Department of Speech Language Pathology, School of Public Health, Honam University, Gwangju 62399, Korea.
Int J Environ Res Public Health. 2020 Apr 10;17(7):2594. doi: 10.3390/ijerph17072594.
Because it is possible to delay the progression of dementia if it is detected and treated in an early stage, identifying mild cognitive impairment (MCI) is an important primary goal of dementia treatment. The objectives of this study were to develop a random forest-based Parkinson's disease with mild cognitive impairment (PD-MCI) prediction model considering health behaviors, environmental factors, medical history, physical functions, depression, and cognitive functions using the Parkinson's Dementia Clinical Epidemiology Data (a national survey conducted by the Korea Centers for Disease Control and Prevention) and to compare the prediction accuracy of our model with those of decision tree and multiple logistic regression models. We analyzed 96 subjects (PD-MCI = 45; Parkinson's disease with normal cognition (PD-NC) = 51 subjects). The prediction accuracy of the model was calculated using the overall accuracy, sensitivity, and specificity. Based on the random forest analysis, the major risk factors of PD-MCI were, in descending order of magnitude, Clinical Dementia Rating (CDR) sum of boxes, Untitled Parkinson's Disease Rating (UPDRS) motor score, the Korean Mini Mental State Examination (K-MMSE) total score, and the K- Korean Montreal Cognitive Assessment (K-MoCA) total score. The random forest method achieved a higher sensitivity than the decision tree model. Thus, it is advisable to develop a protocol to easily identify early stage PDD based on the PD-MCI prediction model developed in this study, in order to establish individualized monitoring to track high-risk groups.
由于在早期发现和治疗痴呆症可以延缓其进展，因此确定轻度认知障碍（MCI）是痴呆症治疗的一个重要的首要目标。本研究的目的是开发一种基于随机森林的帕金森病伴轻度认知障碍（PD-MCI）预测模型，该模型考虑了健康行为、环境因素、病史、身体功能、抑郁和认知功能，使用了帕金森病痴呆症临床流行病学数据（由韩国疾病控制和预防中心进行的一项全国性调查），并将我们的模型与决策树和多逻辑回归模型的预测准确性进行了比较。我们分析了 96 名受试者（PD-MCI=45；帕金森病认知正常（PD-NC）=51 名受试者）。使用整体准确性、敏感性和特异性来计算模型的预测准确性。基于随机森林分析，PD-MCI 的主要危险因素按降序排列依次为：临床痴呆评定量表（CDR）总分、未命名帕金森病评定量表（UPDRS）运动评分、韩国简易精神状态检查（K-MMSE）总分和韩国蒙特利尔认知评估（K-MoCA）总分。随机森林方法的敏感性高于决策树模型。因此，建议根据本研究中开发的 PD-MCI 预测模型制定一项方案，以便于识别早期 PDD，从而建立个体化监测，以跟踪高危人群。
