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.

追踪帕金森氏病（PD）早期阶段的症状进展是一项艰苦的工作，因为该疾病可以表现为截然不同的表型，迫使临床医生在患者评估中遵循多参数方法，不仅要寻找运动症状，还要寻找非运动症状。 -运动并发症，包括认知能力下降，睡眠问题和情绪障碍。PD本质上是神经退行性的，随着时间的流逝，它有望使患者的病情持续恶化。然而，发现症状发展的速度甚至比PD初始阶段可以表达的迥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基线PD特征进行分析，以识别PD症状早期快速发展的预后因素。使用帕金森氏病进展指标计划（PPMI）研究的开放数据，检查了广泛的基线患者评估结果，以隔离PD前两年和四年内快速进展的决定因素。通过追踪运动障碍协会统一的帕金森氏病评分量表（MDS-UPDRS）总分在相应随访期内的变化，可以估计症状的进展速度。根据患者的进展速度对患者进行排名，那些表示随访期内每年MDS-UPDRS总评分增加率最高的患者，则使用5分位数和10分位数划分为快速进展类别。在每个分位数分区分析方案和与分位数无关的方法中评估了针对快速发展类别的分类性能，分别。结果显示，通过分位数划分可以更准确地区分患者，但是在后者中提取了更为紧凑的基线因子子集，这更适合于实践中的实际干预。当使用更长的4年随访期来评估PD进展时，在所有情况下的分类准确性都会提高，这表明延长的患者评估可以在识别快速进展表型方面提供更好的结果。发现非运动性症状是两个随访期内症状快速进展的主要决定因素，在基线评估中，自主神经功能障碍，情绪障碍，焦虑，REM睡眠行为障碍，认知能力下降和记忆障碍是令人震惊的迹象，另外僵硬症状，某些实验室血液检查结果和基因突变。