Biomedical Engineering Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, GR15773, Athens, Greece; Unit of Medical Technology and Intelligent Information Systems, Dept. of Material Science and Engineering, University of Ioannina, GR45110, Ioannina, Greece.
Dept. of Neurology, Medical School, University of Ioannina, GR45110, Ioannina, Greece.
Artif Intell Med. 2020 Mar;103:101807. doi: 10.1016/j.artmed.2020.101807. Epub 2020 Jan 21.
Tracking symptoms progression in the early stages of Parkinson's disease (PD) is a laborious endeavor as the disease can be expressed with vastly different phenotypes, forcing clinicians to follow a multi-parametric approach in patient evaluation, looking for not only motor symptomatology but also non-motor complications, including cognitive decline, sleep problems and mood disturbances. Being neurodegenerative in nature, PD is expected to inflict a continuous degradation in patients' condition over time. The rate of symptoms progression, however, is found to be even more chaotic than the vastly different phenotypes that can be expressed in the initial stages of PD. In this work, an analysis of baseline PD characteristics is performed using machine learning techniques, to identify prognostic factors for early rapid progression of PD symptoms. Using open data from the Parkinson's Progression Markers Initiative (PPMI) study, an extensive set of baseline patient evaluation outcomes is examined to isolate determinants of rapid progression within the first two and four years of PD. The rate of symptoms progression is estimated by tracking the change of the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) total score over the corresponding follow-up period. Patients are ranked according to their progression rates and those who expressed the highest rates of MDS-UPDRS total score increase per year of follow-up period are assigned into the rapid progression class, using 5- and 10-quantiles partition. Classification performance against the rapid progression class was evaluated in a per quantile partition analysis scheme and in quantile-independent approach, respectively. The results shown a more accurate patient discrimination with quantile partitioning, however, a much more compact subset of baseline factors is extracted in the latter, making a more suitable for actual interventions in practice. Classification accuracy improved in all cases when using the longer 4-year follow-up period to estimate PD progression, suggesting that a prolonged patient evaluation can provide better outcomes in identifying rapid progression phenotype. Non-motor symptoms are found to be the main determinants of rapid symptoms progression in both follow-up periods, with autonomic dysfunction, mood impairment, anxiety, REM sleep behavior disorders, cognitive decline and memory impairment being alarming signs at baseline evaluation, along with rigidity symptoms, certain laboratory blood test results and genetic mutations.
追踪帕金森病（PD）早期阶段的症状进展是一项艰巨的任务，因为该疾病可能表现出截然不同的表型，迫使临床医生在患者评估中采用多参数方法，不仅要寻找运动症状，还要寻找非运动并发症，包括认知能力下降、睡眠问题和情绪障碍。PD 是一种神经退行性疾病，预计随着时间的推移，患者的病情会持续恶化。然而，症状进展的速度比 PD 早期可能表现出的截然不同的表型更加混乱。在这项工作中，使用机器学习技术对 PD 的基线特征进行了分析，以确定 PD 症状早期快速进展的预后因素。使用帕金森病进展标志物倡议（PPMI）研究的公开数据，对大量基线患者评估结果进行了检查，以确定 PD 发病后的前两年和四年内快速进展的决定因素。通过跟踪运动障碍协会统一帕金森病评定量表（MDS-UPDRS）总分在相应随访期间的变化来估计症状进展的速度。根据他们的进展速度对患者进行排名，并根据每年的 MDS-UPDRS 总分增加量将那些表达最高进展率的患者分配到快速进展组，使用 5 分和 10 分的分区。分别在逐分位数分区分析方案和分位数独立方法中评估了对快速进展组的分类性能。结果表明，使用分位数分区可以更准确地对患者进行区分，但是在后一种方法中提取的基线因素子集更加紧凑，因此更适合实际干预。当使用更长的 4 年随访期来估计 PD 进展时，所有情况下的分类准确性都有所提高，这表明延长患者评估可以更好地识别快速进展表型。在两个随访期内，非运动症状被发现是快速症状进展的主要决定因素，自主神经功能障碍、情绪障碍、焦虑、快速眼动睡眠行为障碍、认知能力下降和记忆障碍在基线评估时是令人警惕的迹象，同时还有僵硬症状、某些实验室血液检查结果和基因突变。
