Tissue window, not the time window, will guide acute stroke treatment

The Artificial Intelligence Revolution in Stroke Care: A Decade of Scientific Evidence in Review

BACKGROUND

The emergence of artificial intelligence (AI) has significantly influenced the diagnostic evaluation of stroke and has revolutionized acute stroke care delivery. The scientific evidence evaluating the role of AI, especially in areas of stroke treatment and rehabilitation is limited but continues to accumulate. We performed a systemic review of current scientific evidence evaluating the use of AI in stroke evaluation and care and examined the publication trends during the past decade.

METHODS

A systematic search of electronic databases was conducted to identify all studies published from 2012 to 2022 that incorporated AI in any aspect of stroke care. Studies not directly relevant to stroke care in the context of AI and duplicate studies were excluded. The level of evidence and publication trends were examined.

RESULTS

A total of 623 studies were examined, including 101 reviews (16.2%), 9 meta-analyses (1.4%), 140 original articles on AI methodology (22.5%), 2 case reports (0.3%), 2 case series (0.3%), 31 case-control studies (5%), 277 cohort studies (44.5%), 16 cross-sectional studies (2.6%), and 45 experimental studies (7.2%). The highest published area of AI in stroke was diagnosis (44.1%) and the lowest was rehabilitation (12%). A 10-year trend analysis revealed a significant increase in AI literature in stroke care.

CONCLUSIONS

Most research on AI is in the diagnostic area of stroke care, with a recent noteworthy trend of increased research focus on stroke treatment and rehabilitation.

Age and geographic disparities in acute ischaemic stroke prehospital delays in China: a cross-sectional study using national stroke registry data

Background

Stroke has been the leading cause of death in China for decades. The extremely low intravenous thrombolysis rate is largely due to the prehospital delays that make patients ineligible for the time-sensitive therapy. Limited studies evaluated prehospital delays across China. We investigated prehospital delays in the stroke population across China and the associated age, rurality, and geographic disparities.

Methods

A cross-sectional study design was employed using the Bigdata Observatory platform for Stroke of China in 2020, the nationwide, prospective, multicentre registry of patients with acute ischaemic stroke (AIS). Mixed-effect regression models were used to account for the clustered data.

Findings

The sample contained 78,389 AIS patients. The median onset-to-door (OTD) time was 24 h, with only 11.79% (95% confidence interval [CI]: 11.56-12.02%) patients arriving at hospitals within 3 h. About 12.43% (95% CI: 12.11-12.74%) of patients 65 years or older arrived at hospitals within 3 h, which was significantly higher than the young and middle-aged patients (11.03%; 95% CI: 10.71-11.36%). After controlling for potential confounders, young and middle-aged patients were less likely to present to hospitals within 3 h (adjusted odds ratio: 0.95; 95% CI: 0.90-0.99) compared to patients 65 years or older. The 3-h hospital arrival rate was the highest in Beijing (18.40%, 95% CI: 16.01-20.79%), which was almost 5 times higher than that in Gansu (3.45%, 95% CI: 2.69-4.20%). The arrival rate in urban areas was almost 2 times higher than that in rural areas (13.35% versus. 7.66%).

Interpretation

We found that the low rates of timely arrival at hospitals after a stroke is more salient in the younger population, rural settings, or those residing in less developed geographic regions. This study calls for more tailored interventions focusing on younger people, rural areas, and less developed geographic regions.

Funding

The National Natural Science Foundation of China; CIHR, Grant/Award Number: 81973157, PI: JZ. Natural Science Foundation of Shanghai; CIHR, Grant/Award Number: 17dz2308400, PI: JZ. Funding from the University of Pennsylvania; Grant/Award Number: CREF-030, PI: RL.

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

INTRODUCTION

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

Recent Advances in Targeted Nanotherapies for Ischemic Stroke

Ischemic stroke (IS) is a severe neurological disease caused by the narrowing or occlusion of cerebral blood vessels and is known for high morbidity, disability, and mortality rates. Clinically available treatments of stroke include the surgical removal of the thrombus and thrombolysis with tissue fibrinogen activator. Pharmaceuticals targeting IS are uncommon, and the development of new therapies is hindered by the low bioavailability and stability of many drugs. Nanomedicine provides new opportunities for the development of novel neuroprotective and thrombolytic strategies for the diagnosis and treatment of IS. Numerous nanotherapeutics with different physicochemical properties are currently being developed to facilitate drug delivery by accumulation and controlled release and to improve their restorative properties. In this review, we discuss recent developments in IS therapy, including assisted drug delivery and targeting, neuroprotection through regulation of the neuron environment, and sources of endogenous biomimetic specific targeting. In addition, we discuss the role and neurotoxic effects of inorganic metal nanoparticles in IS therapy. This study provides a theoretical basis for the utilization of nano-IS therapies that may contribute to the development of new strategies for a range of embolic diseases.

