Comparison of stroke recognition and stroke severity scores for stroke detection in a single cohort

Enhancing Stroke Recognition: A Comparative Analysis of Balance and Eyes-Face, Arms, Speech, Time (BE-FAST) and Face, Arms, Speech, Time (FAST) in Identifying Posterior Circulation Strokes

Background/Objectives: Posterior circulation stroke (PCS) poses a diagnostic challenge due to the diverse and subtle clinical manifestations. While the FAST (Face, Arms, Speech, Time) mnemonic has proven effective in identifying anterior circulation stroke, its sensitivity to posterior events is less clear. Recently, the addition of Balance and Eyes to the mnemonic has been proposed as a more comprehensive tool for stroke recognition. Despite this, evidence directly comparing the effectiveness of BE-FAST and FAST in identifying PCS remains limited. Methods: A retrospective analysis was performed on stroke calls at a comprehensive stroke centre, Sydney, Australia. BE-FAST symptoms first assessed at an emergency department triage were recorded, along with automated acute computerised tomography perfusion (CTP) imaging findings. Haemorrhagic strokes were excluded from analysis. An ischaemic stroke diagnosis was confirmed 48-72 h later with magnetic resonance imaging (MRI) brain. The performance of 1. BE-FAST and FAST and 2. BE-FAST and CTP in the hyperacute detection of posterior circulation ischaemic stroke was compared. Results: Out of 164 identified ischaemic infarcts confirmed on MRIs, 46 were PCS. Of these, 27 were FAST-positive, while 45 were BE-FAST-positive. Overall, BE-FAST demonstrated a higher sensitivity compared to FAST in identifying PCS (97.8 vs. 58.7) but suffered from a lower specificity (10.0 vs. 39.8). Notably, 39.1% (n = 18) of patients with PCS would have been missed if only FAST were used. Furthermore, of the 26 PCS negative on CTP, 25 were BE-FAST-positive, and 14 were FAST-positive. Conclusions: The incorporation of Balance and Eye assessments into the FAST protocol improves PCS detection, although may yield more false positives.

BE FAST Versus FAST: A Randomized Pilot Trial Comparing Retention of Stroke Symptoms Between 2 Mnemonics

BACKGROUND

Balance, Eye, Face, Arm, Speech, Time (BE FAST) was proposed to increase the public's ability to recognize more signs of stroke by adding balance (B) and eyesight changes (E) to the stroke acronym FAST (Face, Arm, Speech, Time). Previous prospective studies suggested these additions did not result in increased stroke detection.

METHODS AND RESULTS

A randomized, assessor blinded prospective pilot study assessed retention of BE FAST and FAST. The 174 participants were randomized to 1 of 2 education arms, educated similarly visually and auditorily, and retention was tested at 3 time points. Mnemonic recall was similar at 30 days (79.5% versus 69.8%, P=0.104). Significantly lower retention was seen in the BE FAST group's ability to recall all symptoms at 3 to 5 minutes (75% versus 30.2%, P<0.001), 60 minutes (70.5% versus 41.9%, P<0.001), and 30 days (51.1% versus 24.4%, P<0.001). Significantly higher retention was observed in the FAST group for partial recall at 3 to 5 minutes (94.3% versus 84.9%, P=0.041), 60 minutes (86.4% versus 77.9%, P=0.045), and 30 days (76.1% versus 59.3%, P=0.012). For BE FAST, retention of more common symptoms at 30 days was lower for face (78.4% versus 60.5%, P=0.010), speech (65.9% versus 47.7%, P=0.015), and arm (63.6% versus 52.3%, P=0.131).

CONCLUSION

Significantly higher retention and ability to recall stroke symptoms, fully or partially, was found with FAST. Adding B and E to FAST resulted in lower retention of more common symptoms.

REGISTRATION

URL: https://www.clinicaltrials.gov; Unique identifier: NCT06152016.

Prehospital stroke detection scales: A head-to-head comparison of 7 scales in patients with suspected stroke

BACKGROUND

Several prehospital scales have been designed to aid paramedics in identifying stroke patients in the ambulance setting. However, external validation and comparison of these scales are largely lacking.

AIMS

To compare all published prehospital stroke detection scales in a large cohort of unselected stroke code patients.

METHODS

We conducted a systematic literature search to identify all stroke detection scales. Scales were reconstructed with prehospital acquired data from two observational cohort studies: the Leiden Prehospital Stroke Study (LPSS) and PREhospital triage of patients with suspected STrOke (PRESTO) study. These included stroke code patients from four ambulance regions in the Netherlands, including 15 hospitals and serving 4 million people. For each scale, we calculated the accuracy, sensitivity, and specificity for a diagnosis of stroke (ischemic, hemorrhagic, or transient ischemic attack (TIA)). Moreover, we assessed the proportion of stroke patients who received reperfusion treatment with intravenous thrombolysis or endovascular thrombectomy that would have been missed by each scale.

RESULTS

We identified 14 scales, of which 7 (CPSS, FAST, LAPSS, MASS, MedPACS, OPSS, and sNIHSS-EMS) could be reconstructed. Of 3317 included stroke code patients, 2240 (67.5%) had a stroke (1528 ischemic, 242 hemorrhagic, 470 TIA) and 1077 (32.5%) a stroke mimic. Of ischemic stroke patients, 715 (46.8%) received reperfusion treatment. Accuracies ranged from 0.60 (LAPSS) to 0.66 (MedPACS, OPSS, and sNIHSS-EMS), sensitivities from 66% (LAPSS) to 84% (MedPACS and sNIHSS-EMS), and specificities from 28% (sNIHSS-EMS) to 49% (LAPSS). MedPACS, OPSS, and sNIHSS-EMS missed the fewest reperfusion-treated patients (10.3-11.2%), whereas LAPSS missed the most (25.5%).

CONCLUSIONS

Prehospital stroke detection scales generally exhibited high sensitivity but low specificity. While LAPSS performed the poorest, MedPACS, sNIHSS-EMS, and OPSS demonstrated the highest accuracy and missed the fewest reperfusion-treated stroke patients. Use of the most accurate scale could reduce unnecessary stroke code activations for patients with a stroke mimic by almost a third, but at the cost of missing 16% of strokes and 10% of patients who received reperfusion treatment.

YouTube as a source of recognizing acute stroke; progress in 2 years

BACKGROUND

YouTube™ has a great role in providing information, which includes educational videos, to more than 2 billion users, making it the second most popular application in the world. BE-FAST is a modified version of the FAST mnemonic and is used to detect acute ischemic stroke by the patients or their relatives. The purpose of this study is to assess the overall usefulness of the information of YouTube in patients to realize an acute stroke attack.

METHODS

YouTube was searched for the following five terms: "stroke", ''stroke diagnosis", "stroke signs", "brain attack" and "what is stroke" in November 2021 and May 2023, separately. Two independent neurology specialists scored each video by using Global Quality Scale (GQS).

RESULTS

Among the total of 150 videos, the number that met inclusion criteria was 91 for the November 2021 search and 104 for the May 2023 search. For the 2021 search, in 30 videos (33%), the FAST mnemonic or its contents were noticed, whereas BE-FAST was mentioned in only four videos (4.4%). For the 2023 search, the FAST mnemonic or its contents were noticed in 36 videos (34.6%) and BE-FAST was mentioned in 11 videos (10.6%). Among the 2021 and 2023 searches, the mean GQS values were 3.09 and 2.96 points, 50 (54.8%) vs. 56 (53.8%) videos rated 3.5 points or higher (high quality), respectively. GQS scores of the videos mentioning balance, eyes, face, arms, speech, and time, the basic and advanced information about radiology and treatment, and mentioning FAST, BE-FAST, and TPA were significantly higher.

CONCLUSION

We conclude that YouTube is not yet a very useful tool for patients to realize that they may have acute ischemic stroke, though over the years; information available on social media for healthcare information and education has improved.

