Emerging Detection Techniques for Large Vessel Occlusion Stroke: A Scoping Review

Safety and Efficacy Comparison of Tenecteplase and Alteplase for Clinically Suspected Large Vessel Occlusion Strokes without Thrombectomy

INTRODUCTION

Tenecteplase is a thrombolytic with higher fibrin affinity and is potentially better in clot lysis. A higher spontaneous recanalisation rate for large vessel occlusion (LVO) strokes had been shown in comparison studies with alteplase. Results of the LVO studies reflect the composite effect of the thrombolytic and thrombectomy, as patients would be treated by thrombectomy had they not been recanalised by intravenous thrombolysis alone. Thrombectomy is not readily available in many parts of the world. Our study aimed to compare the outcomes of suspected LVO patients treated with tenecteplase versus alteplase only, without the confounding effect of thrombectomy.

METHODS

This is a retrospective review. Data of patients given tenecteplase from May 2020 to August 2023 and those given alteplase 0.9 mg/kg from January 2019 to August 2023 were retrieved. Due to fluctuation in supply of tenecteplase during the COVID pandemic, some LVO patients were given alteplase. Patients with anterior circulation, clinically suspected LVO strokes (defined as National Institutes of Health Stroke Scale (NIHSS) score ≥6, plus cortical signs or hyperdense vessel sign), with thrombolysis given within 4.5 h of stroke onset were analysed. Patients with thrombectomy done were excluded. Safety and efficacy outcomes were compared.

RESULTS

There were 245 tenecteplase-treated patients treated between May 1, 2020, and August 31, 2023, and 732 patients were treated with alteplase between January 1, 2019, to August 31, 2023. Out of these, 148 tenecteplase patients and 138 alteplase 0.9 mg/kg patients fulfilled the study criteria. The symptomatic intracerebral haemorrhage rate was non-significantly lower in the tenecteplase group (2.1% vs. 5.8%, p = 0.13). There were no significant differences in the rate of ≥8-point NIHSS improvement (23.6% vs. 23.7%, p = 1) or the ≥4-point improvement (40.5% vs. 40.7%, p = 1) at 24 h. At 3 months, 21.6% of tenecteplase patients had good functional outcome (modified Rankin scale [mRS] 0-2), compared to 26.3% in the alteplase group (p = 0.40).

CONCLUSION

In this pragmatic study of clinically suspected anterior circulation LVO patients without thrombectomy, outcome solely reflects the effects of tenecteplase. Tenecteplase showed comparable safety and efficacy to alteplase, but the result should be interpreted with caution in view of its small sample size and non-randomised study design.

External Validation of Vision, Aphasia and Neglect, Ventura Emergent Large Vessel Occlusion and Large Artery Intracranial Occlusion Screening Tools for Emergent Large Vessel Occlusion Stroke: A Multicenter, Prospective, Cross-Sectional Study

BACKGROUND

Vision, Aphasia, and Neglect (VAN), Ventura Emergent Large Vessel Occlusion (VES), and Large Artery Intracranial Occlusion (LARIO) are promising stroke screening tools that were shown to have high diagnostic performance to detect Emergent Large Vessel Occlusion (ELVO) in their derivation studies.

OBJECTIVES

This study aimed to assess the validation of VAN, VES, and LARIO in predicting ELVO among patients presenting at emergency department (ED) triage with suspected acute ischemic stroke.

METHODS

This is a prospective multicenter study conducted in five EDs of tertiary stroke centers between June and October 2023. Patients with suspected stroke admitted to ED for triage were evaluated using the VAN, VES, and LARIO stroke screening tools. Diagnostic performances of these tools for predicting ELVO were determined and compared with the National Institute of Health Stroke Scale (NIHSS).

