Holo-Stroke: Assessing for Immersive Stroke Care Through Stroke Hologram Teleportation.

Background: Augmented reality enables the wearer to see both their physical environment and virtual objects. Holograms could allow 3D video of providers to be transmitted to distant sites, allowing patients to interact with virtual providers as if they are in the same physical space. Our aim was to determine if Tele-Stroke augmented with Holo-Stroke, compared with Tele-Stroke alone, could improve satisfaction and perception of immersion for the patient. Methods: Kinect cameras positioned at 90-degree intervals around the hub practitioner were used. Cameras streamed real-time optical video to a unity point-cloud program where the data were stitched together in a 360-degree view. The resultant hologram was positioned in 3D space and was visible through the head-mounted display by the patient. Radiology images were shared in Tele-Stroke and via hologram. Likert satisfaction questions were administered. Wilcoxon signed-rank testing was used. Results: Each of the 30 neurology clinic participants scored both Tele-Stroke and Holo-Stroke. Out of these, 29 patients completed the assessments (1 failure owing to computer reboot). Average age was 52 years, with 53.3% of the patients being female, 70.0% being White, and 13.3% being Hispanic. Likert scale score median "Overall" was 32 Tele-Stroke versus 48 Holo-Stroke (p < 0.00001), "Immersion" was 5 versus 10 (p < 0.00001), "Beneficial Technique" was 6 versus 10 (p < 0.00001), and "Ability to See Images" was 5 versus 10 (p < 0.00001). Discussion: Holo-Stroke 3D holographic Tele-Stroke exams resulted in feasibility, satisfaction, and high perception of immersion for the patient. Patients were enthusiastic for the more immersive, personal discussion with their provider and a robust way to experience radiology images. Though further assessments are needed, Holo-Stroke can help the provider "be there, not just see there!"

Multicenter comparison using two AI stroke CT perfusion software packages for determining thrombectomy eligibility.

BACKGROUND: Stroke AI platforms assess infarcted core and potentially salvageable tissue (penumbra) to identify patients suitable for mechanical thrombectomy. Few studies have compared outputs of these platforms, and none have been multicenter or considered NIHSS or scanner/protocol differences. Our objective was to compare volume estimates and thrombectomy eligibility from two widely used CT perfusion (CTP) packages, Viz.ai and RAPID.AI, in a large multicenter cohort. METHODS: We analyzed CTP data of acute stroke patients with large vessel occlusion (LVO) from four institutions. Core and penumbra volumes were estimated by each software and DEFUSE-3 thrombectomy eligibility assessed. Results between software packages were compared and categorized by NIHSS score, scanner manufacturer/model, and institution. RESULTS: Primary analysis of 362 cases found statistically significant differences in both software's volume estimations, with subgroup analysis showing these differences were driven by results from a single scanner model, the Canon Aquilion One. Viz.ai provided larger estimates with mean differences of 8cc and 18cc for core and penumbra, respectively (p<0.001). NIHSS subgroup analysis also showed systematically larger Viz.ai volumes (p<0.001). Despite volume differences, a significant difference in thrombectomy eligibility was not found. Additional subgroup analysis showed significant differences in penumbra volume for the Phillips Ingenuity scanner, and thrombectomy eligibility for the Canon Aquilion One scanner at one center (7 % increased eligibility with Viz.ai, p=0.03). CONCLUSIONS: Despite systematic differences in core and penumbra volume estimates between Viz.ai and RAPID.AI, DEFUSE-3 eligibility was not statistically different in primary or NIHSS subgroup analysis. A DEFUSE-3 eligibility difference, however, was seen on one scanner at one institution, suggesting scanner model and local CTP protocols can influence performance and cause discrepancies in thrombectomy eligibility. We thus recommend centers discuss optimal scanning protocols with software vendors and scanner manufacturers to maximize CTP accuracy.

INTERP: Interpreter requirements needed for tissue plasminogen activator evaluations and resulting performance: a retrospective review.

