ArticleTurkish journal of emergency medicine
Predictors of 7-day symptomatic hemorrhagic transformation in patients with acute ischemic stroke and proposal of a novel screening tool: A retrospective cohort study.
Article in Turkish journal of emergency medicine. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- 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
- MSKT: multimodal data fusion for improved nursing management in hemorrhagic stroke.PeerJ. Computer science · 2025Article
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Authors and funding
7 authors.
Funding
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Abstract
objectivesHemorrhagic transformation (HT) is significantly related to poor neurological outcomes and mortality. Although variables and models that predict HT have been reported in the literature, the need for a model with high diagnostic performance continues. We aimed to propose a model that can accurately predict symptomatic HT within 7 days of acute ischemic stroke (AIS).
methodsPatients with AIS admitted to the emergency department of a tertiary training and research hospital between November 07, 2021, and August 26, 2022, were included in this single-center retrospective study. For the model, binary logistics with the forced-entry method was used and the model was validated with 3-fold cross-validation. After the final model was created, the optimal cutoff point was determined with Youden's index. Another cut-off point was determined at which the sensitivity was the highest.
resultsThe mean age of the 423 patients included in the study was 70 (60-81) and 53.7% (
conclusionThe diagnostic performance of our model was satisfactory and it seems to be promising for symptomatic HT. External validation studies are required to implement our results into clinical use.
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