Novel Prehospital Prediction Model of Large Vessel Occlusion Using Artificial Neural Network

Background: Identifying large vessel occlusion (LVO) patients in the prehospital triage stage to avoid unnecessary and costly delays is important but still challenging. We aim to develop an artificial neural network (ANN) algorithm to predict LVO using prehospital accessible data including demographics, National Institutes of Health Stroke Scale (NIHSS) items and vascular risk factors.Methods: Consecutive acute ischemic stroke patients who underwent CT angiography (CTA) or time of flight MR angiography (TOF-MRA) and received reperfusion therapy within 8 h from symptom onset were included. The diagnosis of LVO was defined as occlusion of the intracranial internal carotid artery (ICA), M1 and M2 segments of the middle cerebral artery (MCA) and basilar artery on CTA or TOF-MRA before treatment. Patients with and without LVO were randomly selected at a 1:1 ratio. The ANN model was developed using backpropagation algorithm, and 10-fold cross-validation was used to validate the model. The comparison of diagnostic parameters between the ANN model and previously established prehospital prediction scales were performed.Results: Finally, 300 LVO and 300 non-LVO patients were randomly selected for the training and validation of the ANN model. The mean Youden index, sensitivity, specificity and accuracy of the ANN model based on the 10-fold cross-validation analysis were 0.640, 0.807, 0.833 and 0.820, respectively. The area under the curve (AUC), Youden index and accuracy of the ANN model were all higher than other prehospital prediction scales.Conclusions: The ANN can be an effective tool for the recognition of LVO in the prehospital triage stage

Conveniently-Grasped Field Assessment Stroke Triage (CG-FAST): A Modified Scale to Detect Large Vessel Occlusion Stroke

Background and Purpose: Patients with large vessel occlusion stroke (LVOS) need to be rapidly identified and transferred to comprehensive stroke centers (CSC). However, previous pre-hospital strategy remains challenging. We aimed to develop a modified scale to better predict LVOS.Methods: We retrospectively reviewed our prospectively collected database for acute ischemic stroke (AIS) patients who underwent CT angiography (CTA) or time of flight MR angiography (TOF-MRA) and had a detailed National Institutes of Health Stroke Scale (NIHSS) score at admission. Large vessel occlusion (LVO) was defined as the complete occlusion of large vessels, including the intracranial internal carotid artery (ICA), M1, and M2 segments of the middle cerebral artery (MCA), and basilar artery (BA). The Conveniently-Grasped Field Assessment Stroke Triage (CG-FAST) scale consisted of Level of Consciousness (LOC) questions, Gaze deviation, Facial palsy, Arm weakness, and Speech changes. Receiver Operating Characteristic (ROC) analysis was used to obtain the Area Under the Curve (AUC) of CG-FAST and previously established pre-hospital prediction scales.Results: Finally, 1,355 patients were included in the analysis. LVOS was detected in 664 (49.0%) patients. The sensitivity, specificity, positive predictive value, and negative predictive value of CG-FAST were 0.617, 0.810, 0.785, and 0.692 respectively, at the optimal cutoff (≥4). The AUC, Youden index and accuracy of the CG-FAST scale (0.758, 0.428, and 0.728) were all higher than other pre-hospital prediction scales.Conclusions: CG-FAST scale could be an effective and simple scale for accurate identification of LVOS among AIS patients

Patients With Ischemic Core ≥70 ml Within 6 h of Symptom Onset May Still Benefit From Endovascular Treatment

Background: Large core is associated with poor outcome in acute ischemic stroke (AIS) patients. It is unclear whether endovascular treatment (EVT) could bring benefits to patients with core volume ≥70 ml before treatment. We aimed to compare the impact of EVT with intravenous thrombolysis (IVT) on the outcome in patients with core volume ≥70 ml.Methods: We included consecutive anterior circulation AIS patients who underwent MR or CT perfusion within 6 h post stroke onset, which revealed a core ≥70 ml before reperfusion therapy. Good outcome was defined by modified Rankin Scale of 0 to 2 at 90-day. Reperfusion was defined as a reduction in hypoperfusion volume of ≥70% between baseline and 24 h.Results: One hundred four patients were included. Among them, 76 received IVT only, and 28 received EVT. After adjusting for age, NIHSS score, baseline core volume and onset to imaging time, patients in EVT group were more likely to achieve good outcome compared to IVT patients (OR, 3.875; 95% Cl 1.068–14.055, p = 0.039). More patients in EVT group achieved recanalization (84.0 vs. 58.5%, p = 0.027) and reperfusion (66.7 vs. 33.3%, p = 0.010) than in IVT group. Reperfusion also independently predicted good outcome (OR, 7.718; 95% Cl 1.713-34.772, p = 0.008). All patients with good outcome achieved recanalization at 24 h.Conclusions: Our data indicated that patients with core volume ≥70 ml might still benefit from EVT, which was related to its high reperfusion rate

