Design and validation of a clinical scale for prehospital stroke recognition, severity grading and prediction of large vessel occlusion: the shortened NIH Stroke Scale for emergency medical services

Inter-Rater Agreement on Cincinnati Prehospital Stroke Scale (CPSS) and Prehospital Acute Stroke Severity Scale (PASS) Between EMS Providers, Neurology Residents and Neurology Consultants

Objective

To examine the agreement between emergency medical service (EMS) providers, neurology residents and neurology consultants, using the Cincinnati Prehospital Stroke Scale (CPSS) and the Prehospital Acute Stroke Severity Scale (PASS).

Methods

Patients with stroke, transient ischemic attack (TIA) and stroke mimic were included upon primary stroke admission or during rehabilitation. Patients were included from June 2018 to September 2019. Video recordings were made of patients being assessed with CPSS and PASS. The recordings were later presented to the healthcare professionals. To determine relative and absolute interrater reliability in terms of inter-rater agreement (IRA), we used generalisability theory. Group-level agreement was determined against a gold standard and presented as an area under the curve (AUC). The gold standard was a consensus agreement between two neurology consultants.

Results

A total of 120 patient recordings were assessed by 30 EMS providers, two neurology residents and two neurology consultants. Using the CPSS and the PASS, a total of 1,800 assessments were completed by EMS providers, 240 by neurology residents and 240 by neurology consultants. The overall relative and absolute IRA for all items combined from the CPSS and PASS score was 0.84 (95% CI 0.80; 0.87) and 0.81 (95% CI 0.77; 0.85), respectively. Using the CPSS, the agreement on a group-level resulted in AUCs of 0.83 (95% CI 0.78; 0.88) for the EMS providers and 0.86 (95% CI 0.82; 0.90) for the neurology residents when compared with the gold standard. Using the PASS, the AUC was 0.82 (95% CI 0.77; 0.87) for the EMS providers and 0.88 (95% CI 0.84; 0.93) for the neurology residents.

Conclusion

The high relative and absolute inter-rater agreement underpins a high robustness/generalisability of the two scales. A high agreement exists across individual raters and different groups of healthcare professionals supporting widespread applicability of the stroke scales.

Emergency medical service utilization and timely treatment among acute ischemic stroke patients in Beijing from 2018 to 2021

BACKGROUND AND IMPORTANCE

Activation of emergency medical services (EMS) is recommended for timely reperfusion therapy for acute ischemic stroke (AIS). The association of EMS utilization and time intervals from hospital arrival to a series of necessary procedures before reperfusion therapy was rarely investigated.

OBJECTIVE

The objective of this study is to investigate the association of EMS utilization with the time intervals from hospital arrival to therapy in patients with AIS.

DESIGN

Observational study.

SETTINGS AND PARTICIPANTS

Medical records for AIS in all emergency departments in Beijing were obtained from January 2018 to December 2021.

INTERVENTION OR EXPOSURE

Patients transported by ambulance were defined as the EMS group, whereas others as the non-EMS group.

OUTCOME MEASURES AND ANALYSIS

Door-to-imaging time (DIT), door-to-needle time (DTN) and door-to-puncture time (DTP) were compared between the two groups.

MAIN RESULTS

There were 11 190 (46%) and 13 106 (54%) AIS patients in the EMS and non-EMS groups. Compared with the non-EMS group, patients in the EMS group were more likely to receive intravenous thrombolysis or endovascular therapy (OR, 1.81; 95% CI, 1.68-1.94). For intravenous thrombolysis therapy, the DIT, ITN (time in minutes from obtaining the first brain imaging to tPA delivery) and DTN times in the EMS group were significantly shorter with time differences between the two groups of -1.1 (95% CI, -1.1 to -1.1) min, -2.6 (-2.6 to -2.6) min, and -3.7 (-3.8, -3.7) min, respectively. The proportion of DIT ≤25 min, DTN ≤45 min or DTN ≤60 min was significantly higher in the EMS group (OR, 1.03, 95% CI, 1.02-1.05; 1.11, 1.07-1.14; 1.05, 1.03-1.07). For endovascular therapy, the differences in DIT, ITP (time in minutes from obtaining the first brain imaging to groin puncture) and DTP times between the EMS and non-EMS groups were +1.1 (1.0-1.2) min, -3.8 (-4.2 to -3.5) min, -2.7 (-3.1 to -2.4) min, respectively, but no significant association was observed between EMS usage and the proportion of DIT ≤25 min or DTP ≤90 min.

