Design and validation of a prehospital scale to predict stroke severity: Cincinnati Prehospital Stroke Severity Scale

The enhanced reliability of higher national institute of health stroke scale thresholds over the conventional 6-point scale

INTRODUCTION

It is still uncertain if higher thresholds on National Institute of Health Stroke Scale (NIHSS) are better predictors of large infarctions than the conventional 6-point cutoff.

METHODS

We used 6-point and higher NIHSS thresholds including 8, 9, and 10-point to predict relative infarct areas, expressed as percentage of the affected hemisphere on axial brain computed tomography images, beginning at 5% with 5% increments each time until reaching the 40% cutoff for large infarctions, or achieving 100% sensitivity. Results were compared using area under the receiver operating characteristic curves (AUROC).

RESULTS

We enrolled 151 patients of acute ischemic stroke (Mean age: 62.88 years ± 12.71; Female: 48.34%). 77 patients (50.99%) exhibited left hemisphere strokes, while 74 (49%) had right hemisphere involvement. Sensitivity values of the 6-point for infarcts measuring 5%, 10%, 20%, 30%, and 40% were 62%, 64%, 77%, 82%, and 100%, respectively. At 40% infarct-size, 8-point achieved comparable results (52%, 55%, 69%, 76%, 100%), closely aligning with the 9-point (50%, 53%, 69%, 76%, 100%). The10-point was slightly trailing behind in sensitivity at 40% infarct-core (96%). Moreover, higher thresholds exhibited improved false-positive rates (FPR). At 40% infarct size, the FPRs of 6, 8, 9, and 10 points were 39%, 27%, 27%, and 21% respectively. Higher thresholds had augmented AUROC values (0.86, 0.86, 0.89) as compared to the 6-point (0.80). Logistic regression identified 14-point as definitive cutoff for large infarctions.

CONCLUSION

Higher thresholds can better differentiate small and medium infarcts as true-negatives and substantially reduce false-positive referrals for mechanical thrombectomy.

Numerical Cincinnati Stroke Scale versus Stroke Severity Screening Tools for the Prehospital Determination of LVO

Background

Previous research demonstrated that the numerical Cincinnati Prehospital Stroke Scale (CPSS) identifies large vessel occlusion (LVO) at similar rates compared to a limited number of stroke severity screening tools. We aimed to compare numerical CPSS to additional stroke scales using a national EMS database.

Methods

Using the ESO Data Collaborative, the largest EMS database with hospital linked data, we retrospectively analyzed prehospital patient records for the year 2022. Stroke and LVO diagnoses were determined by ICD-10 codes from linked hospital discharge and emergency department records. Prehospital CPSS was compared to the Cincinnati Stroke Triage Assessment Tool (C-STAT), the Field Assessment Stroke Triage for Emergency Destination (FAST-ED), and the Balance Eyes Face Arm Speech Time (BE-FAST). The optimal prediction cut-points for LVO screening were determined by intersecting the sensitivity and specificity curves for each scale. To compare the discriminative abilities of each scale among those diagnosed with LVO, we used the area under the receiver operating curve (AUROC).

Results

We identified 17,442 prehospital records from 754 EMS agencies with ≥ 1 documented stroke scale of interest: 30.3% (n=5,278) had a hospital diagnosis of stroke, of which 71.6% (n=3,781) were ischemic; of those, 21.6% (n=817) were diagnosed with LVO. CPSS score ≥ 2 was found to be predictive of LVO with 76.9% sensitivity, 68.0% specificity, and AUROC 0.787 (95% CI 0.722-0.801). All other tools had similar predictive abilities, with sensitivity / specificity / AUROC of: C-STAT 62.5% / 76.5% / 0.727 (0.555-0.899); FAST-ED 61.4% / 76.1%/ 0.780 (0.725-0.836); BE-FAST 70.4% / 67.1% / 0.739 (0.697-0.788).

Conclusion

The less complex CPSS exhibited comparable performance to three frequently employed LVO detection tools. EMS agency leadership, medical directors, stroke system directors, and other stroke leaders may consider the complexity of stroke severity instruments and challenges with ensuring accurate recall and consistent application when selecting which instrument to implement. Use of the simpler CPSS may enhance compliance with the utilization of LVO screening instruments while maintaining the accuracy of prehospital LVO determination.

Headpulse measurement can reliably identify large-vessel occlusion stroke in prehospital suspected stroke patients: Results from the EPISODE-PS-COVID study

BACKGROUND

Large-vessel occlusion (LVO) stroke represents one-third of acute ischemic stroke (AIS) in the United States but causes two-thirds of poststroke dependence and >90% of poststroke mortality. Prehospital LVO stroke detection permits efficient emergency medical systems (EMS) transport to an endovascular thrombectomy (EVT)-capable center. Our primary objective was to determine the feasibility of using a cranial accelerometry (CA) headset device for prehospital LVO stroke detection. Our secondary objective was development of an algorithm capable of distinguishing LVO stroke from other conditions.

METHODS

We prospectively enrolled consecutive adult patients suspected of acute stroke from 11 study hospitals in four different U.S. geographical regions over a 21-month period. Patients received device placement by prehospital EMS personnel. Headset data were matched with clinical data following informed consent. LVO stroke diagnosis was determined by medical chart review. The device was trained using device data and Los Angeles Motor Scale (LAMS) examination components. A binary threshold was selected for comparison of device performance to LAMS scores.

RESULTS

A total of 594 subjects were enrolled, including 183 subjects who received the second-generation device. Usable data were captured in 158 patients (86.3%). Study subjects were 53% female and 56% Black/African American, with median age 69 years. Twenty-six (16.4%) patients had LVO and 132 (83.6%) were not LVO (not-LVO AIS, 33; intracerebral hemorrhage, nine; stroke mimics, 90). COVID-19 testing and positivity rates (10.6%) were not different between groups. We found a sensitivity of 38.5% and specificity of 82.7% for LAMS ≥ 4 in detecting LVO stroke versus a sensitivity of 84.6% (p < 0.0015 for superiority) and specificity of 82.6% (p = 0.81 for superiority) for the device algorithm (CA + LAMS).

CONCLUSIONS

Obtaining adequate recordings with a CA headset is highly feasible in the prehospital environment. Use of the device algorithm incorporating both CA and LAMS data for LVO detection resulted in significantly higher sensitivity without reduced specificity when compared to the use of LAMS alone.

Validation of the prehospital stroke scales as a tool for in-hospital large vessel occlusion stroke: whether we satisfied?

BACKGROUND

Prehospital stroke severity scales have been widely used to identify whether community stroke patients presented with large vessel occlusion (LVO) or not. However, whether these scales are also applicable to in-hospital stroke patients remains unknown.

PURPOSE

We aim to validate and compare the predictive capability of these scales for these patients.

MATERIAL AND METHODS

From January 2016 to October 2020, a total of 243 patients who activated in-hospital stroke alerts, were included in this study. The area under the curve (AUC) was used to assess the predictive ability of five scales (Field Assessment Stroke Triage for Emergency Destination [FAST-ED], Rapid Arterial Occlusion Evaluation [RACE], Los Angeles Motor Scale [LAMS], Cincinnati Prehospital Stroke Severity Scale [CPSSS], and Prehospital Acute Stroke Severity scale [PASS]) for LVO. In addition, multivariable logistic analysis was adopted to determine the predictors of LVO in our patients cohort.

RESULTS

Finally, 94 (38.7%) patients were confirmed presence of persistent LVO. The AUC for the FAST-ED, RACE, LAMS, CPSSS, and PASS scales to predict the presence of LVO in patients activating in-hospital stroke alerts were 0.82, 0.89, 0.86, 0.81, and 0.79, respectively. After multivariable analysis, baseline NIHSS (adjusted odds ratio [OR] = 1.160, 95% confidence interval [CI] = 1.110-1.212; P < 0.001) atrial fibrillation (adjusted OR = 2.940, 95% CI = 1.387-6.230; P = 0.005) and cardiac/pulmonary procedure (adjusted OR = 6.861, 95% CI = 2.437-19.315; P < 0.001) remained independent predictors of LVO.

CONCLUSION

The prehospital stroke scales also showed good predictive capabilities in discriminating LVO among inpatients who activated stroke alerts. However, given that inpatients' history is more readily available, a specifically designed in-hospital stroke scale that combines stroke severity and history is warranted.

Sex Differences in Prehospital Identification of Large Vessel Occlusion in Patients With Suspected Stroke

BACKGROUND

Differences in clinical presentation of acute ischemic stroke between men and women may affect prehospital identification of anterior circulation large vessel occlusion (aLVO). We assessed sex differences in diagnostic performance of 8 prehospital scales to detect aLVO.

