Predictors Of Hemorrhagic Transformation Research Articles

Nomogram prediction model for the risk of intracranial hemorrhagic transformation after intravenous thrombolysis in patients with acute ischemic stroke.

Hemorrhagic transformation (HT) after intravenous thrombolysis (IVT) might worsen the clinical outcomes, and a reliable predictive system is needed to identify the risk of hemorrhagic transformation after IVT. Retrospective collection of patients with acute cerebral infarction treated with intravenous thrombolysis in our hospital from 2018 to 2022. 197 patients were included in the research study. Multivariate logistic regression analysis was used to screen the factors in the predictive nomogram. The performance of nomogram was assessed on the area under the receiver operating characteristic curve (AUC-ROC), calibration plots and decision curve analysis (DCA). A total of 197 patients were recruited, of whom 24 (12.1%) developed HT. In multivariate logistic regression model National Institute of Health Stroke Scale (NIHSS) (OR, 1.362; 95% CI, 1.161-1.652; p = 0.001), N-terminal pro-brain natriuretic peptide (NT-pro BNP) (OR, 1.012; 95% CI, 1.004-1.020; p = 0.003), neutrophil to lymphocyte ratio (NLR) (OR, 3.430; 95% CI, 2.082-6.262; p < 0.001), systolic blood pressure (SBP) (OR, 1.039; 95% CI, 1.009-1.075; p = 0.016) were the independent predictors of HT which were used to generate nomogram. The nomogram showed good discrimination due to AUC-ROC values. Calibration plot showed good calibration. DCA showed that nomogram is clinically useful. Nomogram consisting of NIHSS, NT-pro BNP, NLR, SBP scores predict the risk of HT in AIS patients treated with IVT.

Clinical and imaging predictors for hemorrhagic transformation of acute ischemic stroke after endovascular thrombectomy.

Hemorrhagic transformation (HT) is a common complication of endovascular thrombectomy (EVT) in patients with acute ischemic stroke (AIS). Our study aims to investigate the clinical and imaging predictors of HT and symptomatic intracranial hemorrhage (sICH) in patients who underwent EVT. A retrospective analysis of 118 patients undergoing EVT for acute anterior circulation stroke was performed. Potential clinical and imaging predictors of all patients were collected and multivariate logistic regression was performed. The risk prediction system was constructed according to the multivariate logistic regression results. The incidence of HT and sICH after EVT were 46.6% and 15.3%, respectively. The multivariate logistic regression results showed that Alberta Stroke Program Early CT Score (ASPECTS) (p = .001, odds ratio [OR] = 0.367, 95% [confidence interval] CI, 0.201-0.670), collateral status (p<.001, OR = 0.117, 95% CI, 0.042-0.325), relative cerebral blood flow (CBF) ratio (p = .025, OR = 0.943, 95% CI, 0.895-0.993), and blood glucose on admission (p = .012, OR = 1.258, 95% CI, 1.053-1.504) were associated with HT. While for sICH, collateral circulation (p = .007, OR = 0.148, 95% CI, 0.037-0.589), ASPECTS (p = .033, OR = 0.510, 95% CI, 0.274-0.946), and blood glucose (p = .005, OR = 1.304, 95% CI, 1.082-1.573) were independent factors. The predictive model for HT after EVT was established, and the sensitivity and specificity of it were 90.9% and 79.4%, respectively, with the area under the curve of 90.0% (84.5%-95.4%). Collateral status, ASPECTS, relative CBF ratio, and blood glucose on admission were predictors for HT in AIS patients, while collateral status, ASPECTS, and blood glucose on admission were also predictors for sICH. In addition, the established predictive model showed good diagnostic value for prediction of HT after EVT.

Predictive Role of Pre-Thrombolytic Neutrophil-Platelet Ratio on Hemorrhagic Transformation After Intravenous Thrombolysis in Acute Ischemic Stroke.

