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.

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.

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.

(VISIION-S): Viz.ai Implementation of Stroke augmented Intelligence and communications platform to improve Indicators and Outcomes for a comprehensive stroke center and Network - Sustainability.

OBJECTIVES: As Comprehensive Stroke Centers (CSCs) strive to improve neuro-intervention (NIR) times, process improvements are put in place to streamline workflows. Our prior publication (VISIION) demonstrated improvements in key performance indicators (KPIs). The purpose VISIION-S was to analyze whether those results were sustainable. MATERIALS AND METHODS: Consecutive Direct Arriving LVO (DALVO) and telemedicine transfer LVO (BEMI) stroke NIR cases were assessed, including subgroups of DALVO-OnHours, DALVO-OffHours, BEMI-OnHours, and BEMI-OffHours. We analyzed times for the original 6 months pre (6/10/20-1/15/21) and compared them to a 17 month post-implementation period (1/16/21- 6/25/22) to evaluate for sustainability. Mann-Whitney U was utilized. RESULTS: 150 NIR cases were analyzed pre (n = 47) v. post (n = 103) implementation (DALVO-OnHours 7 v. 20, DALVO-OffHours 10 v. 25, BEMI-OnHours 13 v. 20, BEMI-OffHours 17 v. 38). For Door-to-groin (DTG), improvement was noted for DALVO-OffHours 39%(157 min,96 min;p < 0.001), DALVO-ALL 25%(127 min,95 min;p = 0.006), BEMI-OffHours 46%(45 min,25 min;p = 0.023), and BEMI-ALL 40%(42 min,25 min;p = 0.005). Activation-to-groin (ATG), door-to-device (DTD), and door-to-recanalization (DTR) also showed statistical improvements. For DALVO-OffHours, there were reductions in door to CT (DTC) 80%(26 min,5 min;p < 0.001), ATG 32%(90 min,61 min;p = 0.036), DTG 39%(157 min,96 min;p < 0.001), DTD 31%(178 min,123 min;p = 0.002), and DTR 32%(197 min,135 min;p = 0.003). CONCLUSIONS: We noted sustainability over a 17 month period with sustained reduction in KPIs for even more NIR time interval comparisons. In the greatest opportunity subgroup (DALVO-OffHours), we noted a reduction in all 5 time interval metrics. Our sustainability finding is important to show that process improvements continued even after the immediate period, adding credibility to the results. Models such as this could be useful for other centers striving to optimize workflow and improve times.

Digital Phenotypes of Instability and Fatigue Derived From Daily Standing Transitions in Persons With Multiple Sclerosis.

Impairment in persons with multiple sclerosis (PwMS) can often be attributed to symptoms of motor instability and fatigue. Symptom monitoring and queued interventions often target these symptoms. Clinical metrics are currently limited to objective physician assessments or subjective patient reported measures. Recent research has turned to wearables for improving the objectivity and temporal resolution of assessment. Our group has previously observed wearable assessment of supervised and unsupervised standing transitions to be predictive of fall-risk in PwMS. Here we extend the application of standing transition quantification to longitudinal home monitoring of symptoms. Subjects (N=23) with varying degrees of MS impairment were recruited and monitored with accelerometry for a total of  âˆ¼  6 weeks each. These data were processed using a preexisting framework, applying a deep learning activity classifier to isolate periods of standing transition from which descriptive features were extracted for analysis. Participants completed daily and biweekly assessments describing their symptoms. From these data, Canonical Correlation Analysis was used to derive digital phenotypes of MS instability and fatigue. We find these phenotypes capable of distinguishing fallers from non-fallers, and further that they demonstrate a capacity to characterize symptoms at both daily and sub-daily resolutions. These results represent promising support for future applications of wearables, which may soon augment or replace current metrics in longitudinal monitoring of PwMS.

Chest-Based Wearables and Individualized Distributions for Assessing Postural Sway in Persons With Multiple Sclerosis.