METHODS

We analyzed pooled individual patient data from 2 prospective cohort studies (LPSS [Leiden Prehospital Stroke Study] and PRESTO [Prehospital Triage of Patients With Suspected Stroke Study]) conducted in the Netherlands between 2018 and 2019, including consecutive patients ≥18 years suspected of acute stroke who presented within 6 hours after symptom onset. Ambulance paramedics assessed clinical items from 8 prehospital aLVO detection scales: Los Angeles Motor Scale, Rapid Arterial Occlusion Evaluation, Cincinnati Stroke Triage Assessment Tool, Cincinnati Prehospital Stroke Scale, Prehospital Acute Stroke Severity, gaze-face-arm-speech-time, Conveniently Grasped Field Assessment Stroke Triage, and Face-Arm-Speech-Time Plus Severe Arm or Leg Motor Deficit. We assessed the diagnostic performance of these scales for identifying aLVO at prespecified cut points for men and women.

RESULTS

Of 2358 patients with suspected stroke (median age, 73 years; 47% women), 231 (10%) had aLVO (100/1114 [9%] women and 131/1244 [11%] men). The area under the curve of the scales ranged from 0.70 (95% CI, 0.65-0.75) to 0.77 (95% CI, 0.73-0.82) in women versus 0.69 (95% CI, 0.64-0.73) to 0.75 (95% CI, 0.71-0.79) in men. Positive predictive values ranged from 0.23 (95% CI, 0.20-0.27) to 0.29 (95% CI, 0.26-0.31) in women versus 0.29 (95% CI, 0.24-0.33) to 0.37 (95% CI, 0.32-0.43) in men. Negative predictive values were similar (0.95 [95% CI, 0.94-0.96] to 0.98 [95% CI, 0.97-0.98] in women versus 0.94 [95% CI, 0.93-0.95] to 0.96 [95% CI, 0.94-0.97] in men). Sensitivity of the scales was slightly higher in women than in men (0.53 [95% CI, 0.43-0.63] to 0.76 [95% CI, 0.68-0.84] versus 0.49 [95% CI, 0.40-0.57] to 0.63 [95% CI, 0.55-0.73]), whereas specificity was lower (0.79 [95% CI, 0.76-0.81] to 0.87 [95% CI, 0.84-0.89] versus 0.82 [95% CI, 0.79-0.84] to 0.90 [95% CI, 0.88-0.91]). Rapid arterial occlusion evaluation showed the highest positive predictive values in both sexes (0.29 in women and 0.37 in men), reflecting the different event rates.

CONCLUSIONS

aLVO scales show similar diagnostic performance in both sexes. The rapid arterial occlusion evaluation scale may help optimize prehospital transport decision-making in men as well as in women with suspected stroke.

Current and future trends in acute ischemic stroke treatment: direct-to-angiography suite, middle vessel occlusion, large core, and minor strokes

Since the publication of the landmark thrombectomy trials in 2015, the field of endovascular therapy for ischemic stroke has been rapidly growing. The very low number needed to treat to provide functional benefits shown by the initial randomized trials has led clinicians and investigators to seek to translate the benefits of endovascular therapy to other patient subgroups. Even if the treatment effect is diminished, currently available data has provided sufficient information to extend endovascular therapy to large infarct core patients. Recently, published data have also shown that sophisticated imaging is not necessary for late time- window patients. As a result, further research into patient selection and the stroke pathway now focuses on dramatically reducing door-to-groin times and improving outcomes by circumventing classical imaging paradigms altogether and employing a direct-to-angio suite approach for selected large vessel occlusion patients in the early time window. While the results of this approach mainly concern patients with severe deficits, there are further struggles to provide evidence of the efficacy and safety of endovascular treatment in minor stroke and large vessel occlusion, as well as in patients with middle vessel occlusions. The current lack of good quality data regarding these patients provides significant challenges for accurately selecting potential candidates for endovascular treatment. However, current and future randomized trials will probably elucidate the efficacy of endovascular treatment in these patient populations.

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.

Prehospital stroke scales outperform National Institutes of Health Stroke Scale in predicting large vessel occlusion in a large academic telestroke network

INTRODUCTION

Prehospital telestroke evaluations may improve stroke triage compared to paramedic-applied large vessel occlusion scales, but ambulance-based video National Institutes of Health Stroke Scale assessments are challenging. The accuracy of telestroke-administered large vessel occlusion scales has not been investigated, so we sought to evaluate this further.

METHODS

This retrospective study included all in-hospital telestroke encounters in a large academic telestroke network from 2019 to 2020. We retrospectively calculated seven large vessel occlusion scales using the in-hospital telestroke National Institutes of Health Stroke Scale (Rapid Arterial oCclusion Evaluation, Cincinnati Stroke Triage Assessment Tool, Field Assessment Stroke Triage for Emergency Destination, 3-Item Stroke Scale, Prehospital Acute Stroke Severity, Vision-Aphasia-Neglect, and Gaze-Face-Arm-Speech-Time). Diagnostic performance was assessed via sensitivity, specificity, negative predictive value, positive predictive value, positive likelihood ratio, negative likelihood ratio, and accuracy using established scale thresholds. These results were compared to the National Institutes of Health Stroke Scale at thresholds of 6, 8, and 10. The area under curve was calculated using c-statistics by treating scales as continuous variables.

RESULTS

A total of 625 patients were included; 111 (17.8%) patients had an anterior large vessel occlusion, 118 (18.9%) patients had any large vessel occlusion, and 182 (29.1%) patients had stroke mimic diagnosis. The mean age (SD) was 67.9 (15.9), 48.3% were female, and 93.4% were white. The Mean National Institutes of Health Stroke Scale (SD) was 14.9 (8.4) for patients with anterior large vessel occlusion, 4.7 (5.0) for patients with non-large vessel occlusion ischemic stroke, and 4.4 (5.8) for stroke mimic (p < 0.001). Compared to the National Institutes of Health Stroke Scale, Field Assessment Stroke Triage for Emergency Destination, and Rapid Arterial oCclusion Evaluation scales demonstrated higher accuracy and area under curve for large vessel occlusion detection.

DISCUSSION

Both the Field Assessment Stroke Triage for Emergency Destination and Rapid Arterial oCclusion Evaluation scales outperformed the National Institutes of Health Stroke Scale for large vessel occlusion detection in patients evaluated by in-hospital telestroke. These scales may be valid alternatives to the National Institutes of Health Stroke Scale examination in this setting.

Clinical Research in Prehospital Care: Current and Future Challenges

Prehospital care plays a critical role in improving patient outcomes, particularly in cases of time-sensitive emergencies such as trauma, cardiac failure, stroke, bleeding, breathing difficulties, systemic infections, etc. In recent years, there has been a growing interest in clinical research in prehospital care, and several challenges and opportunities have emerged. There is an urgent need to adapt clinical research methodology to a context of prehospital care. At the same time, there are many barriers in prehospital research due to the complex context, posing unique challenges for research, development, and evaluation. Among these, this review allows the highlighting of limited resources and infrastructure, ethical and regulatory considerations, time constraints, privacy, safety concerns, data collection and analysis, selection of a homogeneous study group, etc. The analysis of the literature also highlights solutions such as strong collaboration between emergency medical services (EMS) and hospital care, use of (mobile) health technologies and artificial intelligence, use of standardized protocols and guidelines, etc. Overall, the purpose of this narrative review is to examine the current state of clinical research in prehospital care and identify gaps in knowledge, including the challenges and opportunities for future research.

Management of possible stroke by laypeople in pre-hospital setting: who should we train?

BACKGROUND AND AIM

The pre-hospital management of a possible stroke is fundamental for the transport of patients to the correct HUB facility; thus, they must be transported to the Emergency Department (ED) by EMS vehicles. Our study aims to analyze the factors correlated with a higher probability of accessing the ED through the EMS in this event.

METHODS

This is a retrospective observational study. All accesses in the 120 EDs of the Lombardy region, with a diagnosis of discharge whose symptoms could resemble CPSS, were analyzed between 1 January 2019 and 31 December 2019.

RESULTS

We identified an increased probability of using the EMS vehicles of 0,05% (I.C. 95%: 0.04% - 0.06%; p<0.0001) for each additional year of age, considering patients aged 20 to 100 years and the percentage was significantly higher in the female population (58% vs 49%; p<0.001). Moreover, we calculated that the incidence of stroke was approximately 140 cases per 100,000 inhabitants.

CONCLUSIONS

Only half of the citizens in the Lombardy region use the EMS in case of suspicion of stroke; further information campaigns are essential to educate citizens. Information strategies should be directed especially at men between 30 and 59 years old.

Effect of using G-FAST to recognize emergent large vessel occlusion: A city-wide community experience

BACKGROUND/PURPOSE

A prehospital bypass strategy was suggested for large vessel occlusion. This study aimed to evaluate the effect of a bypass strategy using the gaze-face-arm-speech-time test (G-FAST) implemented in a metropolitan community.

METHODS

Pre-notified patients with positive Cincinnati Prehospital Stroke Scale and symptom onset <3 h from July 2016 to December 2017 (pre-intervention period) and those with positive G-FAST and symptom onset <6 h from July 2019 to December 2020 (intervention period) were included. Patients aged <20 years and those with missing in-hospital data were excluded. The primary outcomes were the rates of receiving endovascular thrombectomy (EVT) and intravenous thrombolysis (IVT). The secondary outcomes were total prehospital time, door-to-computed tomography (CT) time, door-to-needle (DTN) time, and door-to-puncture (DTP) time.

