Prehospital Stroke Scale Research Articles

Patients with acute intracerebral hemorrhage and severe symptoms are highly sensitive to prehospital delay. A subgroup analysis from the RESIST and TRIAGE-STROKE trials

BackgroundPatients with a positive prehospital stroke severity score and underlying intracerebral hemorrhage (ICH) may be harmed by longer onset-to-admission time. We aimed to investigate the interaction between ICH severity and time from onset to admission on functional outcome.MethodsThis is an individual patient data analysis with data from two randomized prehospital stroke trials using the same prehospital stroke scale. Patients were stratified according to the presence of a positive stroke severity score. They were grouped into early arrivers (admitted ≤ 90 min from onset) and late arrivers (admitted ≥90 min after onset). The primary outcome was a shift toward a better functional outcome on the modified Rankin Scale (mRS).ResultsA total of 212 patients had ICH. A positive stroke severity score was seen in 123 of these patients. Patients with ICH and a positive prehospital stroke severity score had a significantly worse outcome if they arrived 90 min or later at the hospital (adjusted odds ratio [aOR]: 2.02, 95% CI [1.01, 4.12]). This difference was not observed in patients without a positive severity score (aOR: 0.50, 95% CI [0.22, 1.14]). Patients with a positive score also had an increased risk of death or severe dependency (mRS of 5–6) of 9.1 percentage points (95% CI [−1.6%, 19.8%]) per hour if they were diagnosed with ICH.ConclusionLonger onset-to-admission time was harmful for patients with ICH and a positive prehospital stroke severity score.

Systematic Review and Meta-Analysis of Prehospital Machine Learning Scores as Screening Tools for Early Detection of Large Vessel Occlusion in Patients With Suspected Stroke.

Enhanced detection of large vessel occlusion (LVO) through machine learning (ML) for acute ischemic stroke appears promising. This systematic review explored the capabilities of ML models compared with prehospital stroke scales for LVO prediction. Six bibliographic databases were searched from inception until October 10, 2023. Meta-analyses pooled the model performance using area under the curve (AUC), sensitivity, specificity, and summary receiver operating characteristic curve. Of 1544 studies screened, 8 retrospective studies were eligible, including 32 prehospital stroke scales and 21 ML models. Of the 9 prehospital scales meta-analyzed, the Rapid Arterial Occlusion Evaluation had the highest pooled AUC (0.82 [95% CI, 0.79-0.84]). Support Vector Machine achieved the highest AUC of 9 ML models included (pooled AUC, 0.89 [95% CI, 0.88-0.89]). Six prehospital stroke scales and 10 ML models were eligible for summary receiver operating characteristic analysis. Pooled sensitivity and specificity for any prehospital stroke scale were 0.72 (95% CI, 0.68-0.75) and 0.77 (95% CI, 0.72-0.81), respectively; summary receiver operating characteristic curve AUC was 0.80 (95% CI, 0.76-0.83). Pooled sensitivity for any ML model for LVO was 0.73 (95% CI, 0.64-0.79), specificity was 0.85 (95% CI, 0.80-0.89), and summary receiver operating characteristic curve AUC was 0.87 (95% CI, 0.83-0.89). Both prehospital stroke scales and ML models demonstrated varying accuracies in predicting LVO. Despite ML potential for improved LVO detection in the prehospital setting, application remains limited by the absence of prospective external validation, limited sample sizes, and lack of real-world performance data in a prehospital setting.

Prospective Validation of Glial Fibrillary Acidic Protein, d ‐Dimer, and Clinical Scales for Acute Large‐Vessel Occlusion Ischemic Stroke Detection