Typical assessments of balance impairment are subjective or require data from cumbersome and expensive force platforms. Researchers have utilized lower back (sacrum) accelerometers to enable more accessible, objective measurement of postural sway for use in balance assessment. However, new sensor patches are broadly being deployed on the chest for cardiac monitoring, opening a need to determine if measurements from these devices can similarly inform balance assessment. Our aim in this work is to validate postural sway measurements from a chest accelerometer. To establish concurrent validity, we considered data from 16 persons with multiple sclerosis (PwMS) asked to stand on a force platform while also wearing sensor patches on the sacrum and chest. We found five of 15 postural sway features derived from the chest and sacrum were significantly correlated with force platform-derived features, which is in line with prior sacrum-derived findings. Clinical significance was established using a sample of 39 PwMS who performed eyes-open, eyes-closed, and tandem standing tasks. This cohort was stratified by fall status and completed several patient-reported measures (PRM) of balance and mobility impairment. We also compared sway features derived from a single 30-second period to those derived from a one-minute period with a sliding window to create individualized distributions of each postural sway feature (ID method). We find traditional computation of sway features from the chest is sensitive to changes in PRMs and task differences. Distribution characteristics from the ID method establish additional relationships with PRMs, detect differences in more tasks, and distinguish between fall status groups. Overall, the chest was found to be a valid location to monitor postural sway and we recommend utilizing the ID method over single-observation analyses.

Tele-Untethered: Telemedicine Without Waiting Rooms.

BACKGROUND AND OBJECTIVES: Telemedicine bridges the gap between care needs and provider availability. The value of telemedicine can be eclipsed by long wait times, especially if patients are stuck in virtual waiting rooms. UCSD Tele-Untethered allows patients to join visits without waiting in virtual waiting rooms. Tele-Untethered uses a text-to-video link to improve clinic flow, decrease virtual waiting room reliance, improve throughput, and potentially improve satisfaction. METHODS: This institutional review board (IRB)-approved quality improvement pilot (IRB #210364QI) included patients seen in a single vascular neurology clinic, within the pilot period, if they had a smartphone/cell phone, and agreed to participate in a flexible approach to telehealth visits. Standard work was disseminated (patient instructions, scripting, and workflows). Patients provided a cell phone number to receive a text link when the provider was ready to see them. Metrics included demographics, volumes, visit rates, percentage seen early/late, time savings, and satisfaction surveys. RESULTS: Over 2.5 months, 22 patients were scheduled. Of those arriving, 76% were "Tele-Untethered" and 24% were "Standard Telemedicine." Text-for-video link was used for 94% of Tele-Untethered. Fifty-five percent were seen early. There was a 55-minute-per-session time savings. CONCLUSION: This UCSD Tele-Untethered pilot benefitted patients by allowing scheduling flexibility while not being tied to a "virtual waiting room." It benefited providers as it allowed them to see patients in order/not tied to exact times, improved throughput, and saved time. Even modest time savings for busy providers, coupled with Lean workflows, can provide critical value. High Tele-Untethered uptake and use of verbal check-in highlight that patients expect flexibility and ease of use. As our initial UCSD Tele-Untethered successes included patient flexibility and time savings for patients and providers, it can serve as a model as enterprises strive for optimal care and improved satisfaction. Expansion to other clinic settings is underway with a mantra of "UCSD Tele-Untethered: Your provider can see you now."

How Much Data Is Enough? A Reliable Methodology to Examine Long-Term Wearable Data Acquisition in Gait and Postural Sway.