To investigate the predictive role of the neutrophil-platelet ratio (NPR) before intravenous thrombolysis (IVT) on hemorrhagic transformation (HT) in patients with acute ischemic stroke (AIS). AIS patients treated with IVT without endovascular therapy between June 2019 and February 2023 were included. Patients were divided into high NPR (>35) and low NPR (≤35) groups according to the optimal threshold NPR value for identifying high-risk patients before IVT. The baseline data and the incidence of HT and symptomatic intracranial hemorrhage (sICH) were compared between the two groups. The predictive role of the NPR and other related factors on HT after IVT was analyzed by multivariate logistic regression. A total of 247 patients were included, with an average age of 67.5 ± 12.4 years. Post-thrombolytic HT was observed in 18.6% of the patients, and post-thrombolytic sICH was observed in 1.2% of the patients. There were 69 patients in the high NPR group and 178 patients in the low NPR group. The incidence of HT in the high NPR group was significantly higher than that in the low NPR group (30.4% vs 16.3%, P < .05). The incidence of sICH was significantly higher in the high NPR group than in the low NPR group (14.5% vs 1.7%, P < .001). Multivariate logistic regression analysis showed that NPR > 35 was positively correlated with HT (odds ratio (OR) = 3.236, 95% confidence interval (CI): 1.481-7.068, P = .003) and sICH (OR = 13.644, 95% CI: 2.392-77.833, P = .003). A high NPR (>35) before IVT may be a predictor of HT in AIS patients. This finding may help clinicians make clinical decisions before IVT in AIS patients.

Abstract 204: On Old Dogs and New Tricks: Connecticut Perfusion Predicts Hemorrhagic Transformation After Thrombectomy

Introduction Hemorrhagic transformation is a major complication of large vessel occlusion (LVO) recanalization after mechanical thrombectomy in acute ischemic stroke. We investigated whether computed tomography perfusion (CTP) derived hypoperfusion index ratio correlates with the rate of hemorrhagic transformation. Methods We conducted a retrospective cohort analysis of a prospectively maintained patient database. Included patients underwent mechanical thrombectomy for large vessel ischemic stroke from January 2019 to December 2022. Patients were separated into 2 groups depending on whether or not hemorrhagic transformation developed. Hypoperfusion index ratio on admission CTP was determined using VizAI software. Data were analyzed using Chi‐square and Mann‐Whitney U tests. Results Among the 289 patients included (median age, 72.5; 41.5% female), 55 (19%) had hemorrhagic transformation. Patients with hemorrhagic transformation had a significantly higher hypoperfusion index ratio (median, 0 vs 0.2; P= 0.004) compared with those with no hemorrhagic transformation. Multivariable analysis showed that for every 0.1 increase in the hypoperfusion index ratio, there was a significant 4.64‐fold increase in hemorrhagic transformation (OR 4.64; 95% CI 1.40 to 15.18; p=0.011). Conclusion In patients with LVO who underwent mechanical thrombectomy, a higher hypoperfusion index ratio on admission CTP was associated with an increased rate of hemorrhagic transformation. This suggests that the hypoperfusion index ratio could be used as a predictor for hemorrhagic transformation after mechanical thrombectomy.

The role of red blood cell distribution width to platelet ratio in predicting hemorrhagic transformation after mechanical thrombectomy therapy in acute ischemic stroke patients

Objective: Hemorrhagic transformation (HT) is common complication after mechanical thrombectory (MT) for acute ischemic stroke (AIS). To our knowledge, there has been no study on the correlation between baseline red blood cell distribution width (RDW) to platelet ratio (RPR) and HT after MT. Methods: This study recruited 126 AIS patients with anterior or posterior circulation large-vessel occlusion who underwent MT therapy at the Department of Neurology, Taizhou Hospital, Zhejiang province between September 2019 and April 2021. Patients were divided into two groups: patients with HT and those without HT (wHT), and their laboratory and clinical data were compared. Results: We found no significant differences in sex, age, alcohol consumption, diabetes mellitus, atrial fibrillation, systolic blood pressure, diastolic blood pressure, triglycerides, total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, serum creatinine, blood urea nitrogen, fibrinogen, neutrophil count, lymphocyte count, neutrophil-to-lymphocyte ratio (NLR), National Institutes of Health Stroke Scale (NIHSS) score, Alberta Stroke Program Early Computed Tomography Score (ASPECTS), whether intravenous thrombolysis was accepted, and TOAST classification between the two groups. Compared with patients without HT, we found that the admission blood glucose, RDW and RPR levels were higher in patients with HT after MT in AIS patients, multivariate logistic regression analysis revealed that baseline RPR (odds ratio (OR), 1.290; 95% CI, 1.062–1.567; P=0.010) and glucose level (OR, 1.177; 95% CI, 1.013–1.369; P=0.034) are independent predictors for HT after MT. Conclusion: Higher baseline RPR and higher admission blood glucose levels might be related to HT in AIS patients who received MT therapy.

