Prediction of Hemorrhagic Transformation After Ischemic Stroke: Development and Validation Study of a Novel Multi-biomarker Model

Neutrophil-to-Lymphocyte Ratio, Lymphocyte-to-Monocyte Ratio and Platelet-to-Lymphocyte Ratio as Predictors of Short- and Long-Term Outcomes in Ischemic Stroke Patients with Atrial Fibrillation

Purpose

Inflammatory response plays essential roles in the pathophysiology of both ischemic stroke and atrial fibrillation (AF). We aimed to investigate whether composite inflammatory markers, including neutrophil to lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR) and platelet-to-lymphocyte ratio (PLR), can serve as early predictors of short- and long-term outcomes in ischemic stroke patients with AF.

Patients and Methods

Ischemic stroke patients with AF were enrolled in this cohort study. The primary outcome was 1-year functional dependence or death (modified Rankin scale (mRS) score 3-6). Secondary outcomes included hemorrhagic transformation (HT) and early neurological deterioration (END, increase in the National Institutes of Health Stroke Scale (NIHSS) ≥4 within 7 days). Partial correlations were performed to assess the correlation between systemic inflammation markers and admission NIHSS scores. Univariate and multivariate logistic analyses were performed to investigate whether systemic inflammatory markers were independent predictors of adverse outcomes.

Results

A total of 408 patients were included. Partial correlation analysis revealed statistically significant but weak correlations between the NLR (r = 0.287; P < 0.001), PLR (r = 0.158; P = 0.001) and admission NIHSS score. Compared with patients without HT or END, patients who developed HT or END had higher NLR and PLR, and lower LMR. Patients in the functional dependence or death group had significantly higher NLR and PLR, and lower LMR than those in the functional independence group (all P < 0.001). Multivariate logistic analysis indicated that NLR, LMR and PLR were independent predictors of HT (OR = 1.069, 0.814 and 1.003, respectively), END (OR = 1.100, 0.768 and 1.006, respectively) and adverse 1-year functional outcome (OR = 1.139, 0.760 and 1.005, respectively).

Conclusion

NLR, LMR and PLR were independent predictors for in-hospital HT, END and long-term functional outcome in ischemic stroke patients with AF. Close monitoring of these inflammatory markers may help guide risk stratification and clinical treatment strategies.

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

Purpose

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.

Methods

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.

Results

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.

Conclusion

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.

Circulating miRNA profiles and the risk of hemorrhagic transformation after thrombolytic treatment of acute ischemic stroke: a pilot study

Background

Hemorrhagic transformation (HT) in acute ischemic stroke is likely to occur in patients treated with intravenous thrombolysis (IVT) and may lead to neurological deterioration and symptomatic intracranial hemorrhage (sICH). Despite the complex inclusion and exclusion criteria for IVT and some useful tools to stratify HT risk, sICH still occurs in approximately 6% of patients because some of the risk factors for this complication remain unknown.

Objective

This study aimed to explore whether there are any differences in circulating microRNA (miRNA) profiles between patients who develop HT after thrombolysis and those who do not.

Methods

Using qPCR, we quantified the expression of 84 miRNAs in plasma samples collected prior to thrombolytic treatment from 10 individuals who eventually developed HT and 10 patients who did not. For miRNAs that were downregulated (fold change (FC) <0.67) or upregulated (FC >1.5) with p < 0.10, we investigated the tissue specificity and performed KEGG pathway annotation using bioinformatics tools. Owing to the small patient sample size, instead of multivariate analysis with all major known HT risk factors, we matched the results with the admission NIHSS scores only.

Results

We observed trends towards downregulation of miR-1-3p, miR-133a-3p, miR-133b and miR-376c-3p, and upregulation of miR-7-5p, miR-17-3p, and miR-296-5p. Previously, the upregulated miR-7-5p was found to be highly expressed in the brain, whereas miR-1, miR-133a-3p and miR-133b appeared to be specific to the muscles and myocardium.

Conclusion

miRNA profiles tend to differ between patients who develop HT and those who do not, suggesting that miRNA profiling, likely in association with other omics approaches, may increase the current power of tools predicting thrombolysis-associated sICH in acute ischemic stroke patients. This study represents a free hypothesis-approach pilot study as a continuation from our previous work. Herein, we showed that applying mathematical analyses to extract information from raw big data may result in the identification of new pathophysiological pathways and may complete standard design works.