RESULTS

A total of 614 patients were included. The prevalence of ELVO was found to be 23.5% in the study population. VAN exhibited a sensitivity of 70.1% and specificity of 78.7%, VES showed a higher sensitivity (79.1%) with lower specificity (63.4%), while LARIO displayed high specificity (86%) with lower sensitivity (56.3%). Receiver operating characteristic curve analysis showed that LARIO and NIHSS had similar diagnostic performance (areas under the curve [AUC] 0.801 and 0.805, p = 0.7, respectively), while VES showed a modestly poorer performance (AUC 0.746, p < 0.001 and p = 0.003).

CONCLUSION

The comparable diagnostic performance of VAN, VES, and LARIO to the NIHSS, in addition to their straightforwardness and rapid evaluation time, can facilitate optimal care for patients with ELVO in prehospital or ED triage settings.

Prediction of Large Vessel Occlusion Stroke Using Clinical Registries for Research

OBJECTIVES

A reliable method of predicting large vessel occlusion (LVO) stroke in data sets without neuroimaging could be retrospectively applied to expand research efforts.

METHODS

We conducted a retrospective, cross-sectional cohort analysis of the Get With The Guidelines (GWTG)-Stroke registry. We included adult patients with a final diagnosis of ischemic stroke from 2016 to 2021 who had brain and vascular imaging and excluded those with missing data or posterior circulation stroke.

RESULTS

We included 416,022 patients of which 125,381 (30.1%) had LVO. The mean age was 71 years, and 48.2% were female. The area under the receiver operating curve (AUC) for the final model, including age, sex, hypertension, dyslipidemia, atrial fibrillation, diabetes, TOAST stroke mechanism, and NIH Stroke Scale (NIHSS), was 0.79 (95% CI 0.79-0.80). Without TOAST mechanism, the AUC was 0.74. The specificity did not exceed 0.5 using different cut points for the NIHSS.

DISCUSSION

We found that 30% of adult acute ischemic stroke patients in GWTG-Stroke have LVO and that the combination of clinical covariates and NIHSS is only moderately predictive of LVO status. These results are consistent with previous studies and suggest it may not be possible to retrospectively predict LVO with high accuracy in data sets without vascular neuroimaging.

Differentiating ischemic stroke patients from healthy subjects using a large-scale, retrospective EEG database and machine learning methods

OBJECTIVES

We set out to develop a machine learning model capable of distinguishing patients presenting with ischemic stroke from a healthy cohort of subjects. The model relies on a 3-min resting electroencephalogram (EEG) recording from which features can be computed.

MATERIALS AND METHODS

Using a large-scale, retrospective database of EEG recordings and matching clinical reports, we were able to construct a dataset of 1385 healthy subjects and 374 stroke patients. With subjects often producing more than one recording per session, the final dataset consisted of 2401 EEG recordings (63% healthy, 37% stroke).

RESULTS

Using a rich set of features encompassing both the spectral and temporal domains, our model yielded an AUC of 0.95, with a sensitivity and specificity of 93% and 86%, respectively. Allowing for multiple recordings per subject in the training set boosted sensitivity by 7%, attributable to a more balanced dataset.

CONCLUSIONS

Our work demonstrates strong potential for the use of EEG in conjunction with machine learning methods to distinguish stroke patients from healthy subjects. Our approach provides a solution that is not only timely (3-minutes recording time) but also highly precise and accurate (AUC: 0.95).

Video support for prehospital stroke consultation: implications for system design and clinical implementation from prehospital simulations

BACKGROUND

Video consultations between hospital-based neurologists and Emergency Medical Services (EMS) have potential to increase precision of decisions regarding stroke patient assessment, management and transport. In this study we explored the use of real-time video streaming for neurologist-EMS consultation from the ambulance, using highly realistic full-scale prehospital simulations including role-play between on-scene EMS teams, simulated patients (actors), and neurologists specialized in stroke and reperfusion located at the remote regional stroke center.

