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Hemorrhagic Transformation in Acute Ischemic Stroke: A Quantitative Systematic Review.

ABSTRACT: The prevalence and risk factors of hemorrhagic transformation (HT) after acute ischemic stroke HT have not been adequately delineated. We performed a systematic review and meta-analysis to identify English-language prospective observational MEDLINE and EMBASE-listed reports of acute ischemic stroke with HT published from 1985-2017. Studies that used the ECASS-2 definitions of hemorrhagic transformation subtypes, hemorrhagic infarction (HI), and parenchymal hematoma (PH) were included. Patients treated with intravenous thrombolysis with tissue plasminogen activator (IV-tPA) were compared with those who did not receive thrombolysis. A total of 65 studies with 17,259 patients met inclusion criteria. Overall, HT prevalence was 27%; 32% in patients receiving IV-tPA vs. 20% in those without. Overall PH prevalence was 9%; 12% in IV-tPA treated patients vs. 5% in those without. HT was associated with a history of atrial fibrillation (OR 2.94) and use of anticoagulants (OR 2.47). HT patients had higher NIHSS (Hedge's-G 0.96) and larger infarct volume (diffusion-weighted MRI, Hedge's-G 0.8). In IV-tPA treated patients, PH correlated with antiplatelet (OR 3) and statin treatment (OR 4). HT (OR 3) and PH (OR 8) were associated with a poor outcome at 90-day (mRS 5-6). Hemorrhagic transformation is a frequent complication of acute ischemic stroke and is associated with poor outcome. Recognition of risk factors for HT and PH may reduce their incidence and severity.

SUBMITTER: Honig A

PROVIDER: S-EPMC8910828 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

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Hemorrhagic Transformation in Acute Ischemic Stroke: A Quantitative Systematic Review.

Honig Asaf A Percy Jennifer J Sepehry Amir A AA Gomez Alejandra G AG Field Thalia S TS Benavente Oscar R OR

Journal of clinical medicine 20220222 5

The prevalence and risk factors of hemorrhagic transformation (HT) after acute ischemic stroke HT have not been adequately delineated. We performed a systematic review and meta-analysis to identify English-language prospective observational MEDLINE and EMBASE-listed reports of acute ischemic stroke with HT published from 1985-2017. Studies that used the ECASS-2 definitions of hemorrhagic transformation subtypes, hemorrhagic infarction (HI), and parenchymal hematoma (PH) were included. Patients t ...[more]

PMID: 35268253

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Project description:Introduction: Hemorrhagic transformation (HT) is a complication of reperfusion therapy for acute ischemic stroke. Blood-brain barrier (BBB) disruption is a crucial step toward HT; however, in clinical studies, there is still uncertainty about this relation. Hence, we conducted a systematic review and meta-analysis to summarize the current evidence. Methods: We performed systematic review and meta-analysis of observational studies from January 1990 to March 2020 about the relation between BBB disruption and HT in patients with acute ischemic stroke with both computed tomography (CT) and magnetic resonance (MR) assessment of BBB. The outcome of interest was HT at follow-up imaging evaluation (within 48 h from symptom onset). We pooled data from available univariate odds ratios (ORs) in random-effects models with DerSimonian-Laird weights and extracted cumulative ORs. Results: We included 30 eligible studies (14 with CT and 16 with MR), N = 2,609 patients, with 88% and 70% of patients included in CT and MR studies treated with acute stroke therapy, respectively. The majority of studies were retrospective and had high or unclear risk of bias. BBB disruption was measured with consistent methodology in CT studies, whereas in MR studies, there was more variability. All CT studies provided a BBB disruption cutoff predictive of HT. Four CT and 10 MR studies were included in the quantitative analysis. We found that BBB disruption was associated with HT with both CT (OR = 3.42; 95%CI = 1.62-7.23) and MR (OR = 9.34; 95%CI = 3.16-27.59). There was a likely publication bias particularly for MR studies. Conclusion: Our results confirm that BBB disruption is associated with HT in both CT and MR studies. Compared with MR, CT has been more uniformly applied in the literature and has resulted in more consistent results. However, more efforts are needed for harmonization of protocols and methodology for implementation of BBB disruption as a neuroradiological marker in clinical practice.

