Headpulse measurement can reliably identify large-vessel occlusion stroke in prehospital suspected stroke patients: Results from the EPISODE-PS-COVID study.

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.

Large vessel occlusion prediction scale thresholds that are sensitive for DAWN Trial patients.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Large vessel occlusion prediction scales provide high negative but low positive predictive values in prehospital suspected stroke patients.

Introduction: We 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. Methods: All 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%. Results: Of 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 <50%. Several standard and lower alternative scale thresholds achieved NPVs ≥95%, but false positives were common. Conclusions: Diagnostic performance tradeoffs of LVO prediction scales limited their ability to achieve high PPVs without missing most LVO strokes. Multiple scales provided high NPV thresholds, but these were associated with many false positives.

A Comparison of Time to Treatment between an Emergency Department Focused Stroke Protocol and Mobile Stroke Units.

BACKGROUND: San Francisco (California USA) is a relatively compact city with a population of 884,000 and nine stroke centers within a 47 square mile area. Emergency Medical Services (EMS) transport distances and times are short and there are currently no Mobile Stroke Units (MSUs). METHODS: This study evaluated EMS activation to computed tomography (CT [EMS-CT]) and EMS activation to thrombolysis (EMS-TPA) times for acute stroke in the first two years after implementation of an emergency department (ED) focused, direct EMS-to-CT protocol entitled "Mission Protocol" (MP) at a safety net hospital in San Francisco and compared performance to published reports from MSUs. The EMS times were abstracted from ambulance records. Geometric means were calculated for MP data and pooled means were similarly calculated from published MSU data. RESULTS: From July 2017 through June 2019, a total of 423 patients with suspected stroke were evaluated under the MP, and 166 of these patients were either ultimately diagnosed with ischemic stroke or were treated as a stroke but later diagnosed as a stroke mimic. The EMS and treatment time data were available for 134 of these patients with 61 patients (45.5%) receiving thrombolysis, with mean EMS-CT and EMS-TPA times of 41 minutes (95% CI, 39-43) and 63 minutes (95% CI, 57-70), respectively. The pooled estimates for MSUs suggested a mean EMS-CT time of 35 minutes (95% CI, 27-45) and a mean EMS-TPA time of 48 minutes (95% CI, 39-60). The MSUs achieved faster EMS-CT and EMS-TPA times (P <.0001 for each). CONCLUSIONS: In a moderate-sized, urban setting with high population density, MP was able to achieve EMS activation to treatment times for stroke thrombolysis that were approximately 15 minutes slower than the published performance of MSUs.

Large Vessel Occlusion Stroke Detection in the Prehospital Environment.

Purpose of Review: Endovascular 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 Findings: Clinical 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. Summary: The 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.

The Neurological Examination Improves Cranial Accelerometry Large Vessel Occlusion Prediction Accuracy.

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.

Validation and iteration of CT perfusion defined malignant profile thresholds for acute ischemic stroke.

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.

A Unique Signature of Cardiac-Induced Cranial Forces During Acute Large Vessel Stroke and Development of a Predictive Model.

BACKGROUND: Cranial accelerometry is used to detect cerebral vasospasm and concussion. We explored this technique in a cohort of code stroke patients to see whether a signature could be identified to aid in the diagnosis of large vessel occlusion (LVO) stroke. METHODS: A military-grade three-axis accelerometer was affixed to a headset. Accelerometer and electrocardiogram (ECG) outputs were digitized at 1.6 kHz. We call the resulting digitized signals the "headpulse." Three-minute recordings were performed immediately after computed tomography (CT) angiography (CTA) and/or immediately before and after attempted mechanical thrombectomy in patents with suspected stroke. The resulting waveforms were inspected by eye and then subjected to supervised machine learning (MATLAB Classification Learner R2018a) to train a model using fivefold cross-validation. RESULTS: Of 42 code stroke subjects with recordings, 19 (45%) had LVO and 23 (55%) had normal CTAs. In patients without LVO, ECG-triggered waveforms followed a self-similar time course revealing that the headpulse is highly coupled to the cardiac contraction. However, in most patients with LVO, headpulses showed little cardiac contraction correlation. We term this abnormality "chaos" and parameterized it with 156 measures of trace-by-trace variation from the ECG-signal-averaged mean for machine learning model training. Selecting the best model, using biometric data only, we properly classified 15/19 LVOs and 20/23 non-LVO patients, with receiver operating characteristic curve area = 0.79, sensitivity of 73%, and specificity of 87%, P < 0.0001. Headpulse waveforms following thrombectomy showed return of cardiac contraction correlation. CONCLUSIONS: Headpulse recordings performed on patients with suspected acute stroke significantly identify those with LVO. The lack of temporal correlation of the headpulse with cardiac contraction and resolution to normal may reflect changes in cerebral blood flow and may provide a useful technique to triage stroke patients for thrombectomy using a noninvasive device.