Utilizing machine learning to facilitate the early diagnosis of posterior circulation stroke

BACKGROUND

Posterior Circulation Syndrome (PCS) presents a diagnostic challenge characterized by its variable and nonspecific symptoms. Timely and accurate diagnosis is crucial for improving patient outcomes. This study aims to enhance the early diagnosis of PCS by employing clinical and demographic data and machine learning. This approach targets a significant research gap in the field of stroke diagnosis and management.

METHODS

We collected and analyzed data from a large national Stroke Registry spanning from January 2014 to July 2022. The dataset included 15,859 adult patients admitted with a primary diagnosis of stroke. Five machine learning models were trained: XGBoost, Random Forest, Support Vector Machine, Classification and Regression Trees, and Logistic Regression. Multiple performance metrics, such as accuracy, precision, recall, F1-score, AUC, Matthew's correlation coefficient, log loss, and Brier score, were utilized to evaluate model performance.

RESULTS

The XGBoost model emerged as the top performer with an AUC of 0.81, accuracy of 0.79, precision of 0.5, recall of 0.62, and F1-score of 0.55. SHAP (SHapley Additive exPlanations) analysis identified key variables associated with PCS, including Body Mass Index, Random Blood Sugar, ataxia, dysarthria, and diastolic blood pressure and body temperature. These variables played a significant role in facilitating the early diagnosis of PCS, emphasizing their diagnostic value.

CONCLUSION

This study pioneers the use of clinical data and machine learning models to facilitate the early diagnosis of PCS, filling a crucial gap in stroke research. Using simple clinical metrics such as BMI, RBS, ataxia, dysarthria, DBP, and body temperature will help clinicians diagnose PCS early. Despite limitations, such as data biases and regional specificity, our research contributes to advancing PCS understanding, potentially enhancing clinical decision-making and patient outcomes early in the patient's clinical journey. Further investigations are warranted to elucidate the underlying physiological mechanisms and validate these findings in broader populations and healthcare settings.

Machine learning-based prognostication of mortality in stroke patients

Objectives

Predicting stroke mortality is crucial for personalized care. This study aims to design and evaluate a machine learning model to predict one-year mortality after a stroke.

Materials and methods

Data from the National Multiethnic Stroke Registry was utilized. Eight machine learning (ML) models were trained and evaluated using various metrics. SHapley Additive exPlanations (SHAP) analysis was used to identify the influential predictors.

Results

The final analysis included 9840 patients diagnosed with stroke were included in the study. The XGBoost algorithm exhibited optimal performance with high accuracy (94.5%) and AUC (87.3%). Core predictors encompassed National Institutes of Health Stroke Scale (NIHSS) at admission, age, hospital length of stay, mode of arrival, heart rate, and blood pressure. Increased NIHSS, age, and longer stay correlated with higher mortality. Ambulance arrival and lower diastolic blood pressure and lower body mass index predicted poorer outcomes.

Conclusions

This model's predictive capacity emphasizes the significance of NIHSS, age, hospital stay, arrival mode, heart rate, blood pressure, and BMI in stroke mortality prediction. Specific findings suggest avenues for data quality enhancement, registry expansion, and real-world validation. The study underscores machine learning's potential for early mortality prediction, improving risk assessment, and personalized care. The potential transformation of care delivery through robust ML predictive tools for Stroke outcomes could revolutionize patient care, allowing for personalized plans and improved preventive strategies for stroke patients. However, it is imperative to conduct prospective validation to evaluate its practical clinical effectiveness and ensure its successful adoption across various healthcare environments.

Establishing an MSU service in a medium-sized German urban area-clinical and economic considerations

Background and purpose

Mobile stroke units (MSU) have been demonstrated to improve prehospital stroke care in metropolitan and rural regions. Due to geographical, social and structural idiosyncrasies of the German city of Mannheim, concepts of established MSU services are not directly applicable to the Mannheim initiative. The aim of the present analysis was to identify major determinants that need to be considered when initially setting up a local MSU service.

Methods

Local stroke statistics from 2015 to 2021 were analyzed and circadian distribution of strokes and local incidence rates were calculated. MSU patient numbers and total program costs were estimated for varying operating modes, daytime coverage models, staffing configurations which included several resource sharing models with the hospital. Additional case-number simulations for expanded catchment areas were performed.

Results

Median time of symptom onset of ischemic stroke patients was 1:00 p.m. 54.3% of all stroke patients were admitted during a 10-h time window on weekdays. Assuming that MSU is able to reach 53% of stroke patients, the average expected number of ischemic stroke patients admitted to MSU would be 0.64 in a 10-h shift each day, which could potentially be increased by expanding the MSU catchment area. Total estimated MSU costs amounted to € 815,087 per annum. Teleneurological assessment reduced overall costs by 11.7%.

Conclusion

This analysis provides a framework of determinants and considerations to be addressed during the design process of a novel MSU program in order to balance stroke care improvements with the sustainable use of scarce resources.

Predicting 90-day prognosis for patients with stroke: a machine learning approach

Background

Stroke is a significant global health burden and ranks as the second leading cause of death worldwide.

Objective

This study aims to develop and evaluate a machine learning-based predictive tool for forecasting the 90-day prognosis of stroke patients after discharge as measured by the modified Rankin Score.

Methods

The study utilized data from a large national multiethnic stroke registry comprising 15,859 adult patients diagnosed with ischemic or hemorrhagic stroke. Of these, 7,452 patients satisfied the study's inclusion criteria. Feature selection was performed using the correlation and permutation importance methods. Six classifiers, including Random Forest (RF), Classification and Regression Tree, Linear Discriminant Analysis, Support Vector Machine, and k-Nearest Neighbors, were employed for prediction.

Results

The RF model demonstrated superior performance, achieving the highest accuracy (0.823) and excellent discrimination power (AUC 0.893). Notably, stroke type, hospital acquired infections, admission location, and hospital length of stay emerged as the top-ranked predictors.

Conclusion

The RF model shows promise in predicting stroke prognosis, enabling personalized care plans and enhanced preventive measures for stroke patients. Prospective validation is essential to assess its real-world clinical performance and ensure successful implementation across diverse healthcare settings.

Predicting 90-Day Prognosis in Ischemic Stroke Patients Post Thrombolysis Using Machine Learning

(1) Objective: This study aimed to construct a machine learning model for predicting the prognosis of ischemic stroke patients who underwent thrombolysis, assessed through the modified Rankin Scale (mRS) score 90 days after discharge. (2) Methods: Data were sourced from Qatar's stroke registry covering January 2014 to June 2022. A total of 723 patients with ischemic stroke who had received thrombolysis were included. Clinical variables were examined, encompassing demographics, stroke severity indices, comorbidities, laboratory results, admission vital signs, and hospital-acquired complications. The predictive capabilities of five distinct machine learning models were rigorously evaluated using a comprehensive set of metrics. The SHAP analysis was deployed to uncover the most influential predictors. (3) Results: The Support Vector Machine (SVM) model emerged as the standout performer, achieving an area under the curve (AUC) of 0.72. Key determinants of patient outcomes included stroke severity at admission; admission systolic and diastolic blood pressure; baseline comorbidities, notably hypertension (HTN) and coronary artery disease (CAD); stroke subtype, particularly strokes of undetermined origin (SUO); and hospital-acquired urinary tract infections (UTIs). (4) Conclusions: Machine learning can improve early prognosis prediction in ischemic stroke, especially after thrombolysis. The SVM model is a promising tool for empowering clinicians to create individualized treatment plans. Despite limitations, this study contributes to our knowledge and encourages future research to integrate more comprehensive data. Ultimately, it offers a pathway to improve personalized stroke care and enhance the quality of life for stroke survivors.