CONCLUSION

In this observational study, the use of EMS for patient with AIS was associated with a shorter time from hospital arrival to intravenous thrombolysis and endovascular therapy.

Stroke Scales as Assessment Tools in Emergency Settings: A Narrative Review

In the last 20 years, substantial improvements have been made in stroke recanalization treatment. Good outcomes after modern reperfusion treatment require the rapid and accurate identification of stroke patients. Several stroke rating scales are available or have been proposed for the early recognition of stroke and the evaluation of stroke severity and outcome. This review aims to provide an overview of commonly used stroke scales in emergency and clinical settings. The most commonly used scale in a prehospital setting for stroke recognition is the Face, Arms, Speech, Time (FAST) test. Among many prehospital stroke scales, the Los Angeles Prehospital Stroke Screen has the highest sensitivity and specificity for confirming stroke diagnosis. The National Institutes of Health Stroke Scale (NIHSS) is the most recommended tool for the evaluation of stroke patients in hospital settings and research, and it has two variants: the shortened NIHSS for Emergency Medical Service and the modified NIHSS. The evaluation of comatose patients usually involves assessment with the Glasgow Coma Scale, which is very useful in patients with hemorrhagic stroke or traumatic brain injury. In patients with subarachnoid hemorrhage, the outcome is usually accessed with the Hunt and Hess scale. A commonly used tool for stroke outcome evaluation in clinical/hospital settings and research is the modified Rankin scale. The tools for disability evaluation are the Barthel Index and Functional Independence Measure.

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.

Optimization of Large Vessel Occlusion Detection in Acute Ischemic Stroke Using Machine Learning Methods

The early detection of large-vessel occlusion (LVO) strokes is increasingly important as these patients are potential candidates for endovascular therapy, the availability of which is limited. Prehospital LVO detection scales mainly contain symptom variables only; however, recent studies revealed that other types of variables could be useful as well. Our aim was to comprehensively assess the predictive ability of several clinical variables for LVO prediction and to develop an optimal combination of them using machine learning tools. We have retrospectively analysed data from a prospectively collected multi-centre stroke registry. Data on 41 variables were collected and divided into four groups (baseline vital parameters/demographic data, medical history, laboratory values, and symptoms). Following the univariate analysis, the LASSO method was used for feature selection to select an optimal combination of variables, and various machine learning methods (random forest (RF), logistic regression (LR), elastic net method (ENM), and simple neural network (SNN)) were applied to optimize the performance of the model. A total of 526 patients were included. Several neurological symptoms were more common and more severe in the group of LVO patients. Atrial fibrillation (AF) was more common, and serum white blood cell (WBC) counts were higher in the LVO group, while systolic blood pressure (SBP) was lower among LVO patients. Using the LASSO method, nine variables were selected for modelling (six symptom variables, AF, chronic heart failure, and WBC count). When applying machine learning methods and 10-fold cross validation using the selected variables, all models proved to have an AUC between 0.736 (RF) and 0.775 (LR), similar to the performance of National Institutes of Health Stroke Scale (AUC: 0.790). Our study highlights that, although certain neurological symptoms have the best ability to predict an LVO, other variables (such as AF and CHF in medical history and white blood cell counts) should also be included in multivariate models to optimize their efficiency.

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.

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.

Prehospital Identification of Large Vessel Occlusions Using Modified National Institutes of Health Stroke Scale: A Pilot Study

Stroke identification is a key step in acute ischemic stroke management. Our objectives were to prospectively examine the agreement between prehospital and hospital Modified National Institutes of Health Stroke Scale (mNIHSS) assessments as well as assess the prehospital performance characteristics of the mNIHSS for identification of large vessel occlusion strokes.

Method: In this prospective cohort study conducted over a 20-month period (11/2016–6/2018), we trained 40 prehospital providers (paramedics) in Emergency Neurological Life Support (ENLS) curriculum and in mNIHSS. English-speaking patients aged 18 and above transported for an acute neurological deficit were included. Using unique identifiers, we linked the prehospital assessment records to the hospital record. We calculated the agreement between prehospital and hospital mNIHSS scores using the Bland-Altman analysis and the sensitivity and specificity of the prehospital mNIHSS.