Endovascular Intervention in Acute Ischemic Stroke: History and Evolution

Stroke is a leading cause of serious long-term disability in the US. Endovascular therapy (EVT), in the form of mechanical thrombectomy, is now a standard of care for patients with acute ischemic stroke with a large vessel occlusion. This article reviews the evolution of EVT in the management of acute ischemic stroke and how it has led to the concept of tissue window over the widely publicized time window.

Nomogram to predict haemorrhagic transformation after stroke thrombolysis: a combined brain imaging and clinical study

AIM

To construct a novel nomogram by integrating computed tomography perfusion (CTP) and clinical parameters for individualised prediction of haemorrhagic transformation (HT) in intravenous thrombolysis (IVT)-treated acute ischaemic stroke (AIS) patients.

METHODS

Anterior circulation AIS patients who underwent IVT at a single centre from January 2018 to June 2020 were reviewed retrospectively. The CTP parameters of two regions of interest (ROI), the entire perfusion lesion areas, and the infract core areas, were assessed. HT was documented by follow-up CT 24 ± 2 h after IVT. Multivariable logistic regression was conducted by including clinical variables and CTP parameters to identify the independent predictors of HT. A nomogram was developed based on the independent predictors. The discriminative value and calibration of the nomogram were tested by concordance indexes (C-indexes) and calibration plots. Internal validation was performed using fivefold cross-validation.

RESULTS

The nomogram was generated using the complete data from 341 patients. Seven variables were included in the final nomogram, including: the relative cerebral blood volume (rCBV), permeability surface (PS), and relative PS (rPS) in infract core areas, the relative time to maximum (rTmax) and rPS in entire perfusion lesion areas, the National Institutes of Health Stroke Scale (NIHSS), and atrial fibrillation (AF). The C-indexes were 0.815 and 0.817 for the nomogram and internal validation. The calibration plots showed excellent agreement.

CONCLUSION

This is the first study establishing a nomogram based on CTP and clinical parameters to predict HT after stroke thrombolysis.

Factors affecting the outcome of delayed intravenous thrombolysis (>4.5hours)

INTRODUCTION

Evidence of the intravenous tissue plasminogen activator (tPA) efficacy beyond the 4.5hours window is emerging. We aim to study the factors affecting the outcome of delayed thrombolysis in patients of clear onset acute ischemic stroke (AIS).

METHODS

Data of patients with AIS who received intravenous thrombolytic after 4.5hours were reviewed including: demographics, risk factors, clinical, laboratory, investigational and radiological data, evidence of mismatch, treatment type and onset, National Institutes of Health Stroke Scale (NIHSS) score at baseline, 24hours, 7days after thrombolysis and before discharge, and 3 months follow-up modified Rankin Scale (mRS).

RESULTS

We report 136 patients treated by intravenous tPA between 4.53 and 19.75hours with average duration of 5.7h. The ASPECT score of our patients was≥7. Sixty-four cases showed intracranial arterial occlusion. Perfusion mismatch was detected in 117 (84.6%) patients, while clinical imaging mismatch was detected in 19 (15.4%). Early neurological improvement after 24hours occurred in 114 (83.8%) patients. At 90days, 91 patients (67%) achieved good outcome (mRS 0-2), while 45 (33%) had bad outcome (mRS 3-6). Age, endovascular treatment, NIHSS, AF, and HT were significantly higher in the bad outcome group. Age (P=0.001, OR: 1.099, 95% CI: 1.042-1.160) and baseline NIHSS were predictive of the poor outcome (P=0.002, OR: 1.151, 95% CI: 1.055-1.256). The best cutoff value of age was 72.5 with AUC of 0.76, sensitivity 73.3% and specificity 60.4%. While for NIHSS at admission, the cutoff value of 7 showed the best results with AUC of 0.73, sensitivity 71.1% and specificity 63.7%. Combination of age and admission NIHSS raised the sensitivity and specificity to 84.4% and 63.7%, respectively.

CONCLUSION

Increased age and admission NIHSS may adversely affect the outcome of delayed thrombolysis and narrow the eligibility criteria. Age and baseline NIHSS based stratification of the patients may provide further evidence as regards the efficacy of the delayed thrombolysis.

