Hemorrhagic Transformation Group Research Articles

Constructing machine learning models based on non-contrast CT radiomics to predict hemorrhagic transformation after stoke: a two-center study.

Machine learning (ML) models were constructed according to non-contrast computed tomography (NCCT) images as well as clinical and laboratory information to assess risk stratification for the occurrence of hemorrhagic transformation (HT) in acute ischemic stroke (AIS) patients. A retrospective cohort was constructed with 180 AIS patients who were diagnosed at two centers between January 2019 and October 2023 and were followed for HT outcomes. Patients were analyzed for clinical risk factors for developing HT, infarct texture features were extracted from NCCT images, and the radiomics score (Rad-score) was calculated. Then, five ML models were established and evaluated, and the optimal ML algorithm was used to construct the clinical, radiomics, and clinical-radiomics models. Receiver operating characteristic (ROC) curves were used to compare the performance of the three models in predicting HT. Based on the outcomes of the AIS patients, 104 developed HT, and the remaining 76 had no HT. The HT group consisted of 27 hemorrhagic infarction (HI) and 77 parenchymal-hemorrhage (PH). Patients with HT had a greater neutrophil-to-lymphocyte ratio (NLR), baseline National Institutes of Health Stroke Scale (NIHSS) score, infarct volume, and Rad-score and lower Alberta stroke program early CT score (ASPECTS) (all p < 0.01) than patients without HT. The best ML algorithm for building the model was logistic regression. In the training and validation cohorts, the AUC values for the clinical, radiomics, and clinical-radiomics models for predicting HT were 0.829 and 0.876, 0.813 and 0.898, and 0.876 and 0.957, respectively. In subgroup analyses with different treatment modalities, different infarct sizes, and different stroke time windows, the assessment accuracy of the clinical-radiomics model was not statistically meaningful (all p > 0.05), with an overall accuracy of 79.5%. Moreover, this model performed reliably in predicting the PH and HI subcategories, with accuracies of 82.9 and 92.9%, respectively. ML models based on clinical and NCCT radiomics characteristics can be used for early risk evaluation of HT development in AIS patients and show great potential for clinical precision in treatment and prognostic assessment.

Combinations of Clinical Factors, CT Signs, Radiomics for Differentiating High-density Areas After Mechanical Thrombectomy in Patients with Acute Ischemic Stroke.

Clinically, hemorrhagic transformation (HT) after mechanical thrombectomy (MT) is a common complication. This study is aim to investigate the value of clinical factors, CT signs, and radiomics in the differential diagnosis of high-density areas (HDAs) in the brain after MT in patients with acute ischemic stroke with large vessel occlusion (AIS-LVO). A total of 156 eligible patients with AIS-LVO in Center Ⅰ from December 2015 to June 2023 were retrospectively enrolled and randomly divided into training (n=109) and internal validation (n=47) sets at a ratio of 7:3. The data of 63 patients in Center Ⅱ were collected as an external validation set. According to the diagnostic criteria, the patients in the three datasets were divided into a HT group and a non-HT group. The clinical and imaging data from Centers Ⅰ and Ⅱ were used to construct a clinical factor and CT-sign model, a radiomic model and a combined model by logistic regression (LR). Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic efficacy of each model in the three datasets. Clinical blood glucose (Glu) and the maximum cross-sectional area (Areamax) on CT were associated with the nature of the HDA according to multivariate LR analyses (P < 0.05). Among the three models, the combined model had the highest diagnostic efficiency, with area under the curve (AUC) values of 0.895, 0.882, and 0.820 in the three datasets, which were significantly greater than the AUC values of the radiomic model (0.887, 0.898, 0.798) and clinical factor and CT sign model (0.831, 0.744, 0.684). The combined model based on radiomics had the best performance, indicating that radiomic features can be used as imaging biomarkers to aid in the clinical judgment of the nature of HDA after MT. HDA =high-density area; HT =hemorrhagic transformation; MT =mechanical thrombectomy; AIS-LVO =acute ischemic stroke with large vessel occlusion; LR =logistic regression; AUC =area under the curve; ICE =iodine contrast extravasation; DECT =dual energy CT; IOM =iodine overlay map; VNC =virtual noncontrast; Glu =glucose; LASSO =least absolute shrinkage and selection operator; ICC =intraclass correlation coefficient; ROC =receiver operating characteristic; DCA =decision curve analysis.

