Quantitative Serial CT Imaging-Derived Features Improve Prediction of Malignant Cerebral Edema after Ischemic Stroke
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ORIGINAL WORK
Quantitative Serial CT Imaging‑Derived Features Improve Prediction of Malignant Cerebral Edema after Ischemic Stroke Hossein Mohammadian Foroushani1, Ali Hamzehloo2, Atul Kumar2, Yasheng Chen2, Laura Heitsch3, Agnieszka Slowik4, Daniel Strbian5, Jin‑Moo Lee2, Daniel S. Marcus6 and Rajat Dhar2* © 2020 Springer Science+Business Media, LLC, part of Springer Nature and Neurocritical Care Society
Abstract Introduction: Malignant cerebral edema develops in a small subset of patients with hemispheric strokes, precipitat‑ ing deterioration and death if decompressive hemicraniectomy (DHC) is not performed in a timely manner. Predict‑ ing which stroke patients will develop malignant edema is imprecise based on clinical data alone. Head computed tomography (CT) imaging is often performed at baseline and 24-h. We determined the incremental value of incorpo‑ rating imaging-derived features from serial CTs to enhance prediction of malignant edema. Methods: We identified hemispheric stroke patients at three sites with NIHSS ≥ 7 who had baseline as well as 24-h clinical and CT imaging data. We extracted quantitative imaging features from baseline and follow-up CTs, including CSF volume, intracranial reserve (CSF/cranial volume), as well as midline shift (MLS) and infarct-related hypodensity volume. Potentially lethal malignant edema was defined as requiring DHC or dying with MLS over 5-mm. We built machine-learning models using logistic regression first with baseline data and then adding 24-h data including reduction in CSF volume (ΔCSF). Model performance was evaluated with cross-validation using metrics of recall (sen‑ sitivity), precision (predictive value), as well as area under receiver-operating-characteristic and precision-recall curves (AUROC, AUPRC). Results: Twenty of 361 patients (6%) died or underwent DHC. Baseline clinical variables alone had recall of 60% with low precision (7%), AUROC 0.59, AUPRC 0.15. Adding baseline intracranial reserve improved recall to 80% and AUROC to 0.82 but precision remained only 16% (AUPRC 0.28). Incorporating ΔCSF improved AUPRC to 0.53 (AUROC 0.91) while all imaging features further improved prediction (recall 90%, precision 38%, AUROC 0.96, AUPRC 0.66). Conclusion: Incorporating quantitative CT-based imaging features from baseline and 24-h CT enhances identifica‑ tion of patients with malignant edema needing DHC. Further refinements and external validation of such imagingbased machine-learning models are required. Keywords: Stroke, Cerebral edema, Imaging, Regression, Prediction models
*Correspondence: [email protected] 2 Department of Neurology, Washington University in St. Louis School of Medicine, 660 S Euclid Avenue, Campus Box 8111, Saint Louis, MO 63110, USA Full list of author information is available at the end of the article
This work was performed at Washington University in St. Louis.
Introduction Cerebral edema is one of the most important complications of acute ischemic stroke. The majority of those suffering a stroke exhibit increases in brain volu
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