Pre-treatment risk markers for hemorrhagic transformation in posterior circulation acute ischemic stroke treated with reperfusion therapy.

Hemorrhagic transformation (HT) is an uncommon complication of posterior circulation acute ischemic stroke (PCS) compared to anterior circulation stroke. Nevertheless, it remains a major concern especially following reperfusion therapy. This study aimed at identifying potential predictive factors associated with HT in PCS. Consecutive patients, from a multicenter cohort, with PCS treated by IVT or EVT or the combination of both, were included from December 2015 to May 2019. The European Cooperative Acute Stroke Study criteria was used to identify HT. Potential risk factors were analyzed using univariate and multivariable testing models. A total of 96 patients were included in our study. Median age was 66 (57-83) years, 54 patients (56%) were male and median baseline NIHSS was 8 (4-14). 77 patients (80%) received IVT and 54 patients (56%) benefited from EVT. HT occurred in 19 patients (20%), while sHT occurred in 3 patients (3%). HT was found to be associated with poor functional status at 3months in univariate analysis (p = 0.0084). Multivariable analysis confirmed that higher baseline NIHSS (OR 1.1008; 95% CI [1.0216-1.1862]; p = 0.0117) and lobar topography of ischemia (OR 4.4275; 95% CI [1.3732-14.2753]; p = 0.0127) were independent predictors of the occurrence of HT. HT is associated with increased morbidity in patients with PCS; higher NIHSS and lobar ischemia were independent predictors of HT in our population. Easy-to-use predictive markers may help to tailor therapeutic management of patients with PCS.

Predictors for hemorrhagic transformation and cerebral edema in stroke patients with first-pass complete recanalization.

Predictors of radiological complications attributable to reperfusion injury remain unknown when baseline setting is optimal for endovascular treatment and procedural setting is the best in stroke patients with large vessel occlusion (LVO). To identify clinical and radiological/procedural predictors for hemorrhagic transformation (HT) and cerebral edema (CED) at 24 hr in patients obtaining complete recanalization in one pass of thrombectomy for ischemic stroke ⩽ 6 h from symptom onset with intra-cranial anterior circulation LVO and ASPECTS ⩾ 6. We conducted a cohort study on prospectively collected data from 1400 patients enrolled in the Italian Registry of Endovascular Treatment in Acute Stroke. HT was reported in 248 (18%) patients and early CED was reported in 260 (19.2%) patients. In the logistic regression model including predictors from a first model with clinical variables and from a second model with radiological/procedural variables, diabetes mellitus (odds ratio (OR) = 1.832, 95% confidence interval (CI) = 1.201-2.795), higher National Institutes of Health Stroke Scale (NIHSS) (OR = 1.076, 95% CI = 1.044-1.110), lower Alberta Stroke Program Early CT (ASPECTS) (OR = 0.815, 95% CI = 0.694-0.957), and longer onset-to-groin time (OR = 1.005, 95% CI = 1.002-1.007) were predictors of HT, whereas general anesthesia was inversely associated with HT (OR = 0.540, 95% CI = 0.355-0.820). Higher NIHSS (OR = 1.049, 95% CI = 1.021-1.077), lower ASPECTS (OR = 0.700, 95% CI = 0.613-0.801), intravenous thrombolysis (OR = 1.464, 95% CI = 1.061-2.020), longer onset-to-groin time (OR = 1.002, 95% CI = 1.001-1.005), and longer procedure time (OR = 1.009, 95% CI = 1.004-1.015) were predictors of early CED. After repeating a fourth logistic regression model including also good collaterals, the same variables remained predictors for HT and/or early CED, except diabetes mellitus and thrombolysis, while good collaterals were inversely associated with early CED (OR = 0.385, 95% CI = 0.248-0.599). Higher NIHSS, lower ASPECTS, and longer onset-to-groin time were predictors for both HT and early CED. General anesthesia and good collaterals were inversely associated with HT and early CED, respectively. Longer procedure time was predictor of early CED.

Predictive Factors for Hemorrhagic Transformation in Acute Ischemic Stroke in the REAL-World Clinical Practice.

