A CT imaging-based prediction model of functional outcome and benefit of endovascular thrombectomy for ischemic stroke
Year: 2026
Type: article
Abstract: OBJECTIVE: To investigate the value of baseline CT imaging for the prediction of functional outcome and benefit of endovascular thrombectomy (EVT) for anterior large vessel occlusion (LVO). MATERIALS AND METHODS: We used individual patient data from seven randomized EVT trials and included patients with available baseline CT imaging and outcome data. We developed a model to predict functional outcome and benefit of EVT, including baseline stroke-related and brain frailty CT imaging features alone. We compared the discriminative performance of our model for predicting good functional outcome (modified Rankin Scale [mRS] 0-2) and treatment benefit (difference between the probability of mRS 0-2 with vs without EVT) with MR PREDICTS by calculating the difference in C-statistics (delta C and delta C-for-benefit). RESULTS: We included 1391 patients (median age, 67 years, interquartile range 59-76; 53% male). Discrimination of the model based on CT imaging alone was substantial for the prediction of good functional outcome (C-statistic 0.700, 95% CI: 0.666-0.731) and treatment benefit (C-for-benefit 0.640, 95% CI: 0.586-0.690). After adding the known strongest clinical predictors namely age and National Institutes of Health Stroke Scale score, discrimination improved to slightly lower than MR PREDICTS for prediction of good functional outcome (C-statistic 0.733 vs 0.750; delta C, -0.017 [95% CI: -0.037 to 0.003]) and treatment benefit (C-for-benefit 0.675 vs 0.692; delta C-for-benefit -0.017 [95% CI: -0.084 to 0.050]). CONCLUSIONS: Baseline CT imaging holds considerable predictive value with regard to functional outcome and treatment benefit, but a combination of clinical and imaging features offers the best predictive performance. KEY POINTS: Question The predictive value of baseline CT imaging for the prediction of functional outcome and benefit of EVT for anterior LVO stroke is uncertain. Findings Discrimination of a model based on CT imaging alone is substantial, but can further be improved by the addition of limited clinical characteristics. Clinical relevance Baseline CT imaging holds considerable predictive value with regard to functional outcome and treatment benefit. The addition of limited clinical information is needed to achieve predictive performance similar to an established prediction model.
Source: European Radiology
Authors: Sven PR Luijten, Aravind Ganesh, Adam P. Marcus, Paul Bentley, Daniel Rueckert, Scott Brown, Faysal Benali, Joachim Fladt, Fouzi Bala, Ibrahim Alhabli, Keith W. Muir, Jeffrey Saver, Andrew M. Demchuk, Tudor G. Jovin, Serge Bracard, Bruce CV Campbell, Francis Guillemin, Philip White, Michael D. Hill, Peter J. Mitchell, Charles BLM Majoie, Mayank Goyal, Diederik WJ Dippel, Aad van der Lugt, Theo van Walsum, Hester F. Lingsma, Daniel Bos
Institutions: Erasmus MC, University of Calgary, Hotchkiss Brain Institute, O'Brien Institute for Public Health, Imperial College London, TUM Klinikum, Technical University of Munich, Saint Louis Art Museum, Maastricht University Medical Centre, Maastricht University, Université de Tours, Centre Hospitalier Universitaire de Tours, Queen Elizabeth University Hospital, University of Glasgow, University of California, Los Angeles, University of Pittsburgh, University of Pittsburgh Medical Center, Inserm, Centre Hospitalier Régional et Universitaire de Nancy, Université de Lorraine, The Royal Melbourne Hospital, The University of Melbourne, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle University, Amsterdam University Medical Centers, University of Amsterdam
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $2,890
Funders: Medtronic, University of Pittsburgh, Université de Lorraine, Amsterdam University Medical Centers, Newcastle University, University of Glasgow, Institut National de la Santé et de la Recherche Médicale, Universiteit van Amsterdam, Newcastle upon Tyne Hospitals NHS Foundation Trust, Fakultet Medicinskih Nauka, Univerziteta U Kragujevcu, University of California, Los Angeles, Medical Center, University of Pittsburgh, Faculty of Medical Sciences, Newcastle University, Erasmus Universitair Medisch Centrum Rotterdam
OpenAlex ID: W7124136605