Dynamic evolution of infarct volumes at MRI in ischemic stroke due to large vessel occlusion
Background and ObjectivesThe typical infarct volume trajectories in stroke patients, categorized as slow or fast progressors, remain largely unknown. This study aimed to reveal the characteristic spatiotemporal evolutions of infarct volumes caused by large vessel occlusion (LVO) and show that such growth charts help anticipate clinical outcomes. MethodsWe conducted a secondary analysis from prospectively collected databases (FRAME, 2017- 2019; ETIS, 2015-2022). We selected acute MRI data from anterior LVO stroke patients with witnessed onset which were divided into training- and independent validation-datasets. In the training-dataset, using Gaussian mixture analysis, we classified the patients into three growth groups based on their rate of infarct growth (diffusion volume / time-to-imaging). Subsequently, we extrapolated pseudo-longitudinal models of infarct growth for each group and generated sequential frequency maps to highlight the spatial distribution of infarct growth. We used these charts to attribute a growth group to the independent patients from the validation-dataset. We compared their 3-month modified Rankin scale (mRS) with the predicted values based on a multivariable regression model from the training-dataset that used growth group as independent variable. ResultsWe included 804 patients (median age, 73.0 years [IQR, 61.2-82.0 years]; 409 men). The training-dataset revealed non-supervised clustering into 11% (74/703) slow, 62% (437/703) intermediate, and 27% (192/703) fast progressors. Infarct volume evolutions were best fitted with a linear (r=0.809; P<.001), cubic (r=0.471; P<.001), and power (r=0.63; P<.001) functions for the slow, intermediate and fast progressors, respectively. Notably, the deep nuclei and insular cortex were rapidly affected in the intermediate and fast groups with further cortical involvement in the fast group. The variable "growth group" significantly predicted 3-month mRS (multivariate OR, 0.51; 95% CI: 0.37-0.72, P<.0001) in the training-dataset, yielding a mean AUC of 0.78 (95% CI: 0.66-0.88) in the independent validation-dataset. ConclusionsWe revealed spatiotemporal archetype dynamic evolutions following large vessel occlusion stroke according to three growth phenotypes called slow, intermediate and fast progressors, providing insight into anticipating clinical outcome. We expect this could help in designing neuroprotective trials aiming at modulating infarct growth prior EVT.