Few data exists on predictive factors of hemorrhagic transformation (HT) in real-world acute ischemic stroke patients. The aims of this study were: (i) to identify predictive variables of HT (ii) to develop a score for predicting HT. We retrospectively analyzed the clinical, radiographic, and laboratory data of patients with acute ischemic stroke consecutively admitted to our Stroke Unit along two years. Patients with HT were compared with those without HT. A multivariate logistic regression analysis was performed to identify independent predictors of HT on CT scan at 24 hours to develop a practical score. The study population consisted of 564 patients with mean age 77.5±11.8 years. Fifty-two patients (9.2%) showed HT on brain CT at 24 hours (4.9% symptomatic). NIHSS score ≥8 at Stroke Unit admission (3 points), cardioembolic etiology (2 points), acute revascularization by systemic thrombolysis and/or mechanical thrombectomy (1 point), history of previous TIA/stroke (1 point), and major vessel occlusion (1 point) were found independent risk factors of HT and were included in the score (Hemorrhagic Transformation Empoli score (HTE)). The predictive power of HTE score was good with an AUC of 0.785 (95% CI: 0.749-0.818). Compared with 5 HT predictive scores proposed in the literature (THRIVE, SPAN-100, MSS, GRASPS, SITS-SIC), the HTE score significantly better predicted HT. NIHSS score ≥8 at Stroke Unit admission, cardioembolism, urgent revascularization, previous TIA/stroke, and major vessel occlusion were independent predictors of HT. The HTE score has a good predictive power for HT. Prospective studies are warranted.

Low MHR Is Associated with Hemorrhagic Transformation in Acute Large Artery Atherosclerosis Ischemic Stroke Patients with Intravenous Thrombolysis.

Background: Hemorrhagic transformation (HT) is a common complication of intravenous thrombolysis (IVT) in patients with acute ischemic stroke (AIS) and may lead to neurological deterioration. This article discusses whether monocyte count to high-density lipoprotein ratio (MHR) level is associated with HT in AIS patients. Materials and methods: The clinical data of AIS patients who underwent rt-PA IVT treatment were continuously collected. According to whether HT occurred, patients were divided into HT group and non-HT group. Potential association between MHR and HT in different subtypes AIS was explored by using logistic regression. Results: A total of 444 AIS patients were retrospective analyzed. The MHR level was lower in HT group compared with the non-HT group in all AIS patients (0.28 vs 0.36, P = .031) and in large-artery atherosclerosis (LAA) type AIS patients (0.31 vs 0.37, P = .032). Low MHR was independently related to HT (OR:0.035, 95%CI:0.003-0.390, P = .006). Among all TOAST subtypes, low MHR was only independently associated with HT in patients of LAA-type AIS after adjusting for confounding factors (OR:0.01, 95%CI:0.00-0.62, P = .031), with an optimal cut-off value of 0.41, sensitivity of 85.7%, and specificity of 43.1%. MHR was not correlated with SVO, VE, and CE subtype AIS. Conclusion: Low MHR may be an independent predictor of HT in patients with AIS and this conclusion only existed in LAA-type AIS.

Machine learning approach for hemorrhagic transformation prediction: Capturing predictors' interaction.

Patients with ischemic stroke frequently develop hemorrhagic transformation (HT), which could potentially worsen the prognosis. The objectives of the current study were to determine the incidence and predictors of HT, to evaluate predictor interaction, and to identify the optimal predicting models. A prospective study included 360 patients with ischemic stroke, of whom 354 successfully continued the study. Patients were subjected to thorough general and neurological examination and T2 diffusion-weighted MRI, at admission and 1 week later to determine the incidence of HT. HT predictors were selected by a filter-based minimum redundancy maximum relevance (mRMR) algorithm independent of model performance. Several machine learning algorithms including multivariable logistic regression classifier (LRC), support vector classifier (SVC), random forest classifier (RFC), gradient boosting classifier (GBC), and multilayer perceptron classifier (MLPC) were optimized for HT prediction in a randomly selected half of the sample (training set) and tested in the other half of the sample (testing set). The model predictive performance was evaluated using receiver operator characteristic (ROC) and visualized by observing case distribution relative to the models' predicted three-dimensional (3D) hypothesis spaces within the testing dataset true feature space. The interaction between predictors was investigated using generalized additive modeling (GAM). The incidence of HT in patients with ischemic stroke was 19.8%. Infarction size, cerebral microbleeds (CMB), and the National Institute of Health stroke scale (NIHSS) were identified as the best HT predictors. RFC (AUC: 0.91, 95% CI: 0.85-0.95) and GBC (AUC: 0.91, 95% CI: 0.86-0.95) demonstrated significantly superior performance compared to LRC (AUC: 0.85, 95% CI: 0.79-0.91) and MLPC (AUC: 0.85, 95% CI: 0.78-0.92). SVC (AUC: 0.90, 95% CI: 0.85-0.94) outperformed LRC and MLPC but did not reach statistical significance. LRC and MLPC did not show significant differences. The best models' 3D hypothesis spaces demonstrated non-linear decision boundaries suggesting an interaction between predictor variables. GAM analysis demonstrated a linear and non-linear significant interaction between NIHSS and CMB and between NIHSS and infarction size, respectively. Cerebral microbleeds, NIHSS, and infarction size were identified as HT predictors. The best predicting models were RFC and GBC capable of capturing nonlinear interaction between predictors. Predictor interaction suggests a dynamic, rather than, fixed cutoff risk value for any of these predictors.