Low blood flow ratio is associated with hemorrhagic transformation secondary to mechanical thrombectomy in patients with acute ischemic stroke

Background and purposeA significant decrease of cerebral blood flow (CBF) is a risk factor for hemorrhagic transformation (HT) in acute ischemic stroke (AIS). This study aimed to ascertain whether the ratio of different CBF thresholds derived from computed tomography perfusion (CTP) is an independent risk factor for HT after mechanical thrombectomy (MT). MethodsA retrospective single center cohort study was conducted on patients with AIS undergoing MT at the First Affiliated Hospital of Wenzhou Medical University from August 2018 to December 2023. The perfusion parameters before thrombectomy were obtained according to CTP automatic processing software. The low blood flow ratio (LFR) was defined as the ratio of brain volume with relative CBF <20 % over volume with relative CBF <30 %. HT was evaluated on the follow-up CT images. Binary logistic regression was used to analyze the correlation between parameters that differ between the two groups with regards to HT occurrence. The predictive efficacy was assessed utilizing the receiver operating characteristic curve. ResultsIn total, 243 patients met the inclusion criteria. During the follow-up, 46.5 % of the patients (113/243) developed HT. Compared with the Non-HT group, the HT group had a higher LFR (0.47 (0.34–0.65) vs. 0.32 (0.07–0.56); P < 0.001). According to the binary logistic regression analysis, the LFR (aOR: 6.737; 95 % CI: 1.994–22.758; P = 0.002), Hypertension history (aOR: 2.231; 95 % CI: 1.201–4.142; P = 0.011), plasma FIB levels before MT (aOR: 0.641; 95 % CI: 0.456–0.902; P = 0.011), and the mismatch ratio (aOR: 0.990; 95 % CI: 0.980–0.999; P = 0.030) were independently associated with HT secondary to MT. The area under the curve of the regression model for predicting HT was 0.741. ConclusionLFR, a ratio quantified via CTP, demonstrates potential as an independent risk factor of HT secondary to MT.

The ratio of the maximum density values: a new method for predicting hemorrhagic transformation in acute ischemic stroke patients undergoing mechanical thrombectomy.

It is challenging yet critical to differentiate between hemorrhagic transformation (HT) and contrast extravasation on non-contrast-enhanced computed tomography (NCCT) scans following mechanical thrombectomy (MT) in patients with acute ischemic stroke. We propose a new method called the ratio of maximum density values (RMDV) to minimize the confusion of contrast extravasation and to evaluate the diagnostic significance of RMDV in predicting HT on immediate post-interventional NCCT scans. We conducted a retrospective analysis of the prospective patients' database who received MT for acute ischemic stroke caused by occlusion of the intracranial large artery and showed postinterventional cerebral hyperdensities (PCHDs) on NCCT scans immediately after MT. Based on the subsequent NCCT scans, we divided patients with PCHDs into the HT and the non-HT groups. The clinical characters and radiological details were collected and compared to the two groups. We assessed the ability of RMDV >1 to predict HT by analyzing the receiver operating characteristic curve. One hundred and three patients showed PCHDs; 58 (56.31%) were classified as HT, while 45 (43.69%) were classified as non-HT. The only notable distinction between the two groups was the proportion of RMDV >1 in the HT group. The correlation between HT and RMDV >1 with an area under the curve of 0.826 (95% confidence interval, 0.739 to 0.894). The sensitivity, specificity, positive, and negative predictive values of RMDV >1 on NCCT for predicting HT were 89.66, 75.56, 82.54, and 85.00%, respectively. The utilization of RMDV >1 on immediate NCCT scans after MT can predict early HT with good sensitivity and specificity.

Association between uric acid and the risk of hemorrhagic transformation in patients with acute ischemic stroke: a systematic review and meta-analysis.

