Search

IntroductionWe studied a registry of Emergency Medical Systems (EMS) identified prehospital suspected stroke patients brought to an academic endovascular capable hospital over 1 year to assess the prevalence of disease and externally validate large vessel occlusion (LVO) stroke prediction scales with a focus on predictive values.MethodsAll patients had last known well times within 6 hours and a positive prehospital Cincinnati Prehospital Stroke Scale. LVO prediction scale scores were retrospectively calculated from emergency department arrival National Institutes of Health Stroke Scale scores. Final diagnoses were determined by chart review. Prevalence and diagnostic performance statistics were calculated. We prespecified analyses to identify scale thresholds with positive predictive values (PPVs) ≥80% and negative predictive values (NPVs) ≥95%. A secondary analysis identified thresholds with PPVs ≥50%.ResultsOf 220 EMS transported patients, 13.6% had LVO stroke, 15.9% had intracranial haemorrhage, 20.5% had non-LVO stroke and 50% had stroke mimic diagnoses. LVO stroke prevalence was 15.8% among the 184 diagnostic performance study eligible patients. Only Field Assessment Stroke Triage for Emergency Destination (FAST-ED) ≥7 had a PPV ≥80%, but this threshold missed 83% of LVO strokes. FAST-ED ≥6, Prehospital Acute Severity Scale =3 and Rapid Arterial oCclusion Evaluation ≥7 had PPVs ≥50% but sensitivities were

Purpose of reviewEndovascular therapy for acute ischemic stroke secondary to large vessel occlusion (LVO) is time-dependent. Prehospital patients with suspected LVO stroke should be triaged directly to specialized stroke centers for endovascular therapy. This review describes advances in LVO detection among prehospital suspected stroke patients.Recent findingsClinical prehospital stroke severity tools have been validated in the prehospital setting. Devices including EEG, SSEPs, TCD, cranial accelerometry, and volumetric impedance phase-shift-spectroscopy have recently published data regarding LVO detection in hospital settings. Mobile stroke units bring thrombolysis and vessel imaging to patients.SummaryThe use of a prehospital stroke severity tool for LVO triage is now widely supported. Ease of use should be prioritized as there are no meaningful differences in diagnostic performance amongst tools. LVO diagnostic devices are promising, but none have been validated in the prehospital setting. Mobile stroke units improve patient outcomes and cost-effectiveness analyses are underway.

Early detection of Atrial Fibrillation (AFib) is crucial to prevent stroke recurrence. New tools for monitoring cardiac rhythm are important for risk stratification and stroke prevention. As many of new approaches to long-term AFib detection are now based on photoplethysmogram (PPG) recordings from wearable devices, ensuring high PPG signal-to-noise ratios is a fundamental requirement for a robust detection of AFib episodes. Traditionally, signal quality assessment is often based on the evaluation of similarity between pulses to derive signal quality indices. There are limitations to using this approach for accurate assessment of PPG quality in the presence of arrhythmia, as in the case of AFib, mainly due to substantial changes in pulse morphology. In this paper, we first tested the performance of algorithms selected from a body of studies on PPG quality assessment using a dataset of PPG recordings from patients with AFib. We then propose machine learning approaches for PPG quality assessment in 30-s segments of PPG recording from 13 stroke patients admitted to the University of California San Francisco (UCSF) neuro intensive care unit and another dataset of 3764 patients from one of the five UCSF general intensive care units. We used data acquired from two systems, fingertip PPG (fPPG) from a bedside monitor system, and radial PPG (rPPG) measured using a wearable commercial wristband. We compared various supervised machine learning techniques including k-nearest neighbors, decisions trees, and a two-class support vector machine (SVM). SVM provided the best performance. fPPG signals were used to build the model and achieved 0.9477 accuracy when tested on the data from the fPPG exclusive to the test set, and 0.9589 accuracy when tested on the rPPG data.