Neutrophil to High-Density Lipoprotein Ratio is Associated with Hemorrhagic Transformation in Patients with Acute Ischemic Stroke.

Hemorrhagic transformation (HT) is one of the most common and severe complications in patients with acute ischemic stroke (AIS). It indicates a poor prognosis in AIS patients. However, the association of neutrophil to high-density lipoprotein ratio (NHR) with HT remains unclear. This study examined whether the NHR has a predictive effect on HT in AIS patients and explored the predictive cutoff value of the NHR. This is a retrospective study and consecutively included AIS patients admitted to the Department of Neurology of the First Affiliated Hospital of Wannan Medical College between December 2019 and January 2022. All subjects had blood samples collected within 24 h of admission, and neutrophil counts and high-density lipoprotein counts were detected. HT was diagnosed with hemorrhage on subsequent magnetic resonance imaging (MRI) or computed tomography (CT) of the brain. Univariate logistic regression analysis was performed to identify confounding factors, and multivariate logistic regression analysis determined the correlation between NHR and HT. Receiver operating characteristic (ROC) curves were used to evaluate the clinical predictive value of NHR. A total of 725 patients were finally included in this study, of which 87 (12%) developed HT. The median NHR value in the HT group was 4.31, which was significantly higher than that in the non-HT group, and the difference was statistically significant [4.31 (3.54-6.24) vs 3.63 (2.68-4.64), p < 0.001]. The binary logistic regression analysis showed that NHR was independently associated with HT in AIS patients (OR: 1.180, 95% CI: 1.036-1.344, p = 0.013). The area under ROC curve (AUC) of NHR for predicting HT in AIS patients was 0.633 (95% CI: 0.567-0.699, p < 0.001), and its optimal cutoff were 3.52. The NHR was a reliable and simple independent predictor of HT in AIS patients.

Pre-treatment spectral CT combined with CT perfusion can predict hemorrhagic transformation after thrombolysis in patients with acute ischemic stroke

ObjectiveTo investigate the value of pre-treatment spectral CT angiography (CTA) in predicting hemorrhagic transformation (HT) after intravenous thrombolysis (IVT) treatment in acute ischemic stroke (AIS) patients. Materials and MethodsAIS patients who underwent IVT with recombinant tissue plasminogen activator and pre-treatment head and neck spectral CTA and head CT perfusion (CTP) from January 2018 to June 2020 were reviewed retrospectively. Finally, 20 patients were included in the HT group and 22 age-matched patients were included in the non-HT group. Spectral and CTP parameters of the region of interest on pre-treatment CTA axial raw images and CTP images, including the infarct core (IC) and ischemic penumbral (IP) regions, were recorded. The differences in clinical variables, CTP, collateral scores and spectral parameters between the two groups were analyzed. Three multivariate logistic regression models were then developed, where model 1 included clinical and spectral parameters, model 2 included clinical and CTP parameters and a combined model included clinical, CTP, and spectral parameters. Receiver operating characteristic analysis was used to evaluate the performance of the multivariate model. ResultsPatients with HT had higher Safe Implementation of Treatments in Stroke (SITS) score (p = 0.023), the volume of perfusion lesions (p = 0.005), the volume of IP (p = 0.003), the mean transit time (MIT) in the IC area (p = 0.012), as well as the TTP in IP area (p = 0.015) compared with patients without HT. The HT group showed significantly lower CBF in the IC area (p = 0.019), iodine concentration (p = 0.017) and the effective atomic number (p = 0.024) in the IP area than non-HT group. And the slope of the spectral curve of the HT group in the IP region was larger than that of the non-HT group (p = 0.023). Gender, age, SITS score, the volume of entire perfusion lesion, CBF and MIT in the IC area, TTP in the IP area, as well as iodine concentration in the IP area were included in the final multivariate model for predicting HT. And CBF in the IC area (OR = 0.779, 95 % CI:0.609–0.996, p = 0.046) as well as the iodine concentration of IP area (OR = 0.343, 95 % CI: 0.131–0.901, p = 0.030) were proved to be independent predictors for HT. The combined model including clinical, spectral, and CTP parameters, showed improved accuracy compared to the other two models, while the Delong test did not suggest a statistically significant difference (both p values > 0.05). ConclusionsThe iodine concentration of IP area derived from pre-treatment spectral CTA was an independent predictor of HT after IVT treatment for AIS patients. Moreover, multivariate models combined with clinical, spectral, and CTP parameters may be able to predict HT.