Background Large‐vessel occlusion (LVO) ischemic stroke is responsible for significant morbidity and mortality. We have previously described a novel tool for acute LVO detection that combines blood‐based biomarkers (glial fibrillary acidic protein and d ‐dimer) with stroke severity scales to achieve high accuracy. Accordingly, the present study sought to prospectively validate cutoff values that we had previously established for biomarkers and scales. Methods The TIME (Testing for Identification Markers of Stroke) trial was designed as a prospective observational diagnostic accuracy study. All ambulance‐identified stroke code activations <18 hours from symptom onset were recruited at Brandon Regional Hospital (Brandon, FL) between May 2021 and August 2022. Previously determined cutoff concentrations of plasma glial fibrillary acidic protein (213 pg/mL) and d ‐dimer (600 ng/mL) were used in combination with prehospital stroke scales to detect LVO. We compared rates of LVO detection against a reference standard using computed tomography/magnetic resonance angiography. Results A total of 382 patients with suspected stroke were recruited. The final cohort was composed of 323 patients with suspected stroke with the following distribution: LVO ischemic stroke (n = 29, 9%), non‐LVO ischemic stroke (n = 48, 15%), hemorrhagic stroke (n = 13, 4%), transient ischemic attack (n = 12, 3.9%), and stroke mimics (n = 220, 68.1%). Combining blood‐based biomarkers (glial fibrillary acidic protein and d ‐dimer) with the scale field assessment stroke triage for emergency destination yielded the best performance for LVO detection, with specificity of 94% and sensitivity of 71%. Performance was found to be higher in a subanalysis focusing on patients presenting <6 hours from symptom onset, with 93% specificity and 81% sensitivity. Critically, application of the biomarker and stroke scale algorithms ruled out all patients with hemorrhage. Conclusion The present work prospectively validated the potential utility of previously defined glial fibrillary acidic protein and d ‐dimer cutoff levels (ie, 213 pg/mL and 600 ng/mL, respectively), demonstrating their value for discrimination of LVO stroke from differential diagnoses during code stroke workups. (ClinicalTrials.gov number, NCT04292600.)

MAMAF-Net: Motion-aware and multi-attention fusion network for stroke diagnosis

Stroke is a major cause of mortality and disability worldwide from which one in four people are in danger of incurring in their lifetime. The pre-hospital stroke assessment plays a vital role in identifying stroke patients accurately to accelerate further examination and treatment in hospitals. Accordingly, the National Institutes of Health Stroke Scale (NIHSS), Cincinnati Pre-hospital Stroke Scale (CPSS) and Face Arm Speed Time (F.A.S.T.) are globally known tests for stroke assessment. However, the validity of these tests is skeptical in the absence of neurologists and access to healthcare may be limited. Therefore, in this study, we propose a motion-aware and multi-attention fusion network (MAMAF-Net) that can detect stroke from multiple examination videos. Contrary to other studies on stroke detection from video analysis, our study for the first time collected a dataset encapsulating transient ischemic attack (TIA), stroke, and healthy controls, and proposes an end-to-end solution using multiple video recordings of each subject. The proposed MAMAF-Net consists of motion-aware modules to sense the mobility of patients, attention modules to fuse the multi-input video data, and 3D convolutional layers to perform diagnosis from the attention-based extracted features. Experimental results over the collected Stroke-data dataset show that the proposed MAMAF-Net achieves a successful detection of stroke with the highest levels of 93.62% sensitivity, 91.19% F1-Score, and 0.7472 Kappa measure in addition to 3.92% increase in the AUC score compared to state-of-the-art deep learning models.

Portable cerebral blood flow monitor to detect large vessel occlusion in patients with suspected stroke

BackgroundEarly detection of large vessel occlusion (LVO) facilitates triage to an appropriate stroke center to reduce treatment times and improve outcomes. Prehospital stroke scales are not sufficiently sensitive, so we...

Use of the SONAS Ultrasound Device for the Assessment of Cerebral Perfusion in Acute Ischemic Stroke