Prehospital screening of acute stroke with the National Institutes of Health Stroke Scale (ParaNASPP): a stepped-wedge, cluster-randomised controlled trial

BACKGROUND

Timely treatment of acute stroke depends on early identification and triage. Improved methods for recognition of stroke in the prehospital setting are needed. We aimed to assess whether use of the National Institutes of Health Stroke Scale (NIHSS) by paramedics in the ambulance could improve communication with the hospital, augment triage, and enhance diagnostic accuracy of acute stroke.

METHODS

The Paramedic Norwegian Acute Stroke Prehospital Project (ParaNASPP) was a stepped-wedge, single-blind, cluster-randomised controlled trial. Patients with suspected acute stroke, who were evaluated by paramedics from five ambulance stations in Oslo, Norway, were eligible for inclusion. The five ambulance stations (defined as clusters) all initially managed patients according to a standard stroke protocol (control group), with randomised sequential crossover of each station to the intervention group. The intervention consisted of supervised training on NIHSS scoring, a mobile application to aid scoring, and standardised communication with stroke physicians. Random allocation was done via a simple lottery draw by administrators at Oslo University Hospital, who were independent of the research team. Allocation concealment was not possible due to the nature of the intervention. The primary outcome was the positive predictive value (PPV) for prehospital identification of patients with a final discharge diagnosis of acute stroke, analysed by intention to treat. Prespecified secondary safety outcomes were median prehospital on-scene time and median door-to-needle time. This trial is registered with ClinicalTrials.gov, NCT04137874, and is completed.

FINDINGS

Between June 3, 2019, and July 1, 2021, 935 patients were evaluated by paramedics for suspected acute stroke. 134 patients met exclusion criteria or did not consent to participate. The primary analysis included 447 patients in the intervention group and 354 in the control group. There was no difference in PPV for prehospital identification of patients with a final discharge diagnosis of acute stroke between the intervention group (48·1%, 95% CI 43·4-52·8) and control group (45·8%, 40·5-51·1), with an estimated percentage points difference between groups of 2·3 (95% CI -4·6 to 9·3; p=0·51). Median prehospital on-scene time increased by 5 min in the intervention group (29 min [IQR 23-36] vs 24 min [19-31]; p<0·0001), whereas median door-to-needle time was similar between groups (26 min [21-36] vs 27 min [20-36]; p=0·90). No prehospital deaths were reported in either group.

INTERPRETATION

The intervention did not improve diagnostic accuracy in patients with suspected stroke. A general increase in prehospital time during the pandemic and the identification of smaller strokes that require more deliberation are possible explanations for the increased on-scene time. The ParaNASPP model is to be implemented in Norway from 2023, and will provide real-life data for further research.

FUNDING

Norwegian Air Ambulance Foundation and Oslo University Hospital.

Diagnostic Performance of a Mnemonic for Warning Symptoms in Predicting Acute Coronary Syndrome Diagnosis: A Retrospective Cross-Sectional Study

Objectives: We aimed to create a mnemonic for acute coronary syndrome (ACS) warning symptoms and determine its diagnostic performance. Methods: This retrospective cross-sectional study included patients visiting the emergency room with symptoms of suspected ACS during 2020-2021. The mnemonic was created using symptoms with an odds ratio (OR) for predicting ACS >1.0. The mnemonic with the highest OR and sensitivity was identified. Sensitivity analysis was performed to test the diagnostic performance of the mnemonic by patient subgroups commonly exhibiting atypical symptoms. Results: ACS prevalence was 12.2% (415/3,400 patients). The mnemonic, "RUSH ChesT" [if you experience referred pain (R), unexplained sweating (U), shortness of breath (S), or heart fluttering (H) together with chest pain (C), visit the hospital in a timely (T) manner] had the best OR [7.81 (5.93-10.44)] and sensitivity [0.81 (0.77-0.85)]. This mnemonic had equal sensitivity in men and women, the elderly and adults, smokers and non-smokers, and those with and without diabetes or hypertension. Conclusion: The "RUSH ChesT" mnemonic shows good diagnostic performance for patient suspected ACS. It may effectively help people memorize ACS warning symptoms.

Development and validation of a clinical nomogram for differentiating hemorrhagic and ischemic stroke prehospital

OBJECTIVES

The early detection and identification of stroke are essential to the prognosis of patients with suspected stroke symptoms out-of-hospital. We aimed to develop a risk prediction model based on the FAST score to identify the different types of strokes early for emergency medical services (EMS).

METHODS

This retrospective observational study enrolled 394 stroke patients at a single center from January 2020 to December 2021. Demographic data, clinical characteristics, and stroke risk factors with patients were collected from the EMS record database. Univariate and multivariate logistic regression analysis was used to identify the independent risk predictors. The nomogram was developed based on the independent predictors, in which the discriminative value and calibration of the nomogram were verified by the receiver operator characteristic (ROC) curve and calibration plots.

RESULTS

A total of 31.90% (88/276) of patients were diagnosed with hemorrhagic stroke in the training set, while 36.40% (43/118) in the validation set. The nomogram was developed based on the multivariate analysis, including age, systolic blood pressure, hypertension, vomiting, arm weakness, and slurred speech. The area under the curve (AUC) of the ROC with nomogram was 0.796 (95% CI: 0.740-0.852, P < 0.001) and 0.808 (95% CI:0.728-0.887, P < 0.001) in the training set and validation set, respectively. In addition, the AUC with the nomogram was superior to the FAST score in both two sets. The calibration curve showed a good agreement with the nomogram and the decision curves analysis also demonstrated that the nomogram had a wider range of threshold probabilities than the FAST score in the prediction risk of hemorrhagic stroke.

CONCLUSIONS

This novel noninvasive clinical nomogram shows a good performance in differentiating hemorrhagic and ischemic stroke for EMS staff prehospital. Moreover, all of the variables of nomogram are acquired in clinical practice easily and inexpensively out-of-hospital.

An updated review and meta-analysis of screening tools for stroke in the emergency room and prehospital setting

BACKGROUND

Stroke screening tools should have good diagnostic performance for early diagnosis and a proper therapeutic plan. This paper describes and compares various diagnostic tools used to identify stroke in emergency departments and prehospital setting.

METHODS

The meta-analysis was conducted according to the Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA) guidelines. The PubMed and Scopus databases were searched until December 31, 2021, for studies published on stroke screening tools. These tools' diagnostic performance (sensitivity and specificity) was pooled using a bivariate random-effects model whenever appropriate.

RESULTS

Eleven screening tools for stroke were identified in 29 different studies. The various tools had a wide range of sensitivity and specificity in different studies. In the meta-analysis, the Cincinnati Pre-hospital Stroke Scale, Face Arm Speech Test, and Recognition of Stroke in the Emergency Room (ROSIER) had sensitivity (between 83 and 91%) but poor specificity (all below 64%). When comparing all the tools, ROSIER had the highest sensitivity 90.5%. Los Angeles Pre-hospital Stroke Screen performed best in terms of specificity 88.7% but had low sensitivity (73.9%). Melbourne Ambulance Stroke Screen had a balanced performance in terms of sensitivity (86%) and specificity (76%). Sensitivity analysis consisting of only prospective studies showed a similar range of sensitivity and specificity.

CONCLUSION

All the stroke screening tools included in the review were comparable, but no clear superior screening tool could be identified. Simple screening tools like Cincinnati prehospital stroke scale (CPSS) have similar performance compared to more complex tools.

