Bayesian network analysis reveals the interplay of intracranial aneurysm rupture risk factors

• Published in: Computers in biology and medicine 2022-08, Vol.147, p.105740-105740, Article 105740
• Main Authors: Delucchi, Matteo, Spinner, Georg R., Scutari, Marco, Bijlenga, Philippe, Morel, Sandrine, Friedrich, Christoph M., Furrer, Reinhard, Hirsch, Sven
• Format: Article
• Language: English
• Subjects: Algorithms; Aneurysm; Aneurysms; Bayesian analysis; Bayesian network; Decision making; Diagnosis; Health services; Intracranial aneurysm; Machine learning; Markov chains; Mathematical models; Network analysis; Patients; Probabilistic graphical model; Regression analysis; Risk analysis; Risk factors; Rupture; Statistical analysis
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2022.105740

Clinical decision making regarding the treatment of unruptured intracranial aneurysms (IA) benefits from a better understanding of the interplay of IA rupture risk factors. Probabilistic graphical models can capture and graphically display potentially causal relationships in a mechanistic model. In this study, Bayesian networks (BN) were used to estimate IA rupture risk factors influences. From 1248 IA patient records, a retrospective, single-cohort, patient-level data set with 9 phenotypic rupture risk factors (n=790 complete entries) was extracted. Prior knowledge together with score-based structure learning algorithms estimated rupture risk factor interactions. Two approaches, discrete and mixed-data additive BN, were implemented and compared. The corresponding graphs were learned using non-parametric bootstrapping and Markov chain Monte Carlo, respectively. The BN models were compared to standard descriptive and regression analysis methods. Correlation and regression analyses showed significant associations between IA rupture status and patient’s sex, familial history of IA, age at IA diagnosis, IA location, IA size and IA multiplicity. BN models confirmed the findings from standard analysis methods. More precisely, they directly associated IA rupture with familial history of IA, IA size and IA location in a discrete framework. Additive model formulation, enabling mixed-data, found that IA rupture was directly influenced by patient age at diagnosis besides additional mutual influences of the risk factors. This study establishes a data-driven methodology for mechanistic disease modelling of IA rupture and shows the potential to direct clinical decision-making in IA treatment, allowing personalised prediction. •Probabilistic graphical model of risk factors influencing rupture of intracranial aneurysms.•De-confounding of risk factors in a hierarchical model.•Novel insights into intracranial aneurysm rupture and potential for personalised predictions.