RESULTS

We included 802 and 695 pre-notified patients from the pre-intervention and intervention periods, respectively. The characteristics of the patients in the two periods were similar. In the primary outcomes, pre-notified patients during the intervention period showed higher rates of receiving EVT (4.49% vs. 15.25%, p < 0.001) and IVT (15.34% vs. 21.58%, p = 0.002). In the secondary outcomes, pre-notified patients during intervention period had longer total prehospital time (mean 23.38 vs 25.23 min, p < 0.001), longer door-to-CT time (median 10 vs 11 min, p < 0.001), longer DTN time (median 53 vs 54.5 min, p < 0.001) but shorter DTP time (median 141 vs 139.5 min, p < 0.001).

CONCLUSION

The prehospital bypass strategy with G-FAST showed benefits for stroke patients.

Derivation and validation of a predictive scale to expedite endovascular intervention for acute stroke patients with an intervenable vessel occlusion

BACKGROUND

Early endovascular intervention team mobilization may reduce reperfusion times and improve clinical outcomes for patients with acute ischemic stroke (AIS) with a possible intervenable vessel occlusion (IVO). In an emergency department or mobile stroke unit, incorporating rapidly available non-contrast CT (NCCT) information with examination findings may improve the accuracy of arterial occlusion prediction scales. For this purpose, we developed a rapid and straightforward IVO predictive instrument-the T3AM2PA1 scale.

METHODS

The T3AM2PA1 scale was retrospectively derived from our 'Get with the Guidelines' database. We included all patients with acute stroke alert between January 2017 and August 2018 with a National Institutes of Health Stroke Scale (NIHSS) score between 5 and 25 inclusive. Different pre-intervention variables were collected, including itemized NIHSS and NCCT information. The T3AM2PA1 scale was also compared with other commonly used scales and was validated in a separate sequential retrospective cohort of patients with a full range of NIHSS scores.

RESULTS

574 eligible patients from 2115 acute stroke alerts were identified. The scale was established with five items (CT hyperdense sign, parenchymal hypodensity, lateralizing hemiparesis, gaze deviation, and language disturbance), with a total score of 9. To minimize unnecessary angiography, a cut-off of ≥5 for IVO detection yielded a sensitivity of 52%, a specificity of 90%, and a positive predictive value of 76%.

CONCLUSIONS

The T3AM2PA1 scale accurately predicts the presence of clinical IVO in patients with AIS. Adopting the T3AM2PA1 scale could reduce revascularization times, improve treatment outcomes, and potentially reduce disability.

Determinants of door-in-door-out time in patients with ischaemic stroke transferred for endovascular thrombectomy

BACKGROUND

Long door-in-door-out (DIDO) times are an important cause of treatment delay in patients transferred for endovascular thrombectomy (EVT) from primary stroke centres (PSC) to an intervention centre. Insight in causes of prolonged DIDO times may facilitate process improvement interventions. We aimed to quantify different components of DIDO time and to identify determinants of DIDO time.

METHODS

We performed a retrospective cohort study in a Dutch ambulance region consisting of six PSCs and one intervention centre. We included consecutive adult patients with anterior circulation large vessel occlusion, transferred from a PSC for EVT between October 1, 2019 and November 31, 2020. We subdivided DIDO into several time components and quantified contribution of these components to DIDO time. We used univariable and multivariable linear regression models to explore associations between potential determinants and DIDO time.

RESULTS

We included 133 patients. Median (IQR) DIDO time was 66 (52-83) min. The longest component was CTA-to-ambulance notification time with a median (IQR) of 24 (16-37) min. DIDO time increased with age (6 min per 10 years, 95% CI: 2-9), onset-to-door time outside 6 h (20 min, 95% CI: 5-35), M2-segment occlusion (15 min, 95% CI: 4-26) and right-sided ischaemia (12 min, 95% CI: 2-21).

CONCLUSIONS

The CTA-to-ambulance notification time is the largest contributor to DIDO time. Higher age, onset-to-door time longer than 6 h, M2-segment occlusion and right-sided occlusions are independently associated with a longer DIDO time. Future interventions that aim to decrease DIDO time should take these findings into account.

Identifying large vessel occlusion at first glance in telemedicine

BACKGROUND

Telemedicine has rapidly emerged as an important tool in emergency neurology. In particular, reliable biomarkers of large vessel occlusions (LVOs) are critically necessary in order to identify the need for in-hospital mechanical thrombectomy (MT). Based on pathophysiological factors, we propose that the presence of head and/or gaze deviation alone signifies cortical hypoperfusion and is therefore a highly sensitive marker for the presence of LVO.

METHODS

We retrospectively analyzed a cohort of 160 patients, examined via telemedicine and suspected to have had an acute stroke; this included patients with ischemic or hemorrhagic stroke, transient ischemic attack, and stroke mimics. An assessment of head and gaze deviation and NIHSS score evaluation was performed. In a second analysis, patients who only had ischemia in the anterior circulation (n = 110) were evaluated.

RESULTS

Head and/or gaze deviation alone was found to be a reliable marker of LVO (sensitivity: 0.66/specificity: 0.92), as well as a sound indicator for MT (0.82/0.91), in patients with suspected ischemic stroke. The performance of this indicator further improved when patients with ischemia in the anterior circulation only were assessed (LVO: 0.70/0.93; MT: 0.86/0.90). In both analyses, head and/or gaze deviation served as a better indicator for LVO or MT compared to the prevalence of motor deficits or aphasia. Of note, in patients who had ischemia in the anterior circulation, head and/or gaze deviation performed better than the NIHSS score as an indicator for MT.

CONCLUSION

These findings confirm that the presence of head and/or gaze deviation serves as a reliable biomarker in stroke-based telemedicine for the diagnosis of LVO, as well as a strong indicator for MT. Furthermore, this marker is just as reliable as the NIHSS score but easier to assess. We therefore suggest that any stroke patient who displays head and/or gaze deviation should immediately be scheduled for vessel imaging and subsequently transported to a MT-competent center.

Design and Validation of a Novel Evaluation Scale to Predict Inpatient Large Vessel Occlusion Strokes: Clinical Assessment Stroke Severity for Inpatient Scale

OBJECTIVES

A large quantity of ischemic stroke events occur in patients hospitalized for non-stroke-related reason. No scale has been developed to identify the large vessel occlusion (LVO) among inpatient stroke alerts. We aimed to develop a novel evaluation scale to predict LVO from in-hospital stroke alerts.

METHODS

Data from consecutive in-hospital stroke alerts were analyzed at a single high volume stroke center between January 2016 and October 2020. We developed a predictive scale based on the first half of patients (training group) using multivariate logistic regression and evaluated it in the remaining half of patients (validation group) adopting receiver operating curve. Receiver operating characteristics of the scale were analyzed to evaluate its value for the detection of LVO.

RESULTS

A total of 243 patients were enrolled for further study, among them, 94 (38.7%) had confirmed LVO. Three risk factors independently predicted the presence of LVO: recent cardiac or pulmonary procedure (1 point), neurological deficit scale (≥1: 2 points), and history of atrial fibrillation (1 point). The CAPS scale was generated based on predictive factors and demonstrated highly effective discrimination in identifying the presence of LVO in the training group (area under curve = 0.956) and the validation group (area under curve = 0.940). When the score ≥2, CAPS scale showed 97.9% sensitivity, 79.2% specificity, 74.8% positive predictive value, and 98.3% negative predictive value for discriminating LVO.

CONCLUSIONS

CAPS scale was developed for identifying LVO among inpatient stroke alerts with high sensitivity and specificity, which may help to quickly prompt responses by appropriate stroke teams.

Point-of-Care Ultrasound to Detect Acute Large Vessel Occlusions in Stroke Patients: A Proof-of-Concept Study

BACKGROUND AND PURPOSE

A primary admission of patients with suspected acute ischemic stroke and large vessel occlusion (LVO) to centers capable of providing endovascular stroke therapy (EVT) may induce shorter time to treatment and better functional outcomes. One of the limitations in this strategy is the need for accurately identifying LVO patients in the prehospital setting. We aimed to study the feasibility and diagnostic performance of point-of-care ultrasound (POCUS) for the detection of LVO in patients with acute stroke.

METHODS

We conducted a proof-of-concept study and selected 15 acute ischemic stroke patients with angiographically confirmed LVO and 15 patients without LVO. Duplex ultrasonography (DUS) of the common carotid arteries was performed, and flow profiles compatible with LVO were scored independently by one experienced and one junior neurologist.

RESULTS

Among the 15 patients with LVO, 6 patients presented with an occlusion of the carotid-T and 9 patients presented with an M1 occlusion. Interobserver agreement between the junior and the experienced neurologist was excellent (kappa = 0.813, p < 0.001). Flow profiles of the CAA allowed the detection of LVO with a sensitivity of 73%, a positive predictive value of 92 and 100%, and a c-statistics of 0.83 (95%CI = 0.65-0.94) and 0.87 (95%CI = 0.69-0.94) (experienced neurologist and junior neurologist, respectively). In comparison with clinical stroke scales, DUS was associated with better trade-off between sensitivity and specificity.

