Prehospital Stroke Diagnosis Research Articles

Real-Time precision prehospital stroke diagnosis in weightlifting athletes using cutting-edge non-invasive sensors

This research paper presents an investigation into the efficacy of a novel diagnostic technology designed for the real-time monitoring of weightlifting athletes, focusing on precision, time efficiency, and user convenience compared to traditional diagnostic systems. The study introduces an advanced non-invasive sensor system, integrated into a cohesive Internet of Medical Things (IoMT) framework, which facilitates the immediate and accurate assessment of athletes' health parameters. To empirically test the benefits of this new technology, a pedagogical experiment was conducted involving two distinct groups: an experimental group that utilized the proposed technology for medical checkups, and a control group that continued with traditional diagnostic methods. Each group consisted of 30 athletes, and the outcomes were measured across three dimensions: the precision of diagnostic results, the time expended for medical checkups, and the user-reported convenience of the equipment. The findings indicate that the proposed technology not only significantly enhances the precision of health diagnostics but also reduces the time required for medical examinations, thereby increasing overall efficiency. Additionally, the higher convenience scores reported by the experimental group suggest improved user satisfaction and usability. These results demonstrate the potential of the proposed diagnostic system to transform athlete health monitoring by providing more accurate, efficient, and user-friendly medical assessments, suggesting a significant step forward in the application of advanced technologies in sports medicine.

Optimizing Prehospital Stroke Diagnosis: Integrating Machine Learning with the FAST Scoring System

Strokes are a leading cause of disability and death worldwide, and timely diagnosis is critical for effective treatment. Prehospital stroke diagnosis by emergency medical services (EMS) is essential for ensuring prompt care. The FAST (Face, Arms, Speech, and Time) scoring system is a common tool used to identify stroke symptoms quickly. However, the traditional FAST system has limitations, including reliance on subjective assessments and potential misdiagnoses due to symptom variability. This study aims to optimize the FAST scoring system and compare its performance with various machine learning models to enhance stroke prediction accuracy. A dataset from an ambulance service, including prehospital data such as age, sex, and FAST test results, was used. Data preprocessing involved handling missing values, encoding categorical variables, and applying the Synthetic Minority Over-sampling Technique (SMOTE) to balance the dataset. A grid search was conducted to test different weighting schemes for the FAST system. Machine learning models, including Logistic Regression, Random Forest, Support Vector Machine (SVM), XGBoost, and Decision Tree, were trained and evaluated using cross-validation on the training set and tested on a separate test set. The traditional FAST system showed an accuracy of 62.95%. Optimizing the FAST system through grid search marginally increased accuracy to 64.36%. However, machine learning models, particularly Random Forest, significantly outperformed the FAST system, achieving a test set accuracy of 88.54%. XGBoost also demonstrated strong performance with an accuracy of 77.07%, while Logistic Regression, SVM, and Decision Tree showed lower accuracies. These findings suggest that integrating machine learning models into EMS protocols could substantially improve the accuracy of prehospital stroke diagnosis, leading to quicker and more accurate identification and treatment of stroke patients. Although the optimized FAST system offers slight improvements, machine learning models present a promising avenue for enhancing stroke prediction and improving patient outcomes in emergency settings while acknowledging practicality issues.

Artificial intelligence to enhance prehospital stroke diagnosis and triage: a perspective.

As health systems organize to deliver the highest quality stroke care to their patients, there is increasing emphasis being placed on prehospital stroke recognition, accurate diagnosis, and efficient triage to improve outcomes after stroke. Emergency medical services (EMS) personnel currently rely heavily on dispatch accuracy, stroke screening tools, bypass protocols and prehospital notification to care for patients with suspected stroke, but novel tools including mobile stroke units and telemedicine-enabled ambulances are already changing the landscape of prehospital stroke care. Herein, the authors provide our perspective on the current state of prehospital stroke diagnosis and triage including several of these emerging trends. Then, we provide commentary to highlight potential artificial intelligence (AI) applications to improve stroke detection, improve accurate and timely dispatch, enhance EMS training and performance, and develop novel stroke diagnostic tools for prehospital use.

