Prediction of Symptomatic Intracranial Hemorrhage after Intravenous Thrombolysis in Acute Ischemic Stroke: The Symptomatic Intracranial Hemorrhage Score

Early Predictive Accuracy of Machine Learning for Hemorrhagic Transformation in Acute Ischemic Stroke: Systematic Review and Meta-Analysis

BACKGROUND

Hemorrhagic transformation (HT) is commonly detected in acute ischemic stroke (AIS) and often leads to poor outcomes. Currently, there is no ideal tool for early prediction of HT risk. Recently, machine learning has gained traction in stroke management, prompting the exploration of predictive models for HT. However, systematic evidence on these models is lacking.

OBJECTIVE

In this study, we assessed the predictive capability of machine learning models for HT risk in AIS, aiming to inform the development of HT prediction tools.

METHODS

We conducted a thorough search of medical databases, such as Web of Science, Embase, Cochrane, and PubMed up until March 2025. The risk of bias was determined through the Prediction Model Risk of Bias Assessment Tool (PROBAST). Subgroup analysis was performed based on treatment backgrounds, diagnostic criteria, and types of HT.

RESULTS

A total of 83 eligible articles were included, containing 106 models and 88,197 patients with AIS with 9323 HT cases. There were 104 validation sets with a total c-index of 0.832 (95% CI 0.814-0.849), sensitivity of 0.82 (95% CI 0.79-0.84), and specificity of 0.78 (95% CI 0.74-0.81). Subgroup analysis indicated that the combined model achieved superior prediction accuracy. Moreover, we also analyzed the predictive performance of 6 mature models.

CONCLUSIONS

Currently, although several prediction methods for HT have been developed, their predictive values are not satisfactory. Fortunately, our findings suggest that machine learning methods, particularly those combining clinical features and radiomics, hold promise for improving predictive accuracy. Our meta-analysis may provide evidence-based guidance for the subsequent development of more efficient clinical predictive models for HT.

TRIAL REGISTRATION

PROSPERO CRD42024498997; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024498997.

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).

Platelet-to-neutrophil ratio predicts hemorrhagic transformation and unfavorable outcomes in acute ischemic stroke with intravenous thrombolysis

BACKGROUND

Acute ischemic stroke (AIS) retains a notable stance in global disease burden, with thrombolysis via recombinant tissue plasminogen activator (rtPA) serving as a viable management approach, albeit with variable outcomes and the potential for complications like hemorrhagic transformation (HT). The platelet-to-neutrophil ratio (P/NR) has been considered for its potential prognostic value in AIS, yet its capacity to predict outcomes following rtPA administration demands further exploration.

AIM

To elucidate the prognostic utility of P/NR in predicting HT and clinical outcomes following intravenous rtPA administration in AIS patients.

METHODS

Data from 418 AIS patients treated with intravenous rtPA at Thammasat University Hospital from January 2018 to June 2021 were retrospectively analyzed. The relationship between P/NR and clinical outcomes [early neurological deterioration (E-ND), HT, delayed ND (D-ND), and 3-mo outcomes] was scrutinized.

RESULTS

Notable variables, such as age, diabetes, and stroke history, exhibited statistical disparities when comparing patients with and without E-ND, HT, D-ND, and 3-mo outcomes. P/NR prognostication revealed an optimal cutoff of 43.4 with a 60.3% sensitivity and a 52.5% specificity for 90-d outcomes. P/NR prognostic accuracy was statistically significant for 90-d outcomes [area under the curve (AUC) = 0.562], D-ND (AUC = 0.584), and HT (AUC = 0.607).

CONCLUSION

P/NR demonstrated an association with adverse 3-mo clinical outcomes, HT, and D-ND in AIS patients post-rtPA administration, indicating its potential as a predictive tool for complications and prognoses. This infers that a diminished P/NR may serve as a novel prognostic indicator, assisting clinicians in identifying AIS patients at elevated risk for unfavorable outcomes following rtPA therapy.

External Validation and Updating of Published Models for Predicting 7-day Risk of Symptomatic Intracranial Hemorrhage after Receiving Alteplase for Acute Ischemic Stroke: A Retrospective Cohort Study

Background

Prediction scores for symptomatic intracranial hemorrhage (sICH) in acute ischemic stroke patients receiving thrombolytic therapy have been widely developed, but the external validation of these scores, especially in the Thai population, is lacking. This study aims to externally validate existing models and update the selected model to enhance its performance in our specific context.

Methods

This cohort study retrospectively collected data from medical records between 2013 and 2022. Acute ischemic stroke patients who received thrombolysis were included. All predictors were gathered at admission. External validation was performed on eight published prediction models; in addition, the observed and expected probabilities of sICH were compared. The most effective model for discrimination was then chosen for further updating using multivariable logistic regression and was bootstrapped for internal validation. Finally, a points-based system for clinical practice was developed from the optimism-corrected model.