METHODS

Video streams from three angles were used for collaborative assessment of stroke using the National Institutes of Health Stroke Scale (NIHSS) to assess symptoms affecting patient's legs, arms, language, and facial expressions. The aim of the assessment was to determine appropriate management and transport destination based on the combination of geographical location and severity of stroke symptoms. Two realistic patient scenarios were created, with severe and moderate stroke symptoms, respectively. Each scenario was simulated using a neurologist acting as stroke patient and an ambulance team performing patient assessment. Four ambulance teams with two nurses each all performed both scenarios, for a total of eight cases. All scenarios were video recorded using handheld and fixed cameras. The audio from the video consultations was transcribed. Each team participated in a semi-structured interview, and neurologists and actors were also interviewed. Interviews were audio recorded and transcribed.

RESULTS

Analysis of video-recordings and post-interviews (n = 7) show a more thorough prehospital patient assessment, but longer total on-scene time, compared to a baseline scenario not using video consultation. Both ambulance nurses and neurologists deem that video consultation has potential to provide improved precision of assessment of stroke patients. Interviews verify the system design effectiveness and suggest minor modifications.

CONCLUSIONS

The results indicate potential patient benefit based on a more effective assessment of the patient's condition, which could lead to increased precision in decisions and more patients receiving optimal care. The findings outline requirements for pilot implementation and future clinical tests.

A Multi-Parametric Approach for Characterising Cerebral Haemodynamics in Acute Ischaemic and Haemorrhagic Stroke

BACKGROUND AND PURPOSE

Early differentiation between acute ischaemic (AIS) and haemorrhagic stroke (ICH), based on cerebral and peripheral hemodynamic parameters, would be advantageous to allow for pre-hospital interventions. In this preliminary study, we explored the potential of multiple parameters, including dynamic cerebral autoregulation, for phenotyping and differentiating each stroke sub-type.

METHODS

Eighty patients were included with clinical stroke syndromes confirmed by computed tomography within 48 h of symptom onset. Continuous recordings of bilateral cerebral blood velocity (transcranial Doppler ultrasound), end-tidal CO2 (capnography), electrocardiogram (ECG), and arterial blood pressure (ABP, Finometer) were used to derive 67 cerebral and peripheral parameters.

RESULTS

A total of 68 patients with AIS (mean age 66.8 ± SD 12.4 years) and 12 patients with ICH (67.8 ± 16.2 years) were included. The median ± SD NIHSS of the cohort was 5 ± 4.6. Statistically significant differences between AIS and ICH were observed for (i) an autoregulation index (ARI) that was higher in the unaffected hemisphere (UH) for ICH compared to AIS (5.9 ± 1.7 vs. 4.9 ± 1.8 p = 0.07); (ii) coherence function for both hemispheres in different frequency bands (AH, p < 0.01; UH p < 0.02); (iii) a baroreceptor sensitivity (BRS) for the low-frequency (LF) bands that was higher for AIS (6.7 ± 4.2 vs. 4.10 ± 2.13 ms/mmHg, p = 0.04) compared to ICH, and that the mean gain of the BRS in the LF range was higher in the AIS than in the ICH (5.8 ± 5.3 vs. 2.7 ± 1.8 ms/mmHg, p = 0.0005); (iv) Systolic and diastolic velocities of the affected hemisphere (AH) that were significantly higher in ICH than in AIS (82.5 ± 28.09 vs. 61.9 ± 18.9 cm/s), systolic velocity (p = 0.002), and diastolic velocity (p = 0.05).

CONCLUSION

Further multivariate modelling might improve the ability of multiple parameters to discriminate between AIS and ICH and warrants future prospective studies of ultra-early classification (<4 h post symptom onset) of stroke sub-types.