Wearable sensors facilitate the evaluation of gait and balance impairment in the free-living environment, often with observation periods spanning weeks, months, and even years. Data supporting the minimal duration of sensor wear, which is necessary to capture representative variability in impairment measures, are needed to balance patient burden, data quality, and study cost. Prior investigations have examined the duration required for resolving a variety of movement variables (e.g., gait speed, sit-to-stand tests), but these studies use differing methodologies and have only examined a small subset of potential measures of gait and balance impairment. Notably, postural sway measures have not yet been considered in these analyses. Here, we propose a three-level framework for examining this problem. Difference testing and intra-class correlations (ICC) are used to examine the agreement in features computed from potential wear durations (levels one and two). The association between features and established patient reported outcomes at each wear duration is also considered (level three) for determining the necessary wear duration. Utilizing wearable accelerometer data continuously collected from 22 persons with multiple sclerosis (PwMS) for 6 weeks, this framework suggests that 2 to 3 days of monitoring may be sufficient to capture most of the variability in gait and sway; however, longer periods (e.g., 3 to 6 days) may be needed to establish strong correlations to patient-reported clinical measures. Regression analysis indicates that the required wear duration depends on both the observation frequency and variability of the measure being considered. This approach provides a framework for evaluating wear duration as one aspect of the comprehensive assessment, which is necessary to ensure that wearable sensor-based methods for capturing gait and balance impairment in the free-living environment are fit for purpose.

The Sit-to-Stand Transition as a Biomarker for Impairment: Comparison of Instrumented 30-Second Chair Stand Test and Daily Life Transitions in Multiple Sclerosis.

Falls and mobility deficits are common in people with multiple sclerosis (PwMS) across all levels of clinical disability. However, functional mobility observed in supervised settings may not reflect daily life which may impact assessments of fall risk and impairment in the clinic. To investigate this further, we compared the utility of sensor-based performance metrics from sit-stand transitions during daily life and a structured task to inform fall risk and impairment in PwMS. Thirty-seven PwMS instrumented with wearable sensors (thigh and chest) completed supervised 30-second chair stand tests (30CST) and underwent two days of instrumented daily life monitoring. Performance metrics were computed for sit-stand transitions during daily life and 30CSTs. EDSS sub scores and fall history were used to dichotomize participants into groups: pyramidal/no pyramidal impairment, sensory/no sensory impairment and high/low fall risk. The ability of performance metrics to discriminate between groups was assessed using the area under the curve (AUC). The feature that best discriminated between high and low fall risk was a chest acceleration measurement from the supervised instrumented 30CST (AUC = 0.89). Only chest features indicated sensory impairment, however the task was different between supervised and daily life. The metric that best discriminated pyramidal impairment was a chest-derived feature (AUC = 0.89) from supervised 30CSTs. The highest AUC from daily life was observed in faller classification with the average sit-stand time (0.81). While characterizing sit-stand performance during daily life may yield insights into fall risk and may be performed without a clinic visit, there remains value to conducting supervised functional assessments to provide the best classification performance between the investigated impairments in this sample.

Evaluation of unsupervised 30-second chair stand test performance assessed by wearable sensors to predict fall status in multiple sclerosis.

BACKGROUND: One in two people with multiple sclerosis (PwMS) will fall in a three-month period. Predicting which patients will fall remains a challenge for clinicians. Standardized functional assessments provide insight into balance deficits and fall risk but their use has been limited to supervised visits. RESEARCH QUESTION: The study aim was to characterize unsupervised 30-second chair stand test (30CST) performance using accelerometer-derived metrics and assess its ability to classify fall status in PwMS compared to supervised 30CST. METHODS: Thirty-seven PwMS (21 fallers) performed instrumented supervised and unsupervised 30CSTs with a single wearable sensor on the thigh. In unsupervised conditions, participants performed bi-hourly 30CSTs and rated their balance confidence and fatigue over 48-hours. ROC analysis was used to classify fall status for 30CST performance. RESULTS: Non-fallers (p = 0.02) but not fallers (p = 0.23) differed in their average unsupervised 30CST performance (repetitions) compared to their supervised performance. The unsupervised maximum number of 30CST repetitions performed optimized ROC classification AUC (0.79), accuracy (78.4%) and specificity (90.0%) for fall status with an optimal cutoff of 17 repetitions. SIGNIFICANCE: Brief durations of instrumented unsupervised monitoring as an adjunct to routine clinical assessments could improve the ability for predicting fall risk and fluctuations in functional mobility in PwMS.