Key CT markers for predicting haemorrhagic transformation after ischaemic stroke: a prospective cohort study in China

OBJECTIVES

Limited studies have systematically addressed the CT markers of predicting haemorrhagic transformation (HT). We aimed to (1) investigate the predictive ability of the imaging factors on multimodal CT for HT and (2) identify the key CT markers that can accurately predict HT while maintaining easy and rapid assessment in the early stage of stroke.

DESIGN AND SETTING

This was a prospective cohort study conducted in a tertiary hospital in Southwest China.

PARTICIPANTS

Patients with ischaemic stroke admitted within 24 hours after onset were included.

OUTCOME MEASURES

The primary outcome was measured as the overall HT. The secondary outcomes were the presence of parenchymal haematoma, symptomatic HT and spontaneous HT.

RESULTS

A total of 763 patients were included. The early hypodensity >1/3 of the middle cerebral artery (MCA) territory, Alberta Stroke Programme Early CT Score≤7, midline shift, hyperdense middle cerebral artery sign (HMCAS), poor collateral circulation, infarct core and penumbra was independently associated with the increased risk of HT (all p < 0.05). The sensitivity of midline shift for predicting HT was only 3.5%, whereas its specificity was 99.8%. The combination of the early hypodensity >1/3 of the MCA territory, midline shift and HMCAS showed a good predictive performance for HT (area under the curve 0.80, 95% CI 0.75 to 0.84).

CONCLUSIONS

Seven imaging factors on multimodal CT were independently associated with HT. The high specificity of midline shift suggests the need to consider it as an imaging indicator when assessing the risk of HT. The early hypodensity >1/3 of the MCA territory, midline shift and HMCAS was identified as the key CT markers for the early prediction of HT. The coexistence of the three key factors might be a valuable index for identifying individuals at high bleeding risk and guiding further treatments.

Impact of lipid profiles on parenchymal hemorrhage and early outcome after mechanical thrombectomy

OBJECTIVE

We aimed to investigate the association of lipid parameters with parenchymal hemorrhage (PH) and early neurological improvement (ENI) after mechanical thrombectomy (MT) in stroke patients.

METHODS

We retrospectively analyzed consecutive patients who underwent MT between January 2019 and February 2022 at a tertiary stroke center. PH was diagnosed and classified as PH-1 and PH-2 according to the European Cooperative Acute Stroke Study definition. ENI was defined as a decrease in the National Institutes of Health Stroke Scale (NIHSS) score by ≥8 or an NIHSS score of ≤1 at 24 h after MT.

RESULTS

Among 155 patients, PH occurred in 41 (26.5%) patients, and 34 (21.9%) patients achieved ENI. In multivariate analysis, lower triglyceride to high-density lipoprotein cholesterol ratio (TG/HDL-C) value (OR = 0.51; 95% CI 0.30-0.89; p = 0.017) and higher HDL-C level (OR = 5.83; 95% CI 1.26-26.99; p = 0.024) were independently associated with PH. The combination of TG <0.77 mmol/L and HDL-C ≥ 0.85 mmol/L was the strongest predictor of PH (OR = 10.73; 95% CI 2.89-39.87; p < 0.001). A low HDL-C level was an independent predictor of ENI (OR 0.13; 95% CI 0.02-0.95; p = 0.045), and PH partially accounts for the failure of ENI in patients with higher HDL-C levels (estimate: -0.05; 95% CI: -0.11 to -0.01; p = 0.016).

INTERPRETATION

The combination of lower TG level and higher HDL-C level can predict PH after MT. Postprocedural PH partially accounts for the failure of ENI in patients with higher HDL-C levels. Further studies into the pathophysiological mechanisms underlying this observation are of interest.

