Department of Information Engineering, University of Padova, Via Gradenigo 6/b, 35131, Padua, Italy.
Department of Clinical and Experimental Sciences, University of Brescia, Viale Europa 11, 25121, Brescia, Italy.
BMC Med Inform Decis Mak. 2023 Feb 2;22(Suppl 6):346. doi: 10.1186/s12911-023-02113-7.
Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease whose spreading and progression mechanisms are still unclear. The ability to predict ALS prognosis would improve the patients' quality of life and support clinicians in planning treatments. In this paper, we investigate ALS evolution trajectories using Process Mining (PM) techniques enriched to both easily mine processes and automatically reveal how the pathways differentiate according to patients' characteristics.
We consider data collected in two distinct data sources, namely the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) dataset and a real-world clinical register (ALS-BS) including data of patients followed up in two tertiary clinical centers of Brescia (Italy). With a focus on the functional abilities progressively impaired as the disease progresses, we use two Process Discovery methods, namely the Directly-Follows Graph and the CareFlow Miner, to mine the population disease trajectories on the PRO-ACT dataset. We characterize the impairment trajectories in terms of patterns, timing, and probabilities, and investigate the effect of some patients' characteristics at onset on the followed paths. Finally, we perform a comparative study of the impairment trajectories mined in PRO-ACT versus ALS-BS.
We delineate the progression pathways on PRO-ACT, identifying the predominant disabilities at different stages of the disease: for instance, 85% of patients enter the trials without disabilities, and 48% of them experience the impairment of Walking/Self-care abilities first. We then test how a spinal onset increases the risk of experiencing the loss of Walking/Self-care ability as first impairment (52% vs. 27% of patients develop it as the first impairment in the spinal vs. the bulbar cohorts, respectively), as well as how an older age at onset corresponds to a more rapid progression to death. When compared, the PRO-ACT and the ALS-BS patient populations present some similarities in terms of natural progression of the disease, as well as some differences in terms of observed trajectories plausibly due to the trial scheduling and recruitment criteria.
We exploited PM to provide an overview of the evolution scenarios of an ALS trial population and to preliminary compare it to the progression observed in a clinical cohort. Future work will focus on further improving the understanding of the disease progression mechanisms, by including additional real-world subjects as well as by extending the set of events considered in the impairment trajectories.
肌萎缩侧索硬化症（ALS）是一种神经退行性疾病，其传播和进展机制尚不清楚。能够预测 ALS 的预后将提高患者的生活质量，并支持临床医生规划治疗。在本文中，我们使用过程挖掘（PM）技术来研究 ALS 的演变轨迹，这些技术既易于挖掘过程，又能自动揭示路径如何根据患者的特征而有所不同。
我们考虑了从两个不同数据源收集的数据，即汇集资源开放获取肌萎缩侧索硬化症临床试验（PRO-ACT）数据集和一个包含在意大利布雷西亚的两个三级临床中心随访的患者数据的真实世界临床登记（ALS-BS）。我们专注于随着疾病的进展逐渐受损的功能能力，使用两种过程发现方法，即直接跟随图和 CareFlow Miner，在 PRO-ACT 数据集上挖掘人群疾病轨迹。我们根据模式、时间和概率来描述损伤轨迹，并研究患者在发病时的一些特征对所遵循路径的影响。最后，我们对 PRO-ACT 与 ALS-BS 中挖掘出的损伤轨迹进行了比较研究。
我们在 PRO-ACT 上描绘了进展途径，确定了疾病不同阶段的主要残疾：例如，85%的患者进入试验时没有残疾，其中 48%的患者首先经历行走/自理能力的损伤。然后，我们测试了脊髓发病如何增加首先经历行走/自理能力丧失的风险（在脊髓组中，52%的患者首先出现这种损伤，而在延髓组中，27%的患者首先出现这种损伤），以及发病年龄较大如何导致更快地死亡。在比较时，PRO-ACT 和 ALS-BS 患者人群在疾病的自然进展方面存在一些相似之处，在观察到的轨迹方面也存在一些差异，这可能是由于试验计划和招募标准所致。
我们利用 PM 提供了 ALS 试验人群演变场景的概述，并初步将其与临床队列中观察到的进展进行了比较。未来的工作将侧重于通过包括更多的真实世界的对象，并通过扩展损伤轨迹中考虑的事件集，进一步提高对疾病进展机制的理解。
