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.

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.

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.

Objectives

In this 2-center study, we assessed the technical feasibility and reliability of a low cost, tablet-based mobile telestroke option for ambulance transport and hypothesized that the NIH Stroke Scale (NIHSS) could be performed with similar reliability between remote and bedside examinations.

Methods

We piloted our mobile telemedicine system in 2 geographic regions, central Virginia and the San Francisco Bay Area, utilizing commercial cellular networks for videoconferencing transmission. Standardized patients portrayed scripted stroke scenarios during ambulance transport and were evaluated by independent raters comparing bedside to remote mobile telestroke assessments. We used a mixed-effects regression model to determine intraclass correlation of the NIHSS between bedside and remote examinations (95% confidence interval).

Results

We conducted 27 ambulance runs at both sites and successfully completed the NIHSS for all prehospital assessments without prohibitive technical interruption. The mean difference between bedside (face-to-face) and remote (video) NIHSS scores was 0.25 (1.00 to -0.50). Overall, correlation of the NIHSS between bedside and mobile telestroke assessments was 0.96 (0.92-0.98). In the mixed-effects regression model, there were no statistically significant differences accounting for method of evaluation or differences between sites.

Conclusions

Utilizing a low-cost, tablet-based platform and commercial cellular networks, we can reliably perform prehospital neurologic assessments in both rural and urban settings. Further research is needed to establish the reliability and validity of prehospital mobile telestroke assessment in live patients presenting with acute neurologic symptoms.