Results

Fifty patients (10% of the 502 included cohort members) experienced sICH after undergoing thrombolysis. The SICH score outperformed the other seven models in terms of discrimination (area under the receiver operating characteristic [AuROC] curve = 0.74 [95% confidence interval {CI} 0.67 to 0.81]), but it still overstated risk (expected-to-observed outcomes [E/O] ratio = 1.7). Once updated, the optimism-corrected revised SICH model showed somewhat better calibration (E/O = 1 and calibration-in-the-large = 0), slightly worse underprediction in the moderate-to-high risk group (calibration slope = 1.152), and marginally better discrimination (AuROC = 0.78). The points-based system also demonstrated substantial agreement (88.1%) with the risk groups predicted by the logistic regression model (kappa statistic = 0.78).

Conclusion

Since the SICH score outperformed seven models in terms of discrimination, it was then modified to the Revised-SICH score, which predicted that patients with at least 5.5 points were at high risk of having sICH.

Symptomatic Intracranial Hemorrhage after Ischemic Stroke Treated with Bridging Revascularization Therapy

(1) Background: bridging revascularization therapy is now the standard of care in patients with ischemic stroke due to large vessel occlusion. This study aimed to determine the frequency of symptomatic intracranial hemorrhage (sICH) related to this treatment, and to assess contributing factors and patients' outcomes. (2) Methods: consecutive ischemic stroke patients treated with bridging therapy were prospectively enrolled. sICH (intracranial hemorrhage with an increase in NIHSS score of ≥4 points) was assessed on imaging at 24 h. The functional status of patients was measured at 6 months using the mRS score; (3) Results: 176 patients were included (mean age 68.7 ± 1.2 years, 52.3% women), among whom 15 (8.5%) had sICH. Patients with sICH had more frequent alcohol abuse (30.1% versus 9.7%, p = 0.023), prestroke use of dual antiplatelet therapy (14.3% versus 1.3%, p = 0.002), higher NIHSS scores at admission (median score 20.5 versus 15, p = 0.01), greater systolic blood pressure upon admission, more frequent vascular intracranial calcifications (p = 0.004), leukoaraiosis (p = 0.001), and intracranial atheroma (p = 0.02), and higher neutrophil-to-lymphocyte ratios (p = 0.02) and neutrophil-to-platelet ratios (p = 0.04). At 6-month follow-up, 9 (60%) patients with sICH died, versus 18% of patients without sICH (p < 0.001). Only 1 (7%) patient with sICH had a good functional outcome, defined as an mRS score of 0 to 2, versus 51% of patients without sICH. (4) Conclusions: one in twelve ischemic stroke patients treated with bridging therapy suffered sICH. Given the observed poor outcomes after sICH, further studies are required to better identify patients at risk to help clinicians in guiding therapeutic strategies.

Construction and validation of a nomogram model to predict symptomatic intracranial hemorrhage after intravenous thrombolysis in severe white matter lesions

Cerebral white matter lesions (WMLs) increase the risk of bleeding after intravenous thrombolysis (IVT) but are also considered to require IVT. Its risk factors and predictive models are still poorly studied. The aim of this study is to develop a clinically applicable model for post-IVT haemorrhage. It offers the possibility to prevent symptomatic intracranial hemorrhage (sICH) in patients with IVT in severe WMLs. A large single-center observational study conducted a retrospective analysis of IVT in patients with severe WMLs from January 2018 to December 2022. Univariate and multi-factor logistic regression results were used to construct nomogram model, and a series of validations were performed on the model. More than 2,000 patients with IVT were screened for inclusion in this study after cranial magnetic resonance imaging evaluation of 180 patients with severe WMLs, 28 of whom developed sICH. In univariate analysis, history of hypertension (OR 3.505 CI 2.257-4.752, p = 0.049), hyperlipidemia (OR 4.622 CI 3.761- 5.483, p < 0.001), the NIHSS score before IVT (OR 41.250 CI 39.212-43.288, p < 0.001), low-density lipoprotein levels (OR 1.995 CI 1.448-2.543, p = 0.013), cholesterol levels (OR 1.668 CI 1.246-2.090, p = 0.017), platelet count (OR 0.992 CI 0.985-0.999, p = 0.028), systolic blood pressure (OR 1.044 CI 1.022-1.066, p < 0.001), diastolic blood pressure (OR 1.047 CI 1.024-1.070, p < 0.001) were significantly associated with sICH. In a multifactorial analysis, the NIHSS score before IVT (OR 94.743 CI 92.311-97.175, p < 0.001), and diastolic blood pressure (OR 1.051 CI 1.005-1.097, p = 0.033) were considered to be significantly associated with sICH after IVT as risk factors for the occurrence of sICH. The four most significant factors from logistic regression are subsequently fitted to create a predictive model. The accuracy was verified using ROC curves, calibration curves, decision curves, and clinical impact curves, and the model was considered to have high accuracy (AUC 0.932, 95% 0.888-0.976). The NHISS score before IVT and diastolic blood pressure are independent risk factors for sICH after IVT in patients with severe WMLs. The models based on hyperlipidemia, the NIHSS score before IVT, low-density lipoprotein and diastolic blood pressure are highly accurate and can be applied clinically to provide a reliable predictive basis for IVT in patients with severe WMLs.

