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. 2024 Mar 29;11(4):107.
doi: 10.3390/jcdd11040107.

Thrombus Imaging Characteristics to Predict Early Recanalization in Anterior Circulation Large Vessel Occlusion Stroke

Affiliations

Thrombus Imaging Characteristics to Predict Early Recanalization in Anterior Circulation Large Vessel Occlusion Stroke

Nerea Arrarte Terreros et al. J Cardiovasc Dev Dis. .

Abstract

The early management of transferred patients with a large vessel occlusion (LVO) stroke could be improved by identifying patients who are likely to recanalize early. We aim to predict early recanalization based on patient clinical and thrombus imaging characteristics. We included 81 transferred anterior-circulation LVO patients with an early recanalization, defined as the resolution of the LVO or the migration to a distal location not reachable with endovascular treatment upon repeated radiological imaging. We compared their clinical and imaging characteristics with all (322) transferred patients with a persistent LVO in the MR CLEAN Registry. We measured distance from carotid terminus to thrombus (DT), thrombus length, density, and perviousness on baseline CT images. We built logistic regression models to predict early recanalization. We validated the predictive ability by computing the median area-under-the-curve (AUC) of the receiver operating characteristics curve for 100 5-fold cross-validations. The administration of intravenous thrombolysis (IVT), longer transfer times, more distal occlusions, and shorter, pervious, less dense thrombi were characteristic of early recanalization. After backward elimination, IVT administration, DT and thrombus density remained in the multivariable model, with an AUC of 0.77 (IQR 0.72-0.83). Baseline thrombus imaging characteristics are valuable in predicting early recanalization and can potentially be used to optimize repeated imaging workflow.

Keywords: acute ischemic stroke; early recanalization; endovascular treatment; intravenous thrombolysis; transferred patients.

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Conflict of interest statement

N. Arrarte Terreros is co-founder of inSteps, a start-up that focuses on in-silico stroke models. D.W.J. Dippel reports unrestricted grants from Stryker, Penumbra, Medtronic, Cerenovus, Thrombolytic Science, LLC, Dutch Heart Foundation, Brain Foundation Netherlands, The Netherlands Organization for Health Research and Development, Health Holland Top Sector Life Sciences and Health, and Thrombolytic Science, LLC for research, paid to institution. H.A. Marquering is co-founder of inSteps and Nico.lab, a company that focuses on the use of artificial intelligence for medical image analysis. C.B.L.M. Majoie reports grants from the European Commission during the conducting of the study; grants from CVON/Dutch Heart Foundation, TWIN Foundation, Healthcare Evaluation Netherlands and Stryker, outside the submitted work; and is a shareholder of Nico.lab. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Thrombus imaging characteristics. Manually placed markers to compute thrombus characteristics. Left: thick-slab maximum intensity projection of a CT angiography showing a sudden stop of contrast due to the occlusion. Right: thick-slab maximum intensity projection of a non-contrast CT where the hyperdense artery sign of the thrombus is visible. Red marker: placed at internal carotid artery terminus (ICA-T). Green markers: placed between ICA-T and proximal border of the thrombus. Yellow marker: proximal thrombus border. Blue markers: proximal, mid, and distal parts of the thrombus. Cyan marker: distal thrombus border.
Figure 2
Figure 2
Model performance and calibration curves. (a) Median (magenta) and interquartile range (grey) of the ROC curve for 100 5-fold cross-validations. Median AUC was 0.77 (IQR 0.72–0.83). (b) Calibration curve showing the true probability over the predicted probability. AUC, area under the curve; ROC, receiver operating characteristics.

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