ABSTRACT NUMBER: ESOC2026A2243 PREDICTING STROKE MIMICS IN ACUTE STROKE PATHWAYS: CLINICAL AND EARLY IMAGING PREDICTORS FROM AN 18-MONTH MULTICENTRE PROSPECTIVE COHORT

Abstract Background and aims Distinguishing acute ischemic stroke (AIS) from stroke mimics (SM) remains challenging in emergency neurology, with up to one-third of suspected cases ultimately identified as mimics despite standardised pathways and rapid imaging. Misclassification may lead to unnecessary thrombolysis or delayed reperfusion. Unlike previous single-domain models, this study integrates bedside clinical and early imaging data from real-world acute stroke workflows. Methods This prospective multicentre study included 1,662 consecutive patients evaluated for suspected AIS (507 SM, 30.5%). An elastic-net logistic regression model was trained on 1,411 patients using routinely collected clinical variables (demographics, comorbidities, medications, NIHSS, FAST, FAST-Plus) and early imaging data (Brainomix e-Stroke v10.1). NCCT was classified as positive only for ischemic changes, CTA as positive for vessel occlusion or stenosis, and CTP as positive/negative with a “not performed” category, including discordant results. Model performance was assessed on an independent test set (n = 251). Results The combined model achieved strong discrimination (AUC 0.89; 95% CI: 0.85–0.93). Predictors of SM included negative NCCT and CTA, unperformed CTP, epilepsy, and headache at presentation, and missing NIHSS data. Positive NCCT, CTA, CTP, smoking, and antiplatelet therapy predicted AIS/TIA. At the optimal ROC threshold, sensitivity for AIS/TIA was 75.9% and specificity 83.1% (PPV 91.0%, NPV 60.4%). Conclusions A model integrating routine clinical and early imaging data reliably differentiates AIS/TIA from SM. By bridging bedside assessment and imaging confirmation, it supports safer and faster decision-making in acute stroke care. Supported by the MoH of the Czech Republic (NU23-04-00336), and by MEYS, LRI CZECRIN (LM2023049). Conflict of interest Katerina Dvornikova:nothing to disclose, Veronika Kunesova: nothing to disclose, Svatopluk Ostry: nothing to disclose, Petr Novobilsky: nothing to disclose, Lenka Bártová: nothing to disclose, Linda Machova: nothing to disclose, Beata Stanková:nothing to disclose, István Szegedi:nothing to disclose, Adéla Kondé:nothing to disclose, Jana Licha:nothing to disclose, Kristyna Janotova:nothing to disclose, Martin Reiser:nothing to disclose, Tomas Jonszta:nothing to disclose, Jaroslav Havelka:nothing to disclose, Ondrej Volny: nothing to disclose, Michal Bar1:nothing to disclose

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Publication Details

Journal
European Stroke Journal
Published
2026-05-01
DOI
https://doi.org/10.1093/esj/aakag023.1774
Primary Topic
Acute Ischemic Stroke Management
Type
article
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article

ABSTRACT NUMBER: ESOC2026A2243 PREDICTING STROKE MIMICS IN ACUTE STROKE PATHWAYS: CLINICAL AND EARLY IMAGING PREDICTORS FROM AN 18-MONTH MULTICENTRE PROSPECTIVE COHORT

Veronika Kunešová, István Szegedi, Petr Novobílský, Jana Lichá et al.
European Stroke Journal
Acute Ischemic Stroke Management
article

ABSTRACT NUMBER: ESOC2026A2243 PREDICTING STROKE MIMICS IN ACUTE STROKE PATHWAYS: CLINICAL AND EARLY IMAGING PREDICTORS FROM AN 18-MONTH MULTICENTRE PROSPECTIVE COHORT

Veronika Kunešová, István Szegedi, Petr Novobílský, Jana Lichá, Svatopluk Ostrý, Martin Reiser, Adéla Kondé, Kristýna Janotová, Lenka Bártová, Michal Bar, Linda MacHová, Jaroslav Havelka, Beata Stanková, Kateřina Dvorníková, Tomas Jonszta, Ondrej Volny
article en

Abstract

Abstract Background and aims Distinguishing acute ischemic stroke (AIS) from stroke mimics (SM) remains challenging in emergency neurology, with up to one-third of suspected cases ultimately identified as mimics despite standardised pathways and rapid imaging. Misclassification may lead to unnecessary thrombolysis or delayed reperfusion. Unlike previous single-domain models, this study integrates bedside clinical and early imaging data from real-world acute stroke workflows. Methods This prospective multicentre study included 1,662 consecutive patients evaluated for suspected AIS (507 SM, 30.5%). An elastic-net logistic regression model was trained on 1,411 patients using routinely collected clinical variables (demographics, comorbidities, medications, NIHSS, FAST, FAST-Plus) and early imaging data (Brainomix e-Stroke v10.1). NCCT was classified as positive only for ischemic changes, CTA as positive for vessel occlusion or stenosis, and CTP as positive/negative with a “not performed” category, including discordant results. Model performance was assessed on an independent test set (n = 251). Results The combined model achieved strong discrimination (AUC 0.89; 95% CI: 0.85–0.93). Predictors of SM included negative NCCT and CTA, unperformed CTP, epilepsy, and headache at presentation, and missing NIHSS data. Positive NCCT, CTA, CTP, smoking, and antiplatelet therapy predicted AIS/TIA. At the optimal ROC threshold, sensitivity for AIS/TIA was 75.9% and specificity 83.1% (PPV 91.0%, NPV 60.4%). Conclusions A model integrating routine clinical and early imaging data reliably differentiates AIS/TIA from SM. By bridging bedside assessment and imaging confirmation, it supports safer and faster decision-making in acute stroke care. Supported by the MoH of the Czech Republic (NU23-04-00336), and by MEYS, LRI CZECRIN (LM2023049). Conflict of interest Katerina Dvornikova:nothing to disclose, Veronika Kunesova: nothing to disclose, Svatopluk Ostry: nothing to disclose, Petr Novobilsky: nothing to disclose, Lenka Bártová: nothing to disclose, Linda Machova: nothing to disclose, Beata Stanková:nothing to disclose, István Szegedi:nothing to disclose, Adéla Kondé:nothing to disclose, Jana Licha:nothing to disclose, Kristyna Janotova:nothing to disclose, Martin Reiser:nothing to disclose, Tomas Jonszta:nothing to disclose, Jaroslav Havelka:nothing to disclose, Ondrej Volny: nothing to disclose, Michal Bar1:nothing to disclose

European Stroke JournalVol. 11(Supplement_1)
VSB - Technical University of Ostrava (CZ), University of Ostrava (CZ), Masaryk University (CZ), University Hospital Ostrava (CZ), Nemocnice České Budějovice (CZ)
Peace, Justice and strong institutions
Openalex Percentile: Top 8%
Acute Ischemic Stroke Management
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