Post-thrombolysis early neurological deterioration occurs with or without hemorrhagic transformation in acute cerebral infarction: risk factors, prediction model and prognosis

Objectives

Early neurological deterioration (END) after ischemic stroke is a severe clinical event and can be caused by hemorrhagic and ischemic injury. We studied the difference between the risk factors of END occurs with or without hemorrhagic transformation after intravenous thrombolysis.

Materials and methods

Consecutive cerebral infarction patients who underwent intravenous thrombolysis from 2017 to 2020 in our hospital were retrospectively recruited. END was defined as a ≥2 points increase on 24-h National Institutes of Health Stroke Scale (NIHSS) score after therapy compared with the best neurological status after thrombolysis and divided into two types based on the computed tomography (CT): symptomatic intracranial hemorrhage (ENDh) and non-hemorrhagic factors (ENDn). Potential risk factors of ENDh and ENDn were assessed by multiple logistic regression and applied to establish the prediction model.

Results

A total of 195 patients were included. In multivariate analysis, the previous history of cerebral infarction (odds ratio [OR],15.19; 95% confidence interval [CI],1.43-161.17; P = 0.025), previous history of atrial fibrillation (OR,8.43; 95%CI,1.09-65.44; P = 0.043), higher baseline NIHSS score (OR,1.19; 95%CI,1.03-1.39; P = 0.022) and higher alanine transferase level (OR,1.05; 95%CI, 1.01-1.10; P = 0.016) were independently associated with ENDh. While higher systolic blood pressure (OR,1.03; 95%CI,1.01-1.05; P = 0.004), higher baseline NIHSS score (OR,1.13; 95%CI,2.86-27.43; P < 0.000) and large artery occlusion (OR,8.85, 95%CI,2.86-27.43; P < 0.000) were independent risk factors of ENDn. The prediction model showed good specificity and sensitivity in predicting the risk of ENDn.

Conclusions

There are differences between the major contributors to ENDh and ENDn, while a severe stroke can increase the occurrence of both sides.

Validation and comparison of multiple risk scores for prediction of symptomatic intracerebral hemorrhage after intravenous thrombolysis in VISTA

BACKGROUND AND AIMS

Prediction models/scores may help to identify patients at high risk of symptomatic intracerebral hemorrhage (sICH) after intravenous thrombolysis. We aimed to validate and compare the performance of different prediction models for sICH after thrombolysis using direct model estimation in the Virtual International Stroke Trials Archive (VISTA).

METHODS

We searched PubMed for potentially eligible prediction models from inception to 1 June 2019. Simple and practical models/scores were validated in VISTA. The primary outcome was sICH based on two criteria (National Institute of Neurological Diseases and Stroke, NINDS; Safe Implementation of Thrombolysis in Stroke-Monitoring Study, SITS-MOST) and the secondary outcome was parenchymal hematoma (PH). The discrimination performance of each model was evaluated using area under the curve (AUC) and calibration was evaluated by Hosmer-Lemeshow goodness-of-fit tests.

RESULTS

We found 13 prediction models and five models (HAT, MSS, SPAN-100, GRASPS and THRIVE) were finally validated in VISTA. A total of 1884 participants were eligible for our study, of whom the proportion with sICH was 4.6% (87/1884) per NINDS and 3.9% (73/1884) per SITS-MOST, and with PH was 11.3% (213/1884). MSS and GRASPS had the greatest predictive ability for sICH (NINDS criteria: MSS AUC 0.7, 95% CI 0.63-0.77, p < 0.001; GRASPS AUC 0.69, 95% CI 0.63-0.76, p < 0.001; SITS-MOST criteria: MSS, AUC 0.76, 95% CI 0.68-0.85, p < 0.001; GRASPS, AUC 0.79, 95% CI 0.71-0.87, p < 0.001). Similar results were found for PH (MSS AUC 0.68, 95% CI 0.64-0.73, p = 0.017; GRASPS AUC 0.68, 95% CI 0.63-0.72, p = 0.017). The calibration of each model was almost good.