Association of time from last known well to alteplase administration and outcomes in acute stroke

INTRODUCTION

In order to determine if time from onset to treatment time (OTT) to alteplase (tPA) administration is associated with improved outcomes in acute ischemic stroke, we performed a retrospective analysis of all acute ischemic stroke patients who received tPA at our hospital.

METHODS

We performed a retrospective review of our Get With the Guidelines® stroke data to determine OTT was associated with either 1.) modified Rankin scale (mRS) at 90 days or 2.) in hospital hemorrhagic conversion. OTT was examined both as a continuous variable in univariate and multivariate analyses as well as a dichotomous variable with a cutoff of 60 min. We controlled for demographic characteristics, 24 medical history issues, receipt of thrombectomy, and NIH stroke scale at admission.

RESULTS

We included 166 patients with data for mRS at 90 days after discharge and 302 patients for hemorrhagic conversion in hospital. In all analyses, there was no difference between OTT in either outcome. Predictors of improved mRS at 90 days included NIH Stroke Scale at admission, age, and lack of hypertension. The only significant predictors for hemorrhagic conversion was NIH stroke scale at admission.

CONCLUSION

Our small, retrospective data shows no association between OTT and either hemorrhagic conversion or mRS at 90 days. This data lends support that time may not be the ideal indicator for identifying patients who will benefit from tPA administration.

Comparative Assessment of the Proteolytic Stability and Impact of Poly-Arginine Peptides R18 and R18D on Infarct Growth and Penumbral Tissue Preservation Following Middle Cerebral Artery Occlusion in the Sprague Dawley Rat

Poly-arginine peptides R18 and R18D have previously been demonstrated to be neuroprotective in ischaemic stroke models. Here we examined the proteolytic stability and efficacy of R18 and R18D in reducing infarct core growth and preserving the ischaemic penumbra following middle cerebral artery occlusion (MCAO) in the Sprague Dawley rat. R18 (300 or 1000 nmol/kg), R18D (300 nmol/kg) or saline were administered intravenously 10 min after MCAO induced using a filament. Serial perfusion and diffusion-weighted MRI imaging was performed to measure changes in the infarct core and penumbra from time points between 45- and 225-min post-occlusion. Repeated measures analyses of infarct growth and penumbral tissue size were evaluated using generalised linear mixed models (GLMMs). R18D (300 nmol/kg) was most effective in slowing infarct core growth (46.8 mm3 reduction; p < 0.001) and preserving penumbral tissue (21.6% increase; p < 0.001), followed by R18 at the 300 nmol/kg dose (core: 29.5 mm3 reduction; p < 0.001, penumbra: 12.5% increase; p < 0.001). R18 at the 1000 nmol/kg dose had a significant impact in slowing core growth (19.5 mm3 reduction; p = 0.026), but only a modest impact on penumbral preservation (6.9% increase; p = 0.062). The in vitro anti-excitotoxic neuroprotective efficacy of R18D was also demonstrated to be unaffected when preincubated for 1-3 h or overnight, in a cell lysate prepared from dying neurons or with the proteolytic enzyme, plasmin, whereas the neuroprotective efficacy of R18 was significantly reduced after a 2-h incubation. These findings highlight the capacity of poly-arginine peptides to reduce infarct growth and preserve the ischaemic penumbra, and confirm the superior efficacy and proteolytic stability of R18D, which indicates that this peptide is likely to retain its neuroprotective properties when co-administered with alteplase during thrombolysis for acute ischaemic stroke.

Systematic review protocol to assess artificial intelligence diagnostic accuracy performance in detecting acute ischaemic stroke and large-vessel occlusions on CT and MR medical imaging

INTRODUCTION

The use of artificial intelligence (AI) to support the diagnosis of acute ischaemic stroke (AIS) could improve patient outcomes and facilitate accurate tissue and vessel assessment. However, the evidence in published AI studies is inadequate and difficult to interpret which reduces the accountability of the diagnostic results in clinical settings. This study protocol describes a rigorous systematic review of the accuracy of AI in the diagnosis of AIS and detection of large-vessel occlusions (LVOs).