Results: Of the 31 patients, the mean difference (prehospital mNIHSS—hospital mNIHSS) was 2.4, 95% limits of agreement (−5.2 to 10.0); 10 patients (32%) met our a priori imaging definition of large vessel occlusion and the sensitivity of mNIHSS ≥ 8 was 6/10 or 0.60 (95% CI: 0.26–0.88) and the specificity was 13/21 or 0.62 (95% CI: 0.38–0.82), respectively.

Conclusions: We were able to train prehospital providers to use the prehospital mNIHSS. Prehospital and hospital mNIHSS had a reasonable level of agreement and and the scale was able to predict large vessel occlusions with moderate sensitivity.

A Novel Scale for Assessment of Stroke Severity at Symptom Onset: Correlation With Neurological Deterioration and Outcome

Objective: To propose a novel scale for the assessment of stroke severity at symptom onset and to investigate whether it is associated with ultra-early neurological deterioration (UND) and functional outcomes.

Methods: The Chongqing Stroke Scale (CQSS) was constructed based on key aspects of history, emphasizing language, motor function, and level of consciousness to yield a total 0–11 scale. The diagnostic performance of the CQSS was assessed in 215 ischemic stroke patients between June 2017 and October 2017 in a tertiary hospital. Patients were included if they presented within 24 h after onset of symptoms and they or their witness can recall the scenario at symptom onset. UND was defined as an increase ≥2 points on the CQSS between symptom onset and admission. Functional outcomes were assessed using the 3-month modified Rankin scale. The correlation between the CQSS score and baseline National Institutes of Health Stroke Scale (NIHSS) score was assessed. The sensitivity, specificity, and positive and negative predictive values of CQSS for the outcomes were calculated. Logistic regression was used to test the association between the CQSS score and functional outcomes.

Results: A total of 215 patients with available CQSS scores were included. Baseline CQSS scores at symptom onset were correlated with the admission NIHSS score (r = 0.56, p < 0.001) and functional outcome at 3 months (r = 0.47, p < 0.001). Baseline CQSS ≥ 6 was an independent predictor of functional outcome at 3 months (odds ratio, 12.61; 95% confidence interval 5.68–27.97, p < 0.001). UND was observed in 20 (9.30%) patients. The 90-day mortality was significantly higher in patients with UND than those without UND (25.0 vs. 8.2%, p < 0.001). After adjusting for age, admission systolic blood pressure, hypertension, and diabetes, UND independently predicted poor functional outcome in the multivariate logistic regression model (odds ratio, 9.69; 95% confidence interval 3.19–29.45, p < 0.001).

Conclusions: The newly developed CQSS is a simple and easy-to-perform scale that allows a quantitative evaluation of the stroke severity at symptom onset and an assessment of UND before hospital admission. It is associated with NIHSS and predicts functional outcome in patients with acute ischemic stroke.

Design and Derivation of the Austrian Prehospital Stroke Scale (APSS) to Predict Severe Stroke with Large Vessel Occlusion

Objectives: Prediction of large vessel occlusion (LVO) is highly relevant for accurate prehospital transportation triage. The Austrian Prehospital Stroke Scale (APSS) score for LVO prediction was developed using critical synthesis of previously published LVO-scores. The aim of this study was to investigate the accuracy of the APSS and compare it to other LVO-scores. Methods: APSS consists of 5 items: "facial palsy," "motor arm," "language," "motor leg" and "gaze deviation." The score ranges from 0 to 9 points. Data from 741 consecutive stroke patients with acute vessel imaging admitted to an independent comprehensive stroke center was used to test the predictive performance of the APSS in context of other LVO-scores (CPSS, FAST-ED, G-FAST, sNIHSS-EMS and RACE). Results: In the prediction of treatable LVO the APSS showed the highest area under the curve (0.834) with significant difference to CPSS (p = 0.010) and G-FAST (p = 0.006) and showed highest sensitivity (69%) as compared to other LVO scores. Specificity (85%), positive predictive value (75%), negative predictive value (81%) and accuracy (79%) were comparable to other LVO scores. Receiver operating curve analysis revealed an optimal cutoff for LVO prediction at APSS equal to 4 points. Conclusions: The easy assessable 5-item APSS score tended to outperform other LVO scores. Real-life prospective evaluation in prehospital setting is ongoing.