Background Early recognition of large‐vessel occlusion in acute ischemic stroke is pivotal to ensuring timely and effective treatment. However, current prehospital evaluation strategies largely rely on stroke scales that lack sensitivity and specificity. Recently, a novel, portable, noninvasive brain perfusion ultrasound device (SONAS) was developed as a diagnostic aid and demonstrated robust correlation with magnetic resonance imaging–based brain perfusion imaging. The present study aimed to investigate the feasibility and diagnostic performance of the SONAS device in identifying alterations in cerebral blood flow in patients with acute ischemic stroke with suspected or known perfusion deficits. Methods We performed a phase II, single‐arm, prospective study using SONAS in a close timely relation to computed tomography perfusion imaging in subjects diagnosed with acute ischemic stroke and suspected or known large‐vessel pathology, presenting within 24 hours of symptom onset between April 2019 through December 2019. Performance end points included successful measurement of time‐to‐peak in each hemisphere and comparison of perfusion deficits measured by SONAS to the reference standard computed tomography perfusion imaging. Safety end points included frequency, severity, and outcome of adverse events. Results A total of 20 subjects were enrolled. Eighteen subjects (90%) had microbubble signals successfully detected on initial assessment by SONAS. The diagnostic accuracy of the SONAS device in identifying a perfusion deficit was 88.9%. The sensitivity was 14 of 15 (93.3%), and the specificity was 2 of 3 (66.7%). No adverse events were reported secondary to SONAS or the contrast agent used. Conclusion In this clinical study, we demonstrated that the SONAS system is a safe tool that can aid in identifying cerebral perfusion deficits in this selected population of patients with acute ischemic stroke with suspected or known perfusion deficits. Future studies should assess the efficacy and impact on clinical outcomes of using SONAS in a real‐world “in the field” setting on a large subset of patients with suspected stroke and in addition to prehospital stroke scales.

Optimization of the stroke hospital selection strategy and the distribution of endovascular thrombectomy resources.

Nowadays, emergency medical technicians (EMTs) decide to send a suspected stroke patient to a primary stroke center (PSC) or to an endovascular thrombectomy (EVT)-capable hospital, based on the Cincinnati Prehospital Stroke Scale (CPSS) and the number of symptoms a patient presents at the scene. Based on existing studies, the patient is likely to have a better functional outcome after three months if the time between the onset of symptoms and receiving EVT treatment is shorter. However, if an acute ischemic stroke (AIS) patient with large vessel occlusion (LVO) is first sent to a PSC, and then needs to be transferred to an EVT-capable hospital, the time to get definitive treatment is significantly increased. For this purpose, We formulate an integer programming model to minimize the expected time to receive a definitive treatment for stroke patients. We then use real-world data to verify the validity of the model. Also, we expand our model to find the optimal redistribution and centralization of EVT resources. It will enable therapeutic teams to increase their experience and skills more efficiently within a short period of time.

Abstract TMP13: Prehospital Stroke Scales Are Non-Inferior to NIHSS in Prediction of Intravenous Thrombolysis Use in a Large Telestroke Network

Introduction: Recent studies report similar outcomes with antiplatelet drugs and intravenous thrombolysis (IVT) in patients with minor nondisabling ischemic stroke. These patients still meet emergent triage criteria and can increase the burden on stroke systems through prehospital stroke activation. Prehospital large vessel occlusion (LVO) scales can improve triage of LVO patients but have not been evaluated as tools to predict IVT administration. We sought to determine if common LVO scales can predict IVT use in a large academic telestroke practice. Methods: This retrospective study included all video telestroke consults performed in a large academic telestroke network from 2019-2020. Patients were excluded if they were on anticoagulation, presented beyond 4.5 hours from last known well, or family declined IVT due to goals of care. We recorded presenting NIHSS and IVT recommendations. NIHSS was used to calculate 7 LVO scales (RACE, G-FAST, FAST-ED, VAN, PASS, 3I-SS, and C-STAT). Scale performance was assessed by calculating area under curve (AUC) and compared to NIHSS total score using the DeLong test. Youden cutpoint was established for each scale and subsequently used to calculate sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and accuracy. Results: A total of 513 patients were included; 205/513 (40%) were recommended IVT. Median (IQR) NIHSS was 1 (0-3) in non-IVT patients and 6 (4-11) in IVT patients. The RACE and G-FAST scales were noninferior to the NIHSS in predicting IVT recommendation (Table 1). Conclusion: The RACE and G-FAST scales may be useful triage tools to identify patients needing emergent stroke evaluation for consideration of IVT.