Open-source dataset reveals relationship between walking bout duration and fall risk classification performance in persons with multiple sclerosis.

Falls are frequent and associated with morbidity in persons with multiple sclerosis (PwMS). Symptoms of MS fluctuate, and standard biannual clinical visits cannot capture these fluctuations. Remote monitoring techniques that leverage wearable sensors have recently emerged as an approach sensitive to disease variability. Previous research has shown that fall risk can be identified from walking data collected by wearable sensors in controlled laboratory conditions however this data may not be generalizable to variable home environments. To investigate fall risk and daily activity performance from remote data, we introduce a new open-source dataset featuring data collected from 38 PwMS, 21 of whom are identified as fallers and 17 as non-fallers based on their six-month fall history. This dataset contains inertial-measurement-unit data from eleven body locations collected in the laboratory, patient-reported surveys and neurological assessments, and two days of free-living sensor data from the chest and right thigh. Six-month (n = 28) and one-year repeat assessment (n = 15) data are also available for some patients. To demonstrate the utility of these data, we explore the use of free-living walking bouts for characterizing fall risk in PwMS, compare these data to those collected in controlled environments, and examine the impact of bout duration on gait parameters and fall risk estimates. Both gait parameters and fall risk classification performance were found to change with bout duration. Deep learning models outperformed feature-based models using home data; the best performance was observed with all bouts for deep-learning and short bouts for feature-based models when evaluating performance on individual bouts. Overall, short duration free-living walking bouts were found to be the least similar to laboratory walking, longer duration free-living walking bouts provided more significant differences between fallers and non-fallers, and an aggregation of all free-living walking bouts yields the best performance in fall risk classification.

Transcranial Doppler to evaluate postreperfusion therapy following acute ischemic stroke: A literature review.

Cerebral vessel recanalization therapy, either intravenous thrombolysis or mechanical thrombectomy, is the main treatment that can significantly improve clinical outcomes after acute ischemic stroke. The degree of recanalization and cerebral reperfusion of the ischemic penumbra are dependent on cerebral hemodynamics. Currently, the main imaging modalities to assess reperfusion are MRI and CT perfusion. However, these imaging techniques cannot predict reperfusion-associated complications and are not readily available in many centers. It is also not feasible to repeat them frequently for sequential assessments, which is important because of the changing nature of cerebral hemodynamics following stroke. Transcranial Doppler sonography (TCD) is a valid, safe, and inexpensive technique that can assess recanalized vessels and reperfused tissue in real-time at the bedside. Post thrombectomy reocclusion, hyperperfusion syndrome, distal embolization, and remote infarction result in poor outcomes after mechanical or intravenous reperfusion therapy. Managing blood pressure following these endovascular treatments can also be a dilemma. TCD has an important role, with major clinical implications, in evaluating cerebral hemodynamics and collateral vessel status, guiding clinicians in making individualized decisions based on cerebral blood flow during acute stroke care. This review summarizes the most relevant literature on the role of TCD in evaluating patients after reperfusion therapy. We also discuss the importance of performing TCD in the first few hours following thrombolytic therapy in identifying hyperperfusion syndrome and embolic signals, predicting recurrent stroke, and detecting reocclusions, all of which may help improve patient prognosis. We recommend TCD during the hyperacute phase of stroke in comprehensive stroke centers.

Telestroke Across the Continuum of Care: Lessons from the COVID-19 Pandemic.

