ArticleClinical interventions in aging2020
Identification of Predictors for Hemorrhagic Transformation in Patients with Acute Ischemic Stroke After Endovascular Therapy Using the Decision Tree Model.
Article in Clinical interventions in aging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it, 17 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
- Risk factors of hemorrhagic transformation in acute ischaemic stroke: A systematic review and meta-analysis.Frontiers in neurology · 2023Pooled it
- Symptomatic Intracranial Hemorrhage After Endovascular Stroke Treatment: External Validation of Prediction Models.Stroke · 2023Trial
- Relationship between blood inflammatory indices and prognosis in patients with intracerebral hemorrhage.American journal of translational research · 2025Article
- Integrating Neutrophil-To-Albumin Ratio and Triglycerides: A Novel Indicator for Predicting Spontaneous Hemorrhagic Transformation in Acute Ischemic Stroke Patients.CNS neuroscience & therapeutics · 2024Observational
- Functional Outcomes and Symptomatic Intracranial Hemorrhage After Endovascular Treatment in Acute Vertebrobasilar Artery Occlusions: External Validation of Prediction Models.Stroke (Hoboken, N.J.) · 2024Article
- A weakly supervised deep learning model integrating noncontrasted computed tomography images and clinical factors facilitates haemorrhagic transformation prediction after intravenous thrombolysis in acute ischaemic stroke patients.Biomedical engineering online · 2023Article
- Assessing the Relationship between LAMS and CT Perfusion Parameters in Acute Ischemic Stroke Secondary to Large Vessel Occlusion.Journal of clinical medicine · 2023Article
- Baseline Characteristics Associated with Good Collateral Status Using Hypoperfusion Index as an Outcome.Tomography (Ann Arbor, Mich.) · 2022Article
- Role of Neutrophil-Lymphocyte Ratio in the Prognosis of Acute Ischaemic Stroke After Reperfusion Therapy: A Systematic Review and Meta-analysis.Journal of central nervous system disease · 2022Article
- Prognostic Role of the Platelet-Lymphocyte Ratio in Acute Ischemic Stroke Patients Undergoing Reperfusion Therapy: A Meta-Analysis.Journal of central nervous system disease · 2022Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis study aimed to identify independent predictors for the risk of hemorrhagic transformation (HT) in arterial ischemic stroke (AIS) patients.
methodsConsecutive patients with AIS due to large artery occlusion in the anterior circulation treated with mechanical thrombectomy (MT) were enrolled in a tertiary stroke center. Demographic and medical history data, admission lab results, and Circle of Willis (CoW) variations were collected from all patients.
resultsAltogether, 90 patients were included in this study; among them, 34 (37.8%) had HT after MT. The final pruned decision tree (DT) model consisted of collateral score and platelet to lymphocyte ratios (PLR) as predictors. Confusion matrix analysis showed that 82.2% (74/90) were correctly classified by the model (sensitivity, 79.4%; specificity, 83.9%). The area under the ROC curve (AUC) was 81.7%. The DT model demonstrated that participants with collateral scores of 2-4 had a 75.0% probability of HT. For participants with collateral scores of 0-1, if PLR at admission was <302, participants had a 13.0% probability of HT; otherwise, participants had an 75.0% probability of HT. The final adjusted multivariate logistic regression analysis indicated that collateral score 0-1 (OR, 10.186; 95% CI, 3.029-34.248; p < 0.001), PLR (OR, 1.005; 95% CI, 1.001-1.010; p = 0.040), and NIHSS at admission (OR, 1.106; 95% CI, 1.014-1.205; p = 0.022) could be used to predict HT. The AUC for the model was 0.855, with 83.3% (75/90) were correctly classified (sensitivity, 79.4%; specificity, 87.3%). Less patients with HT achieved independent outcomes (mRS, 0-2) in 90 days (20.6% vs. 64.3%, p < 0.001). Rate of poor outcomes (mRS, 4-6) was significantly higher in patients with HT (73.5% vs. 19.6%; p < 0.001).
conclusionBoth the DT model and multivariate logistic regression model confirmed that the lower collateral status and the higher PLR were significantly associated with an increased risk for HT in AIS patients after MT. PLR may be one of the cost-effective and practical predictors for HT. Further prospective multicenter studies are needed to validate our findings.
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