Abstract 44: Portable Optical Blood Flow Monitor for the Detection of Large Vessel Occlusion

Introduction: Early detection of large vessel occlusion (LVO) facilitates triage to appropriate stroke centers and reduces treatment times. Prehospital stroke scales are insensitive, so we investigated the ability of the Openwater Headset, a portable optical blood flow monitor, to detect LVO in patients undergoing acute stroke evaluation. Hypothesis: We hypothesized that the Openwater Headset would detect LVO with greater sensitivity and specificity than prehospital scales. Methods: At two comprehensive stroke centers, eligible patients presented to the emergency department or were transferred from another facility for acute stroke evaluation within 24 hours of symptom onset and NIHSS ≥2. Eligible patients underwent emergent vascular imaging as per routine care. LVO was defined as occlusion of the ICA or the first/second segment of the MCA. Each patient underwent a 70-second bedside optical blood flow evaluation with the Openwater Headset (prior to endovascular therapy, if applicable). The Openwater Headset generated blood flow waveforms based on the relative change in the speckle contrast, acquired at 40 Hz. A deep neural network was trained to identify waveform features that discriminate LVO status ( Figure 1a ), withholding a validation set to test model performance. ROC analysis compared Openwater Headset performance with that of prehospital stroke scales. Results: 135 patients completed the optical evaluation a median of 6 hours after symptom onset (IQR: 3-14). The median NIHSS was 9 (IQR: 4-18). 52 patients (39%) had a LVO. The Openwater LVO stroke alert algorithm had 81% sensitivity and 80% specificity for the presence of LVO (AUC 0.85 by ROC analysis), exceeding that of prehospital stroke scales ( Figure 1b ). Conclusions: The Openwater Headset outperformed prehospital stoke scales for LVO detection in patients who underwent acute stroke evaluation, but further investigation is needed to assess performance in pre-hospital settings.

Abstract WP93: Providing Stroke Reports to Emergency Medical Services (EMS) Agencies Improves Data Quality in Michigan's Statewide EMS Data Repository

Intro: The Michigan Stroke Program (MiSP) monitors statewide EMS stroke care performance and data quality. Starting in 2018, quarterly performance and outcome benchmark data were shared with EMS agency partners to support prehospital quality improvement (QI) for stroke patients. Objective: To improve EMS data quality and prehospital stroke care performance. Methods: This is a retrospective before and after study of the impact of tailored QI interventions to improve EMS compliance with three prehospital stroke quality metrics selected by MiSP EMS partners: (prehospital stroke scale documentation, last known well documentation, and prehospital notification). Five MiSP EMS partners participated in the intervention which consisted of provision of quarterly performance reports generated from linked data from Michigan’s EMS Information System and the Michigan Stroke Registry. The MiSP team reviewed agency performance with EMS leadership to select the target measures for intervention and then assisted in development of a QI plan to improve agency performance. Measure compliance was compared from three months prior to and six months after data quality solutions were implemented. Results: Through data sharing and communication with EMS agencies and EMS software data vendors, common themes were identified related to poor data quality beginning in January 2020. A sample of agencies and resolutions to address these issues are displayed in the Table. Compliance with the targeted metric improved significantly at each agency at three and six months following intervention. Conclusions: Analysis of EMS data and sharing performance data directly with EMS agency partners identified correctable data quality issues negatively impacting EMS quality measures. Correcting these issues led to dramatic and sustained improvement in EMS quality metric compliance. This approach may serve as a model for improving the accuracy of EMS performance data in state-level EMS registries.

Sex Differences in Prehospital Identification of Large Vessel Occlusion in Patients With Suspected Stroke.