While use of telemedicine to guide emergent treatment of ischemic stroke is well established, the COVID-19 pandemic motivated the rapid expansion of care via telemedicine to provide consistent care while reducing patient and provider exposure and preserving personal protective equipment. Temporary changes in re-imbursement, inclusion of home office and patient home environments, and increased access to telehealth technologies by patients, health care staff and health care facilities were key to provide an environment for creative and consistent high-quality stroke care. The continuum of care via telestroke has broadened to include prehospital, inter-facility and intra-facility hospital-based services, stroke telerehabilitation, and ambulatory telestroke. However, disparities in technology access remain a challenge. Preservation of reimbursement and the reduction of regulatory burden that was initiated during the public health emergency will be necessary to maintain expanded patient access to the full complement of telestroke services. Here we outline many of these initiatives and discuss potential opportunities for optimal use of technology in stroke care through and beyond the pandemic.

Wearables and Deep Learning Classify Fall Risk From Gait in Multiple Sclerosis.

Falls are a significant problem for persons with multiple sclerosis (PwMS). Yet fall prevention interventions are not often prescribed until after a fall has been reported to a healthcare provider. While still nascent, objective fall risk assessments could help in prescribing preventative interventions. To this end, retrospective fall status classification commonly serves as an intermediate step in developing prospective fall risk assessments. Previous research has identified measures of gait biomechanics that differ between PwMS who have fallen and those who have not, but these biomechanical indices have not yet been leveraged to detect PwMS who have fallen. Moreover, they require the use of laboratory-based measurement technologies, which prevent clinical deployment. Here we demonstrate that a bidirectional long short-term (BiLSTM) memory deep neural network was able to identify PwMS who have recently fallen with good performance (AUC of 0.88) based on accelerometer data recorded from two wearable sensors during a one-minute walking task. These results provide substantial improvements over machine learning models trained on spatiotemporal gait parameters (21% improvement in AUC), statistical features from the wearable sensor data (16%), and patient-reported (19%) and neurologist-administered (24%) measures in this sample. The success and simplicity (two wearable sensors, only one-minute of walking) of this approach indicates the promise of inexpensive wearable sensors for capturing fall risk in PwMS.

Medical Undistancing Through Telemedicine: A Model Enabling Rapid Telemedicine Deployment in an Academic Health Center During the COVID-19 Pandemic.

Background: The authors draw upon their experience with a successful, enterprise-level, telemedicine program implementation to present a "How To" paradigm for other academic health centers that wish to rapidly deploy such a program in the setting of the COVID-19 pandemic. The advent of social distancing as essential for decreasing viral transmission has made it challenging to provide medical care. Telemedicine has the potential to medically undistance health care providers while maintaining the quality of care delivered and fulfilling the goal of social distancing. Methods: Rather than simply reporting enterprise telemedicine successes, the authors detail key telemedicine elements essential for rapid deployment of both an ambulatory and inpatient telemedicine solution. Such a deployment requires a multifaceted strategy: (1) determining the appropriateness of telemedicine use, (2) understanding the interface with the electronic health record, (3) knowing the equipment and resources needed, (4) developing a rapid rollout plan, (5) establishing a command center for post go-live support, (6) creating and disseminating reference materials and educational guides, (7) training clinicians, patients, and clinic schedulers, (8) considering billing and credentialing implications, (9) building a robust communications strategy, and (10) measuring key outcomes. Results: Initial results are reported, showing a telemedicine rate increase to 45.8% (58.6% video and telephone) in just the first week of rollout. Over a 5-month period, the enterprise has since conducted over 119,500 ambulatory telemedicine evaluations (a 1,000-fold rate increase from the pre-COVID-19 time period). Conclusion: This article is designed to offer a "How To" potential best practice approach for others wishing to quickly implement a telemedicine program during the COVID-19 pandemic.

Characteristics and Outcomes of Patients who Refuse Intravenous Thrombolysis for Acute Ischemic Stroke - The San Diego Experience.

