Detailed severity assessment of Cincinnati Prehospital Stroke Scale to detect large vessel occlusion in acute ischemic stroke.
Ontology highlight
ABSTRACT:
SUBMITTER: Tarkanyi G
PROVIDER: S-EPMC7446055 | biostudies-literature | 2020 Aug
REPOSITORIES: biostudies-literature
Publications
Detailed severity assessment of Cincinnati Prehospital Stroke Scale to detect large vessel occlusion in acute ischemic stroke.
Tarkanyi Gabor G Csecsei Peter P Szegedi Istvan I Feher Evelin E Annus Adam A Molnar Tihamer T Szapary Laszlo L
BMC emergency medicine 20200824 1
<h4>Background</h4>Selecting stroke patients with large vessel occlusion (LVO) based on prehospital stroke scales could provide a faster triage and transportation to a comprehensive stroke centre resulting a favourable outcome. We aimed here to explore the detailed severity assessment of Cincinnati Prehospital Stroke Scale (CPSS) to improve its ability to detect LVO in acute ischemic stroke (AIS) patients.<h4>Methods</h4>A cross-sectional analysis was performed in a prospectively collected regis ...[more]
Similar Datasets
Project description:BACKGROUND AND PURPOSE:Mechanical thrombectomy (MT) is indicated for the treatment of large vessel occlusion (LVO) stroke. MT should be provided as quickly as possible; therefore, a test identifying suspected LVO in the prehospitalization stage is needed to ensure direct transport to a comprehensive stroke center (CSC). We assume that patients with clinically severe hemiparesis have a high probability of LVO stroke. We modified the FAST test into the FAST PLUS test: The first part is the FAST test and the second part evaluates the presence of severe arm or leg motor deficit. This prospective multicenter study evaluates the specificity and sensitivity of the FAST PLUS test in detecting LVO stroke. METHODS:Paramedics were trained through e-learning to conduct the FAST PLUS test. All prehospital suspected stroke patients who were administered the FAST PLUS test were included. Demographics, National Institutes of Health Stroke Scale (NIHSS) score, brain computed tomography (CT), and CT angiography (CTA) were recorded. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and receiver operating curve (ROC) area for LVO were calculated. RESULTS:The study included 435 patients. LVO were found in 124 patients (28%). Sensitivity was 93%, specificity was 47%, PPV was 41%, NPV was 94%, and ROC area for ICA/MCA occlusion was 0.65. Intracerebral hemorrhage (ICH) was identified in 48 patients (11%). CONCLUSION:We found that the FAST PLUS test had a high sensitivity for LVO stroke. Of the 435 patients, 41% were all directly transported to a CSC based on positive FAST PLUS test scores and were potential candidates for MT.
| S-EPMC6160663 | biostudies-literature
Project description:IntroductionTo date, identifying emergent large vessel occlusion (ELVO) patients in the prehospital stage is important but still challenging. In this present study, we aimed to design a modified prehospital acute stroke severity (mPASS) scale to identify ELVO patients and compared the scale to the PASS scale which has been published.MethodsWe retrospectively evaluated a consecutive cohort of acute ischemic stroke (AIS) in our stroke unit who visited the emergercy department. These patients underwent CT angiography (CTA), MR angiography (MRA), or digital subtraction angiography (DSA) at admission. The mPASS scale was calculated based on the National Institutes of Health Stroke Scale (NIHSS) items retrospectively, including the level of consciousness commands, gaze, arm weakness, and aphasia/dysarthria. Receiver operating characteristic (ROC) analysis was used to obtain the area under the curve (AUC) of the mPASS scale, NIHSS, and PASS scale. U-statistics was used to compare the AUC of the mPASS scale to the NIHSS and PASS scale.ResultsA total of 382 AIS patients were enrolled. The AUC and specificity of the mPASS scale (0.92, 84.4) were all higher than those of the PASS scale. Cortical symptoms such as gaze palsy and consciousness disorder were more specific indicators for ELVO than motor deficits.ConclusionsThe mPASS scale had a better discrimination for identifying ELVO than the PASS scale in our retrospective cohort. It might predict ELVO in an effective and simple way for paramedics in the prehospital triage stage or emergency stage. Moreover, cortical symptoms might have relatively high specificities to predict ELVO on their own.
