Identification Of Stroke Research Articles

Automatic Stroke Screening on Mobile Application: Features of Gyroscope and Accelerometer for Arm Factor in FAST.

This study focuses on automatic stroke-screening of the arm factor in the FAST (Face, Arm, Speech, and Time) stroke screening method. The study provides a methodology to collect data on specific arm movements, using signals from the gyroscope and accelerometer in mobile devices. Fifty-two subjects were enrolled in this study (20 stroke patients and 32 healthy subjects). Given in the instructions of the application, the patients were asked to perform two arm movements, Curl Up and Raise Up. The two exercises were classified into three parts: curl part, raise part, and stable part. Stroke patients were expected to experience difficulty in performing both exercises efficiently on the same arm. We proposed 20 handcrafted features from these three parts. Our study achieved an average accuracy of 61.7%-74.2% and an average area under the ROC curve (AUC) of 66.2%-81.5% from the combination of both exercises. Compared to the FAST method used by examiners in a previous study (Kapes et al., 2014) that showed with an accuracy of 69%-77% for every age group, our study showed promising results for early stroke identification, giving that our study is based only on the arm factor.

Identification and Management of Ischemic Stroke in the Postanesthesia Care Unit

A thromboembolic stroke is a debilitating event that can occur with little or no warning. This report details the case of a 63-year-old male experiencing a stroke in the immediate postoperative period after total knee arthroplasty. Risk for perioperative stroke is influenced by age, sex, ethnicity, comorbidities, and some medications. The depressed neurocognitive state of patients recovering from anesthesia warrants special consideration for the identification and management of perioperative stroke.

Understandings of stroke in rural Malaysia: ethnographic insights

Background Stroke is a public health concern in Malaysia but local beliefs and lay understandings of stroke have not been examined before. Explanatory models provide a way for people to make sense of their illness and influence health seeking behaviors, in a locally relevant way. Methods Drawing on ethnographic research from rural Malaysia, this descriptive article explores ethnic Malaysian-Chinese stroke survivors’ lay understandings of stroke. Eighteen community-dwelling stroke survivors aged 50–83 took part in the study. Results Causation of stroke was derived from cultural, biomedical and social sources. Participants also drew simultaneously from both biomedical and traditional explanations of stroke to develop their own understanding of etiology. Similarities with biomedical causation and other studies from different cultures were found. Participants’ typically focused on the more immediate effects of stroke and often do not attribute causation and association with their comorbid conditions which are also risk factors of stroke. Conclusion Lack of knowledge about stroke and its symptoms was evident in participants’ account. Findings emphasize the importance of knowledge based health interventions, especially in health education strategies for stroke survivors to reduce delays to diagnosis and potentially improve health outcomes post-stroke. Implications for rehabilitation Stroke survivors often form explanatory models of stroke that draw from both biomedical and traditional explanations of stroke. Understanding how people derive lay understandings of stroke can contribute towards developing the goals and activities that facilitate recovery and rehabilitation in similar settings. Health practitioners in the community should strengthen communication regarding the identification, etiology and risk factors of stroke with stroke survivors and their carers to improve compliance to medication, exercise and diet for better recovery. Sustained health education which is culturally relevant is recommended. Communication should also include non-physical impact of stroke (such as cognitive deficits and emotional difficulties) as the stroke survivors were unlikely to relate such symptoms to stroke.

Stroke Prehospital Informed Decision-Making Using EEG Recordings (SPIDER)

