ArticleScientific reports2024
Development and validation of a nomogram for predicting early acute hydrocephalus after spontaneous intracerebral hemorrhage: a single-center retrospective study.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Topographical Patterns of Intraventricular Hemorrhage as a Novel Prognostic Imaging Biomarker in Intracerebral Hemorrhage.CNS neuroscience & therapeutics · 2026Article
- Do conventional intracerebral hemorrhage functional scores apply to mild-to-moderate hemorrhage?Frontiers in neurology · 2026Article
- Development and Internal Validation of an Inflammatory-Coagulation Biomarker-Enhanced Nomogram for 90-Day Outcome After Deep Hypertension-Related Intracerebral Hemorrhage.Neuropsychiatric disease and treatment · 2026Article
- Metabolic dysfunction-associated fatty liver disease exacerbates hematoma expansion in intracerebral hemorrhage: an explainable machine learning approach.Frontiers in endocrinology · 2026Article
- The predictive value of the leuko-glycemic index in spontaneous intracerebral hemorrhage patients undergoing neurosurgical operation: a retrospective cohort study.Frontiers in neurology · 2026Article
- Hematoma expansion in intracerebral hemorrhage: neuroimaging biomarkers, radiomics-based prediction models, and clinical translation.Frontiers in human neuroscience · 2026Review
- Development and validation of a clinical-radiomics nomogram for predicting 180-day functional outcomes in patients with spontaneous thalamic hemorrhage.Neurosurgical review · 2025Article
- Clinical characteristics and prognostic analysis of patients with hydrocephalus after hypertensive cerebral haemorrhage: a retrospective case-control study.Neurosurgical review · 2025Article
- Predictive value of the admission IBIL/TBIL ratio for poor prognosis after surgical treatment of intracerebral hemorrhage.Frontiers in neurologyArticle
- Development and internal validation of a machine learning model for predicting intracranial infection after spontaneous intracerebral hemorrhage: a two-center retrospective study.Frontiers in neurologyArticle
- Early multimodal neuromonitoring is associated with outcomes in critically ill patients with hemorrhagic stroke.Frontiers in neurologyArticle
- Machine learning integration of routine inflammatory biomarkers for predicting remote punctate ischemic lesions following intracerebral hemorrhage: a single-center retrospective study.Frontiers in neurologyArticle
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6 authors.
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Abstract
Acute hydrocephalus is a severe complication that may occur early after an intracerebral hemorrhage (ICH). However, clinical factors predicting the occurrence of acute hydrocephalus have rarely been studied. This study aimed to establish a nomogram model to predict early acute hydrocephalus after ICH. We retrospectively analyzed the data of 930 patients with ICH who were treated at our hospital between January 2017 and May 2024. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to screen for risk factors for acute hydrocephalus, and stepwise logistic regression analysis was used to construct the prediction model, which was visualized using a nomogram. Data were randomly divided into training (n = 652) and test (n = 278) sets at a 7:3 ratio. A total of 930 patients were included, of whom 123 (13.2%) developed acute hydrocephalus within 6 h of being diagnosed with ICH. Univariate analysis revealed that 11 indicators were associated with acute hydrocephalus. In the training set, LASSO and stepwise logistic regression analyses identified four independent risk factors that were used to establish a prediction model. These were the modified Graeb score, age, infratentorial hemorrhage > 15 mL, and thalamic hemorrhage > 15 mL. A graphical nomogram was then developed. The area under the receiver operating characteristic (ROC) curve was 0.974 (95% confidence interval 0.961-0.987). In the Hosmer-Lemeshow test, the p-value was 0.887. The mean absolute error of the calibration plot was 0.012. The decision curve analysis (DCA) validated the fitness and clinical application value of this nomogram. Internal validation showed the test set was in good accordance with the training set. The nomogram prediction model showed good accuracy and could be used to predict the risk of early acute hydrocephalus after ICH, thereby aiding neurologist in making rapid clinical decisions.
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