Blood Biomarkers for Triaging Patients for Suspected Stroke: Every Minute Counts

Early stroke diagnosis remains a big challenge in healthcare partly due to the lack of reliable diagnostic blood biomarkers, which in turn leads to increased rates of mortality and disability. Current screening methods are optimised to identify patients with a high risk of cardio-vascular disease, especially among the elderly. However, in young adults and children, these methods suffer low sensitivity and specificity and contribute to further delays in their triage and diagnosis. Accordingly, there is an urgent need to develop reliable blood biomarkers for triaging patients suspected of stroke in all age groups, especially children and young adults. This review explores some of the existing blood biomarkers, as single biomarkers or biomarker panels, and examines their sensitivity and specificity for predicting stroke. A review was performed on PubMed and Web of Science for journal articles published in English during the period 2001 to 2021, which contained information regarding biomarkers of stroke. In this review article, we provide comparative information on the availability, clinical usefulness, and time-window periods of seven single blood biomarkers and five biomarker panels that have been used for predicting stroke in emergency situations. The outcomes of this review can be used in future research for developing more effective stroke biomarkers.

Use of deep artificial neural networks to identify stroke during triage via subtle changes in circulating cell counts

Background

The development of tools that could help emergency department clinicians recognize stroke during triage could reduce treatment delays and improve patient outcomes. Growing evidence suggests that stroke is associated with several changes in circulating cell counts. The aim of this study was to determine whether machine-learning can be used to identify stroke in the emergency department using data available from a routine complete blood count with differential.

Methods

Red blood cell, platelet, neutrophil, lymphocyte, monocyte, eosinophil, and basophil counts were assessed in admission blood samples collected from 160 stroke patients and 116 stroke mimics recruited from three geographically distinct clinical sites, and an ensemble artificial neural network model was developed and tested for its ability to discriminate between groups.

Results

Several modest but statistically significant differences were observed in cell counts between stroke patients and stroke mimics. The counts of no single cell population alone were adequate to discriminate between groups with high levels of accuracy; however, combined classification using the neural network model resulted in a dramatic and statistically significant improvement in diagnostic performance according to receiver-operating characteristic analysis. Furthermore, the neural network model displayed superior performance as a triage decision making tool compared to symptom-based tools such as the Cincinnati Prehospital Stroke Scale (CPSS) and the National Institutes of Health Stroke Scale (NIHSS) when assessed using decision curve analysis.

Conclusions

Our results suggest that algorithmic analysis of commonly collected hematology data using machine-learning could potentially be used to help emergency department clinicians make better-informed triage decisions in situations where advanced imaging techniques or neurological expertise are not immediately available, or even to electronically flag patients in which stroke should be considered as a diagnosis as part of an automated stroke alert system.

Carotid endarterectomy for symptomatic carotid artery stenosis in a patient who presented with psychosis

Acute psychosis is an unusual presentation of stroke particularly in a patient with no history of psychiatric illness. We report a case where an elderly male patient with self-inflicted injuries caused by acute psychosis. The investigation confirmed an acute left hemispherical stroke associated with a high-grade left internal carotid artery stenosis. The patient underwent a successful left carotid endarterectomy. His psychotic symptoms resolved and he was discharged home without the need for neuroleptic medication.

A Systematic Review and Meta-Analysis Comparing FAST and BEFAST in Acute Stroke Patients

Objective

To evaluate and compare the predictive value of Face, Arm, Speech Test (FAST) and Balance, Eyes, Face, Arm, Speech, Time (BEFAST) scale in the acute ischemic stroke (AIS).

Methods

We searched Medline and Ovid databases for relevant literature in the English language. There were no limitations on the date. The sensitivity, specificity, likelihood ratio, and diagnostic odds ratio were pooled for meta-analysis. The symmetric receiver operator characteristic curve and Fagan's Nomogram were drawn, and meta-regression and subgroup analysis were used to explore the source of heterogeneity.

Results

A total of 9 studies, including 6,151 participants, were analyzed. The combined sensitivity of FAST was 0.77 [95% CI (0.64–0.86)], specificity was 0.60 [95% CI (0.38–0.78)], the area under the ROC curve was 0.76, and the diagnostic ratio was 1.57, while the sensitivity of BEFAST was 0.68 [95% CI (0.23–0.93)], specificity was 0.85 [95% CI (0.72–0.92)], the area under the ROC curve was 0.86, and the diagnostic odds ratio was 2.44. No publication bias was detected in Deeks' funnel plot. For FAST, meta-regression analysis showed that the prospective design, satisfactory description of the index test, and a broad spectrum of disease contributed to the heterogeneity in sensitivity, while no sources contributed to the heterogeneity in sensitivity. When the pretest probability was set as 20%, the posterior probability in Fagan's Nomogram was 32%; however, when the pretest probability was set as 20% in BEFAST, the posterior probability in Fagan's Nomogram was 52%.

Conclusions

Our findings indicated that FAST and BEFAST might be useful in the diagnosis of acute ischemic stroke. The diagnostic value of BEFAST in acute ischemic stroke was higher than in FAST; thus, it might have an important role in the fast recognition of acute ischemic stroke.

Role of imaging in early diagnosis of acute ischemic stroke: a literature review

Stroke is a serious health condition that is responsible for more than 5% of total deaths. Near 20% of patients experiencing stroke die every year, resulting in the stroke being at the top of the list of preventable causes of death. Once an acute stroke is suspected, a golden hour of less than an hour is available to prevent the undesirable consequences. Since neuroimaging is mandatory in the diagnosis of stroke, the proper use of neuroimaging could help saving time and planning the right treatment for the patient. Some of the available imaging methods help us with rapid results, while others benefit us from a more accurate diagnosis. Hereby, we aim to provide a clinical review of the advantages and disadvantages of different available neuroimaging methods in approaching acute stroke to help clinicians choose the best method according to the settings.

Prehospital Comprehensive Stroke Center vs Primary Stroke Center Triage in Patients With Suspected Large Vessel Occlusion Stroke

Importance

Endovascular therapy (EVT) improves functional outcomes in acute ischemic stroke (AIS) with large vessel occlusion (LVO). Whether implementation of a regional prehospital transport policy for comprehensive stroke center triage increases use of EVT is uncertain.

Objective

To evaluate the association of a regional prehospital transport policy that directly triages patients with suspected LVO stroke to the nearest comprehensive stroke center with rates of EVT.

Design, Setting, and Participants

This retrospective, multicenter preimplementation-postimplementation study used an interrupted time series analysis to compare treatment rates before and after implementation in patients with AIS arriving at 15 primary stroke centers and 8 comprehensive stroke centers in Chicago, Illinois, via emergency medical services (EMS) transport from December 1, 2017, to May 31, 2019 (9 months before and after implementation in September 2018). Data were analyzed from December 1, 2017, to May 31, 2019.

Interventions

Prehospital EMS transport policy to triage patients with suspected LVO stroke, using a 3-item stroke scale, to comprehensive stroke centers.

Main Outcomes and Measures

Rates of EVT before and after implementation among EMS-transported patients within 6 hours of AIS onset.

Results

Among 7709 patients with stroke, 663 (mean [SD] age, 68.5 [14.9] years; 342 women [51.6%] and 321 men [48.4%]; and 348 Black individuals [52.5%]) with AIS arrived within 6 hours of stroke onset by EMS transport: 310 of 2603 (11.9%) in the preimplementation period and 353 of 2637 (13.4%) in the postimplementation period. The EVT rate increased overall among all patients with AIS (preimplementation, 4.9% [95% CI, 4.1%-5.8%]; postimplementation, 7.4% [95% CI, 7.5%-8.5%]; P < .001) and among EMS-transported patients with AIS within 6 hours of onset (preimplementation, 4.8% [95% CI, 3.0%-7.8%]; postimplementation, 13.6% [95% CI, 10.4%-17.6%]; P < .001). On interrupted time series analysis among EMS-transported patients, the level change within 1 month of implementation was 7.15% (P = .04) with no slope change before (0.16%; P = .71) or after (0.08%; P = .89), which indicates a step rather than gradual change. No change in time to thrombolysis or rate of thrombolysis was observed (step change, 1.42%; P = .82). There were no differences in EVT rates in patients not arriving by EMS in the 6- to 24-hour window or by interhospital transfer or walk-in, irrespective of time window.

