Pomona Large Vessel Occlusion Screening Tool for Prehospital and Emergency Room Settings

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.

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 models improve prediction of large vessel occlusion and mechanical thrombectomy candidacy in acute ischemic stroke

BACKGROUND AND PURPOSE

Early identification of large vessel occlusions (LVO) and timely recanalization are paramount to improved clinical outcomes in acute ischemic stroke. A stroke assessment that maximizes sensitivity and specificity for LVOs is needed to identify these cases and not overburden the health system with unnecessary transfers. Machine learning techniques are being used for predictive modeling in many aspects of stroke care and may have potential in predicting LVO presence and mechanical thrombectomy (MT) candidacy.

METHODS

Ischemic stroke patients treated at Loyola University Medical Center from July 2018 to June 2019 (N = 286) were included. Thirty-five clinical and demographic variables were analyzed using machine learning algorithms, including logistic regression, extreme gradient boosting, random forest (RF), and decision trees to build models predictive of LVO presence and MT candidacy by area of the curve (AUC) analysis. The best performing model was compared with prior stroke scales.

RESULTS

When using all 35 variables, RF best predicted LVO presence (AUC = 0.907 ± 0.856-0.957) while logistic regression best predicted MT candidacy (AUC = 0.930 ± 0.886-0.974). When compact models were evaluated, a 10-feature RF model best predicted LVO (AUC = 0.841 ± 0.778-0.904) and an 8-feature RF model best predicted MT candidacy (AUC = 0.862 ± 0.782-0.942). The compact RF models had sensitivity, specificity, negative predictive value and positive predictive value of 0.81, 0.87, 0.92, 0.72 for LVO and 0.87, 0.97, 0.97, 0.86 for MT, respectively. The 10-feature RF model was superior at predicting LVO to all previous stroke scales (AUC 0.944 vs 0.759-0.878) and the 8-feature RF model was superior at predicting MT (AUC 0.970 vs 0.746-0.834).

CONCLUSION

Random forest machine learning models utilizing clinical and demographic variables predicts LVO presence and MT candidacy with a high degree of accuracy in an ischemic stroke cohort. Further validation of this strategy for triage of stroke patients requires prospective and external validation.

Usefulness of a single-parameter tool for the prediction of large vessel occlusion in acute stroke

BACKGROUND

In acute stroke, large vessel occlusion (LVO) should be promptly identified to guide patient's transportation directly to comprehensive stroke centers (CSC) for mechanical thrombectomy (MT). In many cases, prehospital multi-parameter scores are used by trained emergency teams to identify patients with high probability of LVO. However, in several countries, the first aid organization without intervention of skilled staff precludes the on-site use of such scores. Here, we assessed the accuracy of LVO prediction using a single parameter (i.e. complete hemiplegia) obtained by bystander's telephone-based witnessing.

PATIENTS AND METHODS

This observational, single-center study included consecutive patients who underwent intravenous thrombolysis at the primary stroke center and/or were directly transferred to a CSC for MT, from January 1, 2015 to March 1, 2020. We defined two groups: patients with initial hemiplegia (no movement in one arm and leg and facial palsy) and patients without initial hemiplegia, on the basis of a bystander's witnessing.

RESULTS

During the study time, 874 patients were included [mean age 73 years (SD 13.8), 56.7% men], 320 with initial hemiplegia and 554 without. The specificity of the hemiplegia criterion to predict LVO was 0.88, but its sensitivity was only 0.53.

CONCLUSION

Our results suggest that the presence of hemiplegia as witnessed by a bystander can predict LVO with high specificity. This single criterion could be used for decision-making about direct transfer to CSC for MT when the absence of emergency skilled staff precludes the patient's on-site assessment, especially in regions distant from a CSC.

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.

Validation of Stroke Network of Wisconsin Scale at Aurora Health Care System

BACKGROUND

The Stroke Network of Wisconsin (SNOW) scale, previously called the Pomona scale, was developed to predict large-vessel occlusions (LVOs) in patients with acute ischemic stroke (AIS). The original study showed a high accuracy of this scale. We sought to externally validate the SNOW scale in an independent cohort.

METHODS

We retrospectively reviewed and calculated the SNOW scale, the Vision Aphasia and Neglect Scale (VAN), the Cincinnati Prehospital Stroke Severity (CPSS), the Los Angeles Motor Scale (LAMS), and the Prehospital Acute Stroke Severity Scale (PASS) for all patients who were presented within 24 hours after onset at AHCS (14 hospitals) between January 2015 and December 2016. The predictive performance of all scales and several National Institute of Health Stroke Scale cutoffs (≥6) were determined and compared. LVO was defined by total occlusions involving the intracranial internal carotid artery, middle cerebral artery (MCA; M1), or basilar arteries.

RESULTS

Among 2183 AIS patients, 1381 had vascular imaging and were included in the analysis. LVO was detected in 169 (12%). A positive SNOW scale had comparable accuracy to predict LVO and showed a sensitivity of 0.80, specificity of 0.76, the positive predictive value (PPV) of 0.31, and negative predictive value of 0.96 for the detection of LVO versus CPSS ≥ 2 of 0.64, 0.87, 0.41, and 0.95. A positive SNOW scale had higher accuracy than VAN, LAMS, and PASS.

CONCLUSION

In our large stroke network cohort, the SNOW scale has promising sensitivity, specificity and accuracy to predict LVO. Future prospective studies in both prehospital and emergency room settings are warranted.