CONCLUSION

POCUS in acute stroke setting is feasible, it may serve as a complementary tool for the detection of LVO and is potentially applicable in the prehospital phase.

Prehospital stroke-scale machine-learning model predicts the need for surgical intervention

While the development of prehospital diagnosis scales has been reported in various regions, we have also developed a scale to predict stroke type using machine learning. In the present study, we aimed to assess for the first time a scale that predicts the need for surgical intervention across stroke types, including subarachnoid haemorrhage and intracerebral haemorrhage. A multicentre retrospective study was conducted within a secondary medical care area. Twenty-three items, including vitals and neurological symptoms, were analysed in adult patients suspected of having a stroke by paramedics. The primary outcome was a binary classification model for predicting surgical intervention based on eXtreme Gradient Boosting (XGBoost). Of the 1143 patients enrolled, 765 (70%) were used as the training cohort, and 378 (30%) were used as the test cohort. The XGBoost model predicted stroke requiring surgical intervention with high accuracy in the test cohort, with an area under the receiver operating characteristic curve of 0.802 (sensitivity 0.748, specificity 0.853). We found that simple survey items, such as the level of consciousness, vital signs, sudden headache, and speech abnormalities were the most significant variables for accurate prediction. This algorithm can be useful for prehospital stroke management, which is crucial for better patient outcomes.

Evolving Stroke Systems of Care: Stroke Diagnosis and Treatment in the Post-Thrombectomy Era

Thrombectomy became the gold-standard treatment of acute ischemic stroke caused by large-vessel occlusions (LVO) in 2015 after five clinical trials published that year demonstrated significantly improved patient outcomes. In subsequent years, advances in stroke systems of care have centered around improving access to and expanding patient eligibility for thrombectomy. The prehospital and acute stroke treatment settings have had the greatest emphasis. Numerous prehospital stroke scales now provide emergency medical services with focused physical exams to identify LVOs, and many devices to non-invasively detect LVO are undergoing clinical testing. Mobile stroke units deployed throughout Western Europe and the USA also show promising results by bringing elements of acute stroke care directly to the patient. Numerous clinical trials since 2015 have aimed to increase candidates for thrombectomy by expanding indications and the eligibility time window. Further optimizations of thrombectomy treatment have focused on the role of thrombolytics and other adjunctive therapies that may promote neuroprotection and neurorecovery. While many of these approaches require further clinical investigation, the next decade shows significant potential for further advances in stroke care.

Patterns of Care in Patients with Basilar Artery Occlusion (BAO): A Population-Based Study

Basilar artery occlusion (BAO) is associated with high morbidity and mortality. Endovascular therapy (EVT) has been shown to be beneficial in acute BAO patients. This retrospective observational study used the National Inpatient Sample (NIS) database to identify BAO patients using the International Classification of Diseases (ICD). Multivariable models were used to evaluate the association of risk factors, comorbidities, length of stay (LOS) in hospital, total cost, disposition, and transfer status. A total of 1120 (447 females, 39.95%) patients were identified, with a higher proportion of White individuals (66.8% vs. 57.6%), atrial fibrillation (31.5% vs. 17.2%; p < 0.0001), and peripheral vascular disease (21.2% vs. 13.7%; p = 0.009). A lower proportion of individuals with diabetes mellitus (32.1% vs. 39.5%; p = 0.05) was found in the EVT group. Majority of the patients (924/1120, 82.5%) were treated at the urban teaching facility, which also performed most of the EVT procedures (164, 89.13%), followed by non-academic urban (166, 14.8%) and rural (30, 2.7%) hospitals. Most patients (19/30, 63%) admitted to rural hospitals were transferred to other facilities. Urban academic hospitals also had the highest median LOS (8.9 days), cost of hospitalization (USD 117,261), and disposition to home (32.6%). This study observed distinct patterns and geographical disparities in the acute treatment of BAO patients. There is a need for national- and state-level strategies to improve access to stroke care.

Mild and moderate cardioembolic stroke patients may benefit more from direct mechanical thrombectomy than bridging therapy: A subgroup analysis of a randomized clinical trial (DIRECT-MT)

Background

The benefit of intravenous alteplase before endovascular thrombectomy is unclear in patients with acute cardioembolic stroke.

Methods

We collected cardioembolic (CE) stroke patient data from the multicentre randomized clinical trial of Direct Intra-arterial Thrombectomy to Revascularize Acute Ischaemic Stroke Patients with Large Vessel Occlusion Efficiently in Chinese Tertiary Hospitals (DIRECT-MT). The primary outcome was the 90-day modified Rankin Scale (mRS) score. Five subgroups of cardioembolic stroke patients were analyzed. A multivariable ordinal logistic regression analysis analyzed the difference in the primary outcome between the direct mechanical thrombectomy (MT) and bridging therapy groups. An interaction term was entered into the model to test for subgroup interaction. The DIRECT-MT trial is registered with clinicaltrials.gov Identifier: NCT03469206.

Results

A total of 290 CE stroke patients from the DIRECT-MT trial were enrolled in this study: 146 patients in the direct MT group and 144 patients in the bridging therapy group. No difference between the two treatment groups in the primary outcome was found (adjusted common odds ratio, 1.218; 95% confidence interval, 0.806 to 1.841; P = 0.34). In the subgroup analysis, CE stroke patients with an NIHSS ≤ 15 in the direct MT group were associated with better outcomes (47 vs. 53, acOR, 3.14 [1.497, 6.585]) and lower mortality (47 vs. 53, aOR, 0.16 [0.026, 0.986]) than those in the bridging therapy group, while there were no significant differences between the two treatment groups in the outcome and mortality of CE stroke patients with an NIHSS >15.

Conclusion

Mild and moderate cardioembolic stroke patients may benefit more from direct mechanical thrombectomy than bridging therapy. This need to be confirmed by further prospective studies in a larger number of patients.

FAST-ED scale for prehospital triage of large vessel occlusion: results in the field

BACKGROUND

Acute ischemic stroke (AIS) is an extremely time-sensitive condition. The field triage of stroke patients should consider a careful balance between the best destination for the timely delivery of intravenous and/or endovascular reperfusion therapies. The Field Assessment Stroke Triage for Emergency Destination (FAST-ED) scale has been shown to have an accuracy comparable to that of the National Institutes of Health Stroke Scale (NIHSS). However, it has not been tested in the field.

OBJECTIVE

To evaluate the accuracy of the FAST-ED scale in the detection of AIS due to large vessel occlusion (LVO) in the prehospital setting.

METHODS

A cross-sectional study of consecutive prospective data collected from February 2017 to May 2019 in the city of Porto Alegre, state of Rio Grande do Sul, Southern Brazil, correlating the prehospital FAST-ED scale scores with the hospital diagnosis of LVO. Area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated.

RESULTS

In total, 74 patients were included in the analysis. As compared with the diagnosis of LVO upon hospital discharge, the prehospital FAST-ED scale applied by paramedics had a sensitivity of 80%, a specificity of 47.7%, a PPV of 51.1%, an NPV of 77.8%, and an AUC of 0.68 (95% confidence interval [95%CI]: 0.55-0.80). Among the patients with a final diagnosis of AIS, the accuracy was higher, with an AUC of 0.75 (95%CI: 0.60-0.89), a sensitivity of 80%, a specificity of 60%, a PPV of 80%, and an NPV of 60%.

CONCLUSIONS

In the present study, the FAST-ED scale, which was applied by paramedics in the field, demonstrated moderate accuracy but high sensitivity and NPV, which are essential attributes for a triage scale. While larger studies are still needed, these findings further support the use of the FAST-ED in stroke triage.

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.

Design and validation of a new scale for prehospital evaluation of stroke and large vessel occlusion

Background

Rapid recognition of acute stroke and large vessel occlusion (LVO) is essential in prehospital triage for timely reperfusion treatment.

Objective

This study aimed to develop and validate a new screening tool for both stroke and LVO in an urban Chinese population.

Methods

This study included patients with suspected stroke who were transferred to our hospital by emergency medical services between July 2017 and June 2021. The population was randomly partitioned into training (70%) and validation (30%) groups. The Staring-Hypertension-atrIal fibrillation-sPeech-weakneSs (SHIPS) scale, consisting of both clinical and medical history information, was generated based on multivariate logistic models. The predictive ability of the SHIPS scale was evaluated and compared with other scales using receiver operating characteristic (ROC) curve comparison analysis.

Results

A total of 400 patients were included in this analysis. In the training group (n = 280), the SHIPS scale showed a sensitivity of 90.4% and specificity of 60.8% in predicting stroke and a sensitivity of 75% and specificity of 61.5% in predicting LVO. In the validation group (n = 120), the SHIPS scale was not inferior to Stroke 1-2-0 (p = 0.301) in predicting stroke and was significantly better than the Cincinnati Stroke Triage Assessment Tool (C-STAT; formerly CPSSS) and the Prehospital Acute Stroke Severity scale (PASS) (all p < 0.05) in predicting LVO. In addition, including medical history in the scale was significantly better than using symptoms alone in detecting stroke (training group, 0.853 versus 0.818; validation group, 0.814 versus 0.764) and LVO (training group, 0.748 versus 0.722; validation group, 0.825 versus 0.778).

