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Novel CT Image‐Based Intracerebral Bleeding Risk Score for Patients With Acute Ischemic Stroke Undergoing Thrombolysis

Fang, Shuangfang; Lei, Hanhan; Ambler, Gareth; Werring, David J; Huang, Huapin; Lin, Huiying; Wu, Xiaomin; ... Du, Hou-wei; + view all (2025) Novel CT Image‐Based Intracerebral Bleeding Risk Score for Patients With Acute Ischemic Stroke Undergoing Thrombolysis. Journal of the American Heart Association , 14 (4) , Article e037256. 10.1161/JAHA.124.037256. Green open access

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Abstract

Background: Symptomatic intracerebral hemorrhage (sICH) after intravenous recombinant tissue plasminogen activator in patients with acute ischemic stroke (AIS) remains a feared yet unpredictable complication. We aimed to develop and validate a new predictive model incorporating clinical variables and noncontrast head computed tomography imaging features to predict sICH in patients with AIS receiving intravenous recombinant tissue plasminogen activator. // Methods and Results: The predictive model was derived from 808 patients with AIS in the derivation cohort in Southeast China, based on multivariable logistic regression analysis. External validation was conducted in a validation cohort from Central China. Discrimination, calibration, and clinical usefulness of the predictive model were assessed. We observed 32 sICH events among 808 patients with AIS in the derivation cohort, and 21 sICH events out of 612 participants in the validation cohort. The variables in the predictive model included cerebral small vessel disease burden and early infarct signs on head computed tomography scan, atrial fibrillation, age, systolic blood pressure, and initial National Institutes of Health Stroke Scale score. The fitted model showed promising discrimination (optimism‐corrected C statistic of 0.80) and acceptable calibration (Hosmer and Lemeshow goodness of fit P=0.816) in the derivation cohort. External validation showed similar discrimination (C statistic 0.82 [95% CI, 0.72–0.91]) and calibration (Hosmer and Lemeshow goodness of fit P=0.866). // Conclusions: Our internally and externally validated prediction model for sICH in patients with AIS who received intravenous thrombolysis may facilitate individualized prediction for intracerebral bleeding risk after intravenous thrombolysis for acute ischemic stroke.

Type: Article
Title: Novel CT Image‐Based Intracerebral Bleeding Risk Score for Patients With Acute Ischemic Stroke Undergoing Thrombolysis
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1161/JAHA.124.037256
Publisher version: https://doi.org/10.1161/jaha.124.037256
Language: English
Additional information: Copyright © 2025 The Author(s). Published on behalf of the American Heart Association, Inc., by Wiley. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, https://creativecommons.org/licenses/by-nc-nd/4.0/, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
Keywords: Acute ischemic stroke; intravenous thrombolysis; nomograph; predictive model; symptomatic intracranial hemorrhage
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Brain Repair and Rehabilitation
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10205909
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