CONCLUSION

MSS and GRASPS had good discrimination and calibration for sICH and PH after thrombolysis as assessed in VISTA. These two models could be used in clinical practice and clinical trials to identity individuals with high risk of sICH.

Symptomatic Intracranial Hemorrhage After Endovascular Stroke Treatment: External Validation of Prediction Models

BACKGROUND

Symptomatic intracranial hemorrhage (sICH) is a severe complication of reperfusion therapy for ischemic stroke. Multiple models have been developed to predict sICH or intracranial hemorrhage (ICH) after reperfusion therapy. We provide an overview of published models and validate their ability to predict sICH in patients treated with endovascular treatment in daily clinical practice.

METHODS

We conducted a systematic search to identify models either developed or validated to predict sICH or ICH after reperfusion therapy (intravenous thrombolysis and/or endovascular treatment) for ischemic stroke. Models were externally validated in the MR CLEAN Registry (n=3180; Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands). The primary outcome was sICH according to the Heidelberg Bleeding Classification. Model performance was evaluated with discrimination (c-statistic, ideally 1; a c-statistic below 0.7 is considered poor in discrimination) and calibration (slope, ideally 1, and intercept, ideally 0).

RESULTS

We included 39 studies describing 40 models. The most frequently used predictors were baseline National Institutes of Health Stroke Scale (NIHSS; n=35), age (n=22), and glucose level (n=22). In the MR CLEAN Registry, sICH occurred in 188/3180 (5.9%) patients. Discrimination ranged from 0.51 (SPAN-100 [Stroke Prognostication Using Age and National Institutes of Health Stroke Scale]) to 0.61 (SITS-SICH [Safe Implementation of Treatments in Stroke Symptomatic Intracerebral Hemorrhage] and STARTING-SICH [STARTING Symptomatic Intracerebral Hemorrhage]). Best calibrated models were IST-3 (intercept, -0.15 [95% CI, -0.01 to -0.31]; slope, 0.80 [95% CI, 0.50-1.09]), SITS-SICH (intercept, 0.15 [95% CI, -0.01 to 0.30]; slope, 0.62 [95% CI, 0.38-0.87]), and STARTING-SICH (intercept, -0.03 [95% CI, -0.19 to 0.12]; slope, 0.56 [95% CI, 0.35-0.76]).

CONCLUSIONS

The investigated models to predict sICH or ICH discriminate poorly between patients with a low and high risk of sICH after endovascular treatment in daily clinical practice and are, therefore, not clinically useful for this patient population.

A new machine learning algorithm with high interpretability for improving the safety and efficiency of thrombolysis for stroke patients: A hospital-based pilot study

Background

Thrombolysis is the first-line treatment for patients with acute ischemic stroke. Previous studies leveraged machine learning to assist neurologists in selecting patients who could benefit the most from thrombolysis. However, when designing the algorithm, most of the previous algorithms traded interpretability for predictive power, making the algorithms hard to be trusted by neurologists and be used in real clinical practice.

Methods

Our proposed algorithm is an advanced version of classical k-nearest neighbors classification algorithm (KNN). We achieved high interpretability by changing the isotropy in feature space of classical KNN. We leveraged a cohort of 189 patients to prove that our algorithm maintains the interpretability of previous models while in the meantime improving the predictive power when compared with the existing algorithms. The predictive powers of models were assessed by area under the receiver operating characteristic curve (AUC).

Results

In terms of interpretability, only onset time, diabetes, and baseline National Institutes of Health Stroke Scale (NIHSS) were statistically significant and their contributions to the final prediction were forced to be proportional to their feature importance values by the rescaling formula we defined. In terms of predictive power, our advanced KNN (AUC 0.88) outperformed the classical KNN (AUC 0.75, p = 0.0192 ).

Conclusions

Our preliminary results show that the advanced KNN achieved high AUC and identified consistent significant clinical features as previous clinical trials/observational studies did. This model shows the potential to assist in thrombolysis patient selection for improving the successful rate of thrombolysis.

The feasibility and accuracy of machine learning in improving safety and efficiency of thrombolysis for patients with stroke: Literature review and proposed improvements

In the treatment of ischemic stroke, timely and efficient recanalization of occluded brain arteries can successfully salvage the ischemic brain. Thrombolysis is the first-line treatment for ischemic stroke. Machine learning models have the potential to select patients who could benefit the most from thrombolysis. In this study, we identified 29 related previous machine learning models, reviewed the models on the accuracy and feasibility, and proposed corresponding improvements. Regarding accuracy, lack of long-term outcome, treatment option consideration, and advanced radiological features were found in many previous studies in terms of model conceptualization. Regarding interpretability, most of the previous models chose restrictive models for high interpretability and did not mention processing time consideration. In the future, model conceptualization could be improved based on comprehensive neurological domain knowledge and feasibility needs to be achieved by elaborate computer science algorithms to increase the interpretability of flexible algorithms and shorten the processing time of the pipeline interpreting medical images.