| S-EPMC8315848 | biostudies-literature
Project description:Background and objectivesEndovascular thrombectomy (EVT) is standard treatment for anterior large vessel occlusion stroke (LVO-a stroke). Prehospital diagnosis of LVO-a stroke would reduce time to EVT by allowing direct transportation to an EVT-capable hospital. We aim to evaluate the diagnostic accuracy of dry electrode EEG for the detection of LVO-a stroke in the prehospital setting.MethodsELECTRA-STROKE was an investigator-initiated, prospective, multicenter, diagnostic study, performed in the prehospital setting. Adult patients were eligible if they had suspected stroke (as assessed by the attending ambulance nurse) and symptom onset <24 hours. A single dry electrode EEG recording (8 electrodes) was performed by ambulance personnel. Primary endpoint was the diagnostic accuracy of the theta/alpha frequency ratio for LVO-a stroke (intracranial ICA, A1, M1, or proximal M2 occlusion) detection among patients with EEG data of sufficient quality, expressed as the area under the receiver operating characteristic curve (AUC). Secondary endpoints were diagnostic accuracies of other EEG features quantifying frequency band power and the pairwise derived Brain Symmetry Index. Neuroimaging was assessed by a neuroradiologist blinded to EEG results.ResultsBetween August 2020 and September 2022, 311 patients were included. The median EEG duration time was 151 (interquartile range [IQR] 151-152) seconds. For 212/311 (68%) patients, EEG data were of sufficient quality for analysis. The median age was 74 (IQR 66-81) years, 90/212 (42%) were women, and the median baseline NIH Stroke Scale was 1 (IQR 0-4). Six (3%) patients had an LVO-a stroke, 109/212 (51%) had a non-LVO-a ischemic stroke, 32/212 (15%) had a transient ischemic attack, 8/212 (4%) had a hemorrhagic stroke, and 57/212 (27%) had a stroke mimic. AUC of the theta/alpha ratio was 0.80 (95% CI 0.58-1.00). Of the secondary endpoints, the pairwise derived Brain Symmetry Index in the delta frequency band had the highest diagnostic accuracy (AUC 0.91 [95% CI 0.73-1.00], sensitivity 80% [95% CI 38%-96%], specificity 93% [95% CI 88%-96%], positive likelihood ratio 11.0 [95% CI 5.5-21.7]).DiscussionThe data from this study suggest that dry electrode EEG has the potential to detect LVO-a stroke among patients with suspected stroke in the prehospital setting. Toward future implementation of EEG in prehospital stroke care, EEG data quality needs to be improved.Trial registration informationClinicalTrials.gov identifier: NCT03699397.Classification of evidenceThis study provides Class II evidence that prehospital dry electrode scalp EEG accurately detects LVO-a stroke among patients with suspected acute stroke.