Early Hemorrhagic Transformation after Reperfusion Therapy in Patients with Acute Ischemic Stroke: Analysis of Risk Factors and Predictors

Background: The standard reperfusion therapy for acute ischemic stroke (AIS) is considered to be thrombolysis, but its application is limited by the high risk of hemorrhagic transformation (HT). This study aimed to analyze risk factors and predictors of early HT after reperfusion therapy (intravenous thrombolysis or mechanical thrombectomy). Material and methods: Patients with acute ischemic stroke who developed HT in the first 24 h after receiving rtPA thrombolysis or performing mechanical thrombectomy were retrospectively reviewed. They were divided into two groups, respectively, the early-HT group and the without-early-HT group based on cranial computed tomography performed at 24 h, regardless of the type of hemorrhagic transformation. Results: A total of 211 consecutive patients were enrolled in this study. Among these patients, 20.37% (n = 43; age: median 70.00 years; 51.2% males) had early HT. Multivariate analysis of independent risk factors associated with early HT found that male gender increased the risk by 2.7-fold, the presence of baseline high blood pressure by 2.4-fold, and high glycemic values by 1.2-fold. Higher values of NIHSS at 24 h increased the risk of hemorrhagic transformation by 1.18-fold, while higher values of ASPECTS at 24 h decreased the risk of hemorrhagic transformation by 0.6-fold. Conclusions: In our study, male gender, baseline high blood pressure, and high glycemic values, along with higher values of NIHSS were associated with the increased risk of early HT. Furthermore, the identification of early-HT predictors is critical in patients with AIS for the clinical outcome after reperfusion therapy. Predictive models to be used in the future to select more careful patients with a low risk of early HT need to be developed in order to minimize the impact of HT associated with reperfusion techniques.

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

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Big Data in Stroke: How to Use Big Data to Make the Next Management Decision

The last decade has seen significant advances in the accumulation of medical data, the computational techniques to analyze that data, and corresponding improvements in management. Interventions such as thrombolytics and mechanical thrombectomy improve patient outcomes after stroke in selected patients; however, significant gaps remain in our ability to select patients, predict complications, and understand outcomes. Big data and the computational methods needed to analyze it can address these gaps. For example, automated analysis of neuroimaging to estimate the volume of brain tissue that is ischemic and salvageable can help triage patients for acute interventions. Data-intensive computational techniques can perform complex risk calculations that are too cumbersome to be completed by humans, resulting in more accurate and timely prediction of which patients require increased vigilance for adverse events such as treatment complications. To handle the accumulation of complex medical data, a variety of advanced computational techniques referred to as machine learning and artificial intelligence now routinely complement traditional statistical inference. In this narrative review, we explore data-intensive techniques in stroke research, how it has informed the management of stroke patients, and how current work could shape clinical practice in the future.

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.

S100b in acute ischemic stroke clots is a biomarker for post-thrombectomy intracranial hemorrhages

Background and purpose

Post-thrombectomy intracranial hemorrhages (PTIH) are dangerous complications of acute ischemic stroke (AIS) following mechanical thrombectomy. We aimed to investigate if S100b levels in AIS clots removed by mechanical thrombectomy correlated to increased risk of PTIH.

Methods

We analyzed 122 thrombi from 80 AIS patients in the RESTORE Registry of AIS clots, selecting an equal number of patients having been pre-treated or not with rtPA (40 each group). Within each subgroup, 20 patients had developed PTIH and 20 patients showed no signs of hemorrhage. Gross photos of each clot were taken and extracted clot area (ECA) was measured using ImageJ. Immunohistochemistry for S100b was performed and Orbit Image Analysis was used for quantification. Immunofluorescence was performed to investigate co-localization between S100b and T-lymphocytes, neutrophils and macrophages. Chi-square or Kruskal-Wallis test were used for statistical analysis.

Results

PTIH was associated with higher S100b levels in clots (0.33 [0.08-0.85] vs. 0.07 [0.02-0.27] mm², H1 = 6.021, P = 0.014^*), but S100b levels were not significantly affected by acute thrombolytic treatment (P = 0.386). PTIH was also associated with patients having higher NIHSS at admission (20.0 [17.0-23.0] vs. 14.0 [10.5-19.0], H1 = 8.006, P = 0.005) and higher number of passes during thrombectomy (2 [1-4] vs. 1 [1-2.5], H1 = 5.995, P = 0.014^*). S100b co-localized with neutrophils, macrophages and with T-lymphocytes in the clots.