Challenges Related to the Implementation of an EMS-Administered, Large Vessel Occlusion Stroke Score

Introduction

There is considerable interest in triaging victims of large vessel occlusion (LVO) strokes to comprehensive stroke centers. Timely access to interventional therapy has been linked to improved stroke outcomes. Accurate triage depends upon the use of a validated screening tool in addition to several emergency medical system (EMS)-specific factors. This study examines the integration of a modified Rapid Arterial oCcclusion Evaluation (mRACE) score into an existing stroke treatment protocol.

Methods

We performed a retrospective review of EMS and hospital charts of patients transported to a single comprehensive stroke center. Adult patients with an EMS provider impression of “stroke/TIA,” “CVA,” or “neurological problem” were included for analysis. EMS protocols mandated the use of the Cincinnati Prehospital Stroke Score (CPSS). The novel protocol authorized the use of the mRACE score to identify candidates for triage directly to the comprehensive stroke center. We calculated specificity and sensitivity for various stroke screens (CPSS and a mRACE exam) for the detection of LVO stroke. The score’s metrics were evaluated as a surrogate marker for a successful EMS triage protocol.

Results

We included 312 prehospital charts in the final analysis. The CPSS score exhibited reliable sensitivity at 85%. Specificity of CPSS for an LVO was calculated at 73%. For an mRACE score of five or greater, the sensitivity was 25%. Specificity for mRACE was calculated at 75%. The positive predictive value of the mRACE score for an LVO was estimated at 12.50%.

Conclusion

In this retrospective study of patients triaged to a single comprehensive stroke center, the addition of an LVO-specific screening tool failed to improve accuracy. Reliable triage of LVO strokes in the prehospital setting is a challenging task. In addition to statistical performance of a particular stroke score, a successful EMS protocol should consider system-based factors such as provider education and training. Study limitations can inform future iterations of LVO triage protocols.

Emergent Large Vessel Occlusion Screen Is an Ideal Prehospital Scale to Avoid Missing Endovascular Therapy in Acute Stroke

Background and Purpose- The strong evidence of endovascular therapy in acute ischemic stroke patients with large vessel occlusion (LVO) is revealed. Such patients are required to direct transport to the hospital capable of endovascular therapy. There are several prehospital scales available for paramedics to predict LVO. However, they are time consuming, and several of them include factors caused by other types than LVO. Therefore, we need a fast, simple, and reliable prehospital scale for LVO. Methods- We developed a new prehospital stroke scale, emergent large vessel occlusion (ELVO) screen, for paramedics to predict LVO. The study was prospectively performed by multistroke centers. When paramedics referred to stroke center to accept suspected stroke patients, we obtain the following information over the telephone. ELVO screen was designed focusing on cortical symptoms: 1 observation; presence of eye deviation and 2 questions; paramedics show glasses, what is this? and paramedics show 4 fingers, how many fingers are there? If the presence of eye deviation or ≥1 of the 2 items were incorrect, ELVO screen was identified as positive. We evaluated between results of ELVO screen and presence of LVO on magnetic resonance angiography at hospital arrival. Results- A total of 413 patients (age, 74±13 years; men, 234 [57%]) were enrolled. Diagnosis was ischemic stroke, 271 (66%); brain hemorrhage 73 (18%); subarachnoid hemorrhage, 7 (2%); and not stroke, 62 (15%). One hundred fourteen patients had LVO (internal carotid artery, 33 [29%]; M1, 52 [46%]; M2, 21 [18%]; basilar artery, 5 [4%]; P1, 3 [3%]). Sensitively, specificity, positive predictive value, negative predictive value, and accuracy for ELVO screen to predict LVO were 85%, 72%, 54%, 93% and 76%, respectively. Among 233 patients with negative ELVO screen, only 17 (7%) had LVO, which indicated to be an ideal scale to avoid missing endovascular therapy. Conclusions- The ELVO screen is a simple, fast, and reliable prehospital scale for paramedics to identify stroke patients with LVO for whom endovascular therapy is an effective treatment.

Diagnostic accuracy of clinical tools for assessment of acute stroke: a systematic review

INTRODUCTION

Recanalisation therapy in acute ischaemic stroke is highly time-sensitive, and requires early identification of eligible patients to ensure better outcomes. Thus, a number of clinical assessment tools have been developed and this review examines their diagnostic capabilities.