Timeliness and accuracy of the 7-Item Japan Urgent Stroke Triage (JUST-7) score, a prehospital stroke triage tool, assessed by emergency medical services.

The prompt initiation of stroke treatment significantly influences patient outcomes, highlighting the crucial role of prehospital triage. This study aimed to assess the implementation of the 7-Item Japan Urgent Stroke Triage (JUST-7) score by emergency medical services (EMS) in our region and its effect on emergency transportation for suspected stroke patients. Data were collected from patients suspected of having an acute stroke with a Cincinnati Prehospital Stroke Scale (CPSS) score of 1 or more who were transferred by ambulance within 24 h of symptom onset. Two prehospital stroke scales were employed during different periods: period 1 with CPSS alone (January to December 2020) and period 2 with both CPSS and JUST-7 (January 2021 to March 2023). On-scene time data were obtained from the EMS crews, and data regarding the final diagnosis of patients and their outcomes were obtained from the respective hospitals to which the patients were transferred. These data were compared between periods 1 and 2 and between the CPSS and JUST-7. The results revealed that additional evaluation with JUST-7 did not affect ambulance transport time. The CPSS+JUST-7 approach demonstrated higher specificity in identifying stroke and major artery occlusion than with the CPSS alone; however, an appropriate cut-off value needs to be considered. The JUST-7 achieved a diagnostic concordance rate of 35.9% for the most likely stroke type and 64.0% for the first two most likely types. This research emphasizes the potential of JUST-7 as a valuable addition to prehospital stroke diagnosis protocols. Its flexibility in adapting cut-off values based on regional factors and available medical resources optimizes its utility in diverse healthcare settings. The JUST-7 score is a promising tool for improving patient outcomes through prompt and accurate prehospital assessments.

Evaluation of Pre-hospital Stroke Diagnosis Agreement with Emergency Diagnosis

Evaluation of Pre-hospital Stroke Diagnosis Agreement with Emergency Diagnosis

Prospective collection of blood plasma samples to identify potential biomarkers for the prehospital stroke diagnosis (ProGrEss-Bio): study protocol for a multicenter prospective observational study.

Intravenous thrombolysis (IVT) and mechanical thrombectomy (MT) are well-established, evidence-based, time-critical therapies that reduce morbidity and mortality in acute ischemic stroke (AIS) patients. The exclusion of intracerebral hemorrhage (ICH) is mandatory and has been performed by cerebral imaging to date. Mobile stroke units (MSUs) have been shown to improve functional outcomes by bringing cerebral imaging and IVT directly to the patient, but they have limited coverage. Blood biomarkers clearly distinguishing between AIS, ICH, and stroke mimics (SM) could provide an alternative to cerebral imaging if concentration changes are detectable in the hyperacute phase after stroke with high diagnostic accuracy. In this study, we will take blood samples in a prehospital setting to evaluate potential biomarkers. The study was registered in the German Clinical Trials Register (https://drks.de/search/de) with the identifier DRKS00023063. We plan a prospective, observational study involving 300 patients with suspected stroke and symptom onset of ≤4.5 h before the collection of biomarkers. Study participants will be recruited from three sites in Berlin, Germany during MSU deployments. The focus of the study is the collection of blood samples from participants at the prehospital scene and from participants with AIS or ICH at a second-time point. All samples will be analyzed using targeted and untargeted analytical approaches. Study-related information about participants, including medical information and discharge diagnoses from the subsequent treating hospital, will be collected and documented in an electronic case report form (eCRF). This study will evaluate whether a single blood biomarker or a combination of biomarkers can distinguish patients with AIS and ICH from patients with stroke and SM in the early phase after symptom onset in the prehospital setting. In addition, the kinetics of blood biomarkers in AIS and ICH patients will be investigated. Our goal is to evaluate new ways to reliably diagnose stroke in the prehospital setting and thus accelerate the application of evidence-based therapies to stroke patients.