Emerging artificial intelligence-aided diagnosis and management methods for ischemic strokes and vascular occlusions: A comprehensive review

Large-vessel occlusion (LVO) stroke is a promising field for the use of AI, especially machine learning (ML) because optimal results are highly dependent on timely diagnosis, communication, and treatment. In order to better understand the current state of artificial intelligence (AI) in relation to LVO strokes, its efficacy, and potential future applications, we searched relevant literature to perform a comprehensive evaluation of the topic. The databases PubMed, Embase, and Scopus were extensively searched for this review. Studies were then screened using title and abstract criteria and duplicate studies were excluded. By using pre-established inclusion and exclusion criteria, it was decided whether or not to include full-text papers in the final analysis. The studies were analyzed, and the relevant information was retrieved. In recognizing LVO on computed tomography, ML approaches were very accurate. There is a shortage of AI applications for thrombectomy patient selection, despite the fact that certain research accurately evaluates individual patient eligibility for endovascular therapy. Machine learning algorithms may reasonably predict clinical and angiographic outcomes as well as associated factors. AI has shown promise in the diagnosis and treatment of people who have just suffered a stroke. However, the usefulness of AI in management and forecasting remains restricted, necessitating more studies into machine learning applications that can guide decision making in the future.

A data-driven computational methodology towards a pre-hospital Acute Ischaemic Stroke screening tool using haemodynamics waveforms

BACKGROUND AND OBJECTIVE

Acute Ischaemic Stroke (AIS), a significant global health concern, results from occlusions in cerebral arteries, causing irreversible brain damage. Different type of treatments exist depending on the size and location of the occlusion. Challenges persist in achieving faster diagnosis and treatment, which needs to happen in the first hours after the onset of symptoms to maximize the chances of patient recovery. The current diagnostic pipeline, i.e. "drip and ship", involves diagnostic via advanced imaging tools, only available in large clinical facilities, which poses important delays. This study investigates the feasibility of developing a machine learning model to diagnose and locate occluding blood clots from velocity waveforms, which can be easily be obtained with portable devices such as Doppler Ultrasound. The goal is to explore this approach as a cost-effective and time-efficient alternative to advanced imaging techniques typically available only in large hospitals.

METHODS

Simulated haemodynamic data is used to conduct blood flow simulations representing healthy and different AIS scenarios using a population-based database. A Machine Learning classification model is trained to solve the inverse problem, this is, detect and locate a potentially occluding thrombus from measured waveforms. The classification process involves two steps. First, the region where the thrombus is located is classified into nine groups, including healthy, left or right large vessel occlusion, left or right anterior cerebral artery, and left or right posterior cerebral artery. In a second step, the bifurcation generation of the thrombus location is classified as small, medium, or large vessel occlusion.

RESULTS

The proposed methodology is evaluated for data without noise, achieving a true prediction rate exceeding 95% for both classification steps mentioned above. The inclusion of up to 20% noise reduces the true prediction rate to 80% for region detection and 70% for bifurcation generation detection.

CONCLUSIONS

This study demonstrates the potential effectiveness and efficiency of using haemodynamic data and machine learning to detect and locate occluding thrombi in AIS patients. Although the geometric and topological data used in this study are idealized, the results suggest that this approach could be applicable in real-world situations with appropriate adjustments. Source code is available in https://github.com/ahmetsenemse/Acute-Ischaemic-Stroke-screening-tool-.

Rapid Arterial Occlusion Evaluation (RACE) Tool in Detecting Large Cerebral Vessel Occlusions; a Systematic Review and Meta-Analysis

Introduction

Large vessel occlusion (LVO) strokes are linked to higher mortality rates and a greater risk of long-term disability. This study aimed to evaluate the diagnostic performance of the Rapid Arterial Occlusion Evaluation (RACE) tool in detecting LVO through a systematic review and meta-analysis.

Methods

A comprehensive search was conducted across online databases including PubMed, Embase, Scopus, and Web of Science, up to June 25th, 2023. Additionally, a manual search on Google and Google Scholar was performed to identify studies that assessed the diagnostic accuracy of the RACE scale in detecting LVO among patients with stroke symptoms.

