{"id":2487,"date":"2024-09-10T15:35:37","date_gmt":"2024-09-10T13:35:37","guid":{"rendered":"https:\/\/vbhi-institute.org\/?p=2487"},"modified":"2024-09-13T15:38:29","modified_gmt":"2024-09-13T13:38:29","slug":"infarct-volumes-and-clinical-outcomes","status":"publish","type":"post","link":"https:\/\/vbhi-institute.org\/en\/infarct-volumes-and-clinical-outcomes\/","title":{"rendered":"Infarct volumes and clinical outcomes"},"content":{"rendered":"
[vc_row][vc_column][vc_column_text css=”” el_class=”intro”]The 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.[\/vc_column_text][vc_column_text css=””]\n

Methods<\/h2>\n

The researchers including Pierrick Coup\u00e9<\/a>, member of the VBHI Board of Directors, Vincent Dousset<\/a>, member of the VBHI Management Board, Michel Thi\u00e9baut de Schotten<\/a> and Igor Sibon<\/a>, VBHI Deputy Scientific Directors, and Thomas Tourdias<\/a>, member of the VBHI Board of Directors\u00a0 conducted a secondary analysis from prospectively collected databases (FRAME, 2017\u20132019; ETIS, 2015\u20132022). They selected acute MRI data from anterior LVO stroke patients with witnessed onset, which were divided into training and independent validation datasets.<\/p>\n

In the training dataset, using Gaussian mixture analysis, they classified the patients into 3 growth groups based on their rate of infarct growth (diffusion volume\/time-to-imaging).<\/p>\n

Subsequently, they extrapolated pseudo-longitudinal models of infarct growth for each group and generated sequential frequency maps to highlight the spatial distribution of infarct growth.<\/p>\n

They used these charts to attribute a growth group to the independent patients from the validation dataset.<\/p>\n

They 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 an independent variable.<\/p>\n

Results<\/h2>\n

The researchers included 804 patients (median age 73.0 years [interquartile range 61.2\u201382.0 years]; 409 men).<\/p>\n

The training dataset revealed nonsupervised clustering into :<\/p>\n