Background/Objective: We combined cranial accelerometry, a device-based approach to large vessel occlusion (LVO) prediction, with neurological examination findings to determine if this improves diagnostic accuracy compared to either alone. Methods: Cranial accelerometry recordings and NIHSS scores were obtained during stroke codes and thrombectomy transfers at an academic medical center using convenience sampling. The reference standard was discharge diagnosis of LVO stroke. We compared accuracy statistics between machine learning models trained using cranial accelerometry alone, with asymmetric arm weakness added, with NIHSS scores added, and retrospective examination only LVO prediction scales. An exploratory analysis required asymmetric arm weakness prior to model training or scale testing. Results: Of 68 patients, there were 23 LVO strokes. Cranial accelerometry was 65% sensitive (95% CI 43–84%) and 87% specific (95% CI 73–95%). Adding asymmetric arm weakness increased specificity to 91% (95% CI 79–98%). Adding asymmetric arm weakness and the NIHSS increased sensitivity to 74% (95% CI 52–90%) and decreased specificity to 89% (95% CI 76–96%). LVO prediction scales had wide sensitivity and specificity ranges. The exploratory analysis improved sensitivity to 91% (95% CI 72–99%) and specificity to 93% (95% CI 92–99%) with only three false positives and two false negatives. Conclusions: Cranial accelerometry models are improved by various additions of asymmetric arm weakness and the NIHSS. An exploratory analysis requiring asymmetric arm weakness prior to cranial accelerometry model training minimized false positives and negatives. [ABSTRACT FROM AUTHOR]

Background: Malignant profile computed tomography perfusion (CTP) lesions are associated with poor outcomes after administration of intravenous tissue-plasminogen activator (IV-tPA) for ischemic stroke. Aims: To determine whether published CTP-based lesion thresholds predictive of poor outcomes in a predominantly 8 cm of CTP anatomic coverage cohort would predict poor outcomes in an independent 4 cm of CTP anatomic coverage cohort and to generate optimized 4 cm CTP thresholds. Methods: Ischemic stroke patients with baseline CTP imaging with 4 cm of anatomic coverage before receiving IV-tPA at a single institution were retrospectively studied. Perfusion lesion time to maximum of tissue residue function (Tmax) and cerebral blood flow (CBF) volumes were determined using RAPID automated software. Fisher's exact tests assessed associations between lesion thresholds and outcomes. Receiver operating characteristic (ROC) curves generated optimized thresholds for 4 cm of CTP coverage. Results: Sixty-three patients were included. Poor outcomes were associated with published thresholds of Tmax >6 s > 103 mL, Tmax > 8 s > 86 mL, and Tmax > 10 s > 78 mL but not CBF core >53 mL. Thresholds optimized for 4 cm of CTP coverage and associated with poor outcomes were Tmax > 6 s > 100 mL, Tmax > 8 s > 65 mL, Tmax >10 s > 46 mL, and CBF core >39 mL. Conclusions: We validated the ability of published CTP Tmax lesion volume thresholds to predict poor outcomes despite IV-tPA in an independent cohort using only 4 cm of CTP anatomical coverage. A CBF > 39 mL threshold, rather than the predominantly 8 cm CTP coverage derived CBF threshold of >53 mL, was associated with poor outcomes in this 4 cm CTP coverage cohort. [ABSTRACT FROM AUTHOR]

Introduction The speech arm Vision eyes (SAVE) scale, a 4-item clinical scale emphasizing binary scoring and avoidance of nuanced examination distinctions, predicts LVOs with similar characteristics as more complex scales. Methods receiver operating characteristic analyses of the prospective STOPStroke study assessed the ability of the saVe scale and other published scales to predict LVO. We identified scale thresholds with positive likelihood ratios with 95% confidence intervals of ≥5.0 or negative likelihood ratios with 95% confidence intervals of ≤0.5. Results 735patients were studied. LVO prevalence was 33%. area under the curve was 0.79 for SAVE, 0.82 for FAST-ED, 0.80 for mnihss and NIHSS, and lower for all other scales. SAVE=4, EMSA=6, MNIHSS≥10, NIHSS≥16, and race≥8 had positive likelihood ratios with 95% confidence intervals ≥5.0. SAVE≥2, CPSS≥2, C-STAT≥1, EMSA≥4, FAST-ED≥3, G-FAST≥3, MNIHSS≥6, NIHSS≥9, Pass≥1, RACE≥2, VAN=1, and 3i-SS≥1 had negative likelihood ratios with 95% confidence intervals ≤0.5. Conclusions SAVE=4 performed similarly to more complex scales at predicting LVO. Other simplified scales did not have thresholds with positive likelihood ratios with 95% confidence intervals ≥5.0. Validation is need in a prehospital cohort of patients with suspected stroke. [ABSTRACT FROM AUTHOR]

