ArticleFrontiers in neurology2023
Development and validation of a nomogram for the risk prediction of malignant cerebral edema after acute large hemispheric infarction involving the anterior circulation.
Article in Frontiers in neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- Predictive Value of Machine Learning Models for Cerebral Edema Risk in Stroke Patients: A Meta-Analysis.Brain and behavior · 2025Pooled it
- [Risk Nomogram Prediction Model for Cerebral Edema in Patients With Hypertensive Intracerebral Hemorrhage].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2026Article
- Development and temporal validation of a prediction model for cognitive impairment in older adults with hearing loss based on the population health risk management framework.Frontiers in public health · 2026Article
- Clinical insights on "characteristics and impacts of large artery atherosclerosis (LAA) versus cardioembolism (CE) subtypes in large hemispheric infarction (LHI) of the middle cerebral artery (MCA)".Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Article
- Large artery atherosclerotic versus cardioembolism subtypes of large hemispheric infarction in the middle cerebral artery.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Article
- Development of a nomogram model for predicting dementia risk in the older adult population of Weifang, Shandong Province, China: based on the biopsychosocial model.Frontiers in public health · 2025Article
- Predicting upper limb motor dysfunction after ischemic stroke: a functional near-infrared spectroscopy-based nomogram model.Frontiers in neurology · 2025Article
- Development of a multimodal model combining radiomics and deep learning to predict malignant cerebral edema after endovascular thrombectomy.Frontiers in neurology · 2025Article
- Development and validation of an interpretable machine learning model to predict malignant cerebral edema after endovascular treatment in acute anterior circulation large vessel occlusion stroke.Frontiers in neurology · 2025Article
- An integrated nomogram combining clinical and radiomic features of hyperattenuated imaging markers to predict malignant cerebral edema following endovascular thrombectomy.Quantitative imaging in medicine and surgery · 2024Article
- Validation and refinement of a predictive nomogram using artificial intelligence: assessing in-hospital mortality in patients with large hemispheric cerebral infarction.Frontiers in neurology · 2024Article
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Malignant cerebral edema (MCE) is a life-threatening complication of large hemisphere infarction (LHI). Therefore, a fast, accurate, and convenient tool for predicting MCE can guide triage services and facilitate shared decision-making. In this study, we aimed to develop and validate a nomogram for the early prediction of MCE risk in acute LHI involving the anterior circulation and to understand the potential mechanism of MCE. Methods: This retrospective study included 312 consecutive patients with LHI from 1 January 2019 to 28 February 2023. The patients were divided into MCE and non-MCE groups. MCE was defined as an obvious mass effect with ≥5 mm midline shift or basal cistern effacement. Least absolute shrinkage and selection operator (LASSO) and logistic regression were performed to explore the MCE-associated factors, including medical records, laboratory data, computed tomography (CT) scans, and independent clinic risk factors. The independent factors were further incorporated to construct a nomogram for MCE prediction. Results: Among the 312 patients with LHI, 120 developed MCE. The following eight factors were independently associated with MCE: Glasgow Coma Scale score ( Conclusion: Our nomogram based on LASSO-logistic regression is accurate and useful for the early prediction of MCE after LHI. This model can serve as a precise and practical tool for clinical decision-making in patients with LHI who may require aggressive therapeutic approaches.
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