METHODS AND ANALYSIS

We will perform a systematic review and meta-analysis of the performance of AI models for diagnosing AIS and detecting LVOs. We will adhere to the Preferred Reporting Items for Systematic Reviews and Meta-analyses Protocols guidelines. Literature searches will be conducted in eight databases. For data screening and extraction, two reviewers will use a modified Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist. We will assess the included studies using the Quality Assessment of Diagnostic Accuracy Studies guidelines. We will conduct a meta-analysis if sufficient data are available. We will use hierarchical summary receiver operating characteristic curves to estimate the summary operating points, including the pooled sensitivity and specificity, with 95% CIs, if pooling is appropriate. Furthermore, if sufficient data are available, we will use Grading of Recommendations, Assessment, Development and Evaluations profiler software to summarise the main findings of the systematic review, as a summary of results.

ETHICS AND DISSEMINATION

There are no ethical considerations associated with this study protocol, as the systematic review focuses on the examination of secondary data. The systematic review results will be used to report on the accuracy, completeness and standard procedures of the included studies. We will disseminate our findings by publishing our analysis in a peer-reviewed journal and, if required, we will communicate with the stakeholders of the studies and bibliographic databases.

PROSPERO REGISTRATION NUMBER

CRD42020179652.

Silver Jubilee of Stroke Thrombolysis With Alteplase: Evolution of the Therapeutic Window

In 1995, the results of a landmark clinical trial by National Institute of Neurological Disorders and Stroke (NINDS) made a paradigm shift in managing acute cerebral ischemic stroke (AIS) patients at critical care centers. The study demonstrated the efficacy of tissue-type plasminogen activator (tPA), alteplase in improving neurological and functional outcome in AIS patients when administered within 3 h of stroke onset. After about 12 years of efforts and the results of the ECASS-III trial, it was possible to expand the therapeutic window to 4.5 h, which still represents a major logistic issue, depriving many AIS patients from the benefits of tPA therapy. Constant efforts in this regards are directed toward either speeding up the patient recruitment for tPA therapy or expanding the current tPA window. Efficient protocols to reduce the door-to-needle time and advanced technologies like telestroke services and mobile stroke units are being deployed for early management of AIS patients. Studies have demonstrated benefit of thrombolysis guided by perfusion imaging in AIS patients at up to 9 h of stroke onset, signifying “tissue window.” Several promising pharmacological and non-pharmacological approaches are being explored to mitigate the adverse effects of delayed tPA therapy, thus hoping to further expand the current tPA therapeutic window without compromising safety. With accumulation of scientific data, stroke organizations across the world are amending/updating the clinical recommendations of tPA, the only US-FDA approved drug for managing AIS patients. Alteplase has been a part of our neurocritical care and we intend to celebrate its silver jubilee by dedicating this review article discussing its journey so far and possible future evolution.

Predictors of Good Outcomes in Stroke Thrombolysis Guided by Tissue-Window

BACKGROUND

Recently studies suggested that assessment of tissue-window can effectively guide thrombolysis in acute ischemic stroke patients with unknown time of onset or late presenting. This study aimed to evaluate predictors of good outcomes from thrombolysis in these patients.

METHODS

Acute ischemic stroke patients received thrombolysis guided by computed tomography perfusion (CTP) from October 2018 to August 2019 were reviewed. Baseline characteristics and outcomes were collected. Good outcomes were defined as modified Rankin scale scores of 0 to 2 at 90 days. Logistic regression analysis was performed and the receiver operating characteristics analysis was used to determine cut-off values for the predictors of outcomes.

RESULTS

Sixty-three patients were enrolled. The median age was 64 (interquartile range 57.75 to 72.5) years. The median baseline National Institutes of Health Stroke Scale (NIHSS) score was 8 (interquartile range 3 to 13) and 41 (65.1%) patients had a good outcome at 90 days. Multivariate regression analysis showed smaller perfusion lesion (the sum of ischemic penumbra and infarcted core) (odds ratio: 0.961; 95% confidence interval, 0.931-0.992; P=0.013) and lower baseline NIHSS score (odds ratio: 0.759; 95% confidence interval, 0.644-0.895; P=0.001) were significant predictors for good outcomes. Receiver operating characteristics analysis was utilized to define optimal cut-off values for perfusion lesion [cut-off, 59 mL; area under curve (AUC), 0.761; sensitivity, 0.57; specificity, 0.93; P=0.001], ischemic penumbra (cut-off, 43.5 mL; AUC, 0.761; sensitivity, 0.62; specificity, 0.90; P=0.001), infarcted core (cut-off, 9.5 mL; AUC, 0.665; sensitivity, 0.43; specificity, 0.93; P=0.035), and baseline NIHSS score (cut-off, 8.5; AUC, 0.880; sensitivity, 0.81; specificity, 0.88; P<0.001).

