A Dynamic Bayesian Network model for simulation of disease progression in Amyotrophic Lateral Sclerosis patients
Author and article information
Abstract
Background. Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease primarily affecting upper and lower motor neurons in the brain and spinal cord. The heterogeneity in the course of ALS clinical progression and ultimately survival, coupled with the rarity of this disease, make predicting disease outcome at the level of the individual patient very challenging. Besides, stratification of ALS patients has been known for years as a question of great importance to clinical practice, research and drug development.
Methods. In this work, we present a Dynamic Bayesian Network (DBN) model of ALS progression to detect probabilistic relationships among variables included in the Pooled Resource Open-Access ALS Clinical Trials Database (PRO-ACT), which provides records of over 10,700 patients from different clinical trials, and with over 2,869,973 longitudinally collected data measurements.
Results. Our model unravels new dependencies among clinical variables in relation to ALS progression, such as the influence of basophil count and creatine kinase on patients’ clinical status and the respiratory functional state, respectively. Furthermore, it provided an indication of ALS temporal evolution, in terms of the most probable disease trajectories across time at the level of both patient population and individual patient.
Conclusions. The risk factors identified by out DBN model could allow patients' stratification based on velocity of disease progression and a sensitivity analysis on this latter in response to changes in input variables, i.e. variables measured at diagnosis.
Cite this as
2017. A Dynamic Bayesian Network model for simulation of disease progression in Amyotrophic Lateral Sclerosis patients. PeerJ Preprints 5:e3262v1 https://doi.org/10.7287/peerj.preprints.3262v1Author comment
This is an abstract which has been accepted for the NETTAB 2017 Workshop “Methods, tools & platforms for Personalized Medicine in the Big Data Era”.
Sections
Additional Information
Competing Interests
The authors declare that they have no competing interests.
Author Contributions
Alessandro Zandonà conceived and designed the experiments, performed the experiments, analyzed the data, wrote the paper, prepared figures and/or tables, performed the computation work, reviewed drafts of the paper.
Matilde Francescon conceived and designed the experiments, performed the experiments, analyzed the data, performed the computation work.
Maya Bronfeld wrote the paper, reviewed drafts of the paper.
Andrea Calvo reviewed drafts of the paper.
Adriano Chiò wrote the paper, reviewed drafts of the paper.
Barbara Di Camillo conceived and designed the experiments, performed the experiments, analyzed the data, wrote the paper, reviewed drafts of the paper.
Data Deposition
The following information was supplied regarding data availability:
Further analyses have still to be performed on data
Funding
This work was funded by the bilateral Italian-Israel project CompALS (Computational analysis of the clinical manifestations and predictive modelling of ALS), supported by the Italian Ministry of Foreign Affairs and International Cooperation and the Ministry of Science, Technology and Space of the State of Israel. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