METHODS

Diagnostic performance of currently available clinical tools for identification of acute ischaemic and haemorrhagic strokes and stroke mimicking conditions was reviewed. A systematic search of the literature published in 2015-2018 was conducted using PubMed, EMBASE, Scopus and The Cochrane Library. Prehospital and in-hospital studies with a minimum sample size of 300 patients reporting diagnostic accuracy were selected.

RESULTS

Twenty-five articles were included. Cortical signs (gaze deviation, aphasia and neglect) were shown to be significant indicators of large vessel occlusion (LVO). Sensitivity values for selecting subjects with LVO ranged from 23 to 99% whereas specificity was 24 to 97%. Clinical tools, such as FAST-ED, NIHSS, and RACE incorporating cortical signs as well as motor dysfunction demonstrated the best diagnostic accuracy. Tools for identification of stroke mimics showed sensitivity varying from 44 to 91%, and specificity of 27 to 98% with the best diagnostic performance demonstrated by FABS (90% sensitivity, 91% specificity). Hypertension and younger age predicted intracerebral haemorrhage whereas history of atrial fibrillation and diabetes were associated with ischaemia. There was a variation in approach used to establish the definitive diagnosis. Blinding of the index test assessment was not specified in about 50% of included studies.

CONCLUSIONS

A wide range of clinical assessment tools for selecting subjects with acute stroke has been developed in recent years. Assessment of both cortical and motor function using RACE, FAST-ED and NIHSS showed the best diagnostic accuracy values for selecting subjects with LVO. There were limited data on clinical tools that can be used to differentiate between acute ischaemia and haemorrhage. Diagnostic accuracy appeared to be modest for distinguishing between acute stroke and stroke mimics with optimal diagnostic performance demonstrated by the FABS tool. Further prehospital research is required to improve the diagnostic utility of clinical assessments with possible application of a two-step clinical assessment or involvement of simple brain imaging, such as transcranial ultrasonography.

Clinical Prediction Rules to Classify Types of Stroke at Prehospital Stage

Supplemental Digital Content is available in the text.

Background and Purpose—

Endovascular therapy is effective against acute cerebral large vessel occlusion (LVO). However, many patients do not receive such interventions because of the lack of timely identification of the type of stroke. If the types of stroke (any stroke, LVO, intracranial hemorrhage [ICH], and subarachnoid hemorrhage [SAH]) were to be predicted at the prehospital stage, better access to appropriate interventions would be possible. Japan Urgent Stroke Triage (JUST) score was clinical prediction rule to classify suspected patients of acute stroke into different types at the prehospital stage.

Methods—

We obtained information for signs and symptoms and medical history of consecutive suspected patients of acute stroke at prehospital stage from paramedics and final diagnosis from the receiving hospital. We constructed derivation cohort in the historical multicenter cohort study from June 2015 to March 2016 and validation cohort in the prospective multicenter cohort study from August 2016 to July 2017. The derivation and the validation cohorts included 1229 and 1007 patients, respectively. We constructed multivariate logistic regression models with 21 variables to develop clinical prediction rules, which distinguish between different types of stroke: any stroke, LVO, ICH, and SAH.

Results—

Among the 1229 patients (median age, 72 years; 55% men) in the derivation cohort, 533 stroke, 104 LVO, 169 ICH, and 57 SAH cases were observed. The developed rules showed that the areas under the receiver operating curves were 0.88 for any stroke, 0.92 for LVO, 0.84 for ICH, and 0.89 for SAH. The validation cohort of 1007 patients (median age, 75 years; 56% men) showed that the areas under the curves of any stroke, LVO, ICH, and SAH were 0.80, 0.85, 0.77, and 0.94, respectively.

Conclusions—

These clinical prediction rules can help paramedics classify the suspected patients of stroke into any stroke, LVO, ICH, and SAH groups with excellent accuracy.

Electronic Alberta Stroke Program Early CT score change and functional outcome in a drip-and-ship stroke service

Background

Debate continues as to whether patients with acute ischemic stroke with (suspected) large vessel occlusion benefit from direct referral versus secondary transportation.

Aims

To analyze the change in early infarct signs, collaterals, and acute ischemia volume and their association with transfer time and functional outcome.