CONCLUSIONS

This study suggested that smaller perfusion lesion and lower baseline NIHSS score may be helpful to predict favorable prognosis of stroke patients who receive thrombolysis guided by tissue-window.

Localized prediction of tissue outcome in acute ischemic stroke patients using diffusion- and perfusion-weighted MRI datasets

BACKGROUND

An accurate prediction of tissue outcome in acute ischemic stroke patients is of high interest for treatment decision making. To date, various machine learning models have been proposed that combine multi-parametric imaging data for this purpose. However, most of these machine learning models were trained using voxel information extracted from the whole brain, without taking differences in susceptibility to ischemia into account that exist between brain regions. The aim of this study was to develop and evaluate a local tissue outcome prediction approach, which makes predictions using locally trained machine learning models and thus accounts for regional differences.

MATERIAL AND METHODS

Multi-parametric MRI data from 99 acute ischemic stroke patients were used for the development and evaluation of the local tissue outcome prediction approach. Diffusion (ADC) and perfusion parameter maps (CBF, CBV, MTT, Tmax) and corresponding follow-up lesion masks for each patient were registered to the MNI brain atlas. Logistic regression (LR) and random forest (RF) models were trained employing a local approach, which makes predictions using models individually trained for each specific voxel position using the corresponding local data. A global approach, which uses a single model trained using all voxels of the brain, was used for comparison. Tissue outcome predictions resulting from the global and local RF and LR models, as well as a combined (hybrid) approach were quantitatively evaluated and compared using the area under the receiver operating characteristic curve (ROC AUC), the Dice coefficient, and the sensitivity and specificity metrics.

RESULTS

Statistical analysis revealed the highest ROC AUC and Dice values for the hybrid approach. With 0.872 (ROC AUC; LR) and 0.353 (Dice; RF), these values were significantly higher (p < 0.01) than the values of the two other approaches. In addition, the local approach achieved the highest sensitivity of 0.448 (LR). Overall, the hybrid approach was only outperformed in sensitivity (LR) by the local approach and in specificity by both other approaches. However, in these cases the effect sizes were comparatively small.

CONCLUSION

The results of this study suggest that using locally trained machine learning models can lead to better lesion outcome prediction results compared to a single global machine learning model trained using all voxel information independent of the location in the brain.

Accuracy of time to treatment estimates in the CRASH-3 clinical trial: impact on the trial results

BACKGROUND

Early treatment with tranexamic acid may reduce deaths after traumatic brain injury (TBI). In mild and moderate TBI, there is a time to treatment interaction, with early treatment being most beneficial. Time to treatment was recorded by clinicians and is subject to error. Using monitoring data from the CRASH-3 trial, we examine the impact of errors in time to treatment on estimated treatment effects.

METHODS

The CRASH-3 trial was a randomised trial of the effect of tranexamic acid on death and vascular occlusive events in 12,737 TBI patients. This analysis includes the 8107 patients with a Glasgow coma scale score of 9 to 15 since previous analyses showed that these patients benefit most from early treatment. Clinician-recorded time to treatment was checked against ambulance and hospital records for 1368/12,737 (11%) patients. Patients who died were preferentially selected for monitoring and we monitored 36% of head injury deaths. We describe measurement errors using Bland-Altman graphs. We model the effect of tranexamic acid on head injury death using logistic regression with a time-treatment interaction term. We use regression calibration, multiple imputation and Bayesian analysis to estimate the impact of time to treatment errors.

RESULTS

Clinicians rounded times to the nearest half or full hour in 66% of cases. Monitored times were also rounded and were identical to clinician times in 63% of patients. Times were underestimated by an average of 9 min (95% CI - 85, 66). There was more variability between clinician-recorded and monitored times in low- and middle-income countries than in high-income countries. The treatment effect estimate at 1 h was greater for monitored times OR = 0.61 (95% CI 0.47, 0.81) than for clinician-recorded times OR = 0.63 (95% CI 0.48, 0.83). All three adjustment methods gave similar time to treatment interactions. For Bayesian methods, the treatment effect at 1 h was OR = 0.58 (95% CI 0.43, 0.78). Using monitored times increased the time-treatment interaction term from 1.15 (95% CI 1.03, 1.27) to 1.16 (95% CI 1.05, 1.28).

CONCLUSIONS

Accurate estimation of time from injury to treatment is challenging, particularly in low resource settings. Adjustment for known errors in time to treatment had minimal impact on the trial results.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01402882 . Registered on 25 July 2011.