Predicting functional impairment trajectories in amyotrophic lateral sclerosis: a probabilistic, multifactorial model of disease progression
Erica Tavazzi, Sebastian Daberdaku, Alessandro Zandonà, Rosario Vasta, Beatrice Nefussy, Christian Lunetta, Gabriele Mora, Jessica Mandrioli, Enrico Grisan, Claudia Tarlarini, Andrea Calvo, Cristina Moglia, Vivian E. Drory, Marc Gotkine, Adriano Chiò, Barbara Di Camillo, For the Piemonte, Valle d’Aosta Register for ALS (PARALS), for the Emilia Romagna Registry for ALS (ERRALS), Adriano Chiò, Rita Levi Montalcini, Andrea Calvo, Cristina Moglia, Antonio Canosa, Umberto Manera, Rosario Vasta, Francesca Palumbo, Alessandro Bombaci, Maurizio Grassano, Maura Brunetti, Federico Casale, Giuseppe Fuda, P. Salomone, Barbara Iazzolino, Laura Peotta, Paolo Cugnasco, Giovanni De Marco, Maria Claudia Torrieri, Salvatore Gallone, Marco Barberis, Luca Sbaiz, S Gentile, Alessandro Mauro, Letizia Mazzini, Fabiola De Marchi, Lucia Corrado, Sandra D’Alfonso, Antonio Bertolotto, M. Gionco, D. Leotta, E. Oddenino, R. Cavallo, Marco De Mattei, G. Gusmaroli, Cristoforo Comi, Carmelo Labate, Fabio Poglio, Luigi Ruiz, D. Ferrandi, Lucia Testa, Eugenia Rota, M. Aguggia, Nicoletta Di Vito, P. Meineri, Paolo Ghiglione, Nicola Launaro, Michele Dotta, Alessia Di Sapio, Maria Valentina di Giovanni, Jessica Mandrioli, Jessica Mandrioli, Nicola Fini, Ilaria Martinelli, Elisabetta Zucchi, Giulia Gianferrari, Cecilia Simonini, Marco Vinceti, Stefano Meletti, Veria Vacchiano, Rocco Liguori, Fabrizio Salvi, Ilaria Bartolomei, Roberto Michelucci, P. Cortelli, Annamaria Borghi, Andrea Zini, Rita Rinaldi, P. Cortelli, Elisabetta Sette, V. Tugnoli, Maura Pugliatti, Elena Canali, Luca Codeluppi, Franco Valzania, Lucia Zinno, G. Pavesi, Doriana Medici, Giovanna Pilurzi, Emilio Terlizzi, Donata Guidetti, Silvia De Pasqua, Michele Santangelo
Abstract
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.