评估UPDRS对早期帕金森病跌倒分类的预测能力
Assessing the predictive ability of the UPDRS for falls classification in early stage Parkinson's disease
摘要：
识别与帕金森病（PD）患者跌倒相关的风险因素非常重要，因为他们跌倒的风险很高。在这项研究中，各种方式利用统一帕金森氏病评定量表（CAFRS）进行了评估，以确定危险因素和预测跌倒。考虑了三种分类统计方法：决策树、随机森林和逻辑斯谛回归。分析了51名早期PD参与者的BPRS测量结果，这些参与者在12个月的随访中完成了每月跌倒日记。所有的分类方法产生了类似的结果方面的分类精度和选定的重要变量。最高的分类率是从模型中获得的，具有80%的准确性（85%的灵敏度和77%的特异性），高于任何以前的研究。对《联合国难民事务调查表》四个部分的独立表现进行比较后发现，《联合国难民事务调查表》第二和第三部分的分类率非常高。在6个月和12个月的随访时间内观察到相似的模式，但分类率略有不同。在两次随访时，通过所有分类方法选择的跌倒的一致预测因子是：对于AIRRS I，思维障碍，对于AIRRS II，穿衣和跌倒，对于AIRRS III，手内翻/仰卧，以及对于AIRRS IV，睡眠障碍和症状性直立。而对于总体指标，小计2（AURSII项目之和）和运动迟缓与跌倒/非跌倒高度相关。跌倒/非跌倒发生率与ECORS的单个项目的相关性大于与总体措施的相关性。预测系统第二部分和第三部分对跌倒/非跌倒预测的分类率很高。6个月和12个月随访时的建模数据获得了相似的结果。
英文摘要：
Identification of risk factors associated with falls in people with Parkinson's Disease (PD) is important due to their high risk of falling. In this study, various ways of utilizing the Unified Parkinson's Disease Rating Scale (UPDRS) were assessed for the identification of risk factors and for the prediction of falls. Three statistical methods for classification were considered:decision trees, random forests, and logistic regression. UPDRS measurements on 51 participants with early stage PD, who completed monthly falls diaries for 12 months of follow-up were analyzed. All classification methods applied produced similar results in regards to classification accuracy and the selected important variables. The highest classification rates were obtained from model with individual items of the UPDRS with 80% accuracy (85% sensitivity and 77% specificity), higher than in any previous study. A comparison of the independent performance of the four parts of the UPDRS revealed the comparably high classification rates for Parts II and III of the UPDRS. Similar patterns with slightly different classification rates were observed for the 6- and 12-month of follow-up times. Consistent predictors for falls selected by all classification methods at two follow-up times are: thought disorder for UPDRS I, dressing and falling for UPDRS II, hand pronate/supinate for UPDRS III, and sleep disturbance and symptomatic orthostasis for UPDRS IV. While for the aggregate measures, subtotal 2 (sum of UPDRS II items) and bradykinesia showed high association with fall/non-fall. Fall/non-fall occurrences were more associated with individual items of the UPDRS than with the aggregate measures. UPDRS parts II and III produced comparably high classification rates for fall/non-fall prediction. Similar results were obtained for modelling data at 6-month and 12-month follow-up times.
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