Association of Proton Pump Inhibitor Prophylaxis on Clinical Outcome in Acute Ischemic Stroke in China: A Multicenter Retrospective Cohort Study

Background: Overtreatment with proton pump inhibitors (PPIs) in acute ischemic stroke (AIS) patients continues to grow. We aimed to investigate the frequency of PPI prophylaxis without an appropriate indication in AIS patients in China and clarify the association between PPI prophylaxis and long-term prognosis. Methods: Based on a multicenter stroke registry database, neurological outcomes, stroke events, recurrent ischemic strokes, and all-cause death were compared between patients with and without PPI prophylaxis. Results: A total of 4542 AIS were included, and 3335 (73.4%) received PPI prophylaxis. Patients with PPI prophylaxis were more likely to have a poor outcome at 1 year than those without PPI prophylaxis (33.3% vs. 25.8%, OR 1.321; 95% CI 1.102–1.584; p = 0.003). No significant differences were found in all-cause death, stroke event, or recurrent ischemic stroke at 1 year between the two groups. After propensity score matching, PPI prophylaxis was still independently associated with a higher rate of poor outcome (30.9% vs. 25.8%, OR 1.432; 95% CI 1.151–1.780; p = 0.001). Sensitivity analysis also showed that PPI prophylaxis increased the rate of a poor outcome in minor strokes or at different durations of PPI prophylaxis. Conclusions: Approximately 3/4 of AIS patients were given PPI prophylaxis during hospitalization, which was associated with a poor long-term outcome

Impairment of the Glymphatic Pathway and Putative Meningeal Lymphatic Vessels in the Aging Human

OBJECTIVE: Aging is a major risk factor for numerous neurological disorders, and the mechanisms underlying brain aging remain elusive. Recent animal studies demonstrated a tight relationship between impairment of the glymphatic pathway, meningeal lymphatic vessels, and aging. However, the relationship in the human brain remains uncertain. METHODS: In this observational cohort study, patients underwent magnetic resonance imaging before and at multiple time points after intrathecal administration of a contrast agent. Head T1-weighted imaging was performed to assess the function of the glymphatic pathway and head high-resolution T2-fluid attenuated inversion recovery imaging to visualize putative meningeal lymphatic vessels (pMLVs). We measured the signal unit ratio (SUR) of 6 locations in the glymphatic pathway and pMLVs, defined the percentage change in SUR from baseline to 39 hours as the clearance of the glymphatic pathway and pMLVs, and then analyzed their relationships with aging. RESULTS: In all patients (N = 35), the SUR of the glymphatic pathway and pMLVs changed significantly after intrathecal injection of the contrast agent. The clearance of both the glymphatic pathway and pMLVs was related to aging (all p \u3c 0.05). The clearance of pMLVs was significantly related to the clearance of the glymphatic pathway (all p \u3c 0.05), and the clearance of the glymphatic pathway was significantly faster in patients with early filling of pMLVs than those with late filling (all p \u3c 0.05). INTERPRETATION: We revealed that both the glymphatic pathway and pMLVs might be impaired in the aging human brain through the novel, clinically available method to simultaneously visualize their clearance. Our findings also support that in humans, pMLVs are the downstream of the glymphatic pathway. ANN NEUROL 2020

Table_1_Novel Prehospital Prediction Model of Large Vessel Occlusion Using Artificial Neural Network.DOCX