Methods

We retrospectively analyzed consecutive transfers between 2013 and 2016 for patients with anterior circulation stroke transported from referring hospitals to our center as potential candidates for thrombectomy. Alberta Stroke Programme Early CT Scores (ASPECTS) were automatically calculated on external and in-house CT using the Brainomix e-ASPECTS software, and collaterals were assessed using the e-CTA tool. Functional status after stroke using the modified Rankin scale (mRS) was obtained.

Results

102 patients with CT scans both at the referring hospital and our center were identified. During patient transfer, e-ASPECTS declined by a median of 1 point (0–2). Functional outcome correlated with the change in e-ASPECTS (decline, n=54) (Spearman rs=0.322, 95% CI 0.131 to 0.482, p=0.001). The median image-to-image time was 149 min (IQR 113–190), but did not correlate with change in e-ASPECTS (p=0.754) and mRS score at 3 months (p=0.25). Preserved good collateral status assessed at the comprehensive stroke center was associated with better functional outcome (rs=−0.271, 95% CI −0.485 to −0.037, p=0.02).

Conclusions

Patient transfer in a drip-and-ship network was associated with declines in e-ASPECTS associated with worse functional outcome. Image-to-image time did not influence this association, but worsening collateral status did.

Non-invasive sensor technology for prehospital stroke diagnosis: Current status and future directions

BACKGROUND

The diagnosis of stroke in the prehospital environment is the subject of intense interest and research. There are a number of non-invasive external brain monitoring devices in development that utilize various technologies to function as sensors for stroke and other neurological conditions. Future increased use of one or more of these devices could result in substantial changes in the current processes for stroke diagnosis and treatment, including transportation of stroke patients by emergency medical services.

AIMS

The present review will summarize information about 10 stroke sensor devices currently in development, utilizing various forms of technology, and all of which are external, non-invasive brain monitoring devices.

SUMMARY OF REVIEW

Ten devices are discussed including the technology utilized, the indications for use (stroke and, when relevant, other neurological conditions), the environment(s) indicated for use (with a focus on the prehospital setting), a description of the physical structure of each instrument, and, when available, findings that have been published in peer-reviewed journals or otherwise reported. The review is organized based on the technology utilized by each device, and seven distinct forms were identified: accelerometers, electroencephalography (EEG), microwaves, near-infrared, radiofrequency, transcranial doppler ultrasound, and volumetric impedance phase shift spectroscopy.

CONCLUSIONS

Non-invasive external brain monitoring devices are in various stages of development and have promise as stroke sensors in the prehospital setting. Some of the potential applications include to differentiate stroke from non-stroke, ischemic from hemorrhage stroke, and large vessel occlusion (LVO) from non-LVO ischemic stroke. Successful stroke diagnosis prior to hospital arrival could transform the current diagnostic and treatment paradigm for this disease.

Prehospital stroke scales as screening tools for early identification of stroke and transient ischemic attack

BACKGROUND

Rapid and accurate detection of stroke by paramedics or other emergency clinicians at the time of first contact is crucial for timely initiation of appropriate treatment. Several stroke recognition scales have been developed to support the initial triage. However, their accuracy remains uncertain and there is no agreement which of the scales perform better.

OBJECTIVES

To systematically identify and review the evidence pertaining to the test accuracy of validated stroke recognition scales, as used in a prehospital or emergency room (ER) setting to screen people suspected of having stroke.

SEARCH METHODS

We searched CENTRAL, MEDLINE (Ovid), Embase (Ovid) and the Science Citation Index to 30 January 2018. We handsearched the reference lists of all included studies and other relevant publications and contacted experts in the field to identify additional studies or unpublished data.

SELECTION CRITERIA

We included studies evaluating the accuracy of stroke recognition scales used in a prehospital or ER setting to identify stroke and transient Ischemic attack (TIA) in people suspected of stroke. The scales had to be applied to actual people and the results compared to a final diagnosis of stroke or TIA. We excluded studies that applied scales to patient records; enrolled only screen-positive participants and without complete 2 × 2 data.

