Abstract
BackgroundAmyotrophic 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.MethodsWe 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.ResultsThe 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.ConclusionsThe 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.
Highlights
Amyotrophic lateral sclerosis (ALS) is an adult-onset neurodegenerative disease progressively affecting upper and lower motor neurons in the brain and spinal cord
We focus our attention on the problem of deriving a probabilistic simulator of the progression of ALS and its complications, by learning a Dynamic Bayesian Network (DBN) model from a large public dataset such as the Pooled Resource Open-Access ALS Clinical Trials Database (PRO-ACT)
The DBN clearly evidenced that the loss of independence on the four Milano-Torino functional staging system (MITOS) domains was related to the changes in Forced Vital Capacity (FVC) along time
Summary
Amyotrophic lateral sclerosis (ALS) is an adult-onset neurodegenerative disease progressively affecting upper and lower motor neurons in the brain and spinal cord. Amyotrophic lateral sclerosis (ALS) is an adult-onset neurodegenerative disease characterised by the progressive involvement of motor neurons [1,2,3,4,5,6]. The 70% of ALS patients exhibit limb-onset disease and the 30% of cases present bulbar-onset disease; mean life expectancy after symptom onset is three to five years, with respiratory failure being the most common cause of death [1, 7]. The variability of onset site, the relative mix of upper and lower motor neuron involvement, the rate and the pattern of progression result in heterogeneous ALS phenotypes [8] and a challenging diagnosis. A timely diagnosis is challenging since these criteria require the history of disease progression
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