Large vessel occlusion (LVO) prediction scales are used to triage prehospital suspected stroke patients with a high probability of LVO stroke to endovascular therapy centers. The sensitivities of these scales in the 6-to-24-h time window are unknown. Higher scale score thresholds are typically less sensitive and more specific. Knowing the highest scale score thresholds that remain sensitive could inform threshold selection for clinical use. Sensitivities may also vary between left and right-sided LVOs.LVO prediction scale scores were retrospectively calculated using the National Institutes of Health Stroke Scale (NIHSS) scores of patients enrolled in the DAWN Trial. All patients had last known well times between 6 and 24 h, NIHSS scores ≥ 10, intracranial internal carotid artery or proximal middle cerebral artery occlusions, and mismatches between their clinical severities and infarct core volumes. Scale thresholds with sensitivities ≥ 85% were identified, along with scores ≥ 5% more sensitive for left or right-sided LVOs. Specificities could not be calculated because all patients had LVOs.A total of 201 out of 206 patients had the required NIHSS subitem scores. CPSS = 3, C-STAT ≥ 2, FAST-ED ≥ 4, G-FAST ≥ 3, RACE ≥ 5, and SAVE ≥ 3 were the highest thresholds that were still 85% sensitive for DAWN Trial LVO stroke patients. RACE ≥ 5 was the only typically used score threshold more sensitive for right-sided LVOs, though similar small differences were seen for other scales at higher thresholds.Our findings likely represent the maximum sensitivities of the LVO prediction scales tested for ideal thrombectomy candidates in the 6-to-24-h time window because NIHSS scores were documented in hospitals during a clinical trial rather than in the prehospital setting. Patients with NIHSS scores < 10 or more distal LVOs would lower sensitivities further. Selecting even higher scale thresholds for LVO triage would lead to many missed LVO strokes. [ABSTRACT FROM AUTHOR]

Background: Large-vessel occlusion (LVO) stroke represents one-third of acute ischemic stroke (AIS) in the United States but causes two-thirds of poststroke dependence and >90% of poststroke mortality. Prehospital LVO stroke detection permits efficient emergency medical systems (EMS) transport to an endovascular thrombectomy (EVT)-capable center. Our primary objective was to determine the feasibility of using a cranial accelerometry (CA) headset device for prehospital LVO stroke detection. Our secondary objective was development of an algorithm capable of distinguishing LVO stroke from other conditions., Methods: We prospectively enrolled consecutive adult patients suspected of acute stroke from 11 study hospitals in four different U.S. geographical regions over a 21-month period. Patients received device placement by prehospital EMS personnel. Headset data were matched with clinical data following informed consent. LVO stroke diagnosis was determined by medical chart review. The device was trained using device data and Los Angeles Motor Scale (LAMS) examination components. A binary threshold was selected for comparison of device performance to LAMS scores., Results: A total of 594 subjects were enrolled, including 183 subjects who received the second-generation device. Usable data were captured in 158 patients (86.3%). Study subjects were 53% female and 56% Black/African American, with median age 69 years. Twenty-six (16.4%) patients had LVO and 132 (83.6%) were not LVO (not-LVO AIS, 33; intracerebral hemorrhage, nine; stroke mimics, 90). COVID-19 testing and positivity rates (10.6%) were not different between groups. We found a sensitivity of 38.5% and specificity of 82.7% for LAMS ≥ 4 in detecting LVO stroke versus a sensitivity of 84.6% (p < 0.0015 for superiority) and specificity of 82.6% (p = 0.81 for superiority) for the device algorithm (CA + LAMS)., Conclusions: Obtaining adequate recordings with a CA headset is highly feasible in the prehospital environment. Use of the device algorithm incorporating both CA and LAMS data for LVO detection resulted in significantly higher sensitivity without reduced specificity when compared to the use of LAMS alone., (© 2024 The Authors. Academic Emergency Medicine published by Wiley Periodicals LLC on behalf of Society for Academic Emergency Medicine.)