| S-EPMC10791060 | biostudies-literature
Project description:OBJECTIVE:To develop an NIH Stroke Scale (NIHSS)-compatible, all-in-one scale for rapid and comprehensive prehospital stroke assessment including stroke recognition, severity grading and progression monitoring as well as prediction of large vessel occlusion (LVO). METHODS:Emergency medical services (EMS) personnel and stroke physicians (n=326) rated each item of the NIHSS regarding suitability for prehospital use; best rated items were included. Stroke recognition was evaluated retrospectively in 689 consecutive patients with acute stroke or stroke mimics, prediction of LVO in 741 consecutive patients with ischaemic stroke with acute vessel imaging independent of admission NIHSS score. RESULTS:Nine of the NIHSS items were rated as 'suitable for prehospital use.' After excluding two items in order to increase specificity, the final scale (termed shortened NIHSS for EMS, sNIHSS-EMS) consists of 'level of consciousness', 'facial palsy', 'motor arm/leg', 'sensory', 'language' and 'dysarthria'. Sensitivity for stroke recognition of the sNIHSS-EMS is 91% (95% CI 86 to 94), specificity 52% (95% CI 47 to 56). Receiver operating curve analysis revealed an optimal cut-off point for LVO prediction of ?6 (sensitivity 70% (95% CI 65 to 76), specificity 81% (95% CI 76 to 84), positive predictive value 70 (95% CI 65 to 75), area under the curve 0.81 (95% CI 0.78 to 0.84)). Test characteristics were non-inferior to non-comprehensive scales. CONCLUSIONS:The sNIHSS-EMS may overcome the sequential use of multiple emergency stroke scales by permitting parallel stroke recognition, severity grading and LVO prediction. Full NIHSS-item compatibility allows for evaluation of stroke progression starting at the prehospital phase.
| S-EPMC5589005 | biostudies-literature
Project description:BackgroundRapid recognition of acute stroke and large vessel occlusion (LVO) is essential in prehospital triage for timely reperfusion treatment.ObjectiveThis study aimed to develop and validate a new screening tool for both stroke and LVO in an urban Chinese population.MethodsThis study included patients with suspected stroke who were transferred to our hospital by emergency medical services between July 2017 and June 2021. The population was randomly partitioned into training (70%) and validation (30%) groups. The Staring-Hypertension-atrIal fibrillation-sPeech-weakneSs (SHIPS) scale, consisting of both clinical and medical history information, was generated based on multivariate logistic models. The predictive ability of the SHIPS scale was evaluated and compared with other scales using receiver operating characteristic (ROC) curve comparison analysis.ResultsA total of 400 patients were included in this analysis. In the training group (n = 280), the SHIPS scale showed a sensitivity of 90.4% and specificity of 60.8% in predicting stroke and a sensitivity of 75% and specificity of 61.5% in predicting LVO. In the validation group (n = 120), the SHIPS scale was not inferior to Stroke 1-2-0 (p = 0.301) in predicting stroke and was significantly better than the Cincinnati Stroke Triage Assessment Tool (C-STAT; formerly CPSSS) and the Prehospital Acute Stroke Severity scale (PASS) (all p < 0.05) in predicting LVO. In addition, including medical history in the scale was significantly better than using symptoms alone in detecting stroke (training group, 0.853 versus 0.818; validation group, 0.814 versus 0.764) and LVO (training group, 0.748 versus 0.722; validation group, 0.825 versus 0.778).ConclusionThe SHIPS scale may serve as a superior screening tool for stroke and LVO identification in prehospital triage. Including medical history in the SHIPS scale improves the predictive value compared with clinical symptoms alone.