Results

Data extracted from 43 studies were analyzed. The optimal cut-off points were determined to be 3 and 4, with a sensitivity of 0.86 (95% confidence interval (CI): 0.78, 0.91) and specificity of 0.57 (95% CI: 0.49, 0.67) for cut-off ≥3, and a sensitivity of 0.78 (95% CI: 0.70, 0.84) and specificity of 0.68 (95% CI: 0.59, 0.75) for cut-off ≥4. Subgroup meta-regression analysis revealed significant variations in sensitivity and specificity. RACE scale's sensitivity was significantly higher in LVO detection in suspected stroke cases, in pre-hospital settings, prospective design studies, and when considering both anterior and posterior occlusions for LVO definition. RACE scale's specificity was significantly higher when evaluating confirmed stroke cases, in-hospital settings, and considering only anterior occlusions for LVO definition and retrospective design studies. Notably, RACE exhibited higher sensitivity and specificity when utilized by neurologists and physicians compared to other emergency staff. Despite these variations, our study found comparable diagnostic accuracy across different conditions.

Conclusion

A high level of evidence indicates that the RACE scale lacks promising diagnostic value for detection of LVOs. A sensitivity range of 0.69 to 0.86 is insufficient for a screening tool intended to aid in the diagnosis of strokes, considering the substantial morbidity and mortality associated with this condition.

Stent reconstruction in intracranial atherosclerotic disease related acute ischemic stroke results in high revascularization rates

OBJECTIVES

Intracranial atherosclerotic disease (ICAD) is a major cause of large vessel occlusion (LVO) in acute ischemic stroke (AIS). Our study aimed to analyze the effect of percutaneous transluminal angioplasty and stenting (PTAS) in patients with ICAD undergoing rescue treatment in terms of functional outcome and mortality rate at 90 days and compare the results to LVO with thromboembolic origins.

MATERIALS AND METHODS

A retrospective review of a mechanical thrombectomy (MT) single center database from 01/2019 to 09/2021 was carried out using chart review and angiogram analysis. From 469 acute stroke patients, 361 patients were enroled in the study, of whom twenty-four (6.6%) were diagnosed with underlying ICAD and treated with angioplasty and stent reconstruction (PTAS) with a standardized medication protocol. Successful reperfusion, peri-procedural complications, and functional independence at 90 days were collected as outcomes.

RESULTS

There was no difference in age or admission National Institutes of Health Stroke Scale (NIHSS). Onset to groin puncture (median 460 vs 277 min, P = 0.019) was significantly longer in the ICAD group. The procedure time (median 73 vs 60 min, P = 0.137) did not differ. Successful reperfusion was achieved in 95.8% of ICAD and 91.1% of the remaining patients (P = 0.445). Functional independence (mRS ≤ 2) at 90 days was achieved in 45.8% (11/24) and 42.7% (144/337, (P = 0.767)). The mortality rates (mRS 6) at 90 days were similar (29.2% vs 29.4% (P = 0.983)).

CONCLUSION

Despite significantly longer treatment delays, the outcome and revascularization rates of ICAD patients were similar to the thromboembolic cohort. Our proposed protocol of PTAS and medication protocol in ICAD was effective with a similar safety profile as MT in general.

Early identification and characterisation of stroke to support prehospital decision-making using artificial intelligence: a scoping review protocol

INTRODUCTION

Stroke is a time-critical condition and one of the leading causes of mortality and disability worldwide. To decrease mortality and improve patient outcome by improving access to optimal treatment, there is an emerging need to improve the accuracy of the methods used to identify and characterise stroke in prehospital settings and emergency departments (EDs). This might be accomplished by developing computerised decision support systems (CDSSs) that are based on artificial intelligence (AI) and potential new data sources such as vital signs, biomarkers and image and video analysis. This scoping review aims to summarise literature on existing methods for early characterisation of stroke by using AI.

METHODS AND ANALYSIS

The review will be performed with respect to the Arksey and O'Malley's model. Peer-reviewed articles about AI-based CDSSs for the characterisation of stroke or new potential data sources for stroke CDSSs, published between January 1995 and April 2023 and written in English, will be included. Studies reporting methods that depend on mobile CT scanning or with no focus on prehospital or ED care will be excluded. Screening will be done in two steps: title and abstract screening followed by full-text screening. Two reviewers will perform the screening process independently, and a third reviewer will be involved in case of disagreement. Final decision will be made based on majority vote. Results will be reported using a descriptive summary and thematic analysis.