The relationship between hemorrhagic transformation (HT) and uric acid (UA) remains controversial. This study aimed to investigate the relationship between UA concentrations and the risk of HT following acute ischemic stroke (AIS). Electronic databases were searched for studies on HT and UA from inception to October 31, 2023. Two researchers independently reviewed the studies for inclusion. STATA Software 16.0 was used to compute the standardized mean difference (SMD) and 95% confidence interval (CI) for the pooled and post-outlier outcomes. Heterogeneity was evaluated using the I2 statistic and the Galbraith plot. Additionally, sensitivity analysis was performed. Lastly, Begg's funnel plot and Egger's test were used to assess publication bias. A total of 11 studies involving 4,608 patients were included in the meta-analysis. The pooled SMD forest plot (SMD = -0.313, 95% CI = -0.586--0.039, p = 0.025) displayed that low UA concentrations were linked to a higher risk of HT in post-AIS patients. However, heterogeneity (I2 = 89.8%, p < 0.001) was high among the studies. Six papers fell outside the Galbraith plot regression line, and there exclusive resulted in the absence of heterogeneity (I2 = 52.1%, p = 0.080). Meanwhile, repeated SMD analysis (SMD = -0.517, 95% CI = -0.748--0.285, p = 0.000) demonstrated that the HT group had lower UA concentrations. Finally, Begg's funnel plot and Egger's test indicated the absence of publication bias in our meta-analysis. This meta-analysis illustrated a substantial connection between UA concentrations and HT, with lower UA concentrations independently linked with a higher risk of HT post-AIS. These results lay a theoretical reference for future studies.Systematic review registration:https://www.crd.york.ac.uk/PROSPERO/CRD42023485539.

Magnetic Resonance Perfusion-Weighted Imaging in Predicting Hemorrhagic Transformation of Acute Ischemic Stroke: A Retrospective Study.

Hemorrhagic transformation (HT) is one of the common complications in patients with acute ischemic stroke (AIS). This study aims to investigate the value of different thresholds of Tmax generated from perfusion-weighted MR imaging (PWI) and the apparent diffusion coefficient (ADC) value in the prediction of HT in AIS. A total of 156 AIS patients were enrolled in this study, with 55 patients in the HT group and 101 patients in non-HT group. The clinical baseline data and multi-parametric MRI findings were compared between HT and non-HT groups to identify indicators related to HT. The optimal parameters for predicting HT and the corresponding cutoff values were obtained using the receiver operating characteristic curve analysis of the volumes of ADC < 620 × 10-6 mm2/s and Tmax > 6 s, 8 s, and 10 s. The results showed that the volumes of ADC < 620 × 10-6 mm2/s and Tmax > 6 s, 8 s, and 10 s in the HT group were all significantly larger than that in the non-HT group and were all independent risk factors for HT. Early measurement of the volume of Tmax > 10 s had the highest value, with a cutoff lesion volume of 10.5 mL.

Predictive role of admission serum glucose, baseline NIHSS score, and fibrinogen on hemorrhagic transformation after intravenous thrombolysis with alteplase in acute ischemic stroke.

This study aimed to investigate the predictive role of admission serum glucose, baseline NIHSS score, and fibrinogen on hemorrhagic transformation after intravenous thrombolysis with alteplase in acute ischemic stroke. A total of 254 patients admitted with acute ischemic stroke who received intravenous thrombolysis with alteplase from January 2016 to December 2017 were selected to collect clinical data. Patients were divided into a hemorrhagic transformation group (n=70) and a no-hemorrhagic transformation group (n=184) based on repeat CT/magnetic resonance imaging (MRI) findings during the acute period. The demographic data, past medical history and laboratory examination indexes of the two groups were compared. Multivariate Logistic regression analysis was used to explore the influencing factors of hemorrhage transformation after intravenous thrombolysis in patients with acute ischemic stroke. ROC curve was used to plot the ability of blood glucose at admission, baseline NIHSS score and fibrinogen alone to predict bleeding transformation after intravenous thrombolysis of alteplase, and then the combined model of the three was constructed and the predictive ability of this model to bleeding transformation was evaluated. Among 254 patients, 70% (27.55%) had hemorrhage transformation. Except for DNT, red blood cell count, platelet count, fibrinogen, smoking, atrial fibrillation, baseline NIHSS score and admission serum glucose, there were statistically significant differences between the hemorrhagic transformation group and the non-hemorrhagic transformation group (p<0.05), and there were no statistically significant differences in other indicators between the two groups (p>0.05). The combined model was better than the three models alone in predicting the risk of bleeding conversion (p<0.05). Compared with the group without hemorrhagic transformation, the 90d prognosis was worse in the hemorrhage transformation group (p<0.05). Admission blood glucose, NIHSS score, and fibrinogen are independent risk factors for hemorrhage transformation after intravenous thrombolysis of alteplase in patients with acute ischemic stroke, and the combined model established by them has high predictive efficacy for hemorrhage transformation risk after intravenous thrombolysis of alteplase.