| S-EPMC9251951 | biostudies-literature
Project description:BackgroundTimely and appropriate hospital treatment of acute cerebrovascular diseases (stroke and Transient Ischemic Attacks - TIA) improves patient outcomes. Emergency Medical Service (EMS) dispatchers who can identify cerebrovascular disease symptoms during telephone requests for emergency service also contribute to these improved outcomes. The Italian Ministry of Health issued guidelines on the management of AC patients in pre-hospital emergency service, including Cincinnati Prehospital Stroke Scale (CPSS) use.We measured the sensitivity and Positive Predictive Value (PPV) of EMS dispatchers' ability to recognize stroke/TIA symptoms and evaluated whether the CPSS improves accuracy.MethodsA cross-sectional multicentre study was conducted to collect data from 38 Italian emergency operative centres on all cases identified with stroke/TIA symptoms at the time of dispatch and all cases with stroke/TIA symptoms identified on the scene by the ambulance personnel from November 2010 to May 2011.ResultsThe study included 21760 cases: 18231 with stroke/TIA symptoms at dispatch and 9791 with symptoms confirmed on the scene. The PPV of the dispatch stroke/TIA symptoms identification was 34.3% (95% CI 33.7-35.0; 6262/18231) and the sensitivity was 64.0% (95% CI 63.0-64.9; 6262/9791). Centres using CPSS more often (>10% of cases) had both higher PPV (56%; CI 95% 57-60 vs 18%; CI 95% 17-19) and higher sensitivity (71%; CI 95% 87-89 vs 52%; CI 95% 51-54).In the multivariate regression a centre's CPSS use was associated with PPV (beta 0.48 p = 0.014) and negatively associated with sensitivity (beta -0.36; p = 0.063); centre sensitivity was associated with CPSS (beta 0.32; p = 0.002), adjusting for PPV.ConclusionsCentres that use CPSS more frequently during phone dispatch showed greater agreement with on-the-scene prehospital assessments, both in correctly identifying more cases with stroke/TIA symptoms and in giving fewer false positives for non-stroke/TIA cases. Our study shows an extreme variability in the performance among OCs, highlighting that form many centres there is room for improvement in both sensitivity and positive predictive value of the dispatch. Our results should be used for benchmarking proposals in the effort to identify best practices across the country.
| S-EPMC3867422 | biostudies-literature
Project description:IntroductionPrehospital stroke scales have been proposed to identify stroke patients with a large vessel occlusion to allow direct transport to an intervention centre capable of endovascular treatment (EVT). It is unclear whether these scales are able to detect not only proximal, but also more distal treatable occlusions. Our aim was to assess the sensitivity of prehospital stroke scales for different EVT-eligible occlusion locations in the anterior circulation.Patients and methodsThe MR CLEAN Registry is a prospective, observational study in all centres that perform EVT in the Netherlands. We included adult patients with an anterior circulation stroke treated between March 2014 and November 2017. We used National Institutes of Health Stroke Scale scores at admission to reconstruct previously published prehospital stroke scales. We compared the sensitivity of each scale for different occlusion locations. Occlusions were assessed with CT angiography by an imaging core laboratory blinded to clinical findings.ResultsWe included 3021 patients for the analysis of 14 scales. All scales had the highest sensitivity to detect internal carotid artery terminus occlusions (ranging from 0.21 to 0.97) and lowest for occlusions of the M2 segment (0.08 to 0.84, p-values < 0.001).Discussion and conclusion: Although prehospital stroke scales are generally sensitive for proximal large vessel occlusions, they are less sensitive to detect more distal occlusions.
| S-EPMC8370085 | biostudies-literature
Project description:Background Patients with large vessel occlusion stroke (LVOS) need to be rapidly identified and transferred to comprehensive stroke centers. However, current prehospital evaluation and strategies still remain challenging. Methods and Results We retrospectively reviewed our prospectively collected database of patients with acute ischemic stroke (AIS). Based on the items of National Institutes of Health Stroke Scale and medical history that had a strong association with LVOS, we designed the 4-item Stroke Scale (4I-SS) and validated it in multi-centers. The 4I-SS incorporated gaze, level of consciousness, arm weakness, and atrial fibrillation. Receiver operating characteristic analysis was used to compare the 4I-SS with previously established prehospital prediction scales. Finally, 1630 and 11 440 patients were included in the derivation and validation cohort, respectively. In the validation cohort, Youden Index, area under the curve, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the 4I-SS≥4 to predict LVOS were 0.494, 0.800, 0.657, 0.837, 0.600, 0.868, and 0.788, respectively, and that of the 4I-SS≥7 to predict basilar artery occlusion were 0.200, 0.669, 0.229, 0.971, 0.066, 0.974, and 0.899, respectively. Youden Index and area under the curve were higher than previously published scales for predicting LVOS. Further analysis showed that for predicting whether cardiogenic embolism was the cause, its accuracy was 0.922 when the 4I-SS score, including atrial fibrillation, was ≥6, and its accuracy of predicting the occluded vessel was intracranial internal carotid artery or M1 segment of the middle cerebral artery when it was ≥7 was 0.590. Conclusions The 4I-SS is an effective and simple tool that can identify LVOS and its cause. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT03317639.