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

ObjectiveTo 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 methodsA 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.ResultsOf 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.ConclusionThis 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.

Predictive value of magnetic resonance imaging-based texture analysis for hemorrhage transformation in large cerebral infarction.

Massive cerebral infarction (MCI) is a devastating condition and associated with high rate of morbidity and mortality. Hemorrhagic transformation (HT) is a common complication after acute MCI, and often results in poor outcomes. Although several predictors of HT have been identified in acute ischemic stroke (AIS), the association between the predictors and HT remains controversial. Therefore, we aim to explore the value of texture analysis on magnetic resonance image (MRI) for predicting HT after acute MCI. This retrospective study included a total of 98 consecutive patients who were admitted for acute MCI between January 2019 and October 2020. Patients were divided into the HT group (n = 44) and non-HT group (n = 54) according to the follow-up computed tomography (CT) images. A total of 11 quantitative texture features derived from images of diffusion-weighted image (DWI) or T2-weighted-Fluid-Attenuated Inversion Recovery (T2/FLAIR) were extracted for each patient. Receiver operating characteristic (ROC) analysis were performed to determine the predictive performance of textural features, with HT as the outcome measurement. There was no significant difference in the baseline demographic and clinical characteristics between the two groups. The distribution of atrial fibrillation and National Institutes of Health Stroke Scale (NIHSS) were significantly higher in patients with HT than those without HT. Among the textural parameters extracted from DWI images, six parameters, f2 (contrast), f3 (correlation), f4 (sum of squares), f5 (inverse difference moment), f10 (difference variance), and f11 (difference entropy), differs significantly between the two groups (p < 0.05). Moreover, five of six parameters (f2, f3, f5, f10, and f11) have good predictive performances of HT with the area under the ROC curve (AUC) values of 0.795, 0.779, 0.791, 0.780, and 0.797, respectively. However, the texture features f2, f3, and f10 in T2/FLAIR images were the only three significant predictors of HT in patients with acute MCI, but with a relatively low AUC values of 0.652, 0.652, and 0.670, respectively. In summary, our preliminary results showed DWI-based texture analysis has a good predictive validity for HT in patients with acute MCI. Multiparametric MRI texture analysis model should be developed to improve the prediction performance of HT following acute MCI.

The role of automated computed topography perfusion in prediction of hemorrhagic transformation after acute ischemic stroke.