<p>Background: Identifying large vessel occlusion (LVO) patients in the prehospital triage stage to avoid unnecessary and costly delays is important but still challenging. We aim to develop an artificial neural network (ANN) algorithm to predict LVO using prehospital accessible data including demographics, National Institutes of Health Stroke Scale (NIHSS) items and vascular risk factors.</p><p>Methods: Consecutive acute ischemic stroke patients who underwent CT angiography (CTA) or time of flight MR angiography (TOF-MRA) and received reperfusion therapy within 8 h from symptom onset were included. The diagnosis of LVO was defined as occlusion of the intracranial internal carotid artery (ICA), M1 and M2 segments of the middle cerebral artery (MCA) and basilar artery on CTA or TOF-MRA before treatment. Patients with and without LVO were randomly selected at a 1:1 ratio. The ANN model was developed using backpropagation algorithm, and 10-fold cross-validation was used to validate the model. The comparison of diagnostic parameters between the ANN model and previously established prehospital prediction scales were performed.</p><p>Results: Finally, 300 LVO and 300 non-LVO patients were randomly selected for the training and validation of the ANN model. The mean Youden index, sensitivity, specificity and accuracy of the ANN model based on the 10-fold cross-validation analysis were 0.640, 0.807, 0.833 and 0.820, respectively. The area under the curve (AUC), Youden index and accuracy of the ANN model were all higher than other prehospital prediction scales.</p><p>Conclusions: The ANN can be an effective tool for the recognition of LVO in the prehospital triage stage.</p

Glymphatic clearance function in patients with cerebral small vessel disease

Few studies have focused on the connection between glymphatic dysfunction and cerebral small vessel disease (CSVD), partially due to the lack of non-invasive methods to measure glymphatic function. We established modified index for diffusion tensor image analysis along the perivascular space (mALPS-index), which was calculated on diffusion tensor image (DTI), compared it with the classical detection of glymphatic clearance function calculated on Glymphatic MRI after intrathecal administration of gadolinium (study 1), and analyzed the relationship between CSVD imaging markers and mALPS-index in CSVD patients from the CIRCLE study (ClinicalTrials.gov ID: NCT03542734) (study 2). Among 39 patients included in study 1, mALPS-index were significantly related to glymphatic clearance function calculated on Glymphatic MRI ( r  = -0.772~-0.844, p < 0.001). A total of 330 CSVD patients were included in study 2. Severer periventricular and deep white matter hyperintensities (β = -0.332, p < 0.001; β = -0.293, p < 0.001), number of lacunas (β = -0.215, p < 0.001), number of microbleeds (β = -0.152, p = 0.005), and severer enlarged perivascular spaces in basal ganglia (β = -0.223, p < 0.001) were related to mALPS-index. Our results indicated that non-invasive mALPS-index might represent glymphatic clearance function, which could be applied in clinic in future. Glymphatic clearance function might play a role in the development of CSVD

Image_1_Novel Prehospital Prediction Model of Large Vessel Occlusion Using Artificial Neural Network.TIF

<p>Background: Identifying large vessel occlusion (LVO) patients in the prehospital triage stage to avoid unnecessary and costly delays is important but still challenging. We aim to develop an artificial neural network (ANN) algorithm to predict LVO using prehospital accessible data including demographics, National Institutes of Health Stroke Scale (NIHSS) items and vascular risk factors.</p><p>Methods: Consecutive acute ischemic stroke patients who underwent CT angiography (CTA) or time of flight MR angiography (TOF-MRA) and received reperfusion therapy within 8 h from symptom onset were included. The diagnosis of LVO was defined as occlusion of the intracranial internal carotid artery (ICA), M1 and M2 segments of the middle cerebral artery (MCA) and basilar artery on CTA or TOF-MRA before treatment. Patients with and without LVO were randomly selected at a 1:1 ratio. The ANN model was developed using backpropagation algorithm, and 10-fold cross-validation was used to validate the model. The comparison of diagnostic parameters between the ANN model and previously established prehospital prediction scales were performed.</p><p>Results: Finally, 300 LVO and 300 non-LVO patients were randomly selected for the training and validation of the ANN model. The mean Youden index, sensitivity, specificity and accuracy of the ANN model based on the 10-fold cross-validation analysis were 0.640, 0.807, 0.833 and 0.820, respectively. The area under the curve (AUC), Youden index and accuracy of the ANN model were all higher than other prehospital prediction scales.</p><p>Conclusions: The ANN can be an effective tool for the recognition of LVO in the prehospital triage stage.</p