Patterns and Clinical Implications of Hemorrhagic Transformation After Thrombolysis in Acute Ischemic Stroke: Results From the ENCHANTED Study

Influence of Asymptomatic Hemorrhagic Transformation After Endovascular Treatment on Stroke Outcome: A Population-Based Study

BACKGROUND AND OBJECTIVES

In patients with acute ischemic stroke (AIS), the impact of hemorrhagic transformation (HT) after endovascular treatment (EVT) on poorer stroke outcome is well established when associated with clinical deterioration. However, the influence of asymptomatic HT remains unclear. We aimed to examine the impact of asymptomatic HT after EVT on functional outcome and mortality.

METHODS

Drawing on Catalan (Spain) population-based prospective stroke registry data, we included patients from 10 comprehensive stroke centers with anterior circulation AIS (2017-2023) who underwent EVT, excluding patients without data on the presence of HT or functional outcome at 3 months of follow-up. HT was categorized as parenchymal hemorrhage (PH), hemorrhagic infarct (HI) types 1 and 2, and remote PH (rPH). Asymptomatic HT was defined as any HT not causing death or the NIH Stroke Scale (NIHSS) score to increase by ≥ 4 points. Functional outcome was centrally assessed using the modified Rankin Scale (mRS). The primary end point was a shift in the 3-month mRS score. After excluding symptomatic intracerebral hemorrhage (sICH), multivariable ordinal regression analyses (adjusted by age, mRS, baseline NIHSS score, baseline Alberta Stroke Program Early CT Score, and modified Thrombolysis In Cerebral Infarction score ≥2b) were performed to test for asymptomatic HT association with the primary end point.

RESULTS

We included 3,067 patients (72.0 ± 13.6 years, 50.7% women), 179 (5.8%) with sICH and 612 (20.0%) with asymptomatic HT. HT category frequencies were 8.9% HI1, 7.2% HI2, 4.4% PH1, 3.8% PH2, and 1.5% rPH. The percentage of asymptomatic patients showed a hierarchical distribution, ranging from 93.4% in HI1 to 25.0% in PH2. In the multivariable analysis, asymptomatic HT was associated with poorer outcomes (common odds ratio [cOR] 2.24, 95% CI 1.89-2.66) and higher mortality (adjusted odds ratio 1.50, 95% CI 1.17-1.91). In the sensitivity analyses, the association with functional outcome remained significant for each HT category, with asymptomatic PH2 showing the highest odds of poorer outcomes (cOR 3.15, 95% CI 1.46-6.83).

DISCUSSION

In patients with AIS undergoing EVT, asymptomatic HT was associated with poorer functional outcomes and higher mortality, suggesting that any HT, regardless of its clinical impact or radiologic category, should be considered as an additional EVT safety measure.

Acupuncture alleviates hemorrhagic transformation after delayed rt-PA treatment for acute ischemic stroke by regulating the mitophagy-NLRP3 inflammasome pathway

Background

The clinical application of recombinant tissue plasminogen activator (rt-PA) is significantly constrained by hemorrhagic transformation (HT), a common and severe complication following thrombolysis for ischemic stroke. Notably, the mitochondrial injury-mediated NLRP3 inflammasome plays a crucial role in HT after delayed rt-PA thrombolysis in acute ischemic stroke. Although acupuncture has demonstrated antioxidant and anti-inflammatory effects in acute cerebral infarction, its impact on delayed rt-PA thrombolysis, especially concerning mitophagy and the NLRP3 inflammasome, remains unclear. This study investigates how acupuncture protects against HT resulting from mitochondrial damage and NLRP3 inflammasome activation after delayed rt-PA thrombolysis in acute cerebral stroke.

Methods

We selected an embolic stroke model in rats and assessed brain injury after delayed rt-PA in acute ischemic stroke using neurological deficit score, volume of brain infarct, the permeability assay of the blood-brain barrier (BBB), and HT. Then, the levels of proteins and mRNA involved in mitophagy and the NLRP3 inflammasome pathway were measured by western blot and real-time PCR. The levels of interleukin-18 (IL-18) and interleukin-1β (IL-1β) were assessed using enzyme-linked immunosorbent assay (ELISA). Morphological changes in the BBB and mitochondria of neurons were observed via transmission electron microscopy.

Results

Acupuncture significantly improved neurological deficit scores, volume of cerebral infarction, BBB destruction, and HT in an embolic stroke model rat. Furthermore, acupuncture induced mitophagy and substantially downregulated the activity of the NLRP3 inflammasome. Additionally, the use of mitochondrial inhibitors significantly reversed the suppressive impact of acupuncture on the NLRP3 inflammasome.

Conclusion

Acupuncture can promote mitophagy and suppress NLRP3 inflammasome activation to decrease HT after delayed rt-PA therapy for acute ischemic stroke.

Traditional and machine learning models for predicting haemorrhagic transformation in ischaemic stroke: a systematic review and meta-analysis

BACKGROUND

Haemorrhagic transformation (HT) is a severe complication after ischaemic stroke, but identifying patients at high risks remains challenging. Although numerous prediction models have been developed for HT following thrombolysis, thrombectomy, or spontaneous occurrence, a comprehensive summary is lacking. This study aimed to review and compare traditional and machine learning-based HT prediction models, focusing on their development, validation, and diagnostic accuracy.

METHODS

PubMed and Ovid-Embase were searched for observational studies or randomised controlled trials related to traditional or machine learning-based models. Data were extracted according to Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist and risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Performance data for prediction models that were externally validated at least twice and showed low risk of bias were meta-analysed.

RESULTS

A total of 100 studies were included, with 67 focusing on model development and 33 on model validation. Among 67 model development studies, 44 were traditional model studies involving 47 prediction models (with National Institutes of Health Stroke Scale score being the most frequently used predictor in 35 models), and 23 studies focused on machine learning prediction models (with support vector machines being the most common algorithm, used in 10 models). The 33 validation studies externally validated 34 traditional prediction models. Regarding study quality, 26 studies were assessed as having a low risk of bias, 11 as unclear, and 63 as high risk of bias. Meta-analysis of 15 studies validating eight models showed a pooled area under the receiver operating characteristic curve of approximately 0.70 for predicting HT.

CONCLUSION

While significant progress has been made in developing HT prediction models, both traditional and machine learning-based models still have limitations in methodological rigour, predictive accuracy, and clinical applicability. Future models should undergo more rigorous validation, adhere to standardised reporting frameworks, and prioritise predictors that are both statistically significant and clinically meaningful. Collaborative efforts across research groups are essential for validating these models in diverse populations and improving their broader applicability in clinical practice.

SYSTEMATIC REVIEW REGISTRATION

International Prospective Register of Systematic Reviews (CRD42022332816).