AIM: To examine the influence of interpreter service needs (IS) on rt-PA administration time metrics. METHODS: Retrospectively reviewed prospectively collected data from Comprehensive Stroke Center database (January 2011- April 1, 2021) and EMR. INCLUSION: Subjects for whom a "stroke code" was activated. Excluded in-house strokes. Baseline characteristics, frequency of rt-PA, rt-PA exclusions and time metrics, NIHSS were compared between patients who did or did not require IS. Analyses utilized ANOVA, t-Test, Brown-Mood Median Test, or Pearson's Chi-squared test as appropriate. RESULTS: Of 2,191 patients with stroke code activations, 81 had a documented need for IS. Rt-PA was administered in 9 IS and 358 non-IS patients. Median baseline NIHSS was higher in rt-PA group (9±8 vs 3±9, p<0.005). In IS patients, there were no differences in baseline characteristics between those who received rt-PA and those who did not, including median score for NIHSS aphasia (0±1 vs 0±1, p = 0.46). There were no rt-PA rate differences between those that did not and did require IS (17% vs 11%, p = 0.22). In patients with final diagnosis acute ischemic stroke, patients excluded from rt-PA solely due to being out of the window were more likely to have required IS (59% vs 35%, p = 0.003). Time metrics of rt-PA administration were not different in IS patients. CONCLUSIONS: There was no significant difference in frequency or time metrics of rt-PA administration in patients requiring interpreter services during an acute stroke code. AIS patients requiring an interpreter were more likely to be excluded from rt-PA on the basis of time.

Statin treatment intensity and cerebral vasomotor reactivity response in patients with ischemic stroke.

BACKGROUND AND PURPOSE: Cerebral vasomotor reactivity (VMR) is vital for regulating brain blood flow and maintaining neurological function. Impaired cerebral VMR is linked to a higher risk of stroke and poor post-stroke outcomes. This study explores the relationship between statin treatment intensity and VMR in patients with ischemic stroke. METHODS: Seventy-four consecutive patients (mean age 69.3 years, 59.4% male) with recent ischemic stroke were included. VMR levels were assessed 4 weeks after the index stroke using transcranial Doppler, measuring the breath-holding index (BHI) as an indicator of the percentage increase in middle cerebral artery blood flow (higher BHI signifies higher VMR). Multistep multivariable regression models, adjusted for demographic and cerebrovascular risk factors, were employed to examine the association between statin intensity treatment and BHI levels. RESULTS: Forty-one patients (55%) received high-intensity statins. Patients receiving high-intensity statins exhibited a mean BHI of 0.85, whereas those on low-intensity statins had a mean BHI of 0.67 (mean difference 0.18, 95% confidence interval: 0.13-0.22, p-value<.001). This significant difference persisted in the fully adjusted model (adjusted mean values: 0.84 vs. 0.68, p-value: .008). No significant differences were observed in BHI values within patient groups on high-intensity or low-intensity statin therapy (all p-values>.05). Furthermore, no significant association was found between baseline low-density lipoprotein (LDL) levels and BHI. CONCLUSIONS: High-intensity statin treatment post-ischemic stroke is linked to elevated VMR independent of demographic and clinical characteristics, including baseline LDL level. Further research is needed to explore statin therapy's impact on preserving brain vascular function beyond lipid-lowering effects.

Assessing Free-Living Postural Sway in Persons With Multiple Sclerosis.

Postural instability is associated with disease status and fall risk in Persons with Multiple Sclerosis (PwMS). However, assessments of postural instability, known as postural sway, leverage force platforms or wearable accelerometers, and are most often conducted in laboratory environments and are thus not broadly accessible. Remote measures of postural sway captured during daily life may provide a more accessible alterative, but their ability to capture disease status and fall risk has not yet been established. We explored the utility of remote measures of postural sway in a sample of 33 PwMS. Remote measures of sway differed significantly from lab-based measures, but still demonstrated moderately strong associations with patient-reported measures of balance and mobility impairment. Machine learning models for predicting fall risk trained on lab data provided an Area Under Curve (AUC) of 0.79, while remote data only achieved an AUC of 0.51. Remote model performance improved to an AUC of 0.74 after a new, subject-specific k-means clustering approach was applied for identifying the remote data most appropriate for modelling. This cluster-based approach for analyzing remote data also strengthened associations with patient-reported measures, increasing their strength above those observed in the lab. This work introduces a new framework for analyzing data from remote patient monitoring technologies and demonstrates the promise of remote postural sway assessment for assessing fall risk and characterizing balance impairment in PwMS.