Differences in clinical presentation of acute ischemic stroke between men and women may affect prehospital identification of anterior circulation large vessel occlusion (aLVO). We assessed sex differences in diagnostic performance of 8 prehospital scales to detect aLVO. We analyzed pooled individual patient data from 2 prospective cohort studies (LPSS [Leiden Prehospital Stroke Study] and PRESTO [Prehospital Triage of Patients With Suspected Stroke Study]) conducted in the Netherlands between 2018 and 2019, including consecutive patients ≥18 years suspected of acute stroke who presented within 6 hours after symptom onset. Ambulance paramedics assessed clinical items from 8 prehospital aLVO detection scales: Los Angeles Motor Scale, Rapid Arterial Occlusion Evaluation, Cincinnati Stroke Triage Assessment Tool, Cincinnati Prehospital Stroke Scale, Prehospital Acute Stroke Severity, gaze-face-arm-speech-time, Conveniently Grasped Field Assessment Stroke Triage, and Face-Arm-Speech-Time Plus Severe Arm or Leg Motor Deficit. We assessed the diagnostic performance of these scales for identifying aLVO at prespecified cut points for men and women. Of 2358 patients with suspected stroke (median age, 73 years; 47% women), 231 (10%) had aLVO (100/1114 [9%] women and 131/1244 [11%] men). The area under the curve of the scales ranged from 0.70 (95% CI, 0.65-0.75) to 0.77 (95% CI, 0.73-0.82) in women versus 0.69 (95% CI, 0.64-0.73) to 0.75 (95% CI, 0.71-0.79) in men. Positive predictive values ranged from 0.23 (95% CI, 0.20-0.27) to 0.29 (95% CI, 0.26-0.31) in women versus 0.29 (95% CI, 0.24-0.33) to 0.37 (95% CI, 0.32-0.43) in men. Negative predictive values were similar (0.95 [95% CI, 0.94-0.96] to 0.98 [95% CI, 0.97-0.98] in women versus 0.94 [95% CI, 0.93-0.95] to 0.96 [95% CI, 0.94-0.97] in men). Sensitivity of the scales was slightly higher in women than in men (0.53 [95% CI, 0.43-0.63] to 0.76 [95% CI, 0.68-0.84] versus 0.49 [95% CI, 0.40-0.57] to 0.63 [95% CI, 0.55-0.73]), whereas specificity was lower (0.79 [95% CI, 0.76-0.81] to 0.87 [95% CI, 0.84-0.89] versus 0.82 [95% CI, 0.79-0.84] to 0.90 [95% CI, 0.88-0.91]). Rapid arterial occlusion evaluation showed the highest positive predictive values in both sexes (0.29 in women and 0.37 in men), reflecting the different event rates. aLVO scales show similar diagnostic performance in both sexes. The rapid arterial occlusion evaluation scale may help optimize prehospital transport decision-making in men as well as in women with suspected stroke.

Early identification of stroke through deep learning with multi-modal human speech and movement data.

JOURNAL/nrgr/04.03/01300535-202501000-00031/figure1/v/2024-05-14T021156Z/r/image-tiff Early identification and treatment of stroke can greatly improve patient outcomes and quality of life. Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale (CPSS) and the Face Arm Speech Test (FAST) are commonly used for stroke screening, accurate administration is dependent on specialized training. In this study, we proposed a novel multimodal deep learning approach, based on the FAST, for assessing suspected stroke patients exhibiting symptoms such as limb weakness, facial paresis, and speech disorders in acute settings. We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements, facial expressions, and speech tests based on the FAST. We compared the constructed deep learning model, which was designed to process multi-modal datasets, with six prior models that achieved good action classification performance, including the I3D, SlowFast, X3D, TPN, TimeSformer, and MViT. We found that the findings of our deep learning model had a higher clinical value compared with the other approaches. Moreover, the multi-modal model outperformed its single-module variants, highlighting the benefit of utilizing multiple types of patient data, such as action videos and speech audio. These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke, thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.

Emergency Medical Services Compliance With Prehospital Stroke Quality Metrics Is Associated With Faster Stroke Evaluation and Treatment.

