Background and Purpose: Early detection of large vessel occlusion (LVO) stroke optimizes endovascular therapy and improves outcomes. Clinical stroke severity scales used for LVO identification have variable accuracy. We investigated a portable LVO-detection device (PLD), using electroencephalography and somatosensory-evoked potentials, to identify LVO stroke. Methods: We obtained PLD data in suspected patients with stroke enrolled prospectively via a convenience sample in 8 emergency departments within 24 hours of symptom onset. LVO discriminative signals were integrated into a binary classifier. The National Institutes of Health Stroke Scale was documented, and 4 prehospital stroke scales were retrospectively calculated. We compared PLD and scale performance to diagnostic neuroimaging. Results: Of 109 patients, there were 25 LVO (23%), 38 non-LVO ischemic (35%), 14 hemorrhages (13%), and 32 stroke mimics (29%). The PLD had higher sensitivity (80% [95% CI, 74–85]) and similar specificity (80% [95% CI, 77–83]) to all prehospital scales at their predetermined high probability LVO thresholds. The PLD had high discrimination for LVO ( C -statistic=0.88). Conclusions: The PLD identifies LVO with superior accuracy compared with prehospital stroke scales in emergency department suspected stroke. Future studies need to validate the PLD’s potential as an LVO triage aid in prehospital undifferentiated stroke populations.

Traumatic brain injury (TBI) induces immune dysfunction that can be captured clinically by an increase in the neutrophil-to-lymphocyte ratio (NLR). However, few studies have characterized the temporal dynamics of NLR post-TBI and its relationship with hospital-acquired infections (HAI), resource utilization, or outcome. We assessed NLR and HAI over the first 21 days post-injury in adults with moderate-to-severe TBI (n = 196) using group-based trajectory (TRAJ), changepoint, and mixed-effects multivariable regression analysis to characterize temporal dynamics. We identified two groups with unique NLR profiles: a high (n = 67) versus a low (n = 129) TRAJ group. High NLR TRAJ had higher rates (76.12% vs. 55.04%, p = 0.004) and earlier time to infection (p = 0.003). In changepoint-derived day 0–5 and 6–20 epochs, low lymphocyte TRAJ, early in recovery, resulted in more frequent HAIs (p = 0.042), subsequently increasing later NLR levels (p ≤ 0.0001). Both high NLR TRAJ and HAIs increased hospital length of stay (LOS) and days on ventilation (p ≤ 0.05 all), while only high NLR TRAJ significantly increased odds of unfavorable six-month outcome as measured by the Glasgow Outcome Scale (GOS) (p = 0.046) in multivariable regression. These findings provide insight into the temporal dynamics and interrelatedness of immune factors which collectively impact susceptibility to infection and greater hospital resource utilization, as well as influence recovery.

The objective of this study is to analyze the derived 3-dimensional (3D) VCG spatial loop using a novel chaotic dynamics algorithm to identify the development of acute myocardial acute myocardial infarction (AMI). Methods: This is a case-controlled study in which digitized 12-lead ECGs were acquired from 109 patients presenting with complaints suggestive of AMI. The prevalence of AMI was 13.76% all of which demonstrated positive troponin I serum levels. The VCG 3D spatial loop was derived from each ECG using the VectraplexECG System (VectraCor Inc, Totowa, NJ) and then analyzed using a novel chaotic dynamics algorithm that yields several real-time electrical biomarker parameters including the fractal dimensions of the volume:surface area (FD Vol:SA) and area:length (FD A:L) relations of spatial cardiac vector motion in 3D space. Both full cycle PP and QT intervals were analyzed. ANOVA of means and standard errors were used to compare AMI positive vs. AMI negative cases to evaluate for statistical significance (p < 0.05). Results: The mean PP FD Vol:SA for AMI positive and negative cases were 2.295 AE 0.197 and 2.107 AE 0.177 respectively (p ¼ 0.000269). Mean QT FD Vol:SA for AMI positive and negative cases were 2.465 AE 0.054 and 2.261 AE 0.022 respectively (p ¼ 0.000697). Mean PP FD A:L for AMI positive and negative cases were 1.102 AE 0.033 and 1.157 AE 0.0075 respectively (p ¼ 0.0177). Mean QT FD A:L for AMI positive and negative cases were 1.127 AE 0.031 and 1.083 AE 0.007 respectively (p ¼ 0.0449). FD Vol:SA showed better discrimination than FD A:L for AMI. Conclusion: The VCG 3D spatial loop can be derived from scalar ECG leads {I, II, V2} directly from a cardiac monitor/ECG device in real-time. Chaotic dynamics analysis of the spatial loop suggests that fractal dimension relationships may be continuous, point-of-care cardiac electrical field biomarkers for the detection of AMI in patients presenting for evaluation and observation of chest pain equivalents.

Study Objectives: Several prehospital stroke scales have been developed to provide quick and accurate triage to facilitate timely treatment. This study evaluated a portable, experimental EEG device using AI as a tool for detection of acute stroke and large vessel occlusion (LVO) among patients with neurological deficits. Both device performance and feasibility in the emergent setting were assessed. Methods: This observational study enrolled a convenience sample of emergency department (ED) patients evaluated for suspected stroke within 24 hours of symptom onset. LVO and stroke status were determined by local neuroradiologists blinded to device output. LVO was defined as an acute occlusion of any of the following arteries: ICA/MCA-(M1 or M2)/vertebral/ basilar. Controls were neurologically normal subjects (NIHSS=0). Results: From May 2018 to July 2019, eight urban US stroke centers enrolled 89 subjects being evaluated for stroke. In suspected stroke subjects, 68 had stroke (76%) and 23 had LVO (26%). Mean (± SD) age was 68 (± 14), 36% were female, and the median (IQR) NIH stroke score was 6 (3 - 12) among suspected stroke subjects. The median last known well time was 327 minutes (196 - 577). Device performance for detecting LVO is shown in Table 1. There were no severe adverse events related to use of the device. Conclusion: The neuromonitoring device performed well in identifying LVO in patients presenting with suspected stroke. The performance of the neuromonitoring device in the acute setting indicates that it may be able to support prehospital decision making when triaging suspected stroke subjects. Additional studies with larger sample sizes are needed to validate this study’s findings.

Study Objectives: Tissue plasminogen activator (tPA) may improve ischemic stroke outcomes if administered soon after symptom onset. A computed tomography (CT) scan is required to exclude hemorrhagic stroke. The purpose of this study was to evaluate the feasibility of using an experimental, non-invasive, point-of-care electroencephalography (EEG)-based platform to identify hemorrhage in suspected stroke subjects presenting to the emergency department (ED). Methods: During a study that took place between May 2018 and July 2019, a sensor was added to the EEG-based platform that was hypothesized could differentiate between hemorrhagic and ischemic stroke. Eligible subjects presented within 24 hours of symptom onset and had a National Institute of Health Stroke Score (NIHSS) greater than 1. The performance of an artificial intelligence (AI) algorithm was assessed using five-fold cross validation. Results: Starting in December 2018, 88 subjects were enrolled with the additional sensor. The median (IQR) age was 68 (56 - 78), 56 subjects (64%) were male, and the distribution in terms of ethnicity was 44% white, 38% African American, and 18% non-black latino. There were 14 (16%) intracerebral hemorrhages (ICH) and 52 (59%) ischemic strokes. Median (IQR) time last known well time was 430 (193 - 574) minutes. Performance is shown in Table 1. Conclusion: Preliminary results from a small sample are encouraging. More hemorrhagic stroke patients are expected to improve performance.