Conclusion

The SHIPS scale may serve as a superior screening tool for stroke and LVO identification in prehospital triage. Including medical history in the SHIPS scale improves the predictive value compared with clinical symptoms alone.

Normal Appearing Ischaemic Brain Tissue on CT and Outcome After Intravenous Alteplase

Background and Aims

The visibility of ischaemic brain lesions on non-enhanced CT increases with time. Obviously hypoattenuating lesions likely represent infarction. Conversely, viable ischaemic brain lesions may be non-visible on CT. We tested whether patients with normal appearing ischaemic brain tissue (NAIBT) on their initial CT are identifiable, and if NAIBT yields better outcomes with alteplase.

Methods

With data from the Third International Stroke Trial (IST-3, a large randomized-controlled trial of intravenous alteplase for ischaemic stroke) we used receiver-operating characteristic analysis to find a baseline National Institutes of Health Stroke Scale (NIHSS) threshold for identifying patients who developed medium-large ischaemic lesions within 48 h. From patients with baseline CT (acquired <6 h from stroke onset), we used this NIHSS threshold for selection and tested whether favorable outcome after alteplase (6-month Oxford Handicap Score 0-2) differed between patients with NAIBT vs. with those with visible lesions on baseline CT using binary logistic regression (controlled for age, NIHSS, time from stroke onset to CT).

Results

From 2,961 patients (median age 81 years, median 2.6 h from stroke onset, 1,534 [51.8%] female, 1,484 [50.1%] allocated alteplase), NIHSS>11 best identified those with medium-large ischaemic lesions (area under curve = 0.79, sensitivity = 72.3%, specificity = 71.9%). In IST-3, 1,404/2,961 (47.4%) patients had baseline CT and NIHSS>11. Of these, 745/1,404 (53.1%) had visible baseline ischaemic lesions, 659/1,404 (46.9%) did not (NAIBT). Adjusted odds ratio for favorable outcome after alteplase was 1.54 (95% confidence interval, 1.01-2.36), p = 0.045 among patients with NAIBT vs. 1.61 (0.97-2.67), p = 0.066 for patients with visible lesions, with no evidence of an alteplase-NAIBT interaction (p-value = 0.895).

Conclusions

Patients with ischaemic stroke and NIHSS >11 commonly develop sizeable ischaemic brain lesions by 48 h that may not be visible within 6 h of stroke onset. Invisible ischaemic lesions may indicate tissue viability. In IST-3, patients with this clinical-radiological mismatch allocated to alteplase achieved more favorable outcome than those allocated to control.

Nursing Care of the Acute Ischemic Stroke Endovascular Thrombectomy Patient

Nurses are an integral part of the multidisciplinary team caring for a patient eligible for endovascular thrombectomy. Their care includes obtaining health history, performing clinical assessments, using critical thinking to anticipate the care path, and communicating findings to other team members. The prehospital and emergency department nurses utilize stroke severity scales to identify a possible thrombectomy candidate and help expedite intervention. In the interventional laboratory, nursing collaborates with radiology technologists and interventionalists to ensure patient safety and monitor for intraprocedural complications. Post-procedure, the intensive care nurse delivers complex care to ensure optimal neurological outcome and assess for postprocedural complications. Nursing is essential in every phase of care along with collaboration with other disciplines.

Portable stroke detection devices: a systematic scoping review of prehospital applications

BACKGROUND

The worldwide burden of stroke remains high, with increasing time-to-treatment correlated with worse outcomes. Yet stroke subtype determination, most importantly between stroke/non-stroke and ischemic/hemorrhagic stroke, is not confirmed until hospital CT diagnosis, resulting in suboptimal prehospital triage and delayed treatment. In this study, we survey portable, non-invasive diagnostic technologies that could streamline triage by making this initial determination of stroke type, thereby reducing time-to-treatment.

METHODS

Following PRISMA guidelines, we performed a scoping review of portable stroke diagnostic devices. The search was executed in PubMed and Scopus, and all studies testing technology for the detection of stroke or intracranial hemorrhage were eligible for inclusion. Extracted data included type of technology, location, feasibility, time to results, and diagnostic accuracy.

RESULTS

After a screening of 296 studies, 16 papers were selected for inclusion. Studied devices utilized various types of diagnostic technology, including near-infrared spectroscopy (6), ultrasound (4), electroencephalography (4), microwave technology (1), and volumetric impedance spectroscopy (1). Three devices were tested prior to hospital arrival, 6 were tested in the emergency department, and 7 were tested in unspecified hospital settings. Median measurement time was 3 minutes (IQR: 3 minutes to 5.6 minutes). Several technologies showed high diagnostic accuracy in severe stroke and intracranial hematoma detection.

CONCLUSION

Numerous emerging portable technologies have been reported to detect and stratify stroke to potentially improve prehospital triage. However, the majority of these current technologies are still in development and utilize a variety of accuracy metrics, making inter-technology comparisons difficult. Standardizing evaluation of diagnostic accuracy may be helpful in further optimizing portable stroke detection technology for clinical use.

Prehospital triage optimization of patients with large vessel occlusion by Austrian Prehospital Stroke Scale

OBJECTIVES

The Austrian Prehospital Stroke Scale (APSS) score was developed to predict large vessel occlusion (LVO) and improve prehospital transportation triage. Its accuracy has been previously analyzed retrospectively. We now aimed to investigate the accuracy, as well as the impact of the implementation of a triage strategy using this score on treatment times and outcome in a prospective study.

MATHERIAL & METHODS

Prospective diagnostic test accuracy and before-after interventional study. EMS prospectively evaluated APSS in patients suspected of stroke. Accuracy was compared with other LVO scores. Patients with APSS ≥4 points were brought directly to the comprehensive stroke center. Treatment time frames, neurological, and radiological outcome before and after the APSS implementation were compared.

RESULTS

A total of 307 patients with suspected stroke were included from October 2018 to February 2020. Treatable LVO was present in 79 (26%). Sensitivity of APSS to detect those was 90%, specificity 79%, positive predictive value 66%, negative predictive value 95%, and area under the curve 0.87 (95% CI 0.83-0.91). This was similar to in-hospital NIHSS (AUC 0.89 95% CI 0.89-0.92, p = .06) and superior to CPSS (AUC 0.83 95% CI 0.78-0.87, p = .01). Implementation of APSS triage increased direct transportation rate for LVO patients (21% before vs. 52% after; p < .001) with a significant time benefit (alert to groin puncture time benefit: 51 min (95% CI 28-74; p < .001). Neurological and radiological outcome did not differ significantly.

CONCLUSIONS

Austrian Prehospital Stroke Scale triage showed an accuracy comparable with in-hospital NIHSS, and lead to a significant optimization of prehospital workflows in patients with potential LVO.

Prehospital Translation of Chest Pain Tools (RESCUE Study): Completion Rate and Inter-rater Reliability

INTRODUCTION

Chest pain is a common reason for ambulance transport. Acute coronary syndrome (ACS) and pulmonary embolism (PE) risk assessments, such as history, electrocardiogram, age, risk factors (HEAR); Emergency Department Assessment of Chest Pain Score (EDACS); Pulmonary Embolism Rule-out Criteria (PERC); and revised Geneva score, are well validated for emergency department (ED) use but have not been translated to the prehospital setting. The objectives of this study were to evaluate the 1) prehospital completion rate and 2) inter-rater reliability of chest pain risk assessments.

METHODS

We conducted a prospective observational cohort study in two emergency medical services (EMS) agencies (April 18, 2018 - January 2, 2019). Adults with acute, non-traumatic chest pain without ST-elevation myocardial infarction or unstable vital signs were accrued. Paramedics were trained to use the HEAR, EDACS, PERC, and revised Geneva score assessments. A subset of patients (a priori goal of N = 250) also had the four risk assessments completed by their treating clinicians in the ED, who were blinded to the EMS risk assessments. Outcomes were 1) risk assessments completion rate and 2) inter-rater reliability between EMS and ED assessments. An a priori goal for completion rate was set as >75%. We computed kappa with corresponding 95% confidence intervals (CI) for each risk assessment as a measure of inter-rater reliability. Acceptable agreement was defined a priori as kappa ≥ 0.60.

RESULTS

During the study period, 837 patients with acute chest pain were accrued. The median age was 54 years, interquartile range 43-66, with 53% female and 51% Black. Completion rates for each risk assessment were above goal: the HEAR score was completed on 95.1% (796/837), EDACS on 92.0% (770/837), PERC on 89.4% (748/837), and revised Geneva score on 90.7% (759/837) of patients. We assessed agreement in a subgroup of 260 patients. The HEAR score had a kappa of 0.51 (95% CI, 0.41-0.61); EDACS was 0.60 (95% CI, 0.49-0.72); PERC was 0.71 (95% CI, 0.61-0.81); and revised Geneva score was 0.51 (95% CI, 0.39-0.62).

