Dataset Information

Test characteristics of a 5-element cortical screen for identifying anterior circulation large vessel occlusion ischemic strokes.

ABSTRACT: Objective:Stroke severity screens typically include cortical signs, such as field cut, aphasia, neglect, gaze preference, and dense hemiparesis (FANG-D). The accuracy and reliability of these signs, when assessed by emergency physicians, to identify patients with anterior circulation large vessel occlusion (ACLVO) acute ischemic stroke (AIS) is unknown. We hypothesized that the FANG-D screen applied by emergency physicians would be sensitive and reliable for identifying ACLVO AIS. Methods:We conducted a prospective cohort study enrolling consecutive patients with suspected AIS presenting within 4.5 hours of last known well to the emergency department (ED). Emergency physicians performed the FANG-D screen prior to, and blinded to the results of, imaging. The imaging standard was defined as a non-contrast computed tomography (CT) for identifying hemorrhage and CT angiography for identifying large vessel occlusion. ACLVO was defined as an occlusion of the internal carotid artery, the middle cerebral artery, or its first branch. A convenience sample of patients had a duplicate FANG-D screen performed by a second emergency physician to assess interobserver agreement. Results:We performed 608 FANG-D assessments on 491 patients presenting to the ED, of whom 64 (10%) had an ACLVO. FANG-D had a sensitivity of 91% (confidence interval [CI] = 81%-96%) and a specificity of 35% (CI = 31%-39%) for identifying ACLVO. Interobserver agreement was tested on 133 patients and was found to be substantial, with a Fleiss' kappa of 0.77 (CI = 0.64-0.88). Conclusions:The FANG-D screen is a sensitive test for identifying ACLVO when performed by emergency physicians and demonstrates substantial interrater reliability.

SUBMITTER: Hoglund J

PROVIDER: S-EPMC7593424 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Test characteristics of a 5-element cortical screen for identifying anterior circulation large vessel occlusion ischemic strokes.

Hoglund Jessica J Strong Dale D Rhoten Jeremy J Chang Brenda B Karamchandani Rahul R Dunn Connell C Yang Hongmei H Asimos Andrew W AW

Journal of the American College of Emergency Physicians open 20200724 5

<h4>Objective</h4>Stroke severity screens typically include cortical signs, such as field cut, aphasia, neglect, gaze preference, and dense hemiparesis (FANG-D). The accuracy and reliability of these signs, when assessed by emergency physicians, to identify patients with anterior circulation large vessel occlusion (ACLVO) acute ischemic stroke (AIS) is unknown. We hypothesized that the FANG-D screen applied by emergency physicians would be sensitive and reliable for identifying ACLVO AIS.<h4>Met ...[more]

PMID: 33145539

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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.

Project description:BackgroundEndovascular treatment (EVT) for acute ischemic stroke (AIS) patients presenting with Alberta Stroke Program Early CT Score (ASPECTS) 0-5 has not yet proven safe and effective by clinical trials.ObjectivesThe aim of the study was to assess whether EVT in AIS patients presenting with low ASPECTS is beneficial.DesignSystematic review and meta-analysis of available studies in accordance with the PRISMA statement.Data sources and methodsWe have searched MEDLINE, the Cochrane Central Register of Controlled Trials, and reference lists of articles published until 28 May 2022 with the aim to calculate (1) modified Rankin scale (mRS) score 0-3 at 3 months, (2) mRS score 0-2 at 3 months, (3) symptomatic intracranial hemorrhage (sICH), and (3) mortality at 3 months.ResultsOverall, 24 eligible studies were included in the meta-analysis, comprising a total of 2539 AIS patients with ASPECTS 0-5 treated with EVT. The pooled proportion of EVT-treated patients achieving mRS 0-3 at 3 months was calculated at 38.4%. The pooled proportion of EVT-treated patients achieving mRS 0-2 at 3 months was 25.7%. Regarding safety outcomes, sICH occurred in 12.8% of patients. The 3-month pooled mortality was 30%. In pairwise meta-analysis, patients treated with EVT had a higher likelihood of achieving mRS 0-3 at 3 months compared with patients treated with best medical therapy (BMT, OR: 2.41). sICH occurred more frequently in EVT-treated patients compared with the BMT-treated patients (OR: 2.30). Mortality at 3 months was not different between the two treatment groups (OR: 0.71).ConclusionEVT may be beneficial for AIS patients with low baseline ASPECTS despite an increased risk for sICH. Further data from randomized-controlled clinical trials are needed to elucidate the role of EVT in this subgroup of AIS patients.RegistrationThe protocol has been registered in the International Prospective Register of Ongoing Systematic Reviews PROSPERO; Registration Number: CRD42022334417.

Project description:Background and Purpose- Accurate prediction of acute ischemic stroke (AIS) caused by anterior large vessel occlusion (LVO) that is amendable to mechanical thrombectomy remains a challenge. We developed and validated a prediction model for anterior circulation LVO stroke using past medical history elements present on admission and neurological examination. Methods- We retrospectively reviewed AIS patients admitted between 2009 and 2017 to 3 hospitals within a large healthcare system in the United States. Patients with occlusions of the internal carotid artery or M1 or M2 segments of the middle cerebral artery were randomly split into 2/3 derivation and 1/3 validation cohorts for development of an anterior circulation LVO prediction model and score that was further curtailed for potential use in the prehospital setting. Results- A total of 1654 AIS were reviewed, including 248 (15%) with proximal anterior circulation LVO AIS. In the derivation cohort, National Institutes of Health Stroke Scale score at the time of cerebrovascular imaging, current smoking status, type 2 diabetes mellitus, extracranial carotid, and intracranial atherosclerotic stenosis was significantly associated with anterior circulation LVO stroke. The prehospital score was curtailed to National Institutes of Health Stroke Scale score, current smoking status, and type 2 diabetes mellitus. The areas under the curve for the prediction model, prehospital score, and National Institutes of Health Stroke Scale score alone were 0.796, 0.757, and 0.725 for the derivation cohort and 0.770, 0.689, and 0.665 for the validation cohort, respectively. The Youden index J was 0.46 for a score of >6 with 84.7% sensitivity and 62.0% specificity for the prediction model. Conclusions- Previously reported LVO stroke prediction scores focus solely on elements of the neurological examination. In addition to stroke severity, smoking, diabetes mellitus, extracranial carotid, and intracranial atherosclerotic stenosis were associated with anterior circulation LVO AIS. Although atherosclerotic stenosis may not be known until imaging is obtained, smoking and diabetes mellitus history can be readily obtained in the field and represent important elements of the prehospital score supplementing National Institutes of Health Stroke Scale score.

Dataset Information

Test characteristics of a 5-element cortical screen for identifying anterior circulation large vessel occlusion ischemic strokes.

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Test characteristics of a 5-element cortical screen for identifying anterior circulation large vessel occlusion ischemic strokes.

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