Abstract WMP47: Low Sensitivity Of Widely Used Emergency Dispatch Algorithm For Thrombectomy Patients - Implications For Mobile Stroke Units

Introduction: Widely used emergency dispatch algorithms such as the Advanced Medical Priority Dispatch System (AMPDS) have limited diagnostic accuracy for prehospital diagnosis of stroke. With advent of mobile stroke units (MSU), this inaccuracy prevents optimal dispatch to patients who may benefit. Expedited endovascular thrombectomy (EVT) is a major contributor to net benefit of MSUs. We assessed the accuracy of AMPDS for recognizing stroke in thrombectomy patients in the Australian state of Victoria. Methods: We included consecutive patients accepted for EVT (direct and secondary transfer) to The Royal Melbourne Hospital from 2007-2021 in whom linked AMPDS dispatch codes could be obtained from Ambulance Victoria. The primary outcome was the proportion of cases dispatched as stroke vs non-stroke with subgroup analyses of the effect of baseline clinical severity, metropolitan vs rural dispatch and time to thrombectomy. Chi square and Mann Whitney tests were used as appropriate. Results: A total of n=618 patients were included with baseline NIHSS 16 (IQR 10-20). Of these, only 62% (95% CI 58-66) were initially dispatched as suspected stroke, with the most common non-stroke diagnoses being “Unconscious/Fainting” (19.2%) and “Falls” (6.9%). Those with a higher baseline severity (NIHSS ≥10) were less likely to be classified as stroke than those with lower severity (59% vs 76%, p<0.001), while no difference was found between metropolitan and rural patients (p=0.066). Overall, no significant time differences were found between stroke and non-stroke dispatches for ambulance dispatch to arterial access (median 208 vs 216 min, p=0.593) or hospital arrival to arterial access (median 42 vs 42 min, p=0.851). However, only 32 patients were treated on the MSU, which commenced operation November 2017. Conclusions: Almost 40% of thrombectomy patients did not receive an initial AMPDS dispatch of suspected stroke and those with higher baseline severity were more likely to be misclassified. Although time to thrombectomy was not significantly different between stroke vs non-stroke dispatches, MSU treatment was under-represented. Our findings have implications for emergency medical services and particularly mobile stroke units which rely on accurate stroke dispatch.

Prehospital technologies for early stroke detection - A review.

The rate of neural circuitry loss in a typical large vessel occlusion well emphasizes that 'Time is Brain'. Every untreated minute in a large vessel ischaemic stroke results in loss of 1.9 million neurons and 13.8 billion synapses. As such, it is essential to optimize the flow-limiting steps in delivering the current standard of care. The current diagnostic model involves recognition of symptoms by patients, followed by access to Emergency Medical Services and subsequent physical examination and neuroimaging in the Emergency Department. With more than 50% of stroke patients using Emergency Medical Services as the first point of care contact, it can be deduced that the outcome of the 'stroke chain of survival' can be improved by addressing the bottleneck of prehospital stroke diagnosis. Here we present a review of the existing technologies.

A mobile battery-powered brain perfusion ultrasound (BPU) device designed for prehospital stroke diagnosis: correlation to perfusion MRI in healthy volunteers

BackgroundEarly prehospital stroke identification is crucial for goal directed hospital admission especially in rural areas. However, clinical prehospital stroke scales are designed to identify any stroke but cannot sufficiently differentiate hemorrhagic from ischemic stroke, including large vessel occlusion (LVO) amenable to mechanical thrombectomy. We report on a novel small, portable and battery driven point-of-care ultrasound system (SONAS®) specifically developed for mobile non-invasive brain perfusion ultrasound (BPU) measurement after bolus injection of an echo-enhancing agent suitable for the use in prehospital stroke diagnosis filling a current, unmet and critical need for LVO identification.MethodsIn a phase I study of healthy volunteers we performed comparative perfusion-weighted magnetic resonance imaging (PWI) and BPU measurements, including safety analysis.ResultsTwelve volunteers (n = 7 females, n = 5 males, age ranging between 19 and 55 years) tolerated the measurement extremely well including analysis of blood–brain barrier integrity, and the correlation coefficient between the generated time kinetic curves after contrast agent bolus between PWI and BPU transducers ranged between 0.89 and 0.76.ConclusionsMobile BPU using the SONAS® device is feasible and safe with results comparable to PWI. When applied in conjunction with prehospital stroke scales this may lead to a more accurate stroke diagnosis and patients bypassing regular stroke units to comprehensive stroke centers. Further studies are needed in acute stroke patients and in the prehospital phase including assessment of immediate and long-term morbidity and mortality in stroke.Trial registration: Clinical trials.gov, registered 28.Sep.2017, Identifier: NCT03296852.