ETHICS AND DISSEMINATION

The methodology used in the protocol is based on information publicly available and does not need ethical approval. The results from the review will be submitted for publication in a peer-reviewed journal. The findings will be shared at relevant national and international conferences and meetings in the field of digital health and neurology.

Evaluation of the Rapid Arterial oCclusion Evaluation (RACE) scale in Upstate South Carolina, USA

OBJECTIVES

Several stroke assessments have been designed for Emergency Medical Services to identify stroke patients with large vessel occlusion in the prehospital setting. The Rapid Arterial oCclusion Evaluation scale was developed in Spain, yet only few United States-based studies have confirmed findings from Spain. This study was designed to determine if the Rapid Arterial oCclusion Evaluation scale is a valid prehospital stroke assessment for identifying large vessel occlusion patients in South Carolina, USA.

MATERIALS AND METHODS

The performance of the Rapid Arterial oCclusion Evaluation scale was determined by calculating the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy at each score. The discriminative power of the Rapid Arterial oCclusion Evaluation score was evaluated using receiver operator characteristics. Comparison of the Rapid Arterial oCclusion Evaluation Scale to the National Institute of Health Stroke Scale was assessed using the Spearman's coefficient.

RESULTS

The Rapid Arterial oCclusion Evaluation scale had an acceptable discriminative power (c = 0.71). A score of ≥5 had a sensitivity of 0.71, specificity of 0.65, positive predictive value of 0.24, negative predictive value of 0.93, and accuracy of 0.66. There was a significant correlation between the Rapid Arterial Cclusion Evaluation score and the National Institute of Health Stroke Scale (rho = 0.60).

CONCLUSION

The Rapid Arterial oCclusion Evaluation scale performed comparably to the National Institute of Health Stroke Scale in South Carolina; however, performed lower than Spain. Future studies should investigate patient demographics and emergency medical services training to determine if these variables contribute to the results found in this study.

Cincinnati Prehospital Stroke Scale Implementation of an Urban County Severity-Based Stroke Triage Protocol: Impact and Outcomes on a Comprehensive Stroke Center

BACKGROUND AND PURPOSE

Screening scales are recommended to assist field-based triage of acute stroke patients to designated stroke centers. Cincinnati prehospital stroke scale (CPSS) is a commonly used prehospital stroke screening tool and has been validated to identify large vessel occlusion (LVO). This study addresses the impact of county-based CPSS implementation to triage suspected LVO patients to a comprehensive stroke center (CSC).

MATERIALS AND METHODS

Dekalb County in Atlanta, Georgia, implemented CPSS-based protocol with score of 3 and last seen normal time < 24 h mandating transfer to the nearest CSC if the added bypass time was <15 min. Frequency of stroke codes, LVO, IV-tPA use, and thrombectomy treatment were compared six months before and after protocol change (November 1, 2020).

RESULTS

During the study period, 907 stroke patients presented to the CSC by EMS, including 289 (32%) with CPSS score 3. There was an increase in monthly ischemic stroke volume (pre-16 ± 2 vs.19 ± 3 p = 0.03), LVO (pre-4.3 ± 1.7 vs. post-7.0 ± 2.4; p = 0.03), EVT (pre-15% vs. post-30%; p = 0.001), without significant increase in stroke mimic volume or delay in mean time from last seen normal to IV-tPA (pre-165 ± 66, post-158 ± 49 min; p = 0.35). CPSS score 3 was associated with increased likelihood of LVO diagnosis (OR 8.5, 95% CI 5.0-14.4; p = 0.001) and decreased the likelihood of stroke mimics (OR 0.66, 95% CI 0.50-0.88; p = 0.004).

CONCLUSION

CPSS is a quick, easy to implement, and reliable prehospital severity scale for EMS to triage LVO to CSC without delaying IV-tPA treatment or significantly increasing stroke mimics.