ArticleFrontiers in neurology2024
Nomogram prediction model for the risk of intracranial hemorrhagic transformation after intravenous thrombolysis in patients with acute ischemic stroke.
Article in Frontiers in neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 3 of them syntheses that pooled it.
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Who cites it
9 citing papers in PubMed, 3 syntheses or guidelines pooled it, 10 citations in OpenAlex.
- Early Predictive Accuracy of Machine Learning for Hemorrhagic Transformation in Acute Ischemic Stroke: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Traditional and machine learning models for predicting haemorrhagic transformation in ischaemic stroke: a systematic review and meta-analysis.Systematic reviews · 2025Pooled it
- Pooled it
- Association of Inflammatory Markers With Symptomatic Intracranial Hemorrhage, Malignant Cerebral Edema, and Poor Functional Recovery in Patients With Acute Ischemic Stroke Undergoing Endovascular Thrombectomy.Journal of the American Heart Association · 2026Article
- A quantitative CT perfusion-derived online dynamic nomogram for predicting hemorrhagic transformation after intravenous thrombolysis in acute ischemic stroke.European journal of radiology open · 2025Article
- Serum Biomarkers in Acute Ischemic Stroke: Clinical Applications and Emerging Insights.Journal of clinical medicine · 2025Review
- Delta radiomics modeling based on CTP for predicting hemorrhagic transformation after intravenous thrombolysis in acute cerebral infarction: an 8-year retrospective pilot study.Frontiers in neurology · 2025Article
- An Explainable Two-Stage Machine Learning Model for Predicting the Post-Thrombolysis Complications in Stroke Patients: A Multi-Center Study.Research (Washington, D.C.) · 2025Article
- Artificial intelligence in ischemic stroke images: current applications and future directions.Frontiers in neurology · 2024Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Hemorrhagic transformation (HT) after intravenous thrombolysis (IVT) might worsen the clinical outcomes, and a reliable predictive system is needed to identify the risk of hemorrhagic transformation after IVT. Methods: Retrospective collection of patients with acute cerebral infarction treated with intravenous thrombolysis in our hospital from 2018 to 2022. 197 patients were included in the research study. Multivariate logistic regression analysis was used to screen the factors in the predictive nomogram. The performance of nomogram was assessed on the area under the receiver operating characteristic curve (AUC-ROC), calibration plots and decision curve analysis (DCA). Results: A total of 197 patients were recruited, of whom 24 (12.1%) developed HT. In multivariate logistic regression model National Institute of Health Stroke Scale (NIHSS) (OR, 1.362; 95% CI, 1.161-1.652; Conclusion: Nomogram consisting of NIHSS, NT-pro BNP, NLR, SBP scores predict the risk of HT in AIS patients treated with IVT.
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