Introduction:The acute care of stroke involves the administration of a clot-dissolving drug (thrombolysis) and/or its removal using endovascular clot retrieval. Earlier intervention results in significantly improved patient outcomes. Clinical assessment scores have limitations, and studies have shown that even the most robust scores have a reported false-negative rate of >20% for large vessel occlusive strokes that may be eligible for clot retrieval, while inappropriate bypass may delay delivery of thrombolysis.1 Quantitative Electroencephalography (QEEG) has been shown to have a very high sensitivity and specificity in the identification of acute stroke versus matched controls in an in-hospital setting.(2,3)Aim:The SPIDER study commenced in Brisbane, Queensland on September 3, 2018, and is investigating the use of an EEG recorder to gather data on acute stroke patients presenting to a metropolitan ambulance service.Discussion:The data collected will guide the development of a simple numerical output reference to guide decision making. The data may aid in identifying large vessel occlusive stroke and patients eligible for endovascular intervention. The QEEG will provide a more accurate and cost-effective tool for the prehospital clinician over other imaging technologies and can guide early destination decisions. This presentation discusses the implementation of a pre-hospital research platform, integration with the clinical dispatch matrix, staff engagement, patient recruitment, and the success of the project so far.

Diagnostic accuracy of recognition of stroke in the emergency room scale: a Meta-analysis

To systematically evaluate the diagnostic accuracy and clinical applicability of recognition of stroke in the emergency room (ROSIER) scale by systematic review and Meta-analysis. The Chinese and English literatures concerning the diagnostic accuracy of ROSIER published from January 1st 2005 to December 31st 2018 by PubMed, Embase, Wanfang, VIP and CNKI databases were searched comprehensively and systematically. The sensitivity, specificity, and diagnostic odds ratio (DOR) of ROSIER in total population and subgroup analysis were pooled by using bivariate mixed effects model. Sensitivity analysis was used to evaluate the stability of the results. Deek funnel plot was utilized to evaluate publication bias. The clinical applicability of ROSIER was evaluated by Fagan Nomogram. A total of 28 studies incorporating 7 579 subjects were enrolled in this Meta-analysis. Meta-analysis in total population showed that the pooled sensitivity, specificity and DOR of ROSIER was 0.89 [95% confidence interval (95%CI) = 0.86-0.91, P = 0.00], 0.74 (95%CI = 0.67-0.80, P = 0.00) and 22.09 (95%CI = 14.86-32.82, P = 0.00), respectively. Subgroup analysis of pooled sensitivity of ROSIER showed that Asian patients was significantly higher than European patients [0.89 (95%CI = 0.86-0.92) vs. 0.74 (95%CI = 0.66-0.82), P < 0.01], prospective study was significantly higher than retrospective study [0.89 (95%CI = 0.87-0.92) vs. 0.74 (95%CI = 0.61-0.88), P < 0.05], pre-hospital emergency was significantly higher than emergency department [0.87 (95%CI = 0.80-0.94) vs. 0.85 (95%CI = 0.81-0.90), P < 0.01], study with sample size ≤ 200 was significantly higher than study with sample size > 200 [0.88 (95%CI = 0.83-0.93) vs. 0.82 (95%CI = 0.76-0.88), P < 0.05], but there was no significant difference between different evaluators or different male to female ratio subgroups. Subgroup analysis of pooled specificity of ROSIER showed that European patients was significantly higher than Asian patients [0.81 (95%CI = 0.73-0.89) vs. 0.79 (95%CI = 0.73-0.85), P < 0.05], retrospective study was significantly higher than prospective study [0.88 (95%CI = 0.78-0.97) vs. 0.79 (95%CI = 0.73-0.84), P < 0.05], pre-hospital emergency was significantly higher than emergency department [0.82 (95%CI = 0.73-0.91) vs. 0.79 (95%CI = 0.73-0.85), P < 0.01], emergency physicians was significantly higher than other medical workers [0.80 (95%CI = 0.74-0.86) vs. 0.79 (95%CI = 0.69-0.90), P < 0.05], study with sample size ≤ 200 was significantly higher than study with sample size > 200 [0.82 (95%CI = 0.76-0.89) vs. 0.78 (95%CI = 0.71-0.85), P < 0.05], but there was no significant difference between different male or female ratio subgroups. Sensitivity analysis showed that there was no significant change in pooled DOR before and after excluding each study, indicating that the results were stable. Funnel plot showed that there was a significant publication bias in the total population (P = 0.04), but there was no publication bias in the European population (P = 0.57) or the Asian population (P = 0.08). According to the results of the Fagan Nomogram, with the pretest probability of 50%, when ROSIER was positive, the probability of being diagnosed with stroke increased to 77%, and when ROSIER was negative, the probability of being diagnosed with non-stroke decreased to 13%. It was suggested that ROSIER had good applicability and high clinical diagnostic value. ROSIER has high diagnostic sensitivity and specificity, and has high clinical diagnostic value. It is a valid stroke identification tool which can be widely used in Asian population, pre-hospital emergency and be utilized by trained medical worker.

