ArticleFrontiers in neuroscience2022
Prognostic nomogram for the outcomes in acute stroke patients with intravenous thrombolysis.
Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis 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
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning prediction of motor function in chronic stroke patients: a systematic review and meta-analysis.Frontiers in neurology · 2023Pooled it
- ANGWC Nomogram for Predicting Poor 3-month Outcome after Intravenous Thrombolysis in Young and Middle-Aged Patients with First-Ever Stroke.Thrombosis and haemostasis · 2026Article
- Predictors of unfavorable 3-month functional outcome following intravenous thrombolysis with alteplase in anterior circulation acute ischemic stroke: a prospective cohort study.Frontiers in neuroscience · 2026Article
- Development of a nomogram prediction model for thrombolytic outcomes in acute ischemic stroke.BMC neurology · 2025Article
- Risk prediction model for poor prognosis after intravenous thrombolysis among ischemic stroke patients aged ≥ 80 years and analysis of follow-up.Scientific reports · 2025Article
- Predictive Value of the Modified Comprehensive Immunoinflammatory Indices for Hemorrhagic Transformation in Ischemic Stroke Patients Undergoing Thrombolysis: A Retrospective Study.International journal of general medicine · 2025Article
- A new nomogram for predicting 90-day outcomes of intravenous thrombolysis in patients with acute ischaemic stroke.Frontiers in neurology · 2025Article
- Article
- Integrated single cell-RNA sequencing and Mendelian randomization for ischemic stroke and metabolic syndrome.iScience · 2024Article
- Development and validation of outcome prediction model for reperfusion therapy in acute ischemic stroke using nomogram and machine learning.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2024Article
- Predicting ineffective thrombolysis in acute ischemic stroke with clinical and biochemical markers.Scientific reports · 2024Article
- Predictors of early neurological deterioration in patients with acute ischemic stroke.Frontiers in neurology · 2024Article
- Cuproptosis-related genes are involved in immunodeficiency following ischemic stroke.Archives of medical science : AMS · 2024Article
- Predictive value of the BDH2-MN2 nomogram model for prognosis at 3 months after receiving intravenous thrombolysis in patients with acute ischemic stroke.Archives of medical science : AMS · 2024Article
- Prediction of long-term mortality in patients with ischemic stroke based on clinical characteristics on the first day of ICU admission: An easy-to-use nomogram.Frontiers in neurology · 2023Article
- Risk factors and a model for prognosis prediction after intravenous thrombolysis with alteplase in acute ischemic stroke based on propensity score matching.International journal of immunopathology and pharmacologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and purpose: The prediction of neurological outcomes in ischemic stroke patients is very useful in treatment choices, as well as in post-stroke management. This study is to develop a convenient nomogram for the bedside evaluation of stroke patients with intravenous thrombolysis. Materials and methods: We reviewed all enrolled stroke patients with intravenous thrombolysis retrospectively. Favorable outcome was defined as modified Rankin Score (mRs) less than 2 at 90 days post thrombolysis. We compared the clinical characteristics between patients with favorable outcome and poor outcome. Then, we applied logistic regression models and compared their predictability. Results: A total of 918 patients were enrolled in this study, 448 patients from one hospital were included to develop a nomogram, whereas 470 patients from the other hospital were used for the external validation. Associated risk factors were identified by multivariate logistic regression. The nomogram was validated by the area under the receiver operating characteristic curve (AUC). A nomogram was developed with baseline NIHSS, blood sugar, blood cholesterol level, part-and full anterior circulation infarction (OCSP type). The AUC was 0.767 (95% CI 0.653-0.772) and 0.836 (95% CI 0.697-0.847) in the derivation and external validation cohorts, respectively. The calibration plot for the probability of severe neurological outcome showed an optimal agreement between the prediction by nomogram and actual observation in both derivation and validation cohorts. Conclusion: A convenient outcome evaluation nomogram for patients with intravenous thrombolysis was developed, which could be used by physicians in making clinical decisions and predicting patients' prognosis.
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.