ABSTRACT NUMBER: ESOC2026A1206 PREDICTING IN-HOSPITAL OUTCOMES IN ACUTE CEREBRAL VENOUS THROMBOSIS: THE CLOT-VENUS STRATIFICATION SCORE

Leonardo Cruz-Criollo; Aaron Rodriguez-Calienes; Eric Kontowicz; Anderson Brito; Nashwa Abdelhakim; Vanessa Cano Nigenda; Andres Alberto Mercado; Brian J Smith; Antonio Arauz Gongora; Santiago Ortega-Gutierrez

doi:10.1093/esj/aakag023.595

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ABSTRACT NUMBER: ESOC2026A1206 PREDICTING IN-HOSPITAL OUTCOMES IN ACUTE CEREBRAL VENOUS THROMBOSIS: THE CLOT-VENUS STRATIFICATION SCORE

Leonardo Cruz-Criollo, Aaron Rodriguez-Calienes, Eric Kontowicz, Anderson Brito, Nashwa Abdelhakim, Vanessa Cano Nigenda, Andres Alberto Mercado, Brian J Smith, Antonio Arauz Gongora and Santiago Ortega-Gutierrez

European stroke journal, Vol.11(Suppl 1), pp.i343-i344

05/06/2026

DOI: 10.1093/esj/aakag023.595

PMCID: PMC13146094

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Abstract

Background and aims Early identification of high-risk Acute Cerebral Venous Thrombosis (CVT) patients is vital for aggressive management. We developed and validated machine-learning models to predict in-hospital mortality and poor functional outcome (mRS 3-6) at discharge. Methods This retrospective observational cohort study of patients diagnosed with acute CVT from the international CoLlabOraTion on Cerebral VENoUs Thrombosis Study (CLOT-VENUS) registry (2004-2024), including two comprehensive stroke centers in the USA and Mexico. Most relevant admission clinical, imaging, and laboratory results were collected. We developed machine learning models using LASSO and a gradient-boosted model (GBM), to predict poor functional outcome (mRS 3-6) at discharge and in-hospital mortality. We then developed nomograms for the outcomes. Parsimonious predictors were selected via Bayesian optimization and recursive feature elimination. Models were trained on two-thirds of the available data and tested on the remaining one-third holdout data Results Of 432 patients, 394 met the inclusion criteria. Median age was 40 years [IQR 28-55], and 65% were female. Volume of ICH, Glasgow coma scale, mRS baseline, age, cerebral edema, anemia, leucocytes, hemoglobin, and NLR were among the important predictors identified for discharge score and in-hospital mortality. The final model achieved high predictive accuracy, with ROC-AUCs of 0.87 (95 %CI: 0.83–0.91) for discharge mRS (3-6) and 0.87 (95% CI: 0.8 – 0.94) for in-hospital mortality Conclusions The new scales provide a rapid and reliable tool for risk-stratifying patients with acute CVT upon admission, helping clinicians identify candidates for intensive monitoring and aggressive interventions to prevent secondary injury.

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Title: Subtitle: ABSTRACT NUMBER: ESOC2026A1206 PREDICTING IN-HOSPITAL OUTCOMES IN ACUTE CEREBRAL VENOUS THROMBOSIS: THE CLOT-VENUS STRATIFICATION SCORE
Creators: Leonardo Cruz-Criollo - University of Iowa
Aaron Rodriguez-Calienes - University of Iowa
Eric Kontowicz - University of Iowa
Anderson Brito - University of Iowa
Nashwa Abdelhakim - University of Iowa
Vanessa Cano Nigenda - Instituto Nacional de Neurología y Neurocirugía
Andres Alberto Mercado - Instituto Nacional de Neurología y Neurocirugía
Brian J Smith - University of Iowa
Antonio Arauz Gongora - Instituto Nacional de Neurología y Neurocirugía
Santiago Ortega-Gutierrez - University of Iowa
Resource Type: Abstract
Publication Details: European stroke journal, Vol.11(Suppl 1), pp.i343-i344
DOI: 10.1093/esj/aakag023.595
PMCID: PMC13146094
ISSN: 2396-9873
eISSN: 2396-9881
Publisher: Oxford University Press
Language: English
Date published: 05/06/2026
Academic Unit: Neurology; Radiology; Epidemiology; Emergency Medicine; Iowa Neuroscience Institute; Biostatistics; Neurosurgery; Holden Comprehensive Cancer Center
Record Identifier: 9985161449002771

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