Predictive effects of S100β and CRP levels on hemorrhagic transformation in patients with AIS after intravenous thrombolysis: A concise review based on our center experience.

Hemorrhagic transformation (HT) is one of the most dangerous complications after intravenous thrombolysis in patients with acute ischemic stroke (AIS). Therefore, we want to explore the predictive effects of peripheral blood S100β and C-Reactive Protein (CRP) levels on hemorrhagic transformation after intravenous thrombolysis in AIS patients. Ninety-two AIS patients who had been treated in Huai'an Second People's Hospital from January 2018 to December 2021 were retrospectively selected. Patients were divided into hemorrhagic transformation (HT) groups (24 cases) and no HT groups (68 cases) based on whether there was hemorrhagic transformation within 24 h after intravenous thrombolysis. General clinical data from the HT group and no HT group were compared. A multivariate logistic regression model was used to analyze the potential risk factors of HT after intravenous thrombolysis in patients with AIS. A receiver operating curve (ROC) was used to analyze the predictive value of risk factors for HT. High serum S100β, CRP levels, and National Institutes of Health Stroke Scale (NIHSS) scores were found to be risk factors for HT after intravenous thrombolysis in patients with AIS (all P < .05). The ROC curve analysis showed that critical value of S100β, CRP level, and NIHSS score for predicting intravenous thrombolytic HT in AIS patients were 0.335, 8.700, and 14.50, respectively, and their sensitivities were 0.750, 0.971, and 0.333 (P < .05), respectively. High serum S100β and CRP levels are risk factors for HT after intravenous thrombolysis in AIS patients and have predictive influence of the occurrence of HT in AIS patients.

Hemorrhagic transformation in patients with large-artery atherosclerotic stroke is associated with the gut microbiota and lipopolysaccharide.

Abstract JOURNAL/nrgr/04.03/01300535-202407000-00032/figure1/v/2023-11-20T171125Z/r/image-tiff Hemorrhagic transformation is a major complication of large-artery atherosclerotic stroke (a major ischemic stroke subtype) that worsens outcomes and increases mortality. Disruption of the gut microbiota is an important feature of stroke, and some specific bacteria and bacterial metabolites may contribute to hemorrhagic transformation pathogenesis. We aimed to investigate the relationship between the gut microbiota and hemorrhagic transformation in large-artery atherosclerotic stroke. An observational retrospective study was conducted. From May 2020 to September 2021, blood and fecal samples were obtained upon admission from 32 patients with first-ever acute ischemic stroke and not undergoing intravenous thrombolysis or endovascular thrombectomy, as well as 16 healthy controls. Patients with stroke who developed hemorrhagic transformation (n = 15) were compared to those who did not develop hemorrhagic transformation (n = 17) and with healthy controls. The gut microbiota was assessed through 16S ribosomal ribonucleic acid sequencing. We also examined key components of the lipopolysaccharide pathway: lipopolysaccharide, lipopolysaccharide-binding protein, and soluble CD14. We observed that bacterial diversity was decreased in both the hemorrhagic transformation and non-hemorrhagic transformation group compared with the healthy controls. The patients with ischemic stroke who developed hemorrhagic transformation exhibited altered gut microbiota composition, in particular an increase in the relative abundance and diversity of members belonging to the Enterobacteriaceae family. Plasma lipopolysaccharide and lipopolysaccharide-binding protein levels were higher in the hemorrhagic transformation group compared with the non-hemorrhagic transformation group. lipopolysaccharide, lipopolysaccharide-binding protein, and soluble CD14 concentrations were associated with increased abundance of Enterobacteriaceae. Next, the role of the gut microbiota in hemorrhagic transformation was evaluated using an experimental stroke rat model. In this model, transplantation of the gut microbiota from hemorrhagic transformation rats into the recipient rats triggered higher plasma levels of lipopolysaccharide, lipopolysaccharide-binding protein, and soluble CD14. Taken together, our findings demonstrate a noticeable change in the gut microbiota and lipopolysaccharide-related inflammatory response in stroke patients with hemorrhagic transformation. This suggests that maintaining a balanced gut microbiota may be an important factor in preventing hemorrhagic transformation after stroke.