The role of computed tomography perfusion (CTP) in prediction of hemorrhagic transformation (HT) has been evolving. We aimed to study the role of automated perfusion post-processing software in prediction of HT using the commercially available RAPID software. Two hundred eighty-two patients with anterior circulation ischemic stroke, who underwent CTP with RAPID automated post-processing, were retrospectively enrolled and divided into HT (n = 91) and non-HT groups (n = 191). The automated RAPID-generated perfusion maps were reviewed. Mismatch volume and ratio, time to maximum (Tmax) > 4-10s volumes, hypoperfusion index, cerebral blood flow (CBF) < 20-38% volumes, cerebral blood volume (CBV) < 34%-42% volumes, and CBV index were recorded and analyzed. The volumes of brain tissues suffering from reduction of cerebral blood flow (CBF < 20%-38%), reduction in cerebral blood volumes (CBV < 34-42%), and delayed contrast arrival times (Tmax > 4-10s) were significantly higher in the HT group. The mismatch volumes were also higher in the HT group (p = .001). Among these parameters, the Tmax > 6s volume was the most reliable and sensitive predictor of HT (p = .001, AUC = 0.667). However, the combination of the perfusion parameters can slightly improve the diagnostic efficiency (AUC = 0.703). There was no statistically significant difference between the non-HT group and either the parenchymal or the symptomatic subtypes. The RAPID automated CTP parameters can provide a reliable predictor of HT overall but not the parenchymal or the symptomatic subtypes. The infarct area involving the penumbra and core represented by the Tmax > 6s threshold is the most sensitive predictor; however, the combination of the perfusion parameters can slightly improve the diagnostic efficiency.

Low Fibrinogen Level as a Predictor of Hemorrhagic Transformation after Thrombolytic Therapy for Acute Ischemic Stroke (P1-1.Virtual)

Low Fibrinogen Level as a Predictor of Hemorrhagic Transformation after Thrombolytic Therapy for Acute Ischemic Stroke (P1-1.Virtual)

Predictive accuracy of an ADC map for hemorrhagic transformation in acute ischemic stroke patients after successful recanalization with endovascular therapy.

BackgroundHemorrhagic transformation (HT) of acute ischemic stroke (AIS) is associated with poor outcome. Previous studies only reported the association of mean ischemic severity or total infarct volume with HT after endovascular therapy (EVT). We aimed to investigate the predictive value of preoperative apparent diffusion coefficient (ADC) map for HT by combinated ischemic severity and corresponding volume in AIS after successful recanalization with EVT.MethodsWe retrospectively analyzed 119 consecutive cases of AIS with large vessel occlusion of anterior circulation within 24 hours after symptom onset and successful recanalization after EVT. All cases had baseline magnetic resonance imaging (MRI), follow-up computed tomography (CT), and magnetic resonance angiography (MRA) or computed tomography angiography (CTA). Volumes of ADC <0.6×10−3, 0.5×10−3, 0.4×10−3, and 0.3×10−3 mm2/s, baseline characteristics and outcomes of patients with and without HT identified by European Collaborative Acute Stroke Study (ECASS) were compared. The optimal ADC and volume threshold for predicting HT were analyzed using receiver operating characteristic (ROC) curve, and multivariate logistic regression analysis were performed with clinical characteristics and volumes of optimal ADC threshold to determine risk factors for HT.ResultsAmong 119 patients, 42 patients had HT on follow-up CT, including 24 hemorrhagic infarct (HI) cases and 18 parenchymal hematoma (PH) cases. The optimal volumes were 6.46 mL with ADC <0.4×10−3 mm2/s for predicting both HT and PH, with a larger area under curve (AUC) of 83.3% for HT than that for PH of 80%. In logistic regression analysis, intravenous tissue plasminogen activator (IV tPA) treatment, atrial fibrillation, and volume of ADC <0.4×10−3 mm2/s were identified as independent predictors for HT and volume of ADC <0.4×10−3 mm2/s had the highest odds ratio (OR) value.ConclusionsThe combination of ischemic severity and corresponding volume in ADC map may predict HT after thrombectomy. In addition to the total infarct volume, volume with severe ischemia should be taken into consideration in preoperative patient selection.

Low Serum Magnesium Levels Are Associated With Hemorrhagic Transformation After Mechanical Thrombectomy in Patients With Acute Ischemic Stroke