Radiomics-based infarct features on CT predict hemorrhagic transformation in patients with acute ischemic stroke

Objective

To develop and validate a model based on the radiomics features of the infarct areas on non-contrast-enhanced CT to predict hemorrhagic transformation (HT) in acute ischemic stroke.

Materials and methods

A total of 118 patients diagnosed with acute ischemic stroke in two centers from January 2019 to February 2022 were included. The radiomics features of infarcted areas on non-contrast-enhanced CT were extracted using 3D-Slicer. A univariate analysis and the least absolute shrinkage and selection operator (LASSO) were used to select features, and the radiomics score (Rad-score) was then constructed. The predictive model of HT was constructed by analyzing the Rad-score and clinical and imaging features in the training cohort, and it was verified in the validation cohort. The model was evaluated with the receiver operating characteristic curve, calibration curve and decision curve, and the prediction performance of the model in different scenarios was further discussed hierarchically.

Results

Of the 118 patients, 52 developed HT, including 21 cases of hemorrhagic infarct (HI) and 31 cases of parenchymal hematoma (PH). The Rad-score was constructed from five radiomics features and was the only independent predictor for HT. The predictive model was constructed from the Rad-score. The area under the curve (AUCs) of the model for predicting HT in the training and validation cohorts were 0.845 and 0.750, respectively. Calibration curve and decision curve analyses showed that the model performed well. Further analysis found that the model predicted HT for different infarct sizes or treatment methods in the training and validation cohorts with 78.3 and 71.4% accuracy, respectively. For all samples, the model predicted an AUC of 0.754 for HT in patients within 4.5 h since stroke onset, and predicted an AUC of 0.648 for PH.

Conclusion

This model, which was based on CT radiomics features, could help to predict HT in the setting of acute ischemic stroke for any infarct size and provide guiding suggestions for clinical treatment and prognosis evaluation.

Differences between predictive factors for early neurological deterioration due to hemorrhagic and ischemic insults following intravenous recombinant tissue plasminogen activator

Early neurological deterioration (END) following intravenous recombinant tissue plasminogen activator (rt-PA) treatment is a serious clinical event that can be caused by hemorrhagic or ischemic insult. We investigated the differences in predictive factors for END due to hemorrhagic and END due to ischemic insults. Consecutive patients from four hospitals who received 0.6 mg/kg intravenous rt-PA for acute ischemic stroke were retrospectively recruited. END was defined as a National Institutes of Health Stroke Scale (NIHSS) score ≥ 4 points within 24 h compared with baseline. END was classified into those due to hemorrhagic (END_h) or ischemic (END_i) insult based on computed tomography (CT) or magnetic resonance imaging. Risk factors associated with END_h and END_i were investigated by comparison with non-END cases. A total of 744 patients (452 men, median 75 years old) were included. END was observed in 79 patients (10.6%), including 22 END_h (3.0%) and 57 END_i (7.7%), which occurred within a median of 7 h after treatment. Multivariate analyses showed that higher pretreatment NIHSS score (odds ratio [OR] 1.06, 95% confidence interval [CI] 1.00–1.13) and pretreatment with antiplatelets (OR 2.84, 95% CI 1.08–7.72) were associated with END_h. Extensive early ischemic change (Alberta Stroke Program Early CT Score ≤ 7 on CT or ≤ 6 on diffusion-weighted imaging; OR 2.80, 95% CI 1.36–5.64) and large artery occlusions (OR 3.09, 95% CI 1.53–6.57) were associated with END_i. Distinct factors were predictive for the END subtypes. These findings could help develop preventative measures for END in patients with the identified risk factors.

Safety and efficacy of oral antiplatelet for patients who had acute ischaemic stroke undergoing endovascular therapy

BACKGROUND AND PURPOSE

To investigate the safety and efficacy of oral antiplatelet therapy (APT) for patients who had acute ischaemic stroke (AIS), receiving endovascular therapy (EVT).

METHODS

Patients were divided into non-APT group and APT (single APT or dual APT (DAPT)) group. The safety and efficacy endpoints at 3-month follow-up were symptomatic intracranial haemorrhage (sICH), recanalisation rate, clinical outcome and mortality.