Conclusions and Relevance

Implementation of a prehospital transport policy for comprehensive stroke center triage in Chicago was associated with a significant, rapid, and sustained increase in EVT rate for patients with AIS without deleterious associations with thrombolysis rates or times.

The validity of recognition of stroke in the emergency room (ROSIER) scale in the diagnosis of Iranian patients with acute ischemic stroke in the emergency department

OBJECTIVES:

In this study, we aimed to investigate the accuracy of recognition of stroke in the Emergency Room (ROSIER) Scale in the diagnosis of patients with acute ischemic stroke (AIS) transferred to the emergency department (ED).

METHODS:

The present study was a multicenter study. Records from patients suspected of stroke, who referred to the ED were reviewed. Demographic, clinical, and diagnostic data were extracted and then entered in checklists. ROSIER Scale was used to evaluate the possible diagnosis in this study. The definitive diagnosis of a stroke was made based on neurologist's assessment and clinical and neuroimaging findings, mainly brain magnetic resonance imaging (MRI). Receiver operating characteristic (ROC) curve analysis was conducted for assessing the accuracy of ROSIER in discrimination of stroke.

RESULTS:

The data of 356 suspected stroke patients were analyzed. Of all, 186 patients (52.2%) were male, and the mean age was 65.2 (standard deviation = 14.0) years ranging from 26 to 95 years. One hundred and fifty-one patients (42.4%) had AIS based on the final diagnosis. The area under the ROC curve was 0.85. The best cutoff point for ROSIER scale was ≥1 with a sensitivity of 85.4% (95% confidence interval [CI]: 78.8, 90.6%) and specificity of 65.8% (95% CI: 58.9, 72.3%).

CONCLUSION:

Based on the findings, although the best cutoff point was the same as the original (derivation) study, its sensitivity (85.4% vs. 92%) and specificity (65.8% vs. 86%) were considerably lower.

Public knowledge of stroke and heart attack symptoms in China: a cross-sectional survey

OBJECTIVE

Stroke and ischaemic heart disease have become the leading causes of death in China. We evaluated recognition of stroke and heart attack symptoms and stroke treatment-seeking behaviour in a large representative sample of the Chinese adult population and explored characteristics associated with recognition rates.

DESIGN

Cross-sectional survey.

SETTING

Household interviews.

PARTICIPANT

3051 Chinese adults aged between 18 and 69 (50.7% female) were interviewed between January and March 2019.

PRIMARY AND SECONDARY OUTCOME MEASURES

Primary measures include recognitions of stroke and heart attack symptoms and stroke treatment-seeking behaviour. Secondary measures include numeracy level, sociodemographics and prior history of cardiovascular diseases and high blood pressure.

RESULTS

Participants on average recognised 5.2 out of 14 stroke symptoms and 2.6 out of 6 heart attack symptoms. In the presence of stroke symptoms, three quarters of participants would take immediate action and call an ambulance, yet the second most common action was to advise the person to see a doctor (59%) rather than to consult a doctor immediately (34%). Recognition of atypical heartattack symptoms, such as nausea and feeling of anxiety, was poor. Symptom recognition rates were higher in females, people with a personal or family/friend history of cardiovascular events, those with higher numeracy scores, and for stroke symptoms, participants with high (versus low) education level. Furthermore, symptom recognition rate was negatively correlated with burden of cardiovascular diseases across the four economic regions of China.

CONCLUSION

Recognition of stroke and heart-attack symptoms was moderate and there remains a gap between recognising symptoms and taking immediate action. Interventions focusing on simple symptom detection tools and on building numerical competencies may help reduce the burden of cardiovascular diseases in China.

Asynchronous Distance Learning of the National Institutes of Health Stroke Scale During the COVID-19 Pandemic (E-Learning vs Video): Randomized Controlled Trial

BACKGROUND

The COVID-19 pandemic has considerably altered the regular medical education curriculum while increasing the need for health care professionals. Senior medical students are being incrementally deployed to the front line to address the shortage of certified physicians. These students, some of whom will be fast-tracked as physicians, may lack knowledge regarding the initial management of time-critical emergencies such as stroke.

OBJECTIVE

Our aim was to determine whether an e-learning module could improve asynchronous distance knowledge acquisition of the National Institutes of Health Stroke Scale (NIHSS) in senior medical students compared to the traditional didactic video.

METHODS

A randomized, data analyst-blinded web-based trial was conducted at the University of Geneva Faculty of Medicine between April and June 2020. Fifth year medical students followed a distance learning path designed to teach the NIHSS. The control group followed the traditional didactic video created by Patrick Lyden, while the e-learning group followed the updated version of a previously tested, highly interactive e-learning module. The main outcome was the score on a 50-question quiz displayed upon completion of the learning material. The difference in the proportion of correct answers for each specific NIHSS item was also assessed.

RESULTS

Out of 158 potential participants, 88 started their allocated learning path and 75 completed the trial. Participants who followed the e-learning module performed better than those who followed the video (38 correct answers, 95% CI 37-39, vs 35 correct answers, 95% CI 34-36, P<.001). Participants in the e-learning group scored better on five elements than the video group: key NIHSS concepts (P=.02), the consciousness - global item (P<.001), the facial palsy item (P=.04), the ataxia item (P=.03), and the sensory item (P=.04).

CONCLUSIONS

Compared to the traditional didactic video, a highly interactive e-learning module enhances asynchronous distance learning and NIHSS knowledge acquisition in senior medical students.

Towards evidence-based emergency medicine: best BETs from the Manchester Royal InfirmaryBET 1: Can hands-on defibrillation be performed safely?BET 2: FAST or ROSIER to identify suspected stroke in the prehospital setting?

A review was carried out to see whether hands-on defibrillation could be performed safely. 6 papers presented the best evidence to answer the clinical question. The author, date and country of publication, patient group studied, study type, relevant outcomes, results and study weaknesses of these papers are tabulated. It is concluded that hands-on defibrillation has the potential to be performed safely if the rescuer uses appropriate electrical insulating barriers such as polyethylene gloves or class 1 electrical insulating gloves. The safety profile of nitrile gloves is unclear. Since detection of shock was used as a proxy for safety, additional investigation is warranted before hands-on defibrillation becomes common practice.

Quantifying the Impact of Acute Stroke System of Care Transfer Protocols on Patient Outcomes

BACKGROUND

We quantify the impact of implementing a stroke system of care requiring transport of individuals believed to have stroke to a primary stroke center, in rural and urban settings, based on time from symptom recognition to treatment, probability of receiving treatment within 3 hours of stroke onset, and probability of overcrowding. We use Indiana as an example.

METHODS

We used discrete-event simulation to estimate outcomes for 2 scenarios: stroke system of care with enabling technology (mobile stroke unit, stroke team expansion) and stroke system of care with no enabling technology, as compared with the status quo. We considered patient flow from symptom recognition to treatment. Patient locations and stroke events were generated for the 92 Indiana counties in Indiana, subdivided into 1009 locations. We considered time from emergency medical service (EMS) arrival at onset to treatment, probability of tissue plasminogen activator administered within 3 h of onset, and percentage of patients admitted beyond the occupancy level at the comprehensive stroke center.

RESULTS

Results varied by urbanicity. Under no enabling technology, having a stroke system of care improved outcomes for individuals in urban and suburban settings. However, in rural settings, the implementation of stroke system of care guidelines decreased the average rate of treatment within 3 h of stroke onset and increased the EMS arrival to treatment times compared with sending the individual to the closest provider. Enabling technologies improved outcomes regardless of setting.

DISCUSSION

Geographic disparities tend to increase the number of transfers, decrease the rate of treatment within 3 h of onset, and increase transit time. This could be overcome through federal and state initiatives to reduce quality gaps in stroke care in rural settings and promote care with dedicated stroke wards.

BET 2: FAST or ROSIER to identify suspected stroke in the prehospital setting?