DATA COLLECTION AND ANALYSIS

Two review authors independently conducted a two-stage screening of all publications identified by the searches, extracted data and assessed the methodologic quality of the included studies using a tailored version of QUADAS-2. A third review author acted as an arbiter. We recalculated study-level sensitivity and specificity with 95% confidence intervals (CI), and presented them in forest plots and in the receiver operating characteristics (ROC) space. When a sufficient number of studies reported the accuracy of the test in the same setting (prehospital or ER) and the level of heterogeneity was relatively low, we pooled the results using the bivariate random-effects model. We plotted the results in the summary ROC (SROC) space presenting an estimate point (mean sensitivity and specificity) with 95% CI and prediction regions. Because of the small number of studies, we did not conduct meta-regression to investigate between-study heterogeneity and the relative accuracy of the scales. Instead, we summarized the results in tables and diagrams, and presented our findings narratively.

MAIN RESULTS

We selected 23 studies for inclusion (22 journal articles and one conference abstract). We evaluated the following scales: Cincinnati Prehospital Stroke Scale (CPSS; 11 studies), Recognition of Stroke in the Emergency Room (ROSIER; eight studies), Face Arm Speech Time (FAST; five studies), Los Angeles Prehospital Stroke Scale (LAPSS; five studies), Melbourne Ambulance Stroke Scale (MASS; three studies), Ontario Prehospital Stroke Screening Tool (OPSST; one study), Medic Prehospital Assessment for Code Stroke (MedPACS; one study) and PreHospital Ambulance Stroke Test (PreHAST; one study). Nine studies compared the accuracy of two or more scales. We considered 12 studies at high risk of bias and one with applicability concerns in the patient selection domain; 14 at unclear risk of bias and one with applicability concerns in the reference standard domain; and the risk of bias in the flow and timing domain was high in one study and unclear in another 16.We pooled the results from five studies evaluating ROSIER in the ER and five studies evaluating LAPSS in a prehospital setting. The studies included in the meta-analysis of ROSIER were of relatively good methodologic quality and produced a summary sensitivity of 0.88 (95% CI 0.84 to 0.91), with the prediction interval ranging from approximately 0.75 to 0.95. This means that the test will miss on average 12% of people with stroke/TIA which, depending on the circumstances, could range from 5% to 25%. We could not obtain a reliable summary estimate of specificity due to extreme heterogeneity in study-level results. The summary sensitivity of LAPSS was 0.83 (95% CI 0.75 to 0.89) and summary specificity 0.93 (95% CI 0.88 to 0.96). However, we were uncertain in the validity of these results as four of the studies were at high and one at uncertain risk of bias. We did not report summary estimates for the rest of the scales, as the number of studies per test per setting was small, the risk of bias was high or uncertain, the results were highly heterogenous, or a combination of these.Studies comparing two or more scales in the same participants reported that ROSIER and FAST had similar accuracy when used in the ER. In the field, CPSS was more sensitive than MedPACS and LAPSS, but had similar sensitivity to that of MASS; and MASS was more sensitive than LAPSS. In contrast, MASS, ROSIER and MedPACS were more specific than CPSS; and the difference in the specificities of MASS and LAPSS was not statistically significant.

AUTHORS' CONCLUSIONS

In the field, CPSS had consistently the highest sensitivity and, therefore, should be preferred to other scales. Further evidence is needed to determine its absolute accuracy and whether alternatives scales, such as MASS and ROSIER, which might have comparable sensitivity but higher specificity, should be used instead, to achieve better overall accuracy. In the ER, ROSIER should be the test of choice, as it was evaluated in more studies than FAST and showed consistently high sensitivity. In a cohort of 100 people of whom 62 have stroke/TIA, the test will miss on average seven people with stroke/TIA (ranging from three to 16). We were unable to obtain an estimate of its summary specificity. Because of the small number of studies per test per setting, high risk of bias, substantial differences in study characteristics and large between-study heterogeneity, these findings should be treated as provisional hypotheses that need further verification in better-designed studies.

A Pilot Study Validating Video-Based Training on Pre-Hospital Stroke Recognition

INTRODUCTION

Delays in recognizing stroke during pre-hospital emergency medical system (EMS) care may affect triage and transport time to an appropriate stroke ready hospital and may preclude patients from receiving time dependent treatment. All EMS transports in a large urban area in the stroke belt were evaluated for transport destinations, triage and transport time and stroke recognition following distribution ofan educational training video to local EMS services.

HYPOTHESIS

Following video training, local paramedics will improve stroke recognition and shorten triage and transport time to appropriate stroke centers of care.