Emergency medical services (EMS) is an important link in the stroke chain of recovery. Various prehospital quality metrics have been proposed for prehospital stroke care, but their individual impact is uncertain. We sought to measure associations between EMS quality metrics and downstream stroke care. This is a retrospective analysis of a cohort of EMS-transported stroke patients assembled through a linkage between Michigan's EMS and stroke registries. We used multivariable regression to quantify the independent associations between EMS quality metric compliance (dispatch within 90 seconds of 911 call, prehospital stroke screen documentation [Prehospital stroke scale], glucose check, last known well time, maintenance of scene times ≤15 minutes, hospital prenotification, and intravenous line placement) and shorter door-to-CT times (door-to-CT ≤25), accounting for EMS recognition, age, sex, race, stroke subtype, severity, and duration of symptoms. We then developed a simple EMS quality score based on metrics associated with early CT and examined its associations with hospital stroke evaluation times, treatment, and patient outcomes. Five thousand seven hundred seven EMS-transported stroke cases were linked to prehospital records from January 2018 through June 2019. In multivariable analysis, prehospital stroke scale documentation (adjusted odds ratio, 1.4 [1.2-1.6]), glucose check (1.3 [1.1-1.6]), on-scene time ≤15 minutes (1.6 [1.4-1.9]), hospital prenotification ([2.0 [1.4-2.9]), and intravenous line placement (1.8 [1.5-2.1]) were independently associated with a door-to-CT ≤25 minutes. A 5-point quality score (1 point for each element) was therefore developed. In multivariable analysis, a 1-point higher EMS quality score was associated with a shorter time from EMS contact to CT (-9.2 [-10.6 to -7.8] minutes; P<0.001) and thrombolysis (-4.3 [-6.4 to -2.2] minutes; P<0.001), and higher odds of discharge to home (adjusted odds ratio, 1.1 [1.0-1.2]; P=0.002). Five EMS actions recommended by national guidelines were associated with rapid CT imaging. A simple quality score derived from these measures was also associated with faster stroke evaluation, greater odds of reperfusion treatment, and discharge to home.

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.

Cincinnati Prehospital Stroke Scale (CPSS) as a Screening Tool for Early Identification of Cerebral Large Vessel Occlusions; a Systematic Review and Meta-analysis.

Large vessel occlusion (LVO) strokes are associated with worse functional outcomes and higher mortality rates. In the present systematic review and meta-analysis, we evaluated the diagnostic yield of the Cincinnati Prehospital Stroke Scale (CPSS) in detecting LVO. We performed an extensive systematic search among online databases including Medline, Embase, Web of Science, and Scopus, until July 31st, 2023. We also conducted a manual search on Google and Google scholar, along with citation tracking to supplement the systematic search in retrieving all studies that evaluated the diagnostic accuracy of the CPSS in detecting LVO among patients suspected to stroke. Fourteen studies were included in the present meta-analysis. CPSS showed the sensitivity of 97% (95% CI: 87%-99%) and the specificity of 17% (95% CI: 4%-54%) at the cut-off point of ≥1. The optimal threshold was determined to be ≥2, with a sensitivity of 82% (95% CI: 74%-88%) and specificity of 62% (95% CI: 48%-74%) in detecting LVO. At the highest cut-off point of ≥3, the CPSS had the lowest sensitivity of 60% (95% CI: 51%-69%) and the highest specificity of 81% (95% CI: 71%-88%). Sensitivity analyses showed the robustness of the results regardless of study population, inclusion of hemorrhagic stroke patients, pre-hospital or in-hospital settings, and the definition of LVO. A very low level of evidence demonstrated that CPSS, with a threshold set at ≥2, is a useful tool for identifying LVO stroke and directing patients to CSCs, both in prehospital and in-hospital settings.

Prehospital stroke scales outperform National Institutes of Health Stroke Scale in predicting large vessel occlusion in a large academic telestroke network.

Prehospital telestroke evaluations may improve stroke triage compared to paramedic-applied large vessel occlusion scales, but ambulance-based video National Institutes of Health Stroke Scale assessments are challenging. The accuracy of telestroke-administered large vessel occlusion scales has not been investigated, so we sought to evaluate this further. This retrospective study included all in-hospital telestroke encounters in a large academic telestroke network from 2019 to 2020. We retrospectively calculated seven large vessel occlusion scales using the in-hospital telestroke National Institutes of Health Stroke Scale (Rapid Arterial oCclusion Evaluation, Cincinnati Stroke Triage Assessment Tool, Field Assessment Stroke Triage for Emergency Destination, 3-Item Stroke Scale, Prehospital Acute Stroke Severity, Vision-Aphasia-Neglect, and Gaze-Face-Arm-Speech-Time). Diagnostic performance was assessed via sensitivity, specificity, negative predictive value, positive predictive value, positive likelihood ratio, negative likelihood ratio, and accuracy using established scale thresholds. These results were compared to the National Institutes of Health Stroke Scale at thresholds of 6, 8, and 10. The area under curve was calculated using c-statistics by treating scales as continuous variables. A total of 625 patients were included; 111 (17.8%) patients had an anterior large vessel occlusion, 118 (18.9%) patients had any large vessel occlusion, and 182 (29.1%) patients had stroke mimic diagnosis. The mean age (SD) was 67.9 (15.9), 48.3% were female, and 93.4% were white. The Mean National Institutes of Health Stroke Scale (SD) was 14.9 (8.4) for patients with anterior large vessel occlusion, 4.7 (5.0) for patients with non-large vessel occlusion ischemic stroke, and 4.4 (5.8) for stroke mimic (p < 0.001). Compared to the National Institutes of Health Stroke Scale, Field Assessment Stroke Triage for Emergency Destination, and Rapid Arterial oCclusion Evaluation scales demonstrated higher accuracy and area under curve for large vessel occlusion detection. Both the Field Assessment Stroke Triage for Emergency Destination and Rapid Arterial oCclusion Evaluation scales outperformed the National Institutes of Health Stroke Scale for large vessel occlusion detection in patients evaluated by in-hospital telestroke. These scales may be valid alternatives to the National Institutes of Health Stroke Scale examination in this setting.

