASSO regression analysis and the multivariate stepwise logistic regression analysis showed that MMSE score(OR=0.566,95％CI=0.360-0.889,P＜0.05)and maximum sit-to-stand trunk lean angle(OR=1.079,95％CI=1.010-1.153,P＜0.05)were the influencing factors for the onset of PD.A nomogram model for the diagnosis of PD was established based on the above factors.The ROC curve analysis showed that the model had an area under the ROC curve of 0.806 in the training set and 0.760 in the validation set;the calibration curve showed that the model had high calibration accuracy in both the training set and the validation set;the DCA results showed that the model had good net clinical benefit in the training and validation sets.Conclusion This study establishes a gait-based diagnostic model for PD,which has good performance,consistency,and clinical decision-making benefit in the diagnosis of PD.关键词
帕金森病/步态分析/步态障碍,神经性/认知功能障碍/影响因素分析/列线图/诊断Key words
Parkinson disease/Gait analysis/Gait disor-ders,neurologic/Cognitive dysfunction/Root cause analysis/Nomograms/Diagnosis分类
医药卫生