Reliability of prehospital diagnosis of acute cerebrovascular accident.

Early and correct suspicion of an acute cerebrovascular accident (CVA) is necessary to minimise the time to reperfusion treatment. Our aim was to evaluate the reliability of a prehospital diagnosis of stroke or transient ischaemic attack made by healthcare professionals referring patients directly to a neurological Emergency Department (ED). This retrospective analysis included all consecutive patients referred between 1 January and 31 December 2014 by ambulance physicians, paramedics or outpatient physicians to the neurological ED providing care for the 300-350,000 inhabitants of a highly urbanised area. We calculated sensitivity and positive predictive value (PPV) with 95% confidence intervals (95% CI) for each group of healthcare professionals, and compared the proportions of undetected CVAs. Of 802 patients referred with a prehospital diagnosis of CVA, 544 were confirmed. Additional 95 CVA cases were referred with a diagnosis other than CVA. The highest sensitivity for detection of any CVA was among ambulance physicians (96%; 95% CI 92-98%), followed by paramedics (85%; 95% CI 80-90%; p < 0.001) and outpatient physicians (74%; 95% CI 70-79%; p < 0.001). PPV for stroke was 83% (95% CI 77-87%) among ambulance physicians, 73% (95% CI 65-80%) among paramedics, and 56% (95% CI 47-64%) among outpatient physicians. Ambulance physicians are highly sensitive in diagnosing any CVA, and are correct in 8 out of 10 cases. The inferior performance of paramedics and outpatient physicians indicates the need for regular stroke training for paramedics and the implementation of two-way communication with the stroke team to identify potential candidates for reperfusion therapy before arrival at the ED.

Abstract TP56: Low Cost, Portable Electroencephalograph May Improve The Accuracy Of Prehospital Stroke Diagnosis And Detection Of Large Vessel Occlusion

Accurate and timely prehospital stroke diagnosis and detection of large vessel occlusion (LVO) are essential to ensure stroke patients are transported to hospitals that offer emergent reperfusion therapies. However, symptom based prehospital stroke scales often fail to identify LVO. Thus, a need exists for cost-effective and portable diagnostic tools, such as portable electroencephalography (EEG) to improve the accuracy of prehospital stroke diagnosis. Hypotheses: 1) Quantitative EEG measures will differ between LVO and non-LVO stroke patients, particularly in regards to brain slowing (ratio of low to high frequency oscillatory brain power) and brain asymmetry (ratio between oscillations in the affected and unaffected hemisphere) 2) Combining EEG with prehospital stroke scales will improve the accuracy of LVO detection. We enrolled patients with acute suspected stroke on presentation to an emergency department at a comprehensive stroke centre. Patients were rapidly evaluated with the Los Angeles Motor Scale followed by a 3-minute resting-state EEG recording using a modified Muse EEG headband (InteraXon). The LVO diagnosis and the extent of cerebral blood flow abnormalities were determined from CT angiography and CT perfusion imaging performed in close temporal proximity to the EEG recording. The study enrolled 74 patients (n= 8 LVO, n=66 non-LVO, including stroke mimics). Initial analysis suggests that LVO patients have trends towards brain slowing, as measured by the delta alpha ratio (LVO: mean = 1.21, SEM = 0.03; non-LVO: mean = 1.19, SEM = 0.01; p-value = 0.34). Additionally, LVO patients showed a trend towards increased brain asymmetry from 6-8 Hz, suggesting physiological differences between hemispheres specific to the theta frequency (LVO: mean = 0.02, SEM = 0.006; non-LVO: mean = 0.01, SEM = 0.002; p-value = 0.13). Quantitative measures will be assessed using classification trees to determine which combination of EEG and clinical features is most predictive of LVO. In conclusion, acute differences in brain activity between LVO and non-LVO patients can be detected with portable EEG, which when combined with clinical stroke scales, have the potential to improve the diagnosis and triage of suspected stroke patients in a prehospital setting.