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

Delays in recognizing stroke during pre-hospital emergency medical system (EMS) care may affect triage and transport time to an appropriate stroke ready hospital and may preclude patients from receiving time dependent treatment. All EMS transports in a large urban area in the stroke belt were evaluated for transport destinations, triage and transport time and stroke recognition following distribution ofan educational training video to local EMS services. Following video training, local paramedics will improve stroke recognition and shorten triage and transport time to appropriate stroke centers of care. A training module (<10 min) containing a stroke triage scenario, instruction on the Cincinnati Prehospital Stroke Score (CPSS) and the Los Angeles Prehospital Stroke Score (LAPSS) and 'where to transport' stroke patients was distributed and viewed by 96 paramedics. Data was collected from February to October 2016. Stroke recognition was determined from one primary stroke center (PSC) hospital's confirmation of EMS delivered patients (Site A). Yearly stroke recognition percentages of 44% from Site A in 2014 were used as baseline. A total of 34,833 emergency 911 response transports were made with a total of 502 (1.4%) suspected strokes identified by paramedics. Median [IQR] triage and transport time for stroke transports was 33 [27-41] min. The PSC hospitals received a 5% increase in stroke transports and non-specific care facilities decreased by 7%. From 8,554 transports to site A (PSC) confirmed strokes totalled 107 transports with 139 suspected strokes by paramedics. Of these transports, 60 were correctly identified by paramedics (positive predictive value of 43%, sensitivity of 56%). By the second month following training, recognition percentages increased from baseline to 64%. At five months, percentages of correct stroke identification had dropped to 36%. Video based training improved stroke recognition by an additional 19%, but continual monthly or quarterly training is recommended for maintenance of increased stroke recognition.

Challenges with the use of Prehospital Stroke Scales for early identification of Large Vessel Occlusion Strokes. (P5.9-068)

Challenges with the use of Prehospital Stroke Scales for early identification of Large Vessel Occlusion Strokes. (P5.9-068)

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

This is the protocol for a review and there is no abstract. The objectives are as follows: Among the currently validated prehospital stroke scales, what is the diagnostic accuracy of the index tests for the diagnosis of stroke in prehospital screening of patients suspected of having a stroke? To assess the influence of the following potential sources of heterogeneity. Patient demographics (e.g. age, gender). Type of event (transient ischemic attack (TIA), ischemic or hemorrhagic). The definition of TIA used by the study. Level of training of paramedic staff. Items from the methodological quality checklist.

Early identification of ischemic stroke in noncontrast computed tomography

Early identification of stroke is critical for the treatment and subsequent recovery. Non-contrast computed tomography (ncCT) is a routinely employed imaging modality for stroke evaluation. However, the early identification of stroke in ncCT images is very difficult, since there are subtle differences between lesion and healthy tissue during the hyperacute phase. In this paper, an image patch classification-based method was developed to detect the early ischemic stroke that is invisible to the radiologist in ncCT. First, we proposed radiomics-based patch classification model to identify whether each patch in ncCT is stroke region or not. To improve the identification accuracy, a symmetry image patch classification was developed, in which an image patch in one brain hemisphere was combined with its contralateral image patch for classification. Second, based on the spatial dependencies between neighboring patches, we built a maximum a posteriori identification model which integrated the spatial constraint information to improve the identification performance. Finally, after performing morphological post-processing, we defined the detected results containing more than 300 voxels as the stroke region. 108 stroke cases that were all invisible to radiologists in ncCT were enrolled in the study and divided into one training cohort and two independent testing cohorts to validate the proposed method. The proposed method has achieved identification accuracies of 76.67% and 75.00% on the two independent testing cohorts, respectively. The results proved the potential of the proposed radiomics model in the task which possessed great clinical values.