PURPOSE: Intravenous Thrombolysis (IV rt-PA) is administered in <10% of ischemic stroke patients. In rare cases, patients or caregivers refuse IV rt-PA treatment even when recommended by stroke practitioners. We sought to assess the characteristics and outcomes of patients who refuse IV rt-PA for acute ischemic stroke, and to compare outcomes between those who were treated with IV rt-PA and those who refused. METHODS: We examined data from the prospectively collected, IRB approved UC San Diego Stroke Registry who presented as a "stroke code" from July 2004 to July 2019 at two academic facilities and five community hospitals. Patients were included if they presented within 4.5 hours of symptom onset or last seen normal, had a "stroke code" alert activated, and were either treated with IV rt-PA or the reason for exclusion was patient/family refusal. Patients were considered "refusers" if IV rt-PA was recommended by the provider during the stroke code and the patient and/or legally authorized representative declined treatment. Baseline demographics, baseline National Institutes of Health Stroke Scale (NIHSS), treatment times and 90-day Modified Rankin Scale (mRS) were collected. Patients who refused IV rt-PA were compared to those that were treated with IV rt-PA. Data was examined for frequencies and distribution. Chi squared was used to evaluate nominal variables. Continuous variables were assessed by Pearson correlation and t test. Kruskal-Wallis or ANOVA were used to evaluate group differences. RESULTS: A total of 1056 patients were included in the analysis. Forty-seven patients (4.5%) refused IV rt-PA. There were no significant socio-demographic differences between patients who were treated with IV rt-PA and those who refused. Compared to patients who were treated with IV rt-PA, patients who refused IV rt-PA had a significantly lower baseline NIHSS (4 vs 9, p < 0.0001) and higher baseline mRS (IQR 0-1.0 vs 0-2.8, p < 0.001). The time from arrival to treatment decision was significantly longer in patients who refused IV rt-PA (group mean 57.9 min vs 48.8 min, p = 0.03). Data for 90-day outcome was available for 556 (55.1%) patients treated with IV rt-PA and 20 (42.5%) patients who refused IV rt-PA. There was no difference in 90-day mRS between groups (p = 0.68). CONCLUSIONS: There is a low rate of IV rt-PA refusal in our registry population which is similar to what previous studies have shown. We found that patients who refuse IV rt-PA have significantly milder deficits and significantly worse pre-morbid disability. We speculate that the longer "arrival to decision" time in the refuse IV rt-PA group is due to longer informed consent discussions. This analysis furthers the body of literature regarding rt-PA refusals.

Encephalopathy only stroke codes (EoSC) do not result in rt-PA treatments.

BACKGROUND: Isolated mental status changes as a presenting sign (EoSC+), are not uncommon stroke code triggers. As stroke alerts, they still require the same intensive resources be applied. We previously showed that EoSC+ strokes (EoSC+ Stroke+) account for 0.1-0.2% of all codes. Whether these result in thrombolytic treatment (rt-PA), and the characteristics/ risk factor profiles of EoSC+ Stroke+ patients, have not been reported. METHODS: Retrospective analysis of stroke codes from an IRB approved registry, from 2004 to 2018, was performed. EoSC+ was defined as a NIHSS>0 for Q1a, 1b, or 1c with remaining elements scored 0. Characteristics and risk factors were compared for EoSC+, EoSC-, EoSC+ Stroke+, and rt-PA (EoSC+ Stroke+TPA+) patients. RESULTS: EoSC+ occurred in 55/2982 (1.84%) of all stroke codes. EoSC+ Stroke+ occurred in 8/55 (14.5%) of EoSC+ codes and 8/2982 (0.27%) of all stroke codes. 6/8 (75%) of EoSC+ Stroke+ scored NIHSS=1. When comparing EoSC++versus EoSC-, Hispanic ethnicity (p=0.009), hypertension (p=0.02), and history of stroke/TIA (p=0.002) were less common in EoSC+. No demographic/risk factor differences were noted for EoSC+ Stroke+ vs. EoSC+ Stroke-. No cases of rt-PA eligibility/treatment were noted. In EoSC+ Stroke+ analysis, imaging positive stroke/intracranial hemorrhage was noted on only 3 cases (3/2982=0.10% of all stroke codes) and none were posterior stroke. CONCLUSIONS: EoSC+ rarely results in stroke/TIA (0.27%) or stroke (0.10%), and in our analysis never (0%) resulted in rt-PA. Sub-analysis did not show missed rt-PA or posterior strokes. Understanding characteristics, and knowing that EoSC+ Stroke+ patients are unlikely to receive rt-PA, may help triage stroke resources.