Project description:BackgroundEnlarged perivascular spaces (EPVS) are considered to be neuroimaging markers of cerebral small vessel disease (CSVD). It remains unknown whether EPVS are associated with hemorrhagic transformation (HT) after acute ischemic stroke (AIS). We performed this retrospective cohort study to explore the associations of EPVS with clinical risk factors and other CSVD imaging features, and to investigate the relationship between EPVS and HT in patients with AIS.MethodsAIS patients admitted within 24 hours of stroke onset between January 2016 and December 2017 were consecutively enrolled. EPVS, lacunes, and white matter hyperintensities (WMH) were rated with validated rating scales on magnetic resonance images after the stroke. HT was defined as hemorrhage determined by follow-up brain imaging during the patients' hospital stay. Logistic regression was used to determine the risk factors and associations with other CSVD markers of EPVS in the basal ganglia (BG) and centrum semiovale (CS) regions, and the impact of EPVS on HT was further explored.ResultsAmong 494 included patients (mean age 66.4 years, 58.1% male), 81 (16.4%) experienced HT. In the multivariate logistic analyses, increasing age [odd ratio (OR) 1.041, 95% confidence interval (CI), 1.017-1.066], hypertension (OR 2.174, 95% CI, 1.338-3.532), lacunar stroke (OR 1.968, 95% CI, 1.169-3.314), CS-EPVS (OR 2.474, 95% CI, 1.796-3.407), and periventricular WMH (OR 2.140, 95% CI, 1.441-3.176) were significantly associated with BG-EPVS; whereas only BG-EPVS (OR 4.349, 95% CI, 2.281-8.291) were independently related to CS-EPVS. After adjustment for potential variables, neither BG-EPVS (OR 0.674, 95% CI, 0.336-1.350) nor CS-EPVS (OR 0.792, 95% CI, 0.334-1.879) was significantly associated with the occurrence of HT.ConclusionsOur data showed that EPVS in the BG and CS regions were interrelated and had different risk factors in ischemic stroke patients. EPVS (particularly that in BG) were independently related to other CSVD markers. The presence or burden of EPVS was not significantly associated with HT after AIS.

Project description:Background: Thrombolytic therapy is essential for acute ischemic stroke (AIS) management but poses a risk of hemorrhagic transformation (HT), necessitating accurate prediction to optimize patient care. Methods: A comprehensive search was conducted across PubMed, Web of Science, Scopus, Embase, and Google Scholar, covering studies from inception until July 10, 2024. Studies were included if they used machine learning (ML) or deep learning algorithms to predict HT in AIS patients treated with thrombolysis. Exclusion criteria included studies involving endovascular treatments and those not evaluating model effectiveness. Data extraction and quality assessment were performed following PRISMA guidelines and using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) and Prediction Model Risk of Bias Assessment Tool (PROBAST) tools. Results: Out of 1943 identified records, 12 studies were included in the final analysis, encompassing 18 007 AIS patients who received thrombolytic therapy. The ML models demonstrated high predictive performance, with pooled area under the curve (AUC) values ranging from 0.79 to 0.95. Specifically, XGBoost models achieved AUCs of up to 0.953 and Artificial Neural Network (ANN) models reached up to 0.942. Sensitivity and specificity varied significantly, with the highest sensitivity at 0.90 and specificity at 0.99. Significant predictors of HT included age, glucose levels, NIH Stroke Scale (NIHSS) score, systolic and diastolic blood pressure, and radiomic features. Despite these promising results, methodological disparities and limited external validation highlighted the need for standardized reporting and further rigorous testing. Conclusion: ML techniques, especially XGBoost and ANN, show great promise in predicting HT following thrombolysis in AIS patients, enhancing risk stratification and clinical decision-making. Future research should focus on prospective study designs, standardized reporting, and integrating ML assessments into clinical workflows to improve AIS management and patient outcomes.

Dataset Information

Hemorrhagic Transformation in Acute Ischemic Stroke: A Quantitative Systematic Review.

Publications

Hemorrhagic Transformation in Acute Ischemic Stroke: A Quantitative Systematic Review.

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