Does Left Atrial Appendage Occlusion in LVAD Patients Impact Outcomes: A Single Centre Study

Purpose Results of left atrial appendage occlusion (LAAO) in coronary artery bypass and valvular surgeries from large multicentre studies remain contradictory. No data has been presented on the outcomes of LAAO during left ventricular assist device (LVAD) implant. Therefore, we studied the effects of LAAO during LVAD implantation on postoperative risk of stroke and mortality. Methods This is a single-centre retrospective cohort study including consecutive patients who underwent LVAD implantation between October/2004 and April/2018. We performed a univariate Cox regression analysis to assess the effect of LAAO on the risk of post-operative stroke and mortality. Results Among 156 patients who underwent LVAD implantation (mean age, 52.5 [SD 12.8] years, 34 [21.8%] females), 14 (9.0%) underwent concurrent LAAO. As seen in table one, there were no significant differences in baseline characteristics except for gender (p=0.039). There were no significant differences in intraoperative identification of thrombus, stroke, or embolism between groups. Patients who received LAAO had longer bypass time (p=0.035), ICU stay (p=0.006), and days hospitalized (p=0.019) compared to those without LAAO. Median follow-up was 7 months [range 0 - 92]. A total of 23 strokes and 48 deaths were identified in our cohort and LAAO was not associated with a reduced risk of stroke (HR 1.014 [0.233 - 4.419]; p=0.985) or mortality (HR 1.209 [0.432 - 3.384]; p=0.718). Conclusion Among patients undergoing LVAD implantation, concurrent surgical LAAO was independently associated with an increase in bypass time and ICU and hospital length of stay. Furthermore, LAAO was not associated with a reduced risk of subsequent stroke and all-cause mortality. Further research including data from large registries is required to definitively determine the role of surgical LAAO during LVAD implantation.

Abstract MP15: Validation of Phenotyping Algorithms for Stroke From Electronic Health Records Using Natural Language Processing

Background: Accurate identification of stroke cases from electronic health record (EHR) is needed for efficient and valid clinical and epidemiology research. A number of studies have evaluated the validity of using ICD-9 and ICD-10 codes for stroke identification. Previous research results show that sensitivity and positive predictive value (PPV) for stroke identification is the lowest among all other cerebrovascular diseases, with notable differences observed by stroke pathological subtypes. While most prevalent cerebrovascular disease cases can be detected using 430-438/I60-I69 collectively, more accurate and comprehensive stroke phenotyping algorithm is needed to identify incident stroke cases from EHR. In our work, we compared case identification results using ICD-codes exclusively with ICD plus a natural language processing (NLP) algorithm. Methods: We developed the NLP part of our stroke algorithm using a list of expert provided stroke-related keywords, which covers transient ischemic attack (TIA), ischemic stroke, and hemorrhagic stroke. We validated the hybrid (ICD+NLP) algorithm and compared it with ICD-exclusive algorithm in a previously established atrial fibrillation cohort (n=5,062). Clinical notes and ICD codes of all patients after the incident AF event were reviewed by two nurse abstracters to confirm new stroke incidences. All past clinical notes and ICD codes of a subset of patients (n=402) were reviewed to confirm lifetime (prior and current) stroke cases. Manual abstraction results were considered the gold standard for evaluation of ICD only and ICD+NLP automatic extraction. Sensitivity and positive predictive values (PPV) of both algorithms were calculated. Results: Among 5,062 patients, 593 patients were confirmed to have suffered a stroke after atrial fibrillation while 4,469 patients were confirmed with no EHR evidence of stroke after AF. The ICD-exclusive algorithm had a sensitivity of 47.2% and a PPV of 47.8% for detecting new stroke incidences. The hybrid stroke algorithm achieved a sensitivity of 92.4% and a PPV of 60.6% for extraction of incident stroke. For extraction of lifetime stroke cases, the hybrid approach achieved a sensitivity of 93.9% and a PPV of 88.7%. Performance of new stroke incidence extraction is limited because past/current stroke mentions in clinical notes are difficult to distinguish by our NLP algorithm (may be a future direction). Conclusions: We developed and validated a stroke algorithm that performed well for identifying incident and lifetime stroke cases. The addition of NLP into the stroke algorithm improved the sensitivity and PPV of compared to an ICD-exclusive algorithm.