Metrics extracted from a single wearable sensor during sit-stand transitions relate to mobility impairment and fall risk in people with multiple sclerosis.

BACKGROUND: Approximately half of the 2.3 million people with multiple sclerosis (PwMS) will fall in any three-month period. Currently clinicians rely on self-report measures or simple functional assessments, administered at discrete time points, to assess fall risk. Wearable inertial sensors are a promising technology for increasing the sensitivity of clinical assessments to accurately predict fall risk, but current accelerometer-based approaches are limited. RESEARCH QUESTION: Will metrics derived from wearable accelerometers during a 30-second chair stand test (30CST) correlate with clinical measures of disease severity, balance confidence and fatigue in PwMS, and can these metrics be used to accurately discriminate fallers from non-fallers? METHODS: Thirty-eight PwMS (21 fallers) completed self-report outcome measures then performed the 30CST while triaxial acceleration data were collected from inertial sensors adhered to the thigh and chest. Accelerometer metrics were derived for the sit-to-stand and stand-to-sit transitions and relationships with clinical metrics were assessed. Finally, the metrics were used to develop a logistic regression model to classify fall status. RESULTS: Accelerometer-derived metrics were significantly associated with multiple clinical metrics that capture disease severity, balance confidence and fatigue. Performance of a logistic regression for classifying fall status was enhanced by including accelerometer features (accuracy 74%, AUC 0.78) compared to the standard of care (accuracy 68%, AUC 0.74) or patient reported outcomes (accuracy 71%, AUC 0.75). SIGNIFICANCE: Accelerometer derived metrics were associated with clinically relevant measures of disease severity, fatigue and balance confidence during a balance challenging task. Inertial sensors could feasibly be utilized to enhance the accuracy of functional assessments to identify fall risk in PwMS. Simplicity of these accelerometer-based metrics could facilitate deployment for community-based monitoring.

A Stroke Care Model at an Academic, Comprehensive Stroke Center During the 2020 COVID-19 Pandemic.

BACKGROUND AND PURPOSE: The COVID-19 pandemic has required the adaptation of hyperacute stroke care (including stroke code pathways) and hospital stroke management. There remains a need to provide rapid and comprehensive assessment to acute stroke patients while reducing the risk of COVID-19 exposure, protecting healthcare providers, and preserving personal protective equipment (PPE) supplies. While the COVID infection is typically not a primary cerebrovascular condition, the downstream effects of this pandemic force adjustments to stroke care pathways to maintain optimal stroke patient outcomes. METHODS: The University of California San Diego (UCSD) Health System encompasses two academic, Comprehensive Stroke Centers (CSCs). The UCSD Stroke Center reviewed the national COVID-19 crisis and implications on stroke care. All current resources for stroke care were identified and adapted to include COVID-19 screening. The adjusted model focused on comprehensive and rapid acute stroke treatment, reduction of exposure to the healthcare team, and preservation of PPE. AIMS: The adjusted pathways implement telestroke assessments as a specific option for all inpatient and outpatient encounters and accounts for when telemedicine systems are not available or functional. COVID screening is done on all stroke patients. We outline a model of hyperacute stroke evaluation in an adapted stroke code protocol and novel methods of stroke patient management. CONCLUSIONS: The overall goal of the model is to preserve patient access and outcomes while decreasing potential COVID-19 exposure to patients and healthcare providers. This model also serves to reduce the use of vital PPE. It is critical that stroke providers share best practices via academic and vetted social media platforms for rapid dissemination of tools and care models during the COVID-19 crisis.