ArticleArquivos de neuro-psiquiatria2022
Accuracy of predictive scores of hemorrhagic transformation in patients with acute ischemic stroke.
Article in Arquivos de neuro-psiquiatria, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- Traditional and machine learning models for predicting haemorrhagic transformation in ischaemic stroke: a systematic review and meta-analysis.Systematic reviews · 2025Pooled it
- Cerebrolysin, hemorrhagic transformation, and anticoagulation timing after reperfusion therapy in stroke:Frontiers in pharmacology · 2025Article
- Serum biomarkers to predict hemorrhagic transformation and ischemic stroke outcomes in a prospective cohort study.Journal of medicine and life · 2023Article
- Heterogeneous treatment effects of Cerebrolysin as an early add-on to reperfusion therapy:Frontiers in pharmacology · 2023Article
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Authors and funding
6 authors at 6 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHemorrhagic transformation (HT) is a complication in ischemic strokes, regardless of use of reperfusion therapy (RT). There are many predictive scores for estimating the risk of HT. However, most of them include patients also treated with RT. Therefore, this may lead to a misinterpretation of the risk of HT in patients who did not undergo RT.
objectiveWe aimed to review published predictive scores and analyze their accuracy in our dataset.
methodsWe analyzed the accuracy of seven scales. Our dataset was derived from a cohort of 1,565 consecutive patients from 2015 to 2017 who were admitted to a comprehensive stroke center. All patients were evaluated with follow-up neuroimaging within seven days. Comparison of area under the curve (AUC) was performed on each scale, to analyze differences between patients treated with recombinant tissue plasminogen activator (tPA) and those without this treatment.
resultsOur dataset provided enough data to assess seven scales, among which six were used among patients with and without tPA treatment. HAT (AUC 0.76), HTI (0.73) and SEDAN (0.70) were the most accurate scores for patients not treated with tPA. SPAN-100 (0.55) had the worst accuracy in both groups. Three of these scores had different cutoffs between study groups.
conclusionsThe predictive scores had moderate to fair accuracy for predicting HT in patients treated with tPA. Three scales were more accurate for predicting HT in patients not treated with tPA. Through standardizing these characteristics and including more patients not treated with RT in a large multicenter series, accurate predictive scores may be created.
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