Improving Prehospital Stroke Diagnosis Using Natural Language Processing of Paramedic Reports.

Accurate prehospital diagnosis of stroke by emergency medical services (EMS) can increase treatments rates, mitigate disability, and reduce stroke deaths. We aimed to develop a model that utilizes natural language processing of EMS reports and machine learning to improve prehospital stroke identification. We conducted a retrospective study of patients transported by the Chicago EMS to 17 regional primary and comprehensive stroke centers. Patients who were suspected of stroke by the EMS or had hospital-diagnosed stroke were included in our cohort. Text within EMS reports were converted to unigram features, which were given as input to a support-vector machine classifier that was trained on 70% of the cohort and tested on the remaining 30%. Outcomes included final diagnosis of stroke versus nonstroke, large vessel occlusion, severe stroke (National Institutes of Health Stroke Scale score >5), and comprehensive stroke center-eligible stroke (large vessel occlusion or hemorrhagic stroke). Of 965 patients, 580 (60%) had confirmed acute stroke. In a test set of 289 patients, the text-based model predicted stroke nominally better than models based on the Cincinnati Prehospital Stroke Scale (c-statistic: 0.73 versus 0.67, P=0.165) and was superior to the 3-Item Stroke Scale (c-statistic: 0.73 versus 0.53, P<0.001) scores. Improvements in discrimination were also observed for the other outcomes. We derived a model that utilizes clinical text from paramedic reports to identify stroke. Our results require validation but have the potential of improving prehospital routing protocols.

Predicting stroke severity with a 3-min recording from the Muse portable EEG system for rapid diagnosis of stroke

In this study, we demonstrated the use of low-cost portable electroencephalography (EEG) as a method for prehospital stroke diagnosis. We used a portable EEG system to record data from 25 participants, 16 had acute ischemic stroke events, and compared the results to age-matched controls that included stroke mimics. Delta/alpha ratio (DAR), (delta + theta)/(alpha + beta) ratio (DBATR) and pairwise-derived Brain Symmetry Index (pdBSI) were investigated, as well as head movement using the on-board accelerometer and gyroscope. We then used machine learning to distinguish between different subgroups. DAR and DBATR increased in ischemic stroke patients with increasing stroke severity (p = 0.0021, partial η2 = 0.293; p = 0.01, partial η2 = 0.234). Also, pdBSI decreased in low frequencies and increased in high frequencies in patients who had a stroke (p = 0.036, partial η2 = 0.177). Using classification trees, we were able to distinguish moderate to severe stroke patients and from minor stroke and controls, with a 63% sensitivity, 86% specificity and accuracy of 76%. There are significant differences in DAR, DBATR, and pdBSI between patients with ischemic stroke when compared to controls, and these effects scale with severity. We have shown the utility of a low-cost portable EEG system to aid in patient triage and diagnosis as an early detection tool.

Break in the Stroke Chain of Survival due to COVID-19.

Background and Purpose:Emergency measures to treat patients with coronavirus 2019 (COVID-19) and contain the outbreak is the main priority in each of our hospitals; however, these measures are likely to result in collateral damage among patients with other acute diseases. Here, we investigate whether the COVID-19 pandemic affects acute stroke care through interruptions in the stroke chain of survival.Methods:A descriptive analysis of acute stroke care activity before and after the COVID-19 outbreak is given for a stroke network in southern Europe. To quantify the impact of the pandemic, the number of stroke code activations, ambulance transfers, consultations through telestroke, stroke unit admissions, and reperfusion therapy times and rates are described in temporal relationship with the rising number of COVID-19 cases in the region.Results:Following confinement of the population, our stroke unit activity decreased sharply, with a 25% reduction in admitted cases (mean number of 58 cases every 15 days in previous months to 44 cases in the 15 days after the outbreak, P<0.001). Consultations to the telestroke network declined from 25 every 15 days before the outbreak to 7 after the outbreak (P<0.001). The increasing trend in the prehospital diagnosis of stroke activated by 911 calls stopped abruptly in the region, regressing to 2019 levels. The mean number of stroke codes dispatched to hospitals decreased (78% versus 57%, P<0.001). Time of arrival from symptoms onset to stroke units was delayed >30 minutes, reperfusion therapy cases fell, and door-to-needle time started 16 minutes later than usual.Conclusions:The COVID-19 pandemic is disruptive for acute stroke pathways. Bottlenecks in the access and delivery of patients to our secured stroke centers are among the main challenges. It is critical to encourage patients to continue seeking emergency care if experiencing acute stroke symptoms and to ensure that emergency professionals continue to use stroke code activation and telestroke networks.