METHODS

A training module (<10 min) containing a stroke triage scenario, instruction on the Cincinnati Prehospital Stroke Score (CPSS) and the Los Angeles Prehospital Stroke Score (LAPSS) and 'where to transport' stroke patients was distributed and viewed by 96 paramedics. Data was collected from February to October 2016. Stroke recognition was determined from one primary stroke center (PSC) hospital's confirmation of EMS delivered patients (Site A). Yearly stroke recognition percentages of 44% from Site A in 2014 were used as baseline.

RESULTS

A total of 34,833 emergency 911 response transports were made with a total of 502 (1.4%) suspected strokes identified by paramedics. Median [IQR] triage and transport time for stroke transports was 33 [27-41] min. The PSC hospitals received a 5% increase in stroke transports and non-specific care facilities decreased by 7%. From 8,554 transports to site A (PSC) confirmed strokes totalled 107 transports with 139 suspected strokes by paramedics. Of these transports, 60 were correctly identified by paramedics (positive predictive value of 43%, sensitivity of 56%). By the second month following training, recognition percentages increased from baseline to 64%. At five months, percentages of correct stroke identification had dropped to 36%.

CONCLUSION

Video based training improved stroke recognition by an additional 19%, but continual monthly or quarterly training is recommended for maintenance of increased stroke recognition.

Mandatory Neuroendovascular Evolution: Meeting the New Demands

Background

Traditionally, patients undergoing acute ischemic strokes were candidates for mechanical thrombectomy if they were within the 6-h window from onset of symptoms. This timeframe would exclude many patient populations, such as wake-up strokes. However, the most recent clinical trials, DAWN and DEFUSE3, have expanded the window of endovascular treatment for acute ischemic stroke patients to within 24 h from symptom onset. This expanded window increases the number of potential candidates for endovascular intervention for emergent large vessel occlusions and raises the question of how to efficiently screen and triage this increase of patients.

Summary

Abbreviated pre-hospital stroke scales can be used to guide EMS personnel in quickly deciding if a patient is undergoing a stroke. Telestroke networks connect remote hospitals to stroke specialists to improve the transportation time of the patient to a comprehensive stroke center for the appropriate level of care. Mobile stroke units, mobile interventional units, and helistroke reverse the traditional hub-and-spoke model by bringing imaging, tPA, and expertise to the patient. Smartphone applications and social media aid in educating patients and the public regarding acute and long-term stroke care.

Key Messages

The DAWN and DEFUSE3 trials have expanded the treatment window for certain acute ischemic stroke patients with mechanical thrombectomy and subsequently have increased the number of potential candidates for endovascular intervention. This expansion brings patient screening and triaging to greater importance, as reducing the time from symptom onset to decision-to-treat and groin puncture can better stroke patient outcomes. Several strategies have been employed to address this issue by reducing the time of symptom onset to decision-to-treat time.

Prehospital stroke scales and large vessel occlusion: A systematic review

BACKGROUND AND PURPOSE

Time sensitivity for pharmacological and mechanical arterial recanalization in acute ischemic stroke influences the choice of the reference hospital. The accurate selection and identification of patients with high probability of a large vessel occlusion (LVO) in the prehospital setting improve the rationalization of the transport in the more suitable centers. Aim of this analysis was to determine the diagnostic accuracy of prehospital stroke scales detecting LVO.

MATERIAL AND METHODS

Studies were searched into MEDLINE, EMBASE, and CINHAL databases between January 1990 and September 2017. Principal measurements of the meta-analysis were the overall accuracy level, sensitivity, and specificity of prehospital stroke scales.

RESULTS

Nineteen scoring systems were included in the analysis coming from 13 studies. A total of 9824 patients were considered. Although a higher heterogeneity was observed in the analysis, three scores showed better results in predicting a LVO (the stroke Vision, Aphasia, Neglect assessment, the National Institute of Health Stroke scale and the Los Angeles Motor Scale). We observed significant differences of overall accuracy only for scores including hemineglect as cortical neurological sign (P < .05).

CONCLUSIONS

This meta-analysis suggests that some prehospital scoring systems including cortical signs showed better accuracy to predict stroke due to LVO. However, the assessment of these signs could be difficult to investigate by paramedics and personnel of Emergency Medical Services, and for this reason, further prospective evaluations are needed.