Hemorrhagic risk factors after rt PA thrombolysis in acute cerebral infarction.

This study aims to investigate the risk factors of hemorrhagic transformation (HT) after thrombolysis with recombinant tissue plasminogen activator (rt-PA) in patients with acute cerebral infarction (ACI) and establish a logistic regression equation and the risk prediction model. One hundred and ninety patients with ACI were divided into the HT group (n=20) and non-HT group (n=170) according to whether HT occurred within 24 hours after rt-PA thrombolysis. The clinical data were collected for analyzing the influencing factors, and a logistic regression analysis model was then established. Besides, patients in the HT group were further grouped into symptomatic hemorrhage (n=7) and non-symptomatic hemorrhage (n=13) according to the type of hemorrhage. The clinical diagnostic value of risk factors in symptomatic hemorrhage after thrombolysis in ACI was analyzed using the ROC curve. We found that history of atrial fibrillation, time from onset to thrombolysis, pre-thrombolytic glucose, pre-thrombolytic National Institute of Health Stroke Scale (NIHSS) score, 24-hour post-thrombolytic NIHSS score, and proportion of patients with large cerebral infarction were all the influencing factors of HT risk after rt-PA thrombolysis in patients with ACI (p<0.05). Logistic regression analysis model was established with an accuracy of 88.42% (168/190), a sensitivity of 75.00% (15/20), and a specificity of 90.00% (153/170). The time from onset to thrombolysis, pre-thrombolytic glucose, and 24-hour post-thrombolytic NIHSS score had higher clinical value in predicting the risk of HT after rt-PA thrombolysis, with the AUCs of 0.874, 0.815 and 0.881, respectively. Blood glucose and pre-thrombolytic NIHSS score were independent risk factors related to symptomatic hemorrhage after thrombolysis in ACI (p<0.05). The AUCs for predicting symptomatic hemorrhage alone and in combination were 0.813, 0.835, and 0.907, respectively, with sensitivities of 85.70%, 87.50% and 90.00%, and specificities of 62.50%, 60.00%, and 75.42% respectively. The establishment of a prediction model based on the risk factors of HT after rt-PA thrombolysis had a good predictive value in patients with ACI. This model was helpful in guiding clinical judgment and improving the safety of intravenous thrombolysis. Early identification of symptomatic bleeding risk factors provided a reference for clinical treatment and prognostic measures of patients with ACI.

Machine learning predicts the risk of hemorrhagic transformation of acute cerebral infarction and in-hospital death

BackgroundThe incidence of hemorrhagic transformation (HT) during thrombolysis after acute cerebral infarction (ACI) is very high. We aimed to develop a model to predict the occurrence of HT after ACI and the risk of death after HT. MethodsCohort 1 is divided into HT and non-HT groups, to train the model and perform internal validation. All first laboratory test results of study subjects were used as features to be selected for machine learning, and the models built by four machine learning algorithms were compared to screen the best algorithm and model. Following that, the HT group was divided into death and non-death for subgroup analysis. Receiver operating characteristic (ROC) curves etc. to evaluate the model. ACI patients in cohort 2 for external validation. ResultsIn cohort 1, the HT risk prediction model HT-Lab10 built by the XgBoost algorithm performed the best with AUCROC=0.95 (95% CI, 0.93-0.96). Ten features were included in the model, namely B-type natriuretic peptide precursor, ultrasensitive C-reactive protein, glucose, absolute neutrophil value, myoglobin, uric acid, creatinine, Ca2+, Thrombin time, and carbon dioxide combining power. The model also had the ability to predict death after HT with AUCROC=0.85 (95% CI, 0.78-0.91). The ability of HT-Lab10 to predict the occurrence of HT as well as death after HT was validated in cohort 2. ConclusionsThe model HT-Lab10 built using the XgBoost algorithm showed excellent predictive ability in both the occurrence of HT and the risk of HT death, achieving a model with multiple uses.

