Back to Search Start Over

Development and validation of a nomogram for the risk prediction of malignant cerebral edema after acute large hemispheric infarction involving the anterior circulation

Authors :
Wei Xie
Xiaoming Ma
Geman Xu
Yumei Wang
Wendie Huang
Meng Liu
Shiying Sheng
Jie Yuan
Jing Wang
Source :
Frontiers in Neurology, Vol 14 (2023)
Publication Year :
2023
Publisher :
Frontiers Media S.A., 2023.

Abstract

BackgroundMalignant 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.MethodsThis 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.ResultsAmong the 312 patients with LHI, 120 developed MCE. The following eight factors were independently associated with MCE: Glasgow Coma Scale score (p = 0.007), baseline National Institutes of Health Stroke Scale score (p = 0.006), Alberta Stroke Program Early CT Score (p < 0.001), admission monocyte count (p = 0.004), white blood cell count (p = 0.002), HbA1c level (p < 0.001), history of hypertension (p = 0.027), and history of atrial fibrillation (p = 0.114). These characteristics were further used to establish a nomogram for predicting prognosis. The nomogram achieved an AUC-ROC of 0.89 (95% CI, 0.82–0.96).ConclusionOur 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.

Details

Language :
English
ISSN :
16642295
Volume :
14
Database :
Directory of Open Access Journals
Journal :
Frontiers in Neurology
Publication Type :
Academic Journal
Accession number :
edsdoj.14a4ad32298462eb053d06120d762fe
Document Type :
article
Full Text :
https://doi.org/10.3389/fneur.2023.1221879