Department of Information Engineering, University of Padova, Gradenigo 6/b, 35131, Padova, Italy.
Department of Neuroscience, University of Torino, 10124, Torino, Italy.
BMC Bioinformatics. 2019 Apr 18;20(Suppl 4):118. doi: 10.1186/s12859-019-2692-x.
Amyotrophic lateral sclerosis (ALS) is an adult-onset neurodegenerative disease progressively affecting upper and lower motor neurons in the brain and spinal cord. Mean life expectancy is three to five years, with paralysis of muscles, respiratory failure and loss of vital functions being the common causes of death. Clinical manifestations of ALS are heterogeneous due to the mix of anatomic regions involvement and the variability in disease course; consequently, diagnosis and prognosis at the level of individual patient is really challenging. Prediction of ALS progression and stratification of patients into meaningful subgroups have been long-standing interests to clinical practice, research and drug development.
We developed a Dynamic Bayesian Network (DBN) model on more than 4500 ALS patients included in the Pooled Resource Open-Access ALS Clinical Trials Database (PRO-ACT), in order to detect probabilistic relationships among clinical variables and identify risk factors related to survival and loss of vital functions. Furthermore, the DBN was used to simulate the temporal evolution of an ALS cohort predicting survival and the time to impairment of vital functions (communication, swallowing, gait and respiration). A first attempt to stratify patients by risk factors and simulate the progression of ALS subgroups was also implemented.
The DBN model provided the prediction of ALS most probable trajectories over time in terms of important clinical outcomes, including survival and loss of autonomy in functional domains. Furthermore, it allowed the identification of biomarkers related to patients' clinical status as well as vital functions, and unrevealed their probabilistic relationships. For instance, DBN found that bicarbonate and calcium levels influence survival time; moreover, the model evidenced dependencies over time among phosphorus level, movement impairment and creatinine. Finally, our model provided a tool to stratify patients into subgroups of different prognosis studying the effect of specific variables, or combinations of them, on either survival time or time to loss of autonomy in specific functional domains.
The analysis of the risk factors and the simulation allowed by our DBN model might enable better support for ALS prognosis as well as a deeper insight into disease manifestations, in a context of a personalized medicine approach.
肌萎缩侧索硬化症（ALS）是一种成人起病的神经退行性疾病，逐渐影响大脑和脊髓中的上下运动神经元。平均预期寿命为三到五年，肌肉瘫痪、呼吸衰竭和重要功能丧失是常见的死亡原因。由于受累解剖区域的混合和疾病过程的可变性，ALS 的临床表现存在异质性；因此，个体患者的诊断和预后确实具有挑战性。预测 ALS 的进展并将患者分层为有意义的亚组一直是临床实践、研究和药物开发的长期关注点。
我们在包含在 Pooled Resource Open-Access ALS Clinical Trials Database (PRO-ACT) 中的 4500 多名 ALS 患者中开发了一个动态贝叶斯网络（DBN）模型，以检测临床变量之间的概率关系并确定与生存和重要功能丧失相关的风险因素。此外，该 DBN 用于模拟预测生存和重要功能（沟通、吞咽、步态和呼吸）受损时间的 ALS 队列的时间演变。还首次尝试根据风险因素对患者进行分层并模拟 ALS 亚组的进展。
DBN 模型提供了 ALS 最重要的临床结果（包括生存和自主功能丧失）随时间的最可能轨迹的预测。此外，它允许确定与患者临床状况和重要功能相关的生物标志物，并揭示它们的概率关系。例如，DBN 发现碳酸氢盐和钙水平会影响生存时间；此外，该模型还证明了磷水平、运动障碍和肌酐之间的时间依赖性。最后，我们的模型提供了一种工具，可以通过研究特定变量或它们的组合对生存时间或特定功能领域自主丧失时间的影响，将患者分层为不同预后的亚组。
我们的 DBN 模型分析风险因素和模拟的能力可以更好地支持 ALS 的预后，并在个性化医疗方法的背景下更深入地了解疾病表现。