RESULTS

Among 915 patients who had AIS, those in APT group (n=199) showed shorter puncture-to-recanalisation time, lower frequency of intravenous thrombolysis and more use of tirofiban compared with those in non-antiplatelet group (n=716) (p<0.05 for all). Oral APT was found to be associated with superior clinical outcome compared with non-APT (APT (44.2%) versus non-APT (41.1%)), adjusted OR=2.605, 95% CI 1.244 to 5.455, p=0.011). DAPT showed superior clinical outcome compared with non-APT (DAPT (56.5%) versus non-APT (41.1%), adjusted OR=5.405, 95% CI 1.614 to 18.102, p=0.006) and lower risk of mortality at 3-month follow-up (DAPT (4.8%) versus non-DAPT (17.7%), adjusted OR=0.008, 95% CI 0.000 to 0.441, p=0.019). There was no significant difference in sICH between the two groups.

CONCLUSIONS

Oral APT prior to undergoing EVT is safe and may accompany with superior clinical outcomes. DAPT may associate with superior clinical outcomes and lower risk of mortality.

Reduction in the Ratio of Low-density Lipoprotein Cholesterol to Highdensity Lipoprotein Cholesterol is Associated with Increased Risks of Hemorrhagic Transformation in Patients with Acute Ischemic Stroke

BACKGROUND AND PURPOSE

Hemorrhagic transformation (HT) is a potentially serious complication in patients with acute ischemic stroke (AIS). Whether the ratio of low-density lipoprotein cholesterol to high-density lipoprotein cholesterol (LDL-C/HDL-C) is associated with HT remains unclear.

METHODS

Ischemic stroke patients within 7 days of stroke onset from January 2016 to November 2017 were included in this study. Lipid profiles were measured within 24h after admission. HT was determined by a second computed tomography or magnetic resonance imaging within 7 days after admission. Univariate and multivariate logistic regression analysis was used to assess the association between LDL-C/HDL-C and HT.

RESULTS

We enrolled 1239 patients with AIS (788 males; mean age, 64 ± 15 years), of whom 129 (10.4%) developed HT. LDL-C/HDL-C was significantly lower on admission in patients with HT than those without HT (2.00 ± 0.89 vs. 2.25 ± 1.02, P=0.009). The unadjusted odds ratio (OR) of low LDL-C/HDL-C for HT was 2.07 (95% confidence interval [CI] 1.42-3.01, P＜0.001). After adjustment for possible confounders, lower LDL-C/HDL-C (≤1.52) was significantly associated with HT (OR 1.53, 95% CI: 1.02-2.31, P=0.046). Similar results were observed between lower LDL-C (≤ 4 mmol/L) and HT (OR 4.17, 95% CI: 1.25-13.90, P=0.02). However, no significant association was found between HT and high HDL-C, low triglycerides or low total cholesterol.

CONCLUSION

Lower LDL-C/HDL-C and LDL-C were significantly associated with increased risk of HT after AIS. Further investigations are warranted to confirm these findings and then optimize lipid management in stroke patients with lower LDL/HDL-C or LDL-C.

Risk stratification and outcomes of intracranial hemorrhage in patients with immune thrombocytopenia under 60 years of age

Intracranial hemorrhage (ICH) is a devastating complication of immune thrombocytopenia (ITP). However, information on ICH in ITP patients under the age of 60 years is limited, and no predictive tools are available in clinical practice. A total of 93 adult patients with ITP who developed ICH before 60 years of age were retrospectively identified from 2005 to 2019 by 27 centers in China. For each case, 2 controls matched by the time of ITP diagnosis and the duration of ITP were provided by the same center. Multivariate analysis identified head trauma (OR = 3.216, 95%CI 1.296-7.979, P =.012), a platelet count ≤ 15,000/μL at the time of ITP diagnosis (OR = 1.679, 95%CI 1.044-2.698, P =.032) and severe/life-threatening bleeding (severe bleeding vs. mild bleeding, OR = 1.910, 95%CI 1.088-3.353, P =.024; life-threatening bleeding vs. mild bleeding, OR = 2.620, 95%CI 1.360-5.051, P =.004) as independent risk factors for ICH. Intraparenchymal hemorrhage (OR = 5.191, 95%CI 1.717-15.692, P =.004) and a history of severe bleeding (OR = 4.322, 95%CI 1.532-12.198, P =.006) were associated with the 30-day outcome of ICH. These findings may facilitate ICH risk stratification and outcome prediction in patients with ITP.