A short cut review was carried out to whether the FAST screening tool is more accurate than the ROSIER tool at correctly identifying those with stroke in the prehospital setting. 9 papers presented the best evidence to answer the clinical question. The author, date and country of publication, patient group studied, study type, relevant outcomes, results and study weaknesses of these papers are tabulated. It is concluded that FAST and ROSIER have similar sensitivities in the recognition of stroke, with ROSIER demonstrating a higher specificity in the prehospital setting.

A systematic review and meta-analysis to evaluate the diagnostic accuracy of recognition of stroke in the emergency department (ROSIER) scale

BACKGROUND

The present study aims to evaluate the performance and the clinical applicability of the Recognition of Stroke in the Emergency Department (ROSIER) scale via systematic review and meta-analysis.

METHODS

Electronic databases of Pubmed and Embase were searched between 1st January 2005 (when ROSIER developed) and 8th May 2020. Studies that evaluated the diagnostic accuracy of the ROSIER scale were included. The sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC) were combined using a bivariate mixed-effects model. Fagan nomogram was used to evaluate the clinical applicability of the ROSIER scale.

RESULTS

A total of 14 studies incorporating 15 datasets were included in this meta-analysis. The combined sensitivity, specificity, DOR and AUC were 0.88 [95% confidence interval (CI): 0.83-0.91], 0.66 (95% CI: 0.52-0.77), 13.86 (95% CI, 7.67-25.07) and 0.88 (95% CI, 0.85-0.90), respectively. Given the pre-test probability of 60.0%, Fagan nomogram suggested the post-test probability was increased to 79% when the ROSIER was positive. In comparison, it was decreased to 22% when ROSIER was negative. Subgroup analysis showed that the pooled sensitivity of ROSIER in the European population was higher than that in Asia. In contrast, the pooled specificity was not significantly different between them. Moreover, results also suggested the male-to-female ratio ≤ 1.0 subgroup, prehospital setting subgroup, and other trained medical personnel subgroup had significantly higher sensitivity compared with their counterparts. At the same time, no significant differences were found in the pooled specificity between them.

CONCLUSIONS

ROSIER is a valid scale with high clinical applicability, which has not only good diagnostic accuracy in Europe but also shows excellent performance in Asia. Moreover, the ROSIER scale exhibits good applicability in prehospital settings with other trained medical personnel.

Diagnosis of ischemic stroke using circulating levels of brain-specific proteins measured via high-sensitivity digital ELISA

Limited lower detection ranges associated with traditional immunoassay techniques have prevented the use of brain-specific proteins as blood biomarkers of stroke in the acute phase of care, as these proteins are often only present in circulation at low concentrations. Digital ELISA is a newly developed technique with allows for quantification of proteins in biofluids with up to 1000 times greater sensitivity than conventional ELISA techniques. The purpose of this study was to determine whether the extended lower limits of detection associated with digital ELISA could enable the use of brain-specific proteins as blood biomarkers of ischemic stroke during triage. Blood was sampled from ischemic stroke patients (n = 14) at emergency department admission, as well as from neurologically normal controls matched in terms of risk factors for cardiovascular disease (n = 33). Plasma levels of two brain-specific axonal proteins, neurofilament light chain (NfL) and tau, were measured via digital ELISA, and receiver-operating characteristic analysis was used to determine their ability to discriminate between groups. Plasma levels of NfL and tau were both significantly elevated in stroke patients versus controls, and could respectively discriminate between groups with 92.9% sensitivity / 84.9% specificity, and 85.7% sensitivity / 54.6% specificity. Furthermore, adjustment of measured NfL and Tau levels according to the lower-limits of detection associated with commercially-available conventional ELISA assays resulted in a dramatic and statistically significant decrease in diagnostic performance. Collectively, our results suggest that the increased analytical sensitivity of digital ELISA could enable the use of brain-specific proteins as blood biomarkers of ischemic stroke during triage.

BE-FAST: A Sensitive Screening Tool to Identify In-Hospital Acute Ischemic Stroke

BACKGROUND

Development of acute ischemic stroke in hospitalized patients represents a significant proportion of all cerebral ischemia. Several prehospital stroke scales were developed to screen for acute ischemic stroke in the community. Despite the advent of inpatient stroke alert systems, there is a lack of validated screening tools for the inpatient population. This study aims to assess the validity of BE-FAST (Balance, Eyes, Face, Arm, Speech, Time) as a screening tool for acute ischemic stroke among inpatients.

METHODS

We retrospectively analyzed all stroke alert activations at a single academic medical center between 2012 and 2016. We classified the triggering symptom as: focal neurologic deficit, aphasia, dysarthria, ataxia/vertigo/dizziness, alteration of consciousness, acute confusion, or headache. BE-FAST was applied retrospectively, and patients were classified as BE-FAST positive or negative. The final diagnosis was classified as acute ischemic stroke, transient ischemic attack , intracranial hemorrhage or noncerebrovascular diagnosis.

RESULTS

Of 1965 stroke alerts, 489 were among inpatients. The mean age was 63 ± 16.1 years; 57% of patients were women (n = 1121). Acute ischemic stroke was diagnosed in 29% of all the activations (n = 567), transient ischemic attack in 12% (n = 232), intracranial hemorrhage in 8 % (n = 160) and noncerebrovascular in 51% (n = 1006). When comparing inpatient with community-onset stroke alerts, the sensitivity of BE-FAST for diagnosing acute ischemic stroke was 85% versus 94% (P = .005), with a specificity of 43% versus 23% (P < .001), respectively. However, when evaluating in-patients with an intact level of consciousness separately, BE-FAST sensitivity for diagnosing acute ischemic stroke was 92% compared to 94% in the community (P = .579). Among in-patients with acute ischemic stroke who were (1) candidates for reperfusion therapy and (2) diagnosed with acute large vessel occlusion, the sensitivity of BE-FAST was 83% and 94%, respectively.

CONCLUSIONS

This is the first study to analyze the performance of BE-FAST among hospitalized patients evaluated through the inpatient stroke alert system. We found BE-FAST to be a very sensitive tool for screening for all in-hospital acute ischemic strokes, including inpatients that were candidates for acute reperfusion therapy.

Stepwise stroke recognition through clinical information, vital signs, and initial labs (CIVIL): Electronic health record-based observational cohort study

BACKGROUND

Stroke recognition systems have been developed to reduce time delays, however, a comprehensive triaging score identifying stroke subtypes is needed to guide appropriate management. We aimed to develop a prehospital scoring system for rapid stroke recognition and identify stroke subtype simultaneously.

METHODS AND FINDINGS

In prospective database of regional emergency and stroke center, Clinical Information, Vital signs, and Initial Labs (CIVIL) of 1,599 patients suspected of acute stroke was analyzed from an automatically-stored electronic health record. Final confirmation was performed with neuroimaging. Using multiple regression analyses, we determined independent predictors of tier 1 (true-stroke or not), tier 2 (hemorrhagic stroke or not), and tier 3 (emergent large vessel occlusion [ELVO] or not). The diagnostic performance of the stepwise CIVIL scoring system was investigated using internal validation. A new scoring system characterized by a stepwise clinical assessment has been developed in three tiers. Tier 1: Seven CIVIL-AS3A2P items (total score from -7 to +6) were deduced for true stroke as Age (≥ 60 years); Stroke risks without Seizure or psychiatric disease, extreme Sugar; "any Asymmetry", "not Ambulating"; abnormal blood Pressure at a cut-off point ≥ 1 with diagnostic sensitivity of 82.1%, specificity of 56.4%. Tier 2: Four items for hemorrhagic stroke were identified as the CIVIL-MAPS indicating Mental change, Age below 60 years, high blood Pressure, no Stroke risks with cut-point ≥ 2 (sensitivity 47.5%, specificity 85.4%). Tier 3: For ELVO diagnosis: we applied with CIVIL-GFAST items (Gaze, Face, Arm, Speech) with cut-point ≥ 3 (sensitivity 66.5%, specificity 79.8%). The main limitation of this study is its retrospective nature and require a prospective validation of the CIVIL scoring system.

