Dynamic Evolution of Infarct Volumes at MRI in Ischemic Stroke Due to Large Vessel Occlusion
Neurology. 2024-06-01; 102(12):
DOI: 10.1212/wnl.0000000000209427
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Munsch F(1), Planes D(1), Fukutomi H(1), Marnat G(1), Courret T(1), Micard E(1), Chen B(1), Seners P(1), Dubos J(1), Planche V(1), Coupé P(1), Dousset V(1), Lapergue B(1), Olivot JM(1), Sibon I(1), Thiebaut De Schotten M(1), Tourdias
T(1); FRAME and ETIS investigators(1).
Author information:
(1)From the Institut de Bio-imagerie IBIO (F.M., J.D., V.D., T.T.), University
Bordeaux; Neuroimagerie Diagnostique et Thérapeutique (D.P., G.M., T.C., V.D.,
T.T.), CHU de Bordeaux, France; Kansai Electric Power Hospital (H.F.), Osaka,
Japan; Inserm CIC-IT U1433 (E.M., B.C.), CHRU Nancy; Institut de Psychiatrie et
Neurosciences de Paris (IPNP) (P.S.), INSERM U1266; Département de Neurologie
(P.S.), Hopital Fondation Rothschild, Paris; Institut des Maladies
Neurodégénératives (V.P.), CNRS, UMR 5293, Bordeaux INP (P.C.), LABRI, CNRS,
UMR5800, and Neurocentre Magendie (V.D., T.T.), INSERM U1215, Univ. Bordeaux;
Service de Neurologie et Unité de Neuro Vasculaire (B.L.), Hôpital FOCH,
Suresnes; Unité Neurovasculaire (J.M.O.), CHU de Toulouse; Unité Neurovasculaire
(I.S.), CHU de Bordeaux; CNRS (M.T.D.S.), UMR-5293, Univ. Bordeaux; and Brain
Connectivity and Behaviour Laboratory (M.T.D.S.), Paris, France.
BACKGROUND AND OBJECTIVES: 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.
METHODS: We 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 3 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 an independent variable.
RESULTS: We included 804 patients (median age 73.0 years [interquartile range
61.2-82.0 years]; 409 men). The training dataset revealed nonsupervised
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 < 0.001), cubic (r = 0.471; p < 0.001), and power (r = 0.63; p < 0.001)
function 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 the 3-month mRS
(multivariate odds ratio 0.51; 95% CI 0.37-0.72, p < 0.0001) in the training
dataset, yielding a mean area under the receiver operating characteristic curve
of 0.78 (95% CI 0.66-0.88) in the independent validation dataset.
DISCUSSION: We revealed spatiotemporal archetype dynamic evolutions following
LVO stroke according to 3 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 before EVT.