The association between monocyte to high-density lipoprotein ratio and hemorrhagic transformation in patients with acute ischemic stroke

Hemorrhagic transformation (HT) is a common complication in patients with acute ischemic stroke. We investigated whether the monocyte to high-density lipoprotein ratio (MHR) is related to HT. Consecutive patients with ischemic stroke within 24 h of symptom onset were included in this study. HT was diagnosed by follow-up brain imaging after admission, and was classified as asymptomatic or symptomatic according to whether patients showed any neurologic worsening. Logistic regression was performed to estimate the association between MHR and HT. Of the 974 enrolled patients, 148 (15.2%) developed HT, and 24 (2.5%) patients experienced symptomatic HT. Compared to the highest MHR tertile (> 0.37), the lowest MHR tertile (< 0.22) was associated with 1.81-fold increase (95% CI 1.08-3.01, P = 0.024) in the odds of HT and 3.82-fold increase (95% CI 1.04-14.00, P = 0.043) in the odds of symptomatic HT after adjustment for possible confounders. Using a multivariate logistic regression model with restricted cubic spline, we found that elevated MHR was associated with a decreased risk of HT and symptomatic HT. In summary, lower MHR was independently associated with increased risk of HT and symptomatic HT in patients with ischemic stroke.

Risk Factors and a Nomogram for Predicting Intracranial Hemorrhage in Stroke Patients Undergoing Thrombolysis

PURPOSE

Identifying stroke patients at risk of postthrombolysis intracranial hemorrhage (ICH) in the clinical setting is essential. We aimed to develop and evaluate a nomogram for predicting the probability of ICH in acute ischemic stroke patients undergoing thrombolysis.

PATIENTS AND METHODS

A retrospective observational study was conducted using data from 345 patients at a single center. The patients were randomly dichotomized into training (2/3; n=233) and validation (1/3; n=112) sets. A prediction model was developed by using a multivariable logistic regression analysis.

RESULTS

The nomogram comprised three variables: the presence of atrial fibrillation (odds ratio [OR]: 4.92, 95% confidence interval [CI]: 2.09-11.57), the National Institutes of Health Stroke Scale (NIHSS) score (OR: 1.11, 95% CI: 1.04-1.18) and the glucose level on admission (OR: 1.27, 95% CI: 1.08-1.50). The areas under the receiver operating characteristic curve of the nomogram for the training and validation sets were 0.828 (0.753-0.903) and 0.801 (0.690-0.911), respectively. The Hosmer-Lemeshow test revealed good calibration in both the training and validation sets (P = 0.509 and P = 0.342, respectively). The calibration plot also demonstrated good agreement. A decision curve analysis demonstrated that the nomogram was clinically useful.

CONCLUSION

We developed an easy-to-use nomogram model to predict ICH, and the nomogram may provide risk assessments for subsequent treatment in stroke patients undergoing thrombolysis.

Resting-State Functional Connectivity Magnetic Resonance Imaging and Outcome After Acute Stroke

Background and Purpose- Physiological effects of stroke are best assessed over entire brain networks rather than just focally at the site of structural damage. Resting-state functional magnetic resonance imaging can map functional-anatomic networks by analyzing spontaneously correlated low-frequency activity fluctuations across the brain, but its potential usefulness in predicting functional outcome after acute stroke remains unknown. We assessed the ability of resting-state functional magnetic resonance imaging to predict functional outcome after acute stroke. Methods- We scanned 37 consecutive reperfused stroke patients (age, 69±14 years; 14 females; 3-day National Institutes of Health Stroke Scale score, 6±5) on day 3 after symptom onset. After imaging preprocessing, we used a whole-brain mask to calculate the correlation coefficient matrices for every paired region using the Harvard-Oxford probabilistic atlas. To evaluate functional outcome, we applied the modified Rankin Scale at 90 days. We used region of interest analyses to explore the functional connectivity between regions and graph-computation analysis to detect differences in functional connectivity between patients with good functional outcome (modified Rankin Scale score ≤2) and those with poor outcome (modified Rankin Scale score >2). Results- Patients with good outcome had greater functional connectivity than patients with poor outcome. Although 3-day National Institutes of Health Stroke Scale score was the most accurate independent predictor of 90-day modified Rankin Scale (84.2%), adding functional connectivity increased accuracy to 94.7%. Preserved bilateral interhemispheric connectivity between the anterior inferior temporal gyrus and superior frontal gyrus and decreased connectivity between the caudate and anterior inferior temporal gyrus in the left hemisphere had the greatest impact in favoring good prognosis. Conclusions- These data suggest that information about functional connectivity from resting-state functional magnetic resonance imaging may help predict 90-day stroke outcome.

Low-dose rt-PA may not decrease the incidence of symptomatic intracranial haemorrhage in patients with high risk of symptomatic intracranial haemorrhage

BACKGROUND

Recombinant tissue plasminogen activator (rt-PA) has been used as the standard treatment for acute ischemic stroke (AIS). The following study investigates whether low-dose rt-PA can decrease the incidence of symptomatic intracranial haemorrhage (sICH) in AIS patients with high-risk sICH compared to standard-dose rt-PA.