ObjectiveIn patients with acute ischemic stroke (AIS), hemorrhagic transformation (HT) is a major complication after mechanical thrombectomy (MT). This study aimed to investigate the relationship between serum magnesium levels and HT after MT.MethodsWe collected 199 cases of consecutive AIS that received MT due to acute anterior circulation occlusions in our institution between January 2017 and January 2020. Baseline serum magnesium was obtained from all patients on admission before MT. The patients were divided into two groups based on the occurrence of HT. Univariate and multivariate analyses were performed to investigate whether magnesium was an independent predictor of HT. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were determined.ResultsOf the 199 enrolled patients, 40 (20.1%) presented with HT, and 12 (6%) developed symptomatic intracranial hemorrhage (sICH). Patients with HT had lower serum magnesium levels compared to those without HT (0.76 [0.69–0.80] vs. 0.84 [0.80–0.90], p < 0.001). The multivariate logistic analysis showed that the serum magnesium level (odds ratio, [OR]: 0.000, 95% confidence interval [CI]: 0.000–0.001, p < 0.001) was significantly associated with the occurrence of HT. The ROC curve analysis revealed that the serum magnesium level could predict HT with an AUC of.820 (95% CI: 0.750–0.891 p < 0.001). Serum magnesium ≤ 0.80 mmol/L could predict HT with a sensitivity of 79.2% and a specificity of 70.0%. Of interest, the serum magnesium level was not associated with HT when the baseline of serum magnesium was higher than the cut-off value (0.80 mmol/L) in the subgroup analysis.ConclusionsLower baseline serum magnesium levels (<0.80 mmol/L) on admission are associated with increased risk of HT in AIS patients receiving MT.

ASPECTS-Based Attenuation Changes on CT Angiography as an Imaging Biomarker to Predict Hemorrhagic Transformation in Acute Ischemic Stroke

Introduction: Imaging-based early warning indicators and feasible stratification of acute ischemic stroke (AIS) patients with hemorrhagic transformation (HT), especially high-risk patients with parenchymal hematoma (PH), are crucial in determining subsequent treatment strategies. This study combined automated ASPECTS software with noncontrast CT (NCCT) and CTA source image (CTASI) attenuation changes using Hounsfield unit (HU) values to predict HT and PH in patients with AIS. Materials and Methods: We retrospectively enrolled 172 consecutive patients with anterior circulation large-vessel occlusion between 2016 and 2020. Univariate and multivariate logistic regression and receiver operating characteristic (ROC) analyses were used to investigate the relationship between NCCT and CTASI-ASPECTS-HU, as well as other clinical and radiological parameters of HT and PH. Univariate and multivariate logistic regression analyses were performed to explore risk factors for HT or PH, and an ROC curve was used to evaluate their diagnostic values. Results: A multivariate analysis showed that CTASI-ASPECTS-HU and NIHSS score were independent predictors of HT (CTASI-ASPECTS-HU: odds ratio (OR), 2.22; 95% CI, 1.01–4.92; NIHSS: OR, 1.07; 95% CI, 1.02–1.13) and PH (CTASI-ASPECTS-HU: OR, 6.51; 95% CI, 2.29–18.50; NIHSS: OR, 1.07; 95% CI, 1.01–1.13). According to ROC analysis, CTASI-ASPECTS-HU >0.09 identified HT (area under the curve, 0.70; sensitivity, 70.15%; specificity, 61.90%), and CTASI-ASPECTS-HU >0.10 identified PH (area under the curve, 0.79; sensitivity, 76.19%; specificity, 73.33%). The area under the curve for predicting HT or PH increased when CTASI-ASPECTS-HU was combined with NIHSS score (HT: area under the curve, 0.74; sensitivity, 73.13%; specificity, 70.48%; PH: area under the curve, 0.81; sensitivity, 85.71%; specificity, 72.38%). Conclusion: CTASI-ASPECTS-HU is a reliable radiological predictor of HT and PH in patients with AIS. Its predictive efficacy is moderately improved when combined with NIHSS score.

A clinical study on stroke patients with emphasis on improvement pattern

Introduction: Stroke is a medical emergency and can cause permanent neurological damage, complication, and death if not promptly diagnosed and treated. It is also the third leading cause of death and serious disability, with major societal consequences. Materials and Methods: A prospective observational study on adult patients aged above 18 years diagnosed with acute ischemic stroke to understand the incidence and predictors of hemorrhagic transformation (HT) during hospital stay was conducted over a period of 18 months at a tertiary care hospital in Mangalore, India. The aim of this study was to find out the incidence of HT in acute ischemic stroke during the first week of hospitalization and to find out the predictors associated with the development of HT. Results: The mean age was 61.37 years, 59.28 years, 66.43 years and 48 years in those with mild stroke, moderate stroke, moderate to severe stroke and severe stroke respectively. Overall males had a higher incidence there was a statistically significant difference in the incidence of stroke, 53.33% were males. 16.67% had hemorrhagic transformation in ischemic stroke. Conclusion: Our study found that the risk of hemorrhagic transformation is higher in females than males.