Early Identification of High-Risk TIA or Minor Stroke Using Artificial Neural Network.

Background and Purpose: The risk of recurrent stroke following a transient ischemic attack (TIA) or minor stroke is high, despite of a significant reduction in the past decade. In this study, we investigated the feasibility of using artificial neural network (ANN) for risk stratification of TIA or minor stroke patients.Methods: Consecutive patients with acute TIA or minor ischemic stroke presenting at a tertiary hospital during a 2-year period were recruited. We collected demographics, clinical and imaging data at baseline. The primary outcome was recurrent ischemic stroke within 1 year. We developed ANN models to predict the primary outcome. We randomly down-sampled patients without a primary outcome to 1:1 match with those with a primary outcome to mitigate data imbalance. We used a 5-fold cross-validation approach to train and test the ANN models to avoid overfitting. We employed 19 independent variables at baseline as the input neurons in the ANN models, using a learning algorithm based on backpropagation to minimize the loss function. We obtained the sensitivity, specificity, accuracy and the c statistic of each ANN model from the 5 rounds of cross-validation and compared that of support vector machine (SVM) and Naïve Bayes classifier in risk stratification of the patients.Results: A total of 451 acute TIA or minor stroke patients were enrolled. Forty (8.9%) patients had a recurrent ischemic stroke within 1 year. Another 40 patients were randomly selected from those with no recurrent stroke, so that data from 80 patients in total were used for 5 rounds of training and testing of ANN models. The median sensitivity, specificity, accuracy and c statistic of the ANN models to predict recurrent stroke at 1 year was 75%, 75%, 75%, and 0.77, respectively. ANN model outperformed SVM and Naïve Bayes classifier in our dataset for predicting relapse after TIA or minor stroke.Conclusion: This pilot study indicated that ANN may yield a novel and effective method in risk stratification of TIA and minor stroke. Further studies are warranted for verification and improvement of the current ANN model.

Performance of the RACE Score for the Prehospital Identification of Large Vessel Occlusion Stroke in a Suburban/Rural EMS Service

Introduction: Emergency Medical Services (EMS) providers may identify and preferentially transport patients experiencing large vessel occlusion (LVO) stroke to appropriate treatment centers. The Rapid Arterial oCclusion Evaluation (RACE) scale was created for prehospital LVO detection, yet few studies have evaluated its function in real-world EMS settings. Our objective was to assess the prehospital performance of the RACE scale for detecting LVO stroke following implementation at a large suburban/rural agency in the United States. Methods: In this retrospective analysis, all 9-1-1 patients with an EMS provider primary or secondary impression of stroke treated by the agency between June 1, 2016 and November 1, 2017 were eligible for inclusion. Patient data were abstracted using a standardized form completed by receiving hospitals. Performance for LVO detection at each RACE cutoff value was evaluated using sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). A receiver operating characteristic (ROC) curve was used to evaluate the discrimination of the RACE scale overall. A secondary analysis of RACE for patients experiencing strokes best treated at comprehensive stroke centers (LVO and intracerebral hemorrhage [ICH]) was conducted. Results: There were 440 patients with a documented RACE score and hospital outcome data included in the analysis. About half (51%, n = 220) were female and the median age was 70 years (IQR: 59–81). Last known well time was under 4.5 hours for 76% of patients (n = 261). Over half (61%, n = 269) had a hospital discharge diagnosis of stroke and 64/440 (15%) were classified as LVO. The ROC curve demonstrated adequate discrimination with a c-statistic of 0.72. Performance for identifying LVO in the prehospital setting was greatest for RACE scores ≥5 with a sensitivity of 66% and specificity of 72%, PPV of 29%, and NPV of 93%. A RACE score ≥5 for both LVO and ICH demonstrated sensitivity: 63%, specificity: 77%, PPV: 47% and NPV: 86%. Conclusion: The RACE scale demonstrated acceptable discrimination, yet the sensitivity and positive predictive value were lower in this cohort of EMS professionals in the United States than in the original validation study conducted in Spain. Further work is needed to determine the optimal prehospital screening tool for identification of LVO.