CONCLUSIONS

The CIVIL score is a comprehensive and versatile system that recognizes strokes and identifies the stroke subtype simultaneously.

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.

The role of the Cincinnati Prehospital Stroke Scale in the emergency department: evidence from a systematic review and meta-analysis

INTRODUCTION

Stroke is one of the leading causes of morbidity, disability, and mortality in high-income countries. Early prehospital stroke recognition plays a fundamental role, because most clinical decisions should be made within the first hours after onset of symptoms. The Cincinnati Prehospital Stroke Scale (CPSS) is a validated screening tool whose utilization is suggested during triage. The aim of this study is to review the role of the CPSS by assessing its sensitivity and specificity in prehospital and hospital settings.

METHODS

A systematic review and a meta-analysis of the literature reporting the CPSS sensitivity and specificity among patients suspected of stroke were undertaken. Electronic databases were searched up to December 2018, and the quality assessment was carried out by using the Revised Quality Assessment of Diagnostic Accuracy Studies -2  (QUADAS-2).

RESULTS

Eleven studies were included in the meta-analysis. Results showed an overall sensitivity of 82.46% (95% confidence interval [CI] 74.83-88.09%) and specificity of 56.95% (95% CI 41.78-70.92). No significant differences were found in terms of sensitivity when CPSS was performed by physicians (80.11%, 95% CI 66.14-89.25%) or non-physicians (81.11%, 95% CI 69.78-88.87%). However, administration by physicians resulted in higher specificity (73.57%, 95% CI 65.78-80.12%) when compared to administration by non-physicians (50.07%, 95% CI 31.54-68.58%). Prospective studies showed higher specificity 71.61% (95% CI 61.12-80.18%) and sensitivity 86.82% (95% CI 74.72-93.63) when compared to retrospective studies which showed specificity of 33.37% (95% CI 22.79-45.94%) and sensitivity of 78.52% (95% CI 75.08-81.60).

CONCLUSIONS

The CPSS is a standardized and easy-to-use stroke screening tool whose implementation in emergency systems protocols, along with proper and consistent coordination with local, regional, and state agencies, medical authorities and local experts are suggested.

High-Throughput Profiling of Circulating Antibody Signatures for Stroke Diagnosis Using Small Volumes of Whole Blood

Accurate stroke recognition during triage can streamline care and afford patients earlier access to life-saving interventions. However, the tools currently available to clinicians for prehospital and early in-hospital identification of stroke are limited. The peripheral immune system is intricately involved in stroke pathology and thus may be targetable for the development of immunodiagnostics. In this preliminary study, we sought to determine whether the circulating antibody pool is altered early in stroke, and whether such alterations could be leveraged for diagnosis. One hundred microliters of peripheral whole blood was sampled from 19 ischemic stroke patients, 17 hemorrhagic stroke patients, and 20 stroke mimics in the acute phase of care. A custom-fabricated high-density peptide array comprising 125,000 unique probes was used to assess the binding characteristics of blood-borne antibodies, and a random forest-based approach was used to select a parsimonious set of probes with an optimal ability to discriminate between groups. The coordinate antibody binding intensities of the top 17 probes identified in our analysis displayed an ability to differentiate the total pool of stroke patients from stroke mimics with 92% sensitivity and 90% specificity, as well as detect hemorrhage with 88% sensitivity and 87% specificity, as determined using a same-set cross-validation. These preliminary findings suggest that stroke-associated alterations in the circulating antibody pool may have clinical utility for diagnosis during triage, and that such a possibility warrants further investigation.

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.

Unique Stroke Symptoms in Women: A Review

BACKGROUND

Research suggests that there is a delay in recognizing unique stroke symptoms in women by both healthcare professionals and the general population. The purpose of this review was to identify and summarize the most relevant literature regarding recognition and assessment of unique stroke symptoms in women.

METHODS

Literature review using PubMed, CINAHL, ERIC, MEDLINE, PsycINFO, and Google Scholar was used to search literature describing unique stroke symptoms.

RESULTS

Unique stroke symptoms, female sex, and race are associated with delayed recognition, treatment, misdiagnosis, and outcomes. Women experience unique symptoms of nausea/vomiting, headache, dizziness, and cognitive dysfunction more often than men. Stroke assessment tools and registries recognize 1 to 4 of the 11 unique stroke symptoms in women, no study directly assesses the sensitivity and specificity of these unique symptoms, and all studies included women and men. Conclusions and Nursing Implications: Current assessment tools and registries are not sensitive and specific to measuring unique stroke symptoms in women. Accurately identifying unique stroke symptoms in women may reduce presentation and treatment time, minimizing misdiagnoses and poor patient outcomes.

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.

Towards phenotyping stroke: Leveraging data from a large-scale epidemiological study to detect stroke diagnosis

Objective

1) To develop a machine learning approach for detecting stroke cases and subtypes from hospitalization data, 2) to assess algorithm performance and predictors on real-world data collected by a large-scale epidemiology study in the US; and 3) to identify directions for future development of high-precision stroke phenotypic signatures.

Materials and methods

We utilized 8,131 hospitalization events (ICD-9 codes 430–438) collected from the Greater Cincinnati/Northern Kentucky Stroke Study in 2005 and 2010. Detailed information from patients’ medical records was abstracted for each event by trained research nurses. By analyzing the broad list of demographic and clinical variables, the machine learning algorithms predicted whether an event was a stroke case and, if so, the stroke subtype. The performance was validated on gold-standard labels adjudicated by stroke physicians, and results were compared with stroke classifications based on ICD-9 discharge codes, as well as labels determined by study nurses.

Results

The best performing machine learning algorithm achieved a performance of 88.57%/93.81%/92.80%/93.30%/89.84%/98.01% (accuracy/precision/recall/F-measure/area under ROC curve/area under precision-recall curve) on stroke case detection. For detecting stroke subtypes, the algorithm yielded an overall accuracy of 87.39% and greater than 85% precision on individual subtypes. The machine learning algorithms significantly outperformed the ICD-9 method on all measures (P value<0.001). Their performance was comparable to that of study nurses, with better tradeoff between precision and recall. The feature selection uncovered a subset of predictive variables that could facilitate future development of effective stroke phenotyping algorithms.

Discussion and conclusions

By analyzing a broad array of patient data, the machine learning technologies held promise for improving detection of stroke diagnosis, thus unlocking high statistical power for subsequent genetic and genomic studies.

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

OBJECTIVE

To develop an NIH Stroke Scale (NIHSS)-compatible, all-in-one scale for rapid and comprehensive prehospital stroke assessment including stroke recognition, severity grading and progression monitoring as well as prediction of large vessel occlusion (LVO).

METHODS

Emergency medical services (EMS) personnel and stroke physicians (n=326) rated each item of the NIHSS regarding suitability for prehospital use; best rated items were included. Stroke recognition was evaluated retrospectively in 689 consecutive patients with acute stroke or stroke mimics, prediction of LVO in 741 consecutive patients with ischaemic stroke with acute vessel imaging independent of admission NIHSS score.

RESULTS

Nine of the NIHSS items were rated as 'suitable for prehospital use.' After excluding two items in order to increase specificity, the final scale (termed shortened NIHSS for EMS, sNIHSS-EMS) consists of 'level of consciousness', 'facial palsy', 'motor arm/leg', 'sensory', 'language' and 'dysarthria'. Sensitivity for stroke recognition of the sNIHSS-EMS is 91% (95% CI 86 to 94), specificity 52% (95% CI 47 to 56). Receiver operating curve analysis revealed an optimal cut-off point for LVO prediction of ≥6 (sensitivity 70% (95% CI 65 to 76), specificity 81% (95% CI 76 to 84), positive predictive value 70 (95% CI 65 to 75), area under the curve 0.81 (95% CI 0.78 to 0.84)). Test characteristics were non-inferior to non-comprehensive scales.