Conclusions

Higher S100b expression in AIS clots, higher NIHSS at admission and higher number of passes during thrombectomy are all associated with PTIH. Further investigation of S100b expression in AIS clots by neutrophils, macrophages and T-lymphocytes could provide insight into the role of S100b in thromboinflammation.

Development and Validation of a Predictive Model for Spontaneous Hemorrhagic Transformation After Ischemic Stroke

Background: Hemorrhagic transformation (HT) after reperfusion therapy for acute ischemic stroke (AIS) has been well studied; however, there is scarce research focusing on spontaneous HT (sHT). Spontaneous HT is no less important with a relatively high incidence and could be associated with neurological worsening. We aimed to develop and validate a simple and practical model to predict sHT after AIS (SHAIS) and compared the predictive value of the SHAIS score against the models of post-Reperfusion HT for sHT.

Methods: Patients with AIS admitted within 24 h of onset were prospectively screened to develop and validate the SHAIS score. The primary outcome was sHT during hospitalization (within 30 days after onset), and the secondary outcomes were symptomatic sHT and parenchymal hematoma (PH). Clinical information, laboratory, and neuroimaging data were screened to construct the SHAIS score. We selected six commonly used scales for predicting HT after reperfusion therapy and compared their predictive ability for sHT with the SHAIS score using Delong's test.

Results: The derivation cohort included 539 patients (mean age, 68.1 years; men, 61.4%), of whom 91 (16.9%) patients developed sHT with 25.3% (23/91) being symptomatic sHT and 62.6% (57/91) being PH. Five variables (atrial fibrillation, NIHSS score ≥ 10, hypodensity > 1/3 of middle cerebral artery territory, hyperdense artery sign, and anterior circulation infarction) composed the SHAIS score, which ranged from 0 to 11 points. The area under the receiver-operating characteristic curve (AUC) was 0.86 (95% CI 0.82–0.91, p < 0.001) for the overall sHT, 0.85 (95% CI 0.76–0.92, p < 0.001) for symptomatic sHT, and 0.89 (95% CI 0.85–0.94, p < 0.001) for PH. No evidence of miscalibration of the SHAIS score was found to predict the overall sHT (p = 0.19), symptomatic sHT (p = 0.44), and PH (p = 0.22). The internal (n = 245) and external validation cohorts (n = 200) depicted similar predictive performance compared to the derivation cohort. The SHAIS score had a higher AUC to predict sHT than any of the six pre-Existing models (p < 0.05).

Conclusions: The SHAIS score provides an easy-to-use model to predict sHT, which could help providers with decision-making about treatments with high bleeding risk, and to counsel patients and families on the baseline risk of HT, aligning expectations with probable outcomes.

Prediction of Hemorrhagic Transformation after Ischemic Stroke Using Machine Learning

Hemorrhagic transformation (HT) is one of the leading causes of a poor prognostic marker after acute ischemic stroke (AIS). We compared the performances of the several machine learning (ML) algorithms to predict HT after AIS using only structured data. A total of 2028 patients with AIS, who were admitted within seven days of symptoms onset, were included in this analysis. HT was defined based on the criteria of the European Co-operative Acute Stroke Study-II trial. The whole dataset was randomly divided into a training and a test dataset with a 7:3 ratio. Binary logistic regression, support vector machine, extreme gradient boosting, and artificial neural network (ANN) algorithms were used to assess the performance of predicting the HT occurrence after AIS. Five-fold cross validation and a grid search technique were used to optimize the hyperparameters of each ML model, which had its performance measured by the area under the receiver operating characteristic (AUROC) curve. Among the included AIS patients, the mean age and number of male subjects were 69.6 years and 1183 (58.3%), respectively. HT was observed in 318 subjects (15.7%). There were no significant differences in corresponding variables between the training and test dataset. Among all the ML algorithms, the ANN algorithm showed the best performance in terms of predicting the occurrence of HT in our dataset (0.844). Feature scaling including standardization and normalization, and the resampling strategy showed no additional improvement of the ANN's performance. The ANN-based prediction of HT after AIS showed better performance than the conventional ML algorithms. Deep learning may be used to predict important outcomes for structured data-based prediction.