Lactate Dehydrogenase Predicts Hemorrhagic Transformation in Patients with Acute Ischemic Stroke

Introduction: Hemorrhagic transformation (HT) is a severe but frequent complication of acute ischemic stroke (AIS). This study aimed to evaluate the relationship between serum lactate dehydrogenase (LDH) levels and HT. Methods: We retrospectively included 542 AIS patients with HT and 1,091 age- and gender-matched patients without HT. Demographic and clinical data were obtained from medical records, and blood samples were obtained within 24 h after admission. The characteristics of the groups were compared. With the receiver operating characteristic (ROC) curve analysis, we assessed the discriminating capacity of LDH levels in predicting HT in patients with AIS. The logistic regression model was used to determine the connection between LDH and HT. Results: The HT group had considerably higher LDH levels than the non-HT group (263.0 [216.0–323.3] U/L versus 178.0 [162.0–195.0] U/L, p < 0.001). We also observed that the levels of LDH in the parenchymal hemorrhage subgroup were significantly higher than those in the hemorrhagic infarction subgroup (281.0 [230.0–340.0] U/L versus 258.0 [209.0–311.0] U/L, p < 0.001). The area under the ROC curve of LDH was 0.890 (95% confidence level [CI] 0.874–0.905, p < 0.001). Besides, logistic regression revealed that high LDH levels (LDH >215 U/L) showed a higher risk of HT (odds ratio = 10.958, 95% CI 7.964–15.078, p < 0.001). Conclusion: High LDH levels were linked with an increased risk of HT in AIS patients. Practical measures should be considered in patients with increased LDH levels (LDH >215 U/L).

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.

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.

Glucose to Platelet Ratio: A Potential Predictor of Hemorrhagic Transformation in Patients with Acute Ischemic Stroke.

Glucose and platelet are two easily obtained clinical indicators; the present research aimed to demonstrate their association with hemorrhagic transformation (HT) in acute ischemic stroke (AIS) patients without thrombolytic or thrombectomy therapy. This was a single-center retrospective study. Patients who were diagnosed with HT after AIS were included in the HT group. Meanwhile, using the propensity score matching (PSM) approach, with a ratio of 1:2, matched patients without HT were included in the non-HT group. Serum G/P levels were measured on the first morning after admission (at least eight hours after the last meal). Characteristics were compared between the two groups. Multivariate logistic regression was used to determine the independent relationship between G/P and HT after AIS, with G/P being divided into quartiles. From January 2013 to March 2022, we consecutively included 643 AIS patients with HT (426/643 [66.25%] with HI and 217/643 [33.75%] with PH), and 1282 AIS patients without HT, at the First Affiliated Hospital of Wenzhou Medical University. The HT group had higher G/P levels than the non-HT group (0.04 ± 0.02 vs. 0.03 ± 0.02, p < 0.001). However, there was no difference in G/P levels between HI and PH subgroups (0.04 ± 0.02 vs. 0.04 ± 0.02, p > 0.05). Moreover, the G/P levels were divided into quartiles (Q1 ≤ 0.022; Q2 = 0.023–0.028; Q3 = 0.029–0.039; Q4 ≥ 0.040), with Q1 being settled as the reference layer. After controlling the confounders, multivariate regression analyses showed that the Q4 layer (Q4: G/P ≥ 0.040) was independently associated with elevated HT risk (odds ratio [OR] = 1.85, 95% CI = 1.31–2.63, p < 0.001). G/P levels on admission were independently associated with HT risk in AIS patients. In clinical practice, adequate attention should be paid to AIS patients with elevated G/P levels (G/P ≥ 0.040).

