Details

Autor(en) / Beteiligte

• Tavazzi, Erica
• Daberdaku, Sebastian
• Zandonà, Alessandro
• Vasta, Rosario
• Nefussy, Beatrice
• Lunetta, Christian
• Mora, Gabriele
• Mandrioli, Jessica
• Grisan, Enrico
• Tarlarini, Claudia
• Calvo, Andrea
• Moglia, Cristina
• Drory, Vivian
• Gotkine, Marc
• Chiò, Adriano
• Di Camillo, Barbara

Titel

Predicting functional impairment trajectories in amyotrophic lateral sclerosis: a probabilistic, multifactorial model of disease progression

Ist Teil von

• Journal of neurology, 2022-07, Vol.269 (7), p.3858-3878

Ort / Verlag

Berlin/Heidelberg: Springer Berlin Heidelberg

Erscheinungsjahr

2022

Quelle

Springer Nature - Complete Springer Journals

Beschreibungen/Notizen

• Objective To employ Artificial Intelligence to model, predict and simulate the amyotrophic lateral sclerosis (ALS) progression over time in terms of variable interactions, functional impairments, and survival. Methods We employed demographic and clinical variables, including functional scores and the utilisation of support interventions, of 3940 ALS patients from four Italian and two Israeli registers to develop a new approach based on Dynamic Bayesian Networks (DBNs) that models the ALS evolution over time, in two distinct scenarios of variable availability. The method allows to simulate patients’ disease trajectories and predict the probability of functional impairment and survival at different time points. Results DBNs explicitly represent the relationships between the variables and the pathways along which they influence the disease progression. Several notable inter-dependencies were identified and validated by comparison with literature. Moreover, the implemented tool allows the assessment of the effect of different markers on the disease course, reproducing the probabilistically expected clinical progressions. The tool shows high concordance in terms of predicted and real prognosis, assessed as time to functional impairments and survival (integral of the AU-ROC in the first 36 months between 0.80–0.93 and 0.84–0.89 for the two scenarios, respectively). Conclusions Provided only with measurements commonly collected during the first visit, our models can predict time to the loss of independence in walking, breathing, swallowing, communicating, and survival and it can be used to generate in silico patient cohorts with specific characteristics. Our tool provides a comprehensive framework to support physicians in treatment planning and clinical decision-making.