Location of intracranial aneurysms is the main factor associated with rupture in the ICAN population

Olivia Rousseau (1) , Matilde Karakachoff (1, 2) , Alban Gaignard (1) , Lise Bellanger (3) , Philippe Bijlenga (4) , Pacôme Constant Dit Beaufils (1) , Vincent L'Allinec (1) , Olivier Levrier (5) , Pierre Aguettaz (6) , Jean-Philippe Desilles (7) , Caterina Michelozzi (8) , Gaultier Marnat (9) , Anne-Clémence Vion (1) , Gervaise Loirand (1, 2) , Hubert Desal (1, 2) , Richard Redon (1, 2) , Pierre-Antoine Gourraud (1) , Romain Bourcier (1, 2)

1 ITX - ITX - unité de recherche de l'institut du thorax
2 CHU Nantes - Centre Hospitalier Universitaire de Nantes
3 LMJL - Laboratoire de Mathématiques Jean Leray
4 UNIGE - Université de Genève = University of Geneva
5 Service de Radiologie [Hôpital Clairval - Marseille]
6 CREATIS - Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé
7 LVTS (UMR_S_1148 / U1148) - Laboratoire de Recherche Vasculaire Translationnelle
8 CHU Toulouse - Centre Hospitalier Universitaire de Toulouse
9 CHU Bordeaux

Background and purpose The ever-growing availability of imaging led to increasing incidentally discovered unruptured intracranial aneurysms (UIAs). We leveraged machine-learning techniques and advanced statistical methods to provide new insights into rupture intracranial aneurysm (RIA) risks. Methods We analysed the characteristics of 2505 patients with intracranial aneurysms (IA) discovered between 2016 and 2019. Baseline characteristics, familial history of IA, tobacco and alcohol consumption, pharmacological treatments before the IA diagnosis, cardiovascular risk factors and comorbidities, headaches, allergy and atopy, IA location, absolute IA size and adjusted size ratio (aSR) were analysed with a multivariable logistic regression (MLR) model. A random forest (RF) method globally assessed the risk factors and evaluated the predictive capacity of a multivariate model. Results Among 994 patients with RIA (39.7%) and 1511 patients with UIA (60.3 %), the MLR showed that IA location appeared to be the most significant factor associated with RIA (OR, 95% CI: internal carotid artery, reference; middle cerebral artery, 2.72, 2.02–3.58; anterior cerebral artery, 4.99, 3.61–6.92; posterior circulation arteries, 6.05, 4.41–8.33). Size and aSR were not significant factors associated with RIA in the MLR model and antiplatelet-treatment intake patients were less likely to have RIA (OR: 0.74; 95% CI: 0.55–0.98). IA location, age, following by aSR were the best predictors of RIA using the RF model. Conclusions The location of IA is the most consistent parameter associated with RIA. The use of ‘artificial intelligence’ RF helps to re-evaluate the contribution and selection of each risk factor in the multivariate model.

Mots clés: decision trees; intracranial aneurysm; location; machine learning; risks; rupture

Domaines: Neurosciences [q-bio.NC]; Santé publique et épidémiologie

Olivia Rousseau, Matilde Karakachoff, Alban Gaignard, Lise Bellanger, Philippe Bijlenga, et al.. Location of intracranial aneurysms is the main factor associated with rupture in the ICAN population. Journal of Neurology, Neurosurgery and Psychiatry, 2021, 92 (2), jnnp-2020-324371. ⟨10.1136/jnnp-2020-324371⟩. ⟨hal-03033899⟩