ArticleNeuropsychiatric disease and treatment2020
Risk Factors and a Nomogram for Predicting Intracranial Hemorrhage in Stroke Patients Undergoing Thrombolysis.
Article in Neuropsychiatric disease and treatment, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 4 syntheses or guidelines pooled it, 26 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
- Risk prediction models for intracranial hemorrhage in acute ischemic stroke patients receiving intravenous alteplase treatment: a systematic review.Frontiers in neurology · 2023Pooled 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
- Development and Validation of a New Nomogram for Predicting Medication Nonadherence Risk in a Chinese Hyperlipidemia Population.Patient preference and adherence · 2025Article
- Explainable machine learning model for predicting the outcome of acute ischemic stroke after intravenous thrombolysis.Frontiers in neurology · 2025Article
- 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
- Determinants of acute and subacute case-fatality in elderly patients with hypertensive intracerebral hemorrhage.Heliyon · 2023Article
- A Nomogram Model for Predicting the Polyphenol Content of Pu-Erh Tea.Foods (Basel, Switzerland) · 2023Article
- A Novel Nomogram to Predict Symptomatic Intracranial Hemorrhage in Ischemic Stroke Patients After Intravenous Thrombolysis.Therapeutics and clinical risk management · 2023Article
- A New Nomogram for Predicting the Risk of Intracranial Hemorrhage in Acute Ischemic Stroke Patients After Intravenous Thrombolysis.Frontiers in neurology · 2022Article
- Prognostic nomogram for the outcomes in acute stroke patients with intravenous thrombolysis.Frontiers in neuroscience · 2022Article
- A novel nomogram to predict hemorrhagic transformation in ischemic stroke patients after intravenous thrombolysis.Frontiers in neurology · 2022Article
- Development and validation of a nomogram to predict the 30-day mortality risk of patients with intracerebral hemorrhage.Frontiers in neuroscience · 2022Article
- The feasibility and accuracy of machine learning in improving safety and efficiency of thrombolysis for patients with stroke: Literature review and proposed improvements.Frontiers in neurology · 2022Review
- Levels of Fibrin Degradation Products at Admission With Acute Ischemic Stroke Correlate With the NIH Stroke Scale Score 1 h After Intravenous Thrombolysis.Frontiers in neurology · 2021Article
- Nomogram-based prediction of hemorrhagic transformation risk integrating platelet-to-white blood cell ratio in patients with acute ischemic stroke after intravenous thrombolysis.Frontiers in neurologyArticle
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeIdentifying stroke patients at risk of postthrombolysis intracranial hemorrhage (ICH) in the clinical setting is essential. We aimed to develop and evaluate a nomogram for predicting the probability of ICH in acute ischemic stroke patients undergoing thrombolysis. PATIENTS AND
methodsA retrospective observational study was conducted using data from 345 patients at a single center. The patients were randomly dichotomized into training (2/3; n=233) and validation (1/3; n=112) sets. A prediction model was developed by using a multivariable logistic regression analysis.
resultsThe nomogram comprised three variables: the presence of atrial fibrillation (odds ratio [OR]: 4.92, 95% confidence interval [CI]: 2.09-11.57), the National Institutes of Health Stroke Scale (NIHSS) score (OR: 1.11, 95% CI: 1.04-1.18) and the glucose level on admission (OR: 1.27, 95% CI: 1.08-1.50). The areas under the receiver operating characteristic curve of the nomogram for the training and validation sets were 0.828 (0.753-0.903) and 0.801 (0.690-0.911), respectively. The Hosmer-Lemeshow test revealed good calibration in both the training and validation sets (P = 0.509 and P = 0.342, respectively). The calibration plot also demonstrated good agreement. A decision curve analysis demonstrated that the nomogram was clinically useful.
conclusionWe developed an easy-to-use nomogram model to predict ICH, and the nomogram may provide risk assessments for subsequent treatment in stroke patients undergoing thrombolysis.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.