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

For the Piemonte, Valle d’Aosta Register for ALS (PARALS), for the Emilia Romagna Registry for ALS (ERRALS)

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)3858-3878
Number of pages21
JournalJournal of Neurology
Volume269
Issue number7
DOIs
StatePublished - Jul 2022
Externally publishedYes

Funding

FundersFunder number
Azienda Ospedaliera
Azienda Ospedaliero Universitaria
Azienda Ospedaliero Universitaria San Luigi Gonzaga
Azienda Ospedaliero Universitaria di Modena and Department of Biomedical
Bufalini Hospital
Carpi Hospital
Department of Clinical and Experimental Medicine, Amedeo Avogadro” University of Piemonte Orientale
Department of Health Sciences
Department of Hospital Services
Department of Neurology
Department of Neurology and Multiple Sclerosis Center
Department of Neurology and Stroke Center
Department of Neurology, Faenza and Ravenna Hospital
Department of Neuroscience, University of Ferrara
Department of Neuroscience, University of Parma
Department of Neurosciences
Department of Neurosciences and Rehabilitation
Dipartimento di Scienze Biomediche e Neuromotorie, University of Bologna
Emilia Romagna Regional Health Authority
Emilia Romagna Regional Health Authority, Bologna
Emilia Romagna Registry
Fidenza Hospital
Forlì Hospital
IRCCS Arcispedale Santa Maria Nuova
Imola Hospital, Bologna
Infermi Hospital
Istituto di Ricovero e Cura a Carattere Scientifico
Maggiore Hospital, Bologna
Ministero della Salute, Ricerca Sanitaria FinalizzataRF-2016-02362405
Ospedale Civile di Ivrea e Ospedale Civico di Chivasso
Ospedale Humanitas Gradenigo
Ospedale Maria Vittoria
Ospedale Regionale
Ospedale San Giovanni Bosco
St Anna Hospital, Ferrara
UOC Interaziendale Clinica Neurologica Metropolitana
University of Piemonte Orientale
Department of Anesthesiology, Medical College of Wisconsin
Seventh Framework Programme259867
Ministry of Science, Technology and Space
Ministero della Salute
Ministero dell’Istruzione, dell’Università e della Ricerca2017SNW5MB
Università degli Studi di Padova
Seventh Framework Programme
Ministero degli Affari Esteri e della Cooperazione Internazionale
Università degli Studi di Torino
Istituto Auxologico Italiano
Azienda Ospedaliero-Universitaria Città della Salute e della Scienza di Torino

    Keywords

    • Amyotrophic lateral sclerosis
    • Artificial intelligence
    • Clinical trajectories
    • Dynamic Bayesian Networks
    • Population model
    • Prognosis modelling

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