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.

Targets for improving dispatcher identification of acute stroke

Accurate identification of acute stroke by Emergency Medical Dispatchers (EMD) is essential for timely and purposeful deployment of Emergency Medical Services (EMS), and a prerequisite for operating mobile stroke units. However, precision of EMD stroke recognition is currently modest. We sought to identify targets for improving dispatcher stroke identification. Dispatch codes and EMS patient records were cross-linked to investigate factors associated with an incorrect dispatch code in a prospective observational cohort of 625 patients with a final diagnosis of acute stroke or transient ischemic attack (TIA), transported to our stroke center as candidates for recanalization therapies. Call recordings were analyzed in a subgroup that received an incorrect low-priority dispatch code indicating a fall or unknown acute illness (n = 46). Out of 625 acute stroke/TIA patients, 450 received a high-priority stroke dispatch code (sensitivity 72.0%; 95% CI, 68.5-75.5). Independent predictors of dispatcher missed acute stroke included a bystander caller (aOR, 3.72; 1.48-9.34), confusion (aOR, 2.62; 1.59-4.31), fall at onset (aOR, 1.86; 1.24-2.78), and older age (aOR [per year], 1.02; 1.01-1.04). Of the analyzed call recordings, 71.7% revealed targets for improvement, including failure to recognize a Face Arm Speech Time (FAST) test symptom (21/46 cases, 18 with speech disturbance), or failure to thoroughly evaluate symptoms (12/46 cases). Based on our findings, efforts to improve dispatcher stroke identification should primarily focus on improving recognition of acute speech disturbance, and implementing screening of FAST-symptoms in emergency phone calls revealing a fall or confusion. URL: http://www.clinicaltrials.gov . Unique identifier: NCT02145663.

Abstract 171: Stroke Identification by Emergency Medical Services: Determinants and its Impact on In-Patient Care

Background: Recognizing acute stroke by Emergency Medical Services (EMS) is critical for timely care. We assessed the accuracy and determinants of stroke diagnosis by EMS and evaluated its impact on stroke patient care in Georgia. Methods: Data from the Georgia Coverdell Acute Stroke Registry on 6,586 clinically diagnosed and hospitalized acute stroke patients were linked to the 2016 Georgia EMS Information System. Accuracy of EMS recognition of clinically diagnosed stroke (sensitivity) was estimated using hospital diagnosis as the gold standard. Multivariable logistic regression was applied to identify determinants of stroke diagnosis and assess its impact on stroke patient care using door-to-imaging time. Results: EMS sensitivity of identifying acute stroke was 48.6%. In a multivariable model, considering ischemic stroke patients only, correct EMS diagnosis was positively associated with emergency medical dispatch (EMD) report of stroke (OR=4.67; 95%CI: 3.96-5.50); severity of stroke as measured by NIH stroke scale score (OR 5-14 Vs. 0-5 =2.17, 95%CI: 1.83-2.57; OR 15+ Vs. 0-5 =4.23, 95%CI: 3.37-5.32); clinical signs of slurred speech (OR=2.35, 95%CI: 2.00-2.75); and weakness of the extremities (OR=1.57, 95%CI: 1.33-1.85). Correct diagnosis was negatively associated with altered level of consciousness (OR=0.56, 95%CI: 0.46-0.70). Patients assisted by EMS with higher level of certification had higher odds (OR=1.36, 95%CI: 0.94-1.97) of being identified as having stroke than those attended by emergency medical technicians with basic training. Patients identified as having stroke by EMS had 3.78 times higher odds (95%CI: 3.01-4.75) of receiving imaging within 25 minutes of hospital arrival than those who were missed by EMS. Conclusion: Pre-hospital identification of acute stroke improves in-hospital care; however, about half of stroke patients transported by EMS in 2016 were not identified in the field. Identification of acute stroke is associated with EMS personnel level of certification, EMD’s report of stroke, and presenting symptoms of the disease and its severity. Thus, it is essential to improve EMDs level of understanding of stroke, enhance EMTs skill in stroke identification for swifter cinical care and better patient outcomes.