CONCLUSIONS

The sNIHSS-EMS may overcome the sequential use of multiple emergency stroke scales by permitting parallel stroke recognition, severity grading and LVO prediction. Full NIHSS-item compatibility allows for evaluation of stroke progression starting at the prehospital phase.

Stroke-associated pattern of gene expression previously identified by machine-learning is diagnostically robust in an independent patient population

Our group recently employed genome-wide transcriptional profiling in tandem with machine-learning based analysis to identify a ten-gene pattern of differential expression in peripheral blood which may have utility for detection of stroke. The objective of this study was to assess the diagnostic capacity and temporal stability of this stroke-associated transcriptional signature in an independent patient population. Publicly available whole blood microarray data generated from 23 ischemic stroke patients at 3, 5, and 24 h post-symptom onset, as well from 23 cardiovascular disease controls, were obtained via the National Center for Biotechnology Information Gene Expression Omnibus. Expression levels of the ten candidate genes (ANTXR2, STK3, PDK4, CD163, MAL, GRAP, ID3, CTSZ, KIF1B, and PLXDC2) were extracted, compared between groups, and evaluated for their discriminatory ability at each time point. We observed a largely identical pattern of differential expression between stroke patients and controls across the ten candidate genes as reported in our prior work. Furthermore, the coordinate expression levels of the ten candidate genes were able to discriminate between stroke patients and controls with levels of sensitivity and specificity upwards of 90% across all three time points. These findings confirm the diagnostic robustness of the previously identified pattern of differential expression in an independent patient population, and further suggest that it is temporally stable over the first 24 h of stroke pathology.

Acute Ischemic Stroke Therapy Overview

The treatment of acute ischemic stroke has undergone dramatic changes recently subsequent to the demonstrated efficacy of intra-arterial (IA) device-based therapy in multiple trials. The selection of patients for both intravenous and IA therapy is based on timely imaging with either computed tomography or magnetic resonance imaging, and if IA therapy is considered noninvasive, angiography with one of these modalities is necessary to document a large-vessel occlusion amenable for intervention. More advanced computed tomography and magnetic resonance imaging studies are available that can be used to identify a small ischemic core and ischemic penumbra, and this information will contribute increasingly in treatment decisions as the therapeutic time window is lengthened. Intravenous thrombolysis with tissue-type plasminogen activator remains the mainstay of acute stroke therapy within the initial 4.5 hours after stroke onset, despite the lack of Food and Drug Administration approval in the 3- to 4.5-hour time window. In patients with proximal, large-vessel occlusions, IA device-based treatment should be initiated in patients with small/moderate-sized ischemic cores who can be treated within 6 hours of stroke onset. The organization and implementation of regional stroke care systems will be needed to treat as many eligible patients as expeditiously as possible. Novel treatment paradigms can be envisioned combining neuroprotection with IA device treatment to potentially increase the number of patients who can be treated despite long transport times and to ameliorate the consequences of reperfusion injury. Acute stroke treatment has entered a golden age, and many additional advances can be anticipated.

Clinical Selection Strategies to Identify Ischemic Stroke Patients With Large Anterior Vessel Occlusion

Background and Purpose—

The National Institutes of Health Stroke Scale (NIHSS) correlates with presence of large anterior vessel occlusion (LAVO). However, the application of the full NIHSS in the prehospital setting to select patients eligible for treatment with thrombectomy is limited. Therefore, we aimed to evaluate the prognostic value of simple clinical selection strategies.

Methods—

Data from the Safe Implementation of Thrombolysis in Stroke International Stroke Thrombolysis Registry (January 2012–May 2014) were analyzed retrospectively. Patients with complete breakdown of NIHSS scores and documented vessel status were included. We assessed the association of prehospital stroke scales and NIHSS symptom profiles with LAVO (internal carotid artery, carotid-terminus or M1-segment of the middle cerebral artery).

Results—

Among 3505 patients, 23.6% (n=827) had LAVO. Pathological finding on the NIHSS item best gaze was strongly associated with LAVO (adjusted odds ratio 4.5, 95% confidence interval 3.8–5.3). All 3 face–arm–speech–time test (FAST) items identified LAVO with high sensitivity. Addition of the item best gaze to the original FAST score (G-FAST) or high scores on other simplified stroke scales increased specificity. The NIHSS symptom profiles representing total anterior syndromes showed a 10-fold increased likelihood for LAVO compared with a nonspecific clinical profile. If compared with an NIHSS threshold of ≥6, the prehospital stroke scales performed similarly or even better without losing sensitivity.

Conclusions—

Simple modification of the face–arm–speech–time score or evaluating the NIHSS symptom profile may help to stratify patients’ risk of LAVO and to identify individuals who deserve rapid transfer to comprehensive stroke centers. Prospective validation in the prehospital setting is required.

Treatment of Acute Ischemic Stroke

Although stroke declined from the third to fifth most common cause of death in the United States, the annual incidence and overall prevalence continue to increase. Since the available US Food and Drug Administration-approved treatment options are time dependent, improving early stroke care may have more of a public health impact than any other phase of care. Timely and efficient stroke treatment should be a priority for emergency department and prehospital providers. This article discusses currently available and emerging treatment options in acute ischemic stroke focusing on the preservation of salvageable brain tissue, minimizing complications, and secondary prevention.

Machine-learning approach identifies a pattern of gene expression in peripheral blood that can accurately detect ischaemic stroke

Early and accurate diagnosis of stroke improves the probability of positive outcome. The objective of this study was to identify a pattern of gene expression in peripheral blood that could potentially be optimised to expedite the diagnosis of acute ischaemic stroke (AIS). A discovery cohort was recruited consisting of 39 AIS patients and 24 neurologically asymptomatic controls. Peripheral blood was sampled at emergency department admission, and genome-wide expression profiling was performed via microarray. A machine-learning technique known as genetic algorithm k-nearest neighbours (GA/kNN) was then used to identify a pattern of gene expression that could optimally discriminate between groups. This pattern of expression was then assessed via qRT-PCR in an independent validation cohort, where it was evaluated for its ability to discriminate between an additional 39 AIS patients and 30 neurologically asymptomatic controls, as well as 20 acute stroke mimics. GA/kNN identified 10 genes (ANTXR2, STK3, PDK4, CD163, MAL, GRAP, ID3, CTSZ, KIF1B and PLXDC2) whose coordinate pattern of expression was able to identify 98.4% of discovery cohort subjects correctly (97.4% sensitive, 100% specific). In the validation cohort, the expression levels of the same 10 genes were able to identify 95.6% of subjects correctly when comparing AIS patients to asymptomatic controls (92.3% sensitive, 100% specific), and 94.9% of subjects correctly when comparing AIS patients with stroke mimics (97.4% sensitive, 90.0% specific). The transcriptional pattern identified in this study shows strong diagnostic potential, and warrants further evaluation to determine its true clinical efficacy.

History, Evolution, and Importance of Emergency Endovascular Treatment of Acute Ischemic Stroke

More than 800,000 people in North America suffer a stroke each year, with ischemic stroke making up the majority of these cases. The outcomes of ischemic stroke range from complete functional and cognitive recovery to severe disability and death; outcome is strongly associated with timely reperfusion treatment. Historically, ischemic stroke has been treated with intravenous thrombolytic agents with moderate success. However, five recently published positive trials have established the efficacy of endovascular treatment in acute ischemic stroke. In this review, we will discuss the history of stroke treatments moving from various intravenous thrombolytic drugs to intra-arterial thrombolysis, early mechanical thrombectomy devices, and finally modern endovascular devices. Early endovascular therapy failures, recent successes, and implications for current ischemic stroke management and future research directions are discussed.