A Supervised Approach to Robust Photoplethysmography Quality Assessment.

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.

Prehospital and Emergency Department-Focused Mission Protocol Improves Thrombolysis Metrics for Suspected Acute Stroke Patients.

BACKGROUND: The Mission Protocol was implemented in 2017 to expedite stroke evaluation and reduce door-to-needle (DTN) times at Zuckerberg San Francisco General Hospital. The key system changes were team-based evaluation of suspected stroke patients at ambulance entrance by an Emergency Department (ED) physician, ED nurse, and neurologist and immediate emergency medical service (EMS) provider transport of patients to CT. METHODS: Patients were eligible for a Mission Protocol prehospital stroke activation if an EMS provider found a positive Cincinnati Prehospital Stroke Scale and a last known normal time within 6 hours. We retrospectively compared treatment metrics between the first year of Mission Protocol patients and patients from the year prior also brought in via ambulance with suspected stroke and a last known normal time within 6 hours. Median Door to CT and DTN times were compared using 2 sample Wilcoxon rank-sum (Mann-Whitney) tests. RESULTS: There were 236 patients in the Mission Protocol group and 112 in the comparison group. The Mission Protocol was associated with a 10 minutes faster median door to CT time (P < .00001), a 6 minutes faster median DTN time (P = .0046), a 22% increase in the proportion of patients treated within 45 minutes of arrival (84% versus 62%), and a 12% increase in the proportion of patients treated within 60 minutes (92% versus 80%). There were 8 stroke mimics treated in the Mission Protocol cohort compared to 2 in the comparison cohort. Symptomatic intracranial hemorrhage occurred in one Mission Protocol patient with an ischemic stroke. CONCLUSIONS: The EMS direct to CT based Mission Protocol was associated with faster median door to CT and DTN times. There was a 22% increase in the proportion of thrombolysis patients treated within 45 minutes or less. More stroke mimic patients received thrombolysis but symptomatic intracranial hemorrhage only occurred in 1 ischemic stroke patient.

The Speech Arm Vision Eyes (SAVE) scale predicts large vessel occlusion stroke as well as more complicated scales.

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.

Prehospital Prediction of Large Vessel Occlusion in Suspected Stroke Patients.

PURPOSE OF REVIEW: Recent advances in endovascular thrombectomy have made acute ischemic stroke due to a large vessel occlusion more treatable than ever. Rapid access to treatment remains paramount and multiple large vessel occlusion prediction scales have been created to enhance prehospital identification and triage of these patients. This review summarizes the current state of large vessel occlusion prediction scales, proposes a set of ideal scale features, and discusses the future of these scales and prehospital neurological emergency response systems. RECENT FINDINGS: A meta-analysis of the available data concluded that none of the currently published scales are more accurate than the others. However, other studies provide insight into important qualitative features beyond accuracy. At present, only a few large vessel occlusion prediction scales have been studied in the necessary prehospital suspected stroke patient population. Among these small studies, 26-51% of patients identified by scales had large vessel occlusions and 63-84% qualified for triage to a Comprehensive Stroke Center. Valuable scale features include binary scoring, inclusion of gaze deviation and arm weakness, exclusion of neglect, and prehospital validation in a suspected stroke cohort. Patients with neurological emergencies that mimic large vessel occlusion, such as intracranial hemorrhage, may also benefit from triage to Comprehensive Stroke Centers. Prehospital triage is more complex than ever and guidelines, tools, and systems continue to evolve.