ADC measurement relevance to predict hemorrhage transformation after mechanical thrombectomy

BackgroundADC (apparent coefficient diffusion) value has been known to predict hemorrhage transformation (HT) after thrombolysis and recently, after mechanical thrombectomy (MT). We aimed to evaluate that utility separately in basal ganglia and superficial territory. We used HT occurrence with or without NIHSS change as primary outcome measures. MethodsThis single-center retrospective study included consecutive stroke patients receiving MT for internal carotid artery (ICA) or middle cerebral artery (M1 or M2) occlusion. In patient with or without HT, using the Heidelberg Bleeding Classification, on follow-up CT scan at 24–48 h, we assessed the ADC value separately in basal ganglia and superficial territory on MRI before MT to search for the correlation. Multivariable analysis was performed using variables with significant differences between the HT group and non-HT group. ResultsOne hundred seventeen patients were included in the final analysis. HT distribution was as follows: 9 patients (7.69%) HI1 or 2; 14 patients (11.97%) PH1; 21 patients (17.95%) PH2; 29 patients (24.79%) subarachnoid hemorrhage; and 21 patients (17.95%) symptomatic intracranial hemorrhage (sICH). Mean ADC minimal value in basal ganglia in the HT group was significantly lower than in the non-HT group (377.6 × 10−6 mm2/s [± 52.4] vs 413.3 × 10−6 mm2/s [± 72.5]; p = 0.0229) with an area under the curve (AUC) of 0.6622 (95% CI: 0.5–0.8; p = 0.014). MRI-MT time was significantly longer in the HT group (p = 0.0002), but there was no association between ADC value and onset-MRI or MRI-MT times (Spearman's coefficients <0.7, p > 0.05). Glycemia at admission (>1.5 g/L) (OR = 4.2; 95% CI [1.611; 10.961]) and carotid occlusion (OR = 2.835; 95% CI [1.134; 7.091]) were independently associated with HT. ConclusionsADC value in basal ganglia, unlike brain superficial territory, are correlated to HT risk after MT.

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.

Machine Learning-Based Model for Prediction of Hemorrhage Transformation in Acute Ischemic Stroke After Alteplase.

Hemorrhage transformation (HT) is the most dreaded complication of intravenous thrombolysis (IVT) in acute ischemic stroke (AIS). The prediction of HT after IVT is important in the treatment decision-making for AIS. We designed and compared different machine learning methods, capable of predicting HT in AIS after IVT. A total of 345 AIS patients who received intravenous alteplase between January 2016 and June 2021 were enrolled in this retrospective study. The demographic characteristics, clinical condition, biochemical data, and neuroimaging variables were included for analysis. HT was confirmed by head computed tomography (CT) or magnetic resonance imaging (MRI) within 48 h after IVT. Based on the neuroimaging results, all of the patients were divided into the non-HT group and the HT group. Then, the variables were applied in logistic regression (LR) and random forest (RF) algorithms to establish HT prediction models. To evaluate the accuracy of the machine learning models, the models were compared to several of the common scales used in clinics, including the multicenter stroke survey (MSS) score, safe implementation of treatments in stroke (SITS) score, and SEDAN score. The performance of these prediction models was evaluated using the receiver operating characteristic (ROC) curve (AUC). Forty-five patients had HT (13.0%) within 48 h after IVT. The ROC curve results showed that the AUCs of HT that were predicted by the RF model, LR model, MSS, SITS, and SEDAN scales after IVT were 0.795 (95% CI, 0.647–0.944), 0.703 (95% CI, 0.515–0.892), 0.657 (95% CI, 0.574–0.741), 0.660 (95% CI, 0.580–0.740) and 0.655 (95% CI, 0.571–0.739), respectively. The RF model performed better than the other models and scales. The top four most influential factors in the RF importance matrix plot were triglyceride, Lpa, the baseline NIHSS, and hemoglobin. The SHapley Additive exPlanation values made the RF prediction model clinically interpretable. In this study, an RF machine learning method was successfully established to predict HT in AIS patients after intravenous alteplase, which the sensitivity was 66.7%, and the specificity was 80.7%.