| S-EPMC8649265 | biostudies-literature
Project description:Objectives: Patients with anterior circulation large vessel occlusion are at high risk of acute ischemic stroke, which could be disabling or fatal. In this study, we applied machine learning to develop and validate two prediction models for acute ischemic stroke (Model 1) and severity of neurological impairment (Model 2), both caused by anterior circulation large vessel occlusion (AC-LVO), based on medical history and neuroimaging data of patients on admission. Methods: A total of 1,100 patients with AC- LVO from the Second Hospital of Hebei Medical University in North China were enrolled, of which 713 patients presented with acute ischemic stroke (AIS) related to AC- LVO and 387 presented with the non-acute ischemic cerebrovascular event. Among patients with the non-acute ischemic cerebrovascular events, 173 with prior stroke or TIA were excluded. Finally, 927 patients with AC-LVO were entered into the derivation cohort. In the external validation cohort, 150 patients with AC-LVO from the Hebei Province People's Hospital, including 99 patients with AIS related to AC- LVO and 51 asymptomatic AC-LVO patients, were retrospectively reviewed. We developed four machine learning models [logistic regression (LR), regularized LR (RLR), support vector machine (SVM), and random forest (RF)], whose performance was internally validated using 5-fold cross-validation. The performance of each machine learning model for the area under the receiver operating characteristic curve (ROC-AUC) was compared and the variables of each algorithm were ranked. Results: In model 1, among the included patients with AC-LVO, 713 (76.9%) and 99 (66%) suffered an acute ischemic stroke in the derivation and external validation cohorts, respectively. The ROC-AUC of LR, RLR and SVM were significantly higher than that of the RF in the external validation cohorts [0.66 (95% CI 0.57-0.74) for LR, 0.66 (95% CI 0.57-0.74) for RLR, 0.55 (95% CI 0.45-0.64) for RF and 0.67 (95% CI 0.58-0.76) for SVM]. In model 2, 254 (53.9%) and 31 (37.8%) patients suffered disabling ischemic stroke in the derivation and external validation cohorts, respectively. There was no difference in AUC among the four machine learning algorithms in the external validation cohorts. Conclusions: Machine learning methods with multiple clinical variables have the ability to predict acute ischemic stroke and the severity of neurological impairment in patients with AC-LVO.
| S-EPMC8675605 | biostudies-literature
Project description:The mechanisms and phenotype of ischemic stroke associated with coronavirus disease 2019 (COVID-19) remain uncertain. A retrospective study was conducted in patients with COVID-19 presenting with ischemic stroke from March 1 to May 25, 2020, and cases with large-vessel occlusion were identified. To provide baseline institutional stroke data within and outside the COVID-19 pandemic, all consecutive ischemic stroke and TIA admissions (COVID and non-COVID) to the hospital during a 10-week period from March 1 to May 10, 2020, were collected and compared with data from the same time period in 2019. Among 20 patients with COVID-19 and acute ischemic stroke, 15 (75%) had large-vessel occlusion. These patients were young (mean age, 46.5 years), male (93%), without major burden of traditional cardiovascular risk factors, and had a severe stroke presentation. Large-vessel occlusions were observed in multiple vessels (40%), uncommonly affected vessels, and atypical locations with a large thrombus burden. Systemic thrombosis separate from large-vessel occlusion was not uncommon (26%). At short-term follow-up, stroke etiology remained undetermined in 46% of patients and functional outcome was poor. The above findings raise the possibility of stroke related to mechanisms induced by the COVID-19 infection itself, including a hypercoagulable state and/or endothelial damage. In addition, they document the severe presentation and poor outcomes of large-vessel occlusion in COVID-19 ischemic stroke.
| S-EPMC7963240 | biostudies-literature