CONCLUSION

The completion rate of risk assessments for ACS and PE was high for prehospital field personnel. The PERC and EDACS both demonstrated acceptable agreement between paramedics and clinicians in the ED, although assessments with better agreement are likely needed.

Comparison of large vessel occlusion scales using prehospital patient reports

BACKGROUND

Prehospital identification of large vessel occlusion (LVO) holds significant potential to decrease the onset-to-treatment time. Several prehospital scales have been developed to identify LVO but data on their comparison has been limited. The aim of this study was to review the currently available prehospital LVO scales and compare their performance using prehospital data.

METHODS

All patients transported by ambulance using stroke code on a six-month period were enrolled into the study. The prehospital patient reports were retrospectively evaluated by two investigators using sixteen LVO scales identified by literature search and expert opinion. After the evaluation, the computed tomography angiography results were reviewed by a neuroradiologist to confirm or exclude LVO.

RESULTS

Sixteen different LVO scales met the predetermined study criteria and were selected for further comparison. Using them, a total of 610 evaluations were registered. The sensitivity of the scales varied between 8%-73%, specificity between 71%-97% and overall accuracy between 71%-87%. The areas under curve (AUC) varied between 0.61-0.80 for the whole scale range and 0.53%-0.74 for the scales' binary cut-offs. The Field Assessment Stroke Triage for Emergency Destination (FAST-ED) was the only scale with AUC > 0.8. Regarding scales' binary cut-offs, The FAST-ED (0.70), Gaze - Face Arm Speech Time (G-FAST) (0.74) and Emergency Medical Stroke Assessment (EMSA) (0.72) were the only scales with AUC > 0.7.

CONCLUSIONS

In a comparison of 16 different LVO scales, the FAST-ED, G-FAST and EMSA achieved the highest overall performance.

Rapid Identification of Patients Eligible for Direct Emergent Computed Tomography Angiography during Acute Ischemic Stroke: The DARE-PACE Assessment

Background: We investigated the clinical signs to establish a method for rapid identification of patients with the National Institute of Health Stroke Scale (NIHSS) score ≥ 8 eligible for direct brain CTA study; Methods: We retrospectively enrolled 2895 in patients with acute ischemic stroke (AIS). Four items in the NIHSS were selected as the main clinical signs of stroke; Results: A total of 922 (31.8%) patients had an initial NIHSS score of ≥8. The average door-to-CT time and door-to-CTA time were 13.4 ± 1.8 and 75.5 ± 44.5 min, respectively. Among 658 patients who had the priority signs, namely dense hemiplegia (D), aphasia with right arm drop (AR), and eyeball forced deviation (E), 634 patients (96.4%) with an NIHSS score ≥ 8 were identified. By using a classification and regression tree analysis, 153 patients with an NIHSS ≥ 8 were identified among 175 patients (87.4%) who had the secondary signs, namely hemiparesis with limb falls (P), aphasia (A), drowsy or worse consciousness (C), and eyeball limitation (E). The sensitivity, specificity, and accuracy were 85.4%, 97.7%, and 95.3%, respectively. Conclusions: The DARE-PACE assessment involving a checkbox list provides excellent accuracy for rapid identification of AIS patients with an NIHSS score ≥ 8 for direct CTA study to reduce the time delay for endovascular thrombectomy.

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

Background: Large vessel occlusion (LVO) is the obstruction of large, proximal cerebral arteries and can account for up to 46% of acute ischaemic stroke (AIS) when both the A2 and P2 segments are included (from the anterior and posterior cerebral arteries). It is of paramount importance that LVO is promptly recognised to provide timely and effective acute stroke management. This review aims to scope recent literature to identify new emerging detection techniques for LVO. As a good comparator throughout this review, the commonly used National Institutes of Health Stroke Scale (NIHSS), at a cut-off of ≥11, has been reported to have a sensitivity of 86% and a specificity of 60% for LVO. Methods: Four electronic databases (Medline via OVID, CINAHL, Scopus, and Web of Science), and grey literature using OpenGrey, were systematically searched for published literature investigating developments in detection methods for LVO, reported from 2015 to 2021. The protocol for the search was published with the Open Science Framework (10.17605/OSF.IO/A98KN). Two independent researchers screened the titles, abstracts, and full texts of the articles, assessing their eligibility for inclusion. Results: The search identified 5,082 articles, in which 2,265 articles were screened to assess their eligibility. Sixty-two studies remained following full-text screening. LVO detection techniques were categorised into 5 groups: stroke scales (n = 30), imaging and physiological methods (n = 15), algorithmic and machine learning approaches (n = 9), physical symptoms (n = 5), and biomarkers (n = 3). Conclusions: This scoping review has explored literature on novel and advancements in pre-existing detection methods for LVO. The results of this review highlight LVO detection techniques, such as stroke scales and biomarkers, with good sensitivity and specificity performance, whilst also showing advancements to support existing LVO confirmatory methods, such as neuroimaging.

Machine Learning-Based Model for Predicting Incidence and Severity of Acute Ischemic Stroke in Anterior Circulation Large Vessel Occlusion

Objectives: Patients with anterior circulation large vessel occlusion are at high risk of acute ischemic stroke, which could be disabling or fatal. In this study, we applied machine learning to develop and validate two prediction models for acute ischemic stroke (Model 1) and severity of neurological impairment (Model 2), both caused by anterior circulation large vessel occlusion (AC-LVO), based on medical history and neuroimaging data of patients on admission.

Methods: A total of 1,100 patients with AC- LVO from the Second Hospital of Hebei Medical University in North China were enrolled, of which 713 patients presented with acute ischemic stroke (AIS) related to AC- LVO and 387 presented with the non-acute ischemic cerebrovascular event. Among patients with the non-acute ischemic cerebrovascular events, 173 with prior stroke or TIA were excluded. Finally, 927 patients with AC-LVO were entered into the derivation cohort. In the external validation cohort, 150 patients with AC-LVO from the Hebei Province People's Hospital, including 99 patients with AIS related to AC- LVO and 51 asymptomatic AC-LVO patients, were retrospectively reviewed. We developed four machine learning models [logistic regression (LR), regularized LR (RLR), support vector machine (SVM), and random forest (RF)], whose performance was internally validated using 5-fold cross-validation. The performance of each machine learning model for the area under the receiver operating characteristic curve (ROC-AUC) was compared and the variables of each algorithm were ranked.

Results: In model 1, among the included patients with AC-LVO, 713 (76.9%) and 99 (66%) suffered an acute ischemic stroke in the derivation and external validation cohorts, respectively. The ROC-AUC of LR, RLR and SVM were significantly higher than that of the RF in the external validation cohorts [0.66 (95% CI 0.57–0.74) for LR, 0.66 (95% CI 0.57–0.74) for RLR, 0.55 (95% CI 0.45–0.64) for RF and 0.67 (95% CI 0.58–0.76) for SVM]. In model 2, 254 (53.9%) and 31 (37.8%) patients suffered disabling ischemic stroke in the derivation and external validation cohorts, respectively. There was no difference in AUC among the four machine learning algorithms in the external validation cohorts.

Conclusions: Machine learning methods with multiple clinical variables have the ability to predict acute ischemic stroke and the severity of neurological impairment in patients with AC-LVO.

Assessment of Clinical Scales for Detection of Large Vessel Occlusion in Ischemic Stroke Patients from the Dijon Stroke Registry

(1) Background: The limited availability of thrombectomy-capable stroke centres raises questions about pre-hospital triage of patients with suspected stroke (IS) due to large vessel occlusion (LVO). Aims: This study aimed to evaluate the diagnostic accuracy of clinical stroke severity scales available for LVO detection. (2) Methods: Patients with IS were prospectively identified among residents of Dijon, France, using a population-based registry (2013–2017). Clinical signs and arterial imaging data were collected. LVO was defined as an occlusion site affecting the terminal intracranial internal carotid artery, the M1 segment of the middle cerebral artery (MCA), or the basilar artery (restricted definition). A wide definition of LVO also included the M2 segment of the MCA. For each of the 16 evaluated scales, a receiver operator characteristic (ROC) analysis was performed, and the c-statistic representing the area under the ROC curve was evaluated to assess discrimination for predicting LVO. (3) Results: 971 patients were registered, including 123 patients (12.7%) with an LVO according to the restricted definition. The c-statistic for LVO detection ranged between 0.66 and 0.80 according to the different scales, with a sensibility varying from 70% to 98% and a specificity from 33% to 86%. According to the wide definition of LVO (174 patients, 17.9%), the c-statistic was slightly lower, ranging between 0.64 and 0.79. The sensitivity was 59% to 93%, and the specificity was 34% to 89%. (4) Conclusion: The clinical scales failed to combine a high sensitivity and a high specificity to detect LVO. Further studies are needed to determine the best strategy for pre-hospital triage of IS patients.