Validation of the prehospital stroke scales as a tool for in-hospital large vessel occlusion stroke: whether we satisfied?

Prehospital stroke severity scales have been widely used to identify whether community stroke patients presented with large vessel occlusion (LVO) or not. However, whether these scales are also applicable to in-hospital stroke patients remains unknown. We aim to validate and compare the predictive capability of these scales for these patients. From January 2016 to October 2020, a total of 243 patients who activated in-hospital stroke alerts, were included in this study. The area under the curve (AUC) was used to assess the predictive ability of five scales (Field Assessment Stroke Triage for Emergency Destination [FAST-ED], Rapid Arterial Occlusion Evaluation [RACE], Los Angeles Motor Scale [LAMS], Cincinnati Prehospital Stroke Severity Scale [CPSSS], and Prehospital Acute Stroke Severity scale [PASS]) for LVO. In addition, multivariable logistic analysis was adopted to determine the predictors of LVO in our patients cohort. Finally, 94 (38.7%) patients were confirmed presence of persistent LVO. The AUC for the FAST-ED, RACE, LAMS, CPSSS, and PASS scales to predict the presence of LVO in patients activating in-hospital stroke alerts were 0.82, 0.89, 0.86, 0.81, and 0.79, respectively. After multivariable analysis, baseline NIHSS (adjusted odds ratio [OR] = 1.160, 95% confidence interval [CI] = 1.110-1.212; P < 0.001) atrial fibrillation (adjusted OR = 2.940, 95% CI = 1.387-6.230; P = 0.005) and cardiac/pulmonary procedure (adjusted OR = 6.861, 95% CI = 2.437-19.315; P < 0.001) remained independent predictors of LVO. The prehospital stroke scales also showed good predictive capabilities in discriminating LVO among inpatients who activated stroke alerts. However, given that inpatients' history is more readily available, a specifically designed in-hospital stroke scale that combines stroke severity and history is warranted.

Changing the stroke network during pandemic scenarios does not affect the management of patients with a positive Cincinnati prehospital stroke scale.

Time plays a crucial role in the management of stroke, and changing the prehospital emergency network, altering the HUB and spoke relationship in pandemic scenarios, might have an impact on time to fibrinolysis or thrombectomy. The aim of this study was to evaluate the time-dependent stroke emergency network in Lombardy region (Italy) by comparing 2019 with 2020 and early 2021. Three parameters were investigated: (i) time of arrival of the first vehicle at the scene, (ii) overall duration of missions, and (iii) number of patients transported by emergency vehicles. Data analysis process conducted using the SAS-AREU portal (SAS Institute, USA). The number of patients with a positive CPSS was similar among the different pandemic waves. Mission duration increased from a mean time (SD) of 52.9 (16.1) min in 2019 to 64.1 (19.7) in 2020 and 55.0 (16.8) in 2021. Time to first vehicle on scene increased to 15.7 (8.4) min in 2020 and 16.0 (7.0) in 2021 compared to 2019, 13.6 (7.2) (P < 0.05). The number of hospital with available stroke units decreased from 46 in 2019 to 10 during the first pandemic wave. The pandemic forced changes in the clinical mission of many hospitals by reducing the number of stroke units. Despite this, the organization of the emergency system allowed to identify strategic hospitals and thus avoid excessive transport time. The result was an adequate time for fibrinolysis/thrombectomy, in agreement with the guidelines. Coordinated management in emergency situations makes it possible to maintain service quality standards, despite the unfavorable scenario.