Stroke in children and adolescents: topical problems of pre-hospital diagnostics

The article is devoted to the urgent problem of pediatrics and pediatric neurology, pre-hospital diagnosis of stroke in children. A review of domestic and foreign literature on the early diagnosis of stroke in children and adolescents, as well as epidemiological data on pediatric stroke, is presented. Particular attention is paid to the features of the symptoms of stroke and stroke-like conditions («stroke masks») in the pediatric population and the analysis of the main factors that influence errors in the early diagnosis of stroke in children and adolescents. Currently, in the diagnosis of ischemic stroke, its «masks» are found in 53.9 % of cases, with hemorrhagic stroke, in 36.3 %, and with transient ischemic attacks, in 9.8 % of cases. One of the most common diseases with which it is necessary to differentiate ischemic stroke in children is migraine. This problem is covered in academic writings that highlight the leading differential diagnostic criteria for migraine and stroke, and also represent a diagnostic algorithm. The clinical features of pediatric stroke, especially manifest symptoms, make it difficult to apply adult screening stroke scales in pediatrics. The article discusses the main scales for the early diagnosis of stroke in adults, and their potential application in pediatric practice. Currently accumulated experience in prehospital and early diagnosis of stroke in children determines the main directions of work to reduce the time of diagnosis of acute cerebrovascular accidents in children, followed by the introduction of reperfusion therapy in pediatric practice.

Pre-hospital diagnosis of stroke in children. An analysis of the work of the First aid station in Moscow and the primary pediatric stroke center

The article presents an analysis of pre-hospital diagnosis of stroke in children. Pediatric Stroke Center of Morozov Children's City Clinical Hospital (Center) for the period from October 2018 to December 2019, 502 cases of hospitalization of children by the First aid Station to the Center with directing diagnoses of ischemic stroke (IS), haemorrhagic stroke (HS), transient ischemic attack (TIA), vertebrobasilar syndrome (VBS) were analysed. TIA was the most prevalent diagnosis made by the First aid station, with the lowest rate of confirmation after further examination in the hospital (7.8%). The higher rates of confirmation were observed for VBS (31.5%), IS (11.2%) and HI (9.1%). The so-called «masks of stroke» were diagnosed in 84.3% cases of hospitalizations that was in line with earlier studies, which reported 50 to 93%. Based on the data on the frequency of symptoms, their combinations and specificity, a screening scale for pre-hospital diagnosis of stroke in children was suggested.

The Prehospital Ambulance Stroke Test Vs. The Cincinnati Prehospital Stroke Scale: A Diagnostic Accuracy Study

Introduction Immediate diagnosis of stroke is crucial in reducing its morbidity and mortality rate. There are various pre-hospital assessment tools, such as the Prehospital Ambulance Stroke Test (PreHAST) and the Cincinnati Prehospital Stroke Scale (CPSS) used to identify stroke early in the chain of care. The aim of this study is to compare the accuracy of PreHAST with CPSS in diagnosing stroke. Methods In this diagnostic accuracy study patients with suspicion of stroke were included in this study. In CPSS, the criterion used to indicate stroke are facial droop, speech and arm drift. In PreHAST it is eye position, visual field, facial palsy, right and left arms paresis, right and left legs paresis, sensory and speech. After data collection, sensitivity and specificity were calculated using standard formulae. Different cut-off points for the best diagnostic accuracy were examined in both CPSS and PreHAST. Results In this study, 883 patients were investigated. The results demonstrated that in CPSS, the highest specificity and sensitivity was for facial droop (84.9%) and arm drift (82.7%); and in PreHAST it was eye position (99.6%) and facial palsy (49.2%). The best predictor of stroke in CPSS with highest sensitivity (78.5%) and specificity (66%) was a cut-off point of 1.5 (AUC: 0.744) (p&lt;0.01(. In PreHAST, the highest sensitivity (68.4%) was a cut-off point of 2.5 and the highest specificity (90.2%) was a cut-off point of 5.5 (AUC: 0.775) (p&lt;0.01). Conclusion Both PreHAST and CPSS are useful screening tools in the pre-hospital diagnosis of stroke. In addition to high sensitivity, these tests provide a grading system in which higher cut-off points lead to higher specificity.