Prediction of large vessel occlusion for ischaemic stroke by using the machine learning model random forests

BACKGROUNDS

The timely identification of large vessel occlusion (LVO) in the prehospital stage is extremely important given the disease morbidity and narrow time window for intervention. The current evaluation strategies still remain challenging. The goal of this study was to develop a machine learning (ML) model to predict LVO using prehospital accessible data.

METHODS

Consecutive acute ischaemic stroke patients who underwent CT or MR angiography and received reperfusion therapy within 8 hours from symptom onset in the Computer-based Online Database of Acute Stroke Patients for Stroke Management Quality Evaluation-II dataset from January 2016 to August 2021 were included. We developed eight ML models to integrate National Institutes of Health Stroke Scale (NIHSS) items with demographics, medical history and vascular risk factors to identify LVO and validate its efficiency.

RESULTS

Finally, 15 365 patients were included in the training set and 4215 patients were included in the test set. On the test set, random forests (RF), gradient boosting machine and extreme gradient boosting presented area under the curve (AUC) of 0.831 (95% CI 0.819 to 0.843), which were higher than other models, and RF presented the highest specificity (0.827). In addition, the AUC of RF was higher than other scales, and the accuracy of the model was improved by 6.4% compared with NIHSS. We also found the top five items of identifying LVO were total NIHSS score, gaze deviation, level of consciousness (LOC), LOC commands and motor left leg.

CONCLUSIONS

Our proposed model could be a useful screening tool to predict LVO based on the prehospital accessible medical data.

TRIAL REGISTRATION NUMBER

NCT04487340.

Novel Prehospital Triage Scale for Detecting Large Vessel Occlusion and Its Cause

Background

Patients with large vessel occlusion stroke (LVOS) need to be rapidly identified and transferred to comprehensive stroke centers. However, current prehospital evaluation and strategies still remain challenging.

Methods and Results

We retrospectively reviewed our prospectively collected database of patients with acute ischemic stroke (AIS). Based on the items of National Institutes of Health Stroke Scale and medical history that had a strong association with LVOS, we designed the 4‐item Stroke Scale (4I‐SS) and validated it in multi‐centers. The 4I‐SS incorporated gaze, level of consciousness, arm weakness, and atrial fibrillation. Receiver operating characteristic analysis was used to compare the 4I‐SS with previously established prehospital prediction scales. Finally, 1630 and 11 440 patients were included in the derivation and validation cohort, respectively. In the validation cohort, Youden Index, area under the curve, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the 4I‐SS≥4 to predict LVOS were 0.494, 0.800, 0.657, 0.837, 0.600, 0.868, and 0.788, respectively, and that of the 4I‐SS≥7 to predict basilar artery occlusion were 0.200, 0.669, 0.229, 0.971, 0.066, 0.974, and 0.899, respectively. Youden Index and area under the curve were higher than previously published scales for predicting LVOS. Further analysis showed that for predicting whether cardiogenic embolism was the cause, its accuracy was 0.922 when the 4I‐SS score, including atrial fibrillation, was ≥6, and its accuracy of predicting the occluded vessel was intracranial internal carotid artery or M1 segment of the middle cerebral artery when it was ≥7 was 0.590.

Conclusions

The 4I‐SS is an effective and simple tool that can identify LVOS and its cause.

Registration

URL: https://www.clinicaltrials.gov; Unique identifier: NCT03317639.

Sensitivity of prehospital stroke scales for different intracranial large vessel occlusion locations

Introduction

Prehospital stroke scales have been proposed to identify stroke patients with a large vessel occlusion to allow direct transport to an intervention centre capable of endovascular treatment (EVT). It is unclear whether these scales are able to detect not only proximal, but also more distal treatable occlusions. Our aim was to assess the sensitivity of prehospital stroke scales for different EVT-eligible occlusion locations in the anterior circulation.

Patients and methods

The MR CLEAN Registry is a prospective, observational study in all centres that perform EVT in the Netherlands. We included adult patients with an anterior circulation stroke treated between March 2014 and November 2017. We used National Institutes of Health Stroke Scale scores at admission to reconstruct previously published prehospital stroke scales. We compared the sensitivity of each scale for different occlusion locations. Occlusions were assessed with CT angiography by an imaging core laboratory blinded to clinical findings.

Results

We included 3021 patients for the analysis of 14 scales. All scales had the highest sensitivity to detect internal carotid artery terminus occlusions (ranging from 0.21 to 0.97) and lowest for occlusions of the M2 segment (0.08 to 0.84, p-values < 0.001).Discussion and conclusion: Although prehospital stroke scales are generally sensitive for proximal large vessel occlusions, they are less sensitive to detect more distal occlusions.

Large Vessel Occlusion Prediction in the Emergency Department with National Institutes of Health Stroke Scale Components: A Machine Learning Approach

OBJECTIVE

To determine the feasibility of using a machine learning algorithm to screen for large vessel occlusions (LVO) in the Emergency Department (ED).

MATERIALS AND METHODS

A retrospective cohort of consecutive ED stroke alerts at a large comprehensive stroke center was analyzed. The primary outcome was diagnosis of LVO at discharge. Components of the National Institutes of Health Stroke Scale (NIHSS) were used in various clinical methods and machine learning algorithms to predict LVO, and the results were compared with the baseline method (aggregate NIHSS score with threshold of 6). The Area-Under-Curve (AUC) was used to measure the overall performance of the models. Bootstrapping (n = 1000) was applied for the statistical analysis.

RESULTS

Of 1133 total patients, 67 were diagnosed with LVO. A Gaussian Process (GP) algorithm significantly outperformed other methods including the baseline methods. AUC score for the GP algorithm was 0.874 ± 0.025, compared with the simple aggregate NIHSS score, which had an AUC score of 0.819 ± 0.024. A dual-stage GP algorithm is proposed, which offers flexible threshold settings for different patient populations, and achieved an overall sensitivity of 0.903 and specificity of 0.626, in which sensitivity of 0.99 was achieved for high-risk patients (defined as initial NIHSS score > 6).

CONCLUSION

Machine learning using a Gaussian Process algorithm outperformed a clinical cutoff using the aggregate NIHSS score for LVO diagnosis. Future studies would be beneficial in exploring prospective interventions developed using machine learning in screening for LVOs in the emergent setting.

Development of Machine Learning Models to Predict Probabilities and Types of Stroke at Prehospital Stage: the Japan Urgent Stroke Triage Score Using Machine Learning (JUST-ML)

In conjunction with recent advancements in machine learning (ML), such technologies have been applied in various fields owing to their high predictive performance. We tried to develop prehospital stroke scale with ML. We conducted multi-center retrospective and prospective cohort study. The training cohort had eight centers in Japan from June 2015 to March 2018, and the test cohort had 13 centers from April 2019 to March 2020. We use the three different ML algorithms (logistic regression, random forests, XGBoost) to develop models. Main outcomes were large vessel occlusion (LVO), intracranial hemorrhage (ICH), subarachnoid hemorrhage (SAH), and cerebral infarction (CI) other than LVO. The predictive abilities were validated in the test cohort with accuracy, positive predictive value, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and F score. The training cohort included 3178 patients with 337 LVO, 487 ICH, 131 SAH, and 676 CI cases, and the test cohort included 3127 patients with 183 LVO, 372 ICH, 90 SAH, and 577 CI cases. The overall accuracies were 0.65, and the positive predictive values, sensitivities, specificities, AUCs, and F scores were stable in the test cohort. The classification abilities were also fair for all ML models. The AUCs for LVO of logistic regression, random forests, and XGBoost were 0.89, 0.89, and 0.88, respectively, in the test cohort, and these values were higher than the previously reported prediction models for LVO. The ML models developed to predict the probability and types of stroke at the prehospital stage had superior predictive abilities.

Association of Admission NIHSS Score with Infarct Core Volume and Target Mismatch of Infarct Core/Penumbra Volume on CT Perfusion in Acute Ischaemic Stroke

INTRODUCTION

Neurological impairment is associated with collateral status in acute ischaemic stroke (AIS). We aimed to validate the association between admission National Institutes of Health Stroke Scale (aNIHSS) score and infarct core volume (ICV) and target infarct core/penumbra volume mismatch (TMM) on CT perfusion (CTP) in AIS patients.

METHODS

Patients with acute middle cerebral artery or internal carotid artery occlusion from 2011 to 2020 were included. All patients underwent pretreatment CTP at admission. ICV and TMM were analyzed with MIStar software on CTP maps. aNIHSS scores and clinical characteristics of patients were obtained from our prospectively recorded stroke database.

RESULTS

We recruited 182 patients with a median age of 69.5 years; 85 (63.7%) were male, and the median aNIHSS score was 14. Of those, 149 (81.8%) had an ICV < 70 mL, and 139 (76.3%) had TMM. Lower aNIHSS was associated with an ICV < 70 mL, with an area under the curve (AUC) of 0.74, and TMM with an AUC of 0.76. Among all 15 items of the aNIHSS, the gaze score was the only item independently associated with an ICV < 70 mL (adjusted odds ratio [OR] = 0.42, 95% confidence interval [CI]: 0.22-0.79, p = 0.008) and TMM (adjusted OR = 0.5, 95% CI: 0.28-0.9, p = 0.021). One or both aNIHSS ≤ 16 and gaze score = 0 predicted TMM with a sensitivity of 0.79 and a specificity of 0.62.