Abstract TP508: Neuropsychological Outcomes in Children With Arterial Ischemic Stroke

Objective: Pediatric stroke is a rare disease with potential for lifelong impact on cognition, social function, and financial productivity. Neuropsychological testing is critically important for understanding the impact of acquired brain injury on the component elements of cognition. Unfortunately patients cannot always access comprehensive testing resources due to a variety of barriers including limited behavioral health provider availability, cost, and insurance coverage. Physicians must be familiar with common neurocognitive difficulties after pediatric stroke to aide timely resource referral. In our study we review the neuropsychological profile of pediatric arterial ischemic stroke (AIS) patients. Methods: This is a single center retrospective case series of AIS patients evaluated from 2015-June 2018 by our multidisciplinary cerebrovascular center. Patients were referred for neuropsychology evaluation routinely after identification of stroke. The test battery was determined at the discretion of the evaluating psychologist and administered per standard protocol. Aggregate results are presented with mean ± standard deviation. Medical records have been abstracted and correlated to lesion laterality, time elapsed since injury, and neurologic comorbidities including ADHD and epilepsy. Results: The CV center cares for 120 children with AIS. 38 children have undergone neuropsychological testing in the study interval (23 females, 15 males). Median age at evaluation is 12 years. The mean full scale IQ for this cohort is 81±18 scoring in the low average range compared to the healthy population. The cohort showed lowest performance in visual spatial index (83±15) and working memory (84±16). Verbal comprehension (89±18), fluid reasoning (87±13), and processing speed indices (85±16) showed minimally higher scores. Conclusion Children with stroke are at risk for cognitive deficits with visual spatial and working memory difficulties most prominent in our cohort. Appropriate therapeutic and educational resources should be directed to address cognitive deficits when identified. Clinician recognition of support needs in this population will facilitate timely referral for intervention.

Abstract WP511: Using a Nurse Driven Protocol for Stroke Triage

Background: In 2013, the American Heart Association/American Stroke Association began a public awareness marketing campaign that included a tool; called F.A.S. T. The purpose was used to educate the public on stroke signs and symptoms and to go to the emergency department promptly for treatment. Prehospital personnel has stroke screening tools, such as Cincinnati Stroke Scale utilized locally. The health system stroke center utilized a nursing triage process using the acronym "FLASHED" to identify patients were exhibiting symptoms of stroke so that nurses would triage patients based on the acronym. F stands for facial asymmetry or droop; L is leg weakness or numbness. A is for arm weakness or numbness. S stands for speech deficits. H is for a severe headache without cause. E is for eyes; this includes all or part of the vision in one or both eyes. D stands for dizziness or loss of coordination if accompanied by any of the above symptoms. Purpose: The purpose of this study was to utilize a tool better suited for nursing for stroke identification in the ED setting. Identifying if patients that met the FLASHED criteria for stroke were appropriate for receiving an emergent evaluation for an acute stroke. Methods: A retrospective electronic record review of ED patients was used for this study. A content analysis included identification of symptom description and stroke one activations over a 24 month period. A patient arriving in the ED meeting FLASHED criteria resulted in stroke one activation with their last known well within 6 hours of arrival. Results: Over a 24 month period, nearly 500 Stroke One activations occurred. Of those activations, 3% did not meet the FLASHED criteria. 89.7% of all Stroke One Activations received a stroke workup by a neurologist. 56% of all activations resulted in the patient being discharged from either an inpatient or observation status with a stroke related diagnosis. Conclusion: Recognition of possible stroke patients based on symptoms upon arrival to the ED promptly is a priority for all EDs. A clear triage tool is essential to identify patients with stroke symptoms. While using FLASHED as this tool resulted in assessments, testing, and interventions further study is needed to determine the validity and reliability of the FLASHED tool.