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

To examine the agreement between emergency medical service (EMS) providers, neurology residents and neurology consultants, using the Cincinnati Prehospital Stroke Scale (CPSS) and the Prehospital Acute Stroke Severity Scale (PASS). Patients with stroke, transient ischemic attack (TIA) and stroke mimic were included upon primary stroke admission or during rehabilitation. Patients were included from June 2018 to September 2019. Video recordings were made of patients being assessed with CPSS and PASS. The recordings were later presented to the healthcare professionals. To determine relative and absolute interrater reliability in terms of inter-rater agreement (IRA), we used generalisability theory. Group-level agreement was determined against a gold standard and presented as an area under the curve (AUC). The gold standard was a consensus agreement between two neurology consultants. A total of 120 patient recordings were assessed by 30 EMS providers, two neurology residents and two neurology consultants. Using the CPSS and the PASS, a total of 1,800 assessments were completed by EMS providers, 240 by neurology residents and 240 by neurology consultants. The overall relative and absolute IRA for all items combined from the CPSS and PASS score was 0.84 (95% CI 0.80; 0.87) and 0.81 (95% CI 0.77; 0.85), respectively. Using the CPSS, the agreement on a group-level resulted in AUCs of 0.83 (95% CI 0.78; 0.88) for the EMS providers and 0.86 (95% CI 0.82; 0.90) for the neurology residents when compared with the gold standard. Using the PASS, the AUC was 0.82 (95% CI 0.77; 0.87) for the EMS providers and 0.88 (95% CI 0.84; 0.93) for the neurology residents. The high relative and absolute inter-rater agreement underpins a high robustness/generalisability of the two scales. A high agreement exists across individual raters and different groups of healthcare professionals supporting widespread applicability of the stroke scales.

Comparison of Prehospital Assessment by Paramedics and In-Hospital Assessment by Physicians in Suspected Stroke Patients: Results From 2 Prospective Cohort Studies.

It is unknown if ambulance paramedics adequately assess neurological deficits used for prehospital stroke scales to detect anterior large-vessel occlusions. We aimed to compare prehospital assessment of these stroke-related deficits by paramedics with in-hospital assessment by physicians. We used data from 2 prospective cohort studies: the LPSS (Leiden Prehospital Stroke Study) and PRESTO study (Prehospital Triage of Patients With Suspected Stroke). In both studies, paramedics scored 9 neurological deficits in stroke code patients in the field. Trained physicians scored the National Institutes of Health Stroke Scale (NIHSS) at hospital presentation. Patients with transient ischemic attack were excluded because of the transient nature of symptoms. Spearman rank correlation coefficient (rs) was used to assess correlation between the total prehospital assessment score, defined as the sum of all prehospital items, and the total NIHSS score. Correlation, sensitivity and specificity were calculated for each prehospital item with the corresponding NIHSS item as reference. We included 2850 stroke code patients. Of these, 1528 had ischemic stroke, 243 intracranial hemorrhage, and 1079 stroke mimics. Correlation between the total prehospital assessment score and NIHSS score was strong (rs=0.70 [95% CI, 0.68-0.72]). Concerning individual items, prehospital assessment of arm (rs=0.68) and leg (rs=0.64) motor function correlated strongest with corresponding NIHSS items, and had highest sensitivity (arm 95%, leg 93%) and moderate specificity (arm 71%, leg 70%). Neglect (rs=0.31), abnormal speech (rs=0.50), and gaze deviation (rs=0.51) had weakest correlations. Neglect and gaze deviation had lowest sensitivity (52% and 66%) but high specificity (84% and 89%), while abnormal speech had high sensitivity (85%) but lowest specificity (65%). The overall prehospital assessment of stroke code patients correlates strongly with in-hospital assessment. Prehospital assessment of neglect, abnormal speech, and gaze deviation differed most from in-hospital assessment. Focused training on these deficits may improve prehospital triage.