MATERIALS AND METHODS

Data from the Thrombolysis Implementation and Monitor of Acute Ischemic Stroke in China (TIMS-China) studies were assessed to explore risk factors for sICH after intravenous thrombolysis. For high-risk sICH patients (age ≧70 years old, or with diabetes, or serum glucose on admission >9.0 mmol/L, or NIHSS on admission>20, or with cardioembolism), standard-dose rt-PA (0.85 to 0.95 mg/kg) and low- dose rt-PA (0.5 to 0.7 mg/kg) were compared. Primary outcome measure was the incidence of sICH, and the secondary outcome measures were 7-day mortality and 90-day functional independence outcome (modified Rankin scale, 0-2).

RESULTS

A total of 554 patients were enrolled (60 cases for low dose, and 494 cases for standard dose). Median rt-PA doses were 0.63 and 0.90 mg, respectively. After adjustment for the baseline variables, low-dose rt-PA did not decrease the incidence of sICH (per SITS-MOST criteria, 3.33% versus 2.23%, P = 0.3467) compared to low dose. The low-dose group revealed less functional independence outcomes (modified Rankin scale, 0-2) compared to standard-dose group (36.67% versus 52.43%; odds ratio = 0.49; p = 0.0204) at 90 days.

CONCLUSIONS

Our study suggests that low-dose intravenous rt-PA for high-risk sICH stroke in Chinese patients may not decrease the incidence of sICH, and concomitant with a poor outcome compared to standard-dose rt-PA.

ABBREVIATIONS

rt-PA: recombinant tissue plasminogen activator; AIS: acute ischemic stroke; sICH: symptomatic intracranial haemorrhage.

The Risk of Symptomatic Intracranial Hemorrhage after Thrombolysis for Acute Stroke: Current Concepts and Perspectives

Background

Thrombolysis is the standard of treatment for acute ischemic stroke, with a time window of up to 4½ h from stroke onset. Despite the long experience with the use of recombinant tissue plasminogen activator and the adherence to protocols symptomatic intracranial hemorrhage (SICH) may occur in around 6% of cases, with high-mortality rate and poor-functional outcomes. Many patients are excluded from thrombolysis on the basis of an evaluation of known risk factors, but there are other less known factors involved.

Objective

The purpose of this work is to analyze the less known risk factors for SICH after thrombolysis. A search of articles related with this field has been undertaken in PubMed with the keywords (brain hemorrhage, thrombolysis, and acute ischemic stroke). Some risk factors for SICH have emerged such as previous microbleeds on brain magnetic resonance imaging, leukoaraiosis, and previous antiplatelet drug use or statin use. Serum matrix metalloproteinases have emerged as a promising biomarker for better selection of patients, but further research is needed.

Conclusions

In addition to the already known risk factors considered in the standard protocols, an individualized evaluation of risks is needed to minimize the risk of brain hemorrhage after thrombolysis for ischemic stroke.

Different doses of tenecteplase vs alteplase in thrombolysis therapy of acute ischemic stroke: evidence from randomized controlled trials

Background

Recent studies showed inconsistent results of tenecteplase vs alteplase for acute ischemic stroke (AIS) with safety and efficacy.

Methods

A meta-analysis was performed to explore the value of tenecteplase and alteplase in AIS treatment. Medline, Embase, and Cochrane Library from January 2001 to April 2018 were searched for randomized controlled trials (RCTs) with tenecteplase vs alteplase for AIS.

Results

The primary outcomes were early neurological improvement at 24 h and functional outcome at 3 months. We pooled 1,390 patients from four RCTs. Tenecteplase showed a significant early neurological improvement (P=0.035) compared with alteplase. In addition, tenecteplase showed a neutral effect on excellent outcome (P=0.309), good functional outcome (P=0.275), and recanalization (P=0.3). No significant differences in safety outcomes were demonstrated. In subgroup analysis, 0.25 mg/kg dose of tenecteplase showed a significantly increased early neurological improvement (P<0.001). In serious stroke at baseline (National Institutes of Health Stroke Scale [NIHSS] >12) subgroup, tenecteplase showed a dramatic early neurological improvement (P=0.002) and low risks of any intracranial hemorrhage (ICH) (P=0.027).

Conclusion

Tenecteplase provided better early neurological improvement than alteplase. The 0.25 mg/kg dose of tenecteplase subgroup specially showed better early neurological improvement and lower any ICH tendency than that of alteplase. In addition, in serious stroke at baseline subgroup, tenecteplase showed a lower risk of any ICH.