CONCLUSION

aNIHSS may be a useful tool to predict an ICV < 70 mL and TMM on CTP in AIS patients.

Large Vessel Occlusion Stroke Detection in the Prehospital Environment

Purpose of Review

Endovascular therapy for acute ischemic stroke secondary to large vessel occlusion (LVO) is time-dependent. Prehospital patients with suspected LVO stroke should be triaged directly to specialized stroke centers for endovascular therapy. This review describes advances in LVO detection among prehospital suspected stroke patients.

Recent Findings

Clinical prehospital stroke severity tools have been validated in the prehospital setting. Devices including EEG, SSEPs, TCD, cranial accelerometry, and volumetric impedance phase-shift-spectroscopy have recently published data regarding LVO detection in hospital settings. Mobile stroke units bring thrombolysis and vessel imaging to patients.

Summary

The use of a prehospital stroke severity tool for LVO triage is now widely supported. Ease of use should be prioritized as there are no meaningful differences in diagnostic performance amongst tools. LVO diagnostic devices are promising, but none have been validated in the prehospital setting. Mobile stroke units improve patient outcomes and cost-effectiveness analyses are underway.

Emergency Care of Patients with Acute Ischemic Stroke

Stroke is a leading cause of morbidity and mortality and a major cause of long-term disability. Management of acute ischemic stroke in the first hours is critical to patient outcomes. This review provides an overview of acute ischemic stroke management, with a focus on the golden hour. Additional topics discussed include prehospital considerations and initial evaluation of the patient with history, examination, and imaging as well as treatment options, including thrombolysis and endovascular therapy.

A Novel Combination of Blood Biomarkers and Clinical Stroke Scales Facilitates Detection of Large Vessel Occlusion Ischemic Strokes

Acute ischemic stroke caused by large vessel occlusions (LVOs) is a major contributor to stroke deaths and disabilities; however, identification for emergency treatment is challenging. We recruited two separate cohorts of suspected stroke patients and screened a panel of blood-derived protein biomarkers for LVO detection. Diagnostic performance was estimated by using blood biomarkers in combination with NIHSS-derived stroke severity scales. Multivariable analysis demonstrated that D-dimer (OR 16, 95% CI 5–60; p-value < 0.001) and GFAP (OR 0.002, 95% CI 0–0.68; p-value < 0.05) comprised the optimal panel for LVO detection. Combinations of D-dimer and GFAP with a number of stroke severity scales increased the number of true positives, while reducing false positives due to hemorrhage, as compared to stroke scales alone (p-value < 0.001). A combination of the biomarkers with FAST-ED resulted in the highest accuracy at 95% (95% CI: 87–99%), with sensitivity of 91% (95% CI: 72–99%), and specificity of 96% (95% CI: 90–99%). Diagnostic accuracy was confirmed in an independent cohort, in which accuracy was again shown to be 95% (95% CI: 87–99%), with a sensitivity of 82% (95% CI: 57–96%), and specificity of 98% (95% CI: 92–100%). Accordingly, the combination of D-dimer and GFAP with stroke scales may provide a simple and highly accurate tool for identifying LVO patients, with a potential impact on time to treatment.

Prehospital Stroke Management Optimized by Use of Clinical Scoring vs Mobile Stroke Unit for Triage of Patients With Stroke: A Randomized Clinical Trial

Importance

Transferring patients with large-vessel occlusion (LVO) or intracranial hemorrhage (ICH) to hospitals not providing interventional treatment options is an unresolved medical problem.

Objective

To determine how optimized prehospital management (OPM) based on use of the Los Angeles Motor Scale (LAMS) compares with management in a Mobile Stroke Unit (MSU) in accurately triaging patients to the appropriate hospital with (comprehensive stroke center [CSC]) or without (primary stroke center [PSC]) interventional treatment.

Design, Setting, and Participants

In this randomized multicenter trial with 3-month follow-up, patients were assigned week-wise to one of the pathways between June 15, 2015, and November 15, 2017, in 2 regions of Saarland, Germany; 708 of 824 suspected stroke patients did not meet inclusion criteria, resulting in a study population of 116 adult patients.

Interventions

Patients received either OPM based on a standard operating procedure that included the use of the LAMS (cut point ≥4) or management in an MSU (an ambulance with vascular imaging, point-of-care laboratory, and telecommunication capabilities).

Main Outcomes and Measures

The primary end point was the proportion of patients accurately triaged to either CSCs (LVO, ICH) or PSCs (others).

Results

A predefined interim analysis was performed after 116 patients of the planned 232 patients had been enrolled. Of these, 53 were included in the OPM group (67.9% women; mean [SD] age, 74 [11] years) and 63 in the MSU group (57.1% women; mean [SD] age, 75 [11] years). The primary end point, an accurate triage decision, was reached for 37 of 53 patients (69.8%) in the OPM group and for 63 of 63 patients (100%) in the MSU group (difference, 30.2%; 95% CI, 17.8%-42.5%; P < .001). Whereas 7 of 17 OPM patients (41.2%) with LVO or ICH required secondary transfers from a PSC to a CSC, none of the 11 MSU patients (0%) required such transfers (difference, 41.2%; 95% CI, 17.8%-64.6%; P = .02). The LAMS at a cut point of 4 or higher led to an accurate diagnosis of LVO or ICH for 13 of 17 patients (76.5%; 6 triaged to a CSC) and of LVO selectively for 7 of 9 patients (77.8%; 2 triaged to a CSC). Stroke management metrics were better in the MSU group, although patient outcomes were not significantly different.

Conclusions and Relevance

Whereas prehospital management optimized by LAMS allows accurate triage decisions for approximately 70% of patients, MSU-based management enables accurate triage decisions for 100%. Depending on the specific health care environment considered, both approaches are potentially valuable in triaging stroke patients.

Trial Registration

ClinicalTrials.gov identifier: NCT02465346.

Reliability of Field Assessment Stroke Triage for Emergency Destination Scale Use by Paramedics: Mobile Stroke Unit First-Year Experience

[Figure: see text].

Design and validation of a recognition instrument-the stroke aid for emergency scale-to predict large vessel occlusion stroke

Background and purpose: Rapidly recognizing patients with large-vessel occlusion stroke (LVOS) and transferring them to a center offering recanalization therapy is crucial of maximizing the benefits of early treatment. We therefore aimed to design an easy-to-use recognition instrument for identifying LVOS.

Methods: Prospective data were collected from emergency departments of 12 stroke-center hospitals in China during a 17-month study period. The Stroke Aid for Emergency (SAFE) scale is based on consciousness commands, facial palsy, gaze, and arm motor ability. Receiver operating characteristic analysis was used to obtain the area under the curve for the SAFE scale and previously established scales to predict LVOS.

Results: The SAFE scale could accurately predict LVOS at an accuracy rate comparable to that of the National Institutes of Health Stroke Scale (c-statistics: 0.823 versus 0.831, p = 0.4798). The sensitivity, specificity, positive predictive value, and negative predictive value for the SAFE scale were 0.6875, 0.8577, 0.6937, and 0.8542, respectively, with a cutoff point of 4. The SAFE scale also performed well in a subgroup analysis based on the patients’ ages, occluded vessel locations, and the onset-to-door times.

Conclusions: The SAFE scale can accurately recognize LVOS at a rate comparable to those of other, similar scales.

Performance of the vision, aphasia, neglect (VAN) assessment within a single large EMS system

BACKGROUND

There is limited evidence on the performance of emergent large-vessel occlusion (LVO) stroke screening tools when used by emergency medical services (EMS) and emergency department (ED) providers. We assessed the validity and predictive value of the vision, aphasia, neglect (VAN) assessment when completed by EMS and in the ED among suspected stroke patients.

METHODS

We conducted a retrospective study of VAN performed by EMS providers and VAN inferred from the National Institutes of Health Stroke Scale performed by ED nurses at a single hospital. We calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of VAN by EMS and in the ED for LVO and a combined LVO and intracerebral hemorrhage (ICH) outcome.

RESULTS

From January 2018 to June 2020, 1,547 eligible patients were identified. Sensitivity and specificity of ED VAN were similar for LVO (72% and 74%, respectively), whereas EMS VAN was more sensitive (84%) than specific (68%). PPVs were low for both EMS VAN (26%) and ED VAN (21%) to detect LVO. Due to several VAN-positive ICHs, PPVs were substantially higher for both EMS VAN (44%) and ED VAN (39%) to detect LVO or ICH. EMS and ED VAN had high NPVs (97% and 96%, respectively).

CONCLUSIONS

Among suspected stroke patients, we found modest sensitivity and specificity of VAN to detect LVO for both EMS and ED providers. Moreover, the low PPV in our study suggests a significant number of patients with non-LVO ischemic stroke or ICH could be over-triaged with VAN.