Abstract TP453: Patient Stroke Education: Personal Modifiable Risk Factors

Objective: To improve stroke education by focusing on identification and management of personal modifiable risk factors, through creating a teaching tool for our stroke patient population and those at risk for stroke. Background: One of the challenges facing those involved in stroke care is improving patient understanding of their personal risk factors and reducing their chance of having another stroke. Our Stroke program recognized that some stroke patients were unable to identify their modifiable risk factors during the stroke discharge follow up calls. 2100 staff initiated a nurse-led process on stroke education. With implementation of an interactive teaching tool developed by the staff, the goal is for stroke patients to identify and provide teach back on recognition and response to stroke signs and symptoms as well as their modifiable stroke risk factors Methods: A teaching card was created (adapted from American Heart Association and American Stroke Association), for the nurse to review and educate the patient on stroke recognition and response, identifying and managing their modifiable risk factors. A unit wide audit was conducted at time of discharge. Using the Plan-Do-Study-Act method and the stroke program discharge callback process, the stroke program and the unit staff continue to evaluate the effectiveness of the stroke education card. Results: Starting in 2018 when the teaching tool card was implemented, 2100 Stroke Discharges responses on Stroke Recognition and Response has increased to 100% for the first 3 months of year. More importantly, in the first quarter of 2018, 2100 Stroke Discharges have been able to identify their personal modifiable risk factors. Conclusions: The results of this project suggest that use of this teaching tool can be beneficial on knowledge retention and understanding of education provided on stroke recognition, response, and identification and management of their personal modifiable stroke risk factors.

Abstract TP506: Decreasing the Time to Detection of Stroke After Cardiac Catheterization in Children

Introduction: Children with congenital and acquired heart disease are at high risk for stroke. One-quarter of acute ischemic strokes (AIS) in children with cardiac disease occur in the peri-procedural period. Children with AIS can be treated successfully with hyperacute therapies. However, this requires timely identification of stroke, which has historically proved challenging in children. By developing and implementing a standardized screening assessment, we aim to decrease the time to detection of strokes occurring after cardiac catheterization in children. Methods: We retrospectively reviewed the medical records of children who had a stroke after cardiac catheterization at a tertiary children’s hospital between December 2006 and July 2016. We then developed a standardized screening stroke assessment. This was incorporated into the electronic medical record, and cardiology bedside nurses were trained in its use. The performance of the screen was analyzed retrospectively for children with a known stroke after cardiac catheterization. Results: Twenty children had a clinically-apparent stroke after cardiac catheterization prior to implementation of our screen. The time from the end of catheterization to the first documented assessment of neurologic function in any domain ranged from 22 minutes to 3.3 days (median 126 minutes). Given this variability, we developed a screening assessment that is brief, feasible, and within the scope of practice of cardiology bedside nurses. This was introduced to all nurses involved in the acute assessment of children after cardiac catheterization and is now performed on all children after catheterization. Retrospective application of the screen to the 20 children with a post-catheterization stroke would have resulted in detection of 18 (90%) of these strokes. Conclusion: Stroke after cardiac catheterization in children is rare; however, it is accompanied by a high rate of morbidity and mortality. The diagnosis of stroke after cardiac catheterization is often delayed. Development of screening methods to rapidly identify stroke in this population is feasible and has the potential to decrease the time to detection of stroke, thereby expanding access to hyperacute therapies.