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

The diagnosis of stroke in the prehospital environment is the subject of intense interest and research. There are a number of non-invasive external brain monitoring devices in development that utilize various technologies to function as sensors for stroke and other neurological conditions. Future increased use of one or more of these devices could result in substantial changes in the current processes for stroke diagnosis and treatment, including transportation of stroke patients by emergency medical services. The present review will summarize information about 10 stroke sensor devices currently in development, utilizing various forms of technology, and all of which are external, non-invasive brain monitoring devices. Ten devices are discussed including the technology utilized, the indications for use (stroke and, when relevant, other neurological conditions), the environment(s) indicated for use (with a focus on the prehospital setting), a description of the physical structure of each instrument, and, when available, findings that have been published in peer-reviewed journals or otherwise reported. The review is organized based on the technology utilized by each device, and seven distinct forms were identified: accelerometers, electroencephalography (EEG), microwaves, near-infrared, radiofrequency, transcranial doppler ultrasound, and volumetric impedance phase shift spectroscopy. Non-invasive external brain monitoring devices are in various stages of development and have promise as stroke sensors in the prehospital setting. Some of the potential applications include to differentiate stroke from non-stroke, ischemic from hemorrhage stroke, and large vessel occlusion (LVO) from non-LVO ischemic stroke. Successful stroke diagnosis prior to hospital arrival could transform the current diagnostic and treatment paradigm for this disease.

Abstract TP314: EEG Has High Precision for Diagnosing Stroke Hours After Onset

Background: Improvements are needed in prehospital diagnosis of stroke. The electroencephalogram (EEG) changes immediately after brain ischemia, and advances in EEG technology enable rapid acquisition in the acute care setting. We hypothesized that EEG data increase the accuracy with which patients are correctly classified as having acute stroke or not, and so performed a study in the Emergency Department (ED) as an initial step towards evaluating prehospital utility of EEG. Methods: Patients with suspected stroke in a comprehensive stroke center ED underwent a 3 min EEG using a wireless, dry-electrode system; data were analyzed offline. A model was developed to classify patients as stroke/TIA vs non-stroke using a training set of 60 randomly selected patients. The model was then tested in an independent validation cohort of 40 new patients. EEG variables were selected using Lasso regression. Four models were examined, the first three using logistic regression: [1] clinical data only; [2] EEG data only; [3] combined clinical and EEG data; [4] a deep learning neural network model using clinical and EEG data. Results: Of the 100 ED patients (mean age 64.5 ± 15.7), 63 were ultimately discharged with a diagnosis of stroke (43 ischemic, 7 hemorrhagic) or TIA (13). Median time from last known well (LKW) to EEG was 9.4 hours; from ED arrival to EEG, 3.7 hours. Median time to prepare/place EEG leads then initiate recording was 9 min, shortened during the study (p&lt;0.0001), and was as brief as 36 seconds. To classify patients as stroke/TIA vs non-stroke: [1] The clinical data (including LKW and Rapid Arterial Occlusion Evaluation score) model had AUC=62.3. [2] The EEG data (Lasso selected 4 frontocentral leads, in higher frequencies) model had AUC=78.2. [3] The clinical+EEG data model had AUC=80.3. [4] The deep learning neural network model yielded AUC=87.8. Conclusions: The data support the feasibility of using a dry lead EEG system to rapidly acquire EEG in the acute stroke ED setting and suggest utility to improve prehospital stroke diagnosis once acquisition and analysis approaches are appropriately streamlined. EEG captures a signal that is diagnostically useful, independent from clinical measures, and improves the precision with which acute stroke can be diagnosed.

Prehospital Stroke Diagnosis and Management

Prehospital Stroke Diagnosis and Management