Primary Stroke Center Research Articles

Patient-reported symptoms and admission pathways in stroke mimics versus confirmed stroke or transient ischaemic attack: a cross-sectional observational study.

To determine patient-reported symptoms and clinical factors associated with mimics and differences in health-seeking behaviour versus stroke. This is a post-hoc analysis of a cross-sectional survey of interviews on patient-reported factors in patients admitted with suspected stroke. Patients were categorised as genuine stroke or mimic. The surveys were conducted from February 2018 to January 2019. Two non-comprehensive stroke centres in Denmark. Patients≥18 years (no upper age limit) admitted with symptoms of stroke to one of the non-comprehensive stroke centres or transferred from a comprehensive- or primary stroke centre were eligible for inclusion. 592 patients were included. Symptoms or clinical factors associated with stroke mimics. Logistic regression analysis was performed to identify factors associated with mimics. Secondarily, the number of strokes versus mimics presenting at a healthcare facility within 3 hours contacted the emergency medical service (EMS) and arrived by ambulance. Of 592 suspected patients with stroke, 113 (19.1%) were mimics; most frequently peripheral vertigo (24.7%) and migraine (11.5%). Factors associated with a higher likelihood of mimics were female sex (OR 1.79, 95% CI 1.14 to 2.79), high Scandinavian Stroke Scale scores (OR 1.05, 95% CI 1.02 to 1.09, per point increase), and vertigo (OR 1.86, 95% CI 1.18 to 2.95). Factors associated with a lower likelihood of mimics were increasing age (OR 0.96, 95% CI 0.95 to 0.98 per year increase), reported limb weakness (OR 0.52, 95% CI 0.30 to 0.89) and difficulty steering (OR 0.51, 95% CI 0.28 to 0.93).There was no difference between groups in the proportion of patients for whom time from symptom onset to healthcare services contact exceeded 3 hours (52.2% vs 53.7%, p=0.78). Fewer mimics contacted the EMS first, were accepted at a primary stroke centre and arrived by ambulance (p<0.05 for all variables). Patient-reported vertigo and migraine are common stroke mimics. Increasing age and unilateral limb symptoms increase the likelihood of a stroke. Although symptoms are similar, prehospital pathways differ between mimics and genuine patients with stroke.

Abstract TMP65: Prehospital Comprehensive Stroke Center vs Primary Stroke Center Triage in Patients with Intracranial Hemorrhage

Background: We aimed to evaluate whether the implementation of regional prehospital transport to comprehensive stroke center (CSC) protocol has any impact on inter-hospital transfer rate and neurologic outcomes of intracranial hemorrhage (ICH) patients. Methods: We performed a retrospective multicenter study comparing outcomes in patients with ICH arriving at 15 Primary Stroke Centers (PSC) and 8 CSC in Chicago, Illinois via emergency medical services transport from January 1 2017 and December 31 2023 - before and after implementation of a prehospital transport protocol in September 2018 which uses a 3-item stroke scale to identify patients with more severe stroke types and &lt;6 hours from onset for transport to the closest CSC rather than closest PSC. Outcomes of interest included inter-hospital transfer rates, arrival to CT times, in-hospital mortality rate, favorable disposition location (discharged to home or acute rehabilitation) and favorable neurologic outcome at discharge (modified Rankin score [mRS] of 0-3). Results: Pre-implementation included 853 patients (mean age=63 [SD 15.0], 54% male, and 45% black), of which 232 (27%) were transported to PSCs (mean NIHSS=13 [SD 11.4]) and 621 patients (73%) to CSCs (mean NIHSS=13 [SD 9.3]). Post-implementation included 3506 patients (mean age=63 [SD 15.3], 63% male, 45% black), of which 891 (25%) went to PSCs (mean NIHSS=13 [SD 11.4]) and 2615 (75%) went to CSCs (mean NIHSS=15 [SD 10.5]). Initial NIHSS score was higher post-implementation compared to pre-implementation (13 [SD 9.9] vs. 14 [SD 10.7]), p=0.04). There were similar rates (41% pre-implementation vs 43% post-implementation) of inter-hospital transfer (p=0.39). Time from hospital arrival to CT imaging was significantly faster post-implementation (86 min vs 36 min, p&lt;0.0001). Rates of favorable discharge disposition (49% pre-implementation vs. 47% post-implementation, p=0.28) and neurologic outcome (29% pre vs 26% post, p=0.13) were similar after adjusting for age, sex, time to CT, and initial NIHSS. However, in-hospital mortality was significantly higher post-implementation (15% pre vs. 20% post, odd ratio=1.4, 95% CI[1.1-1.8], p=0.02) after adjusting for initial NIHSS score. Conclusion: Implementation of a regional prehospital transport protocol to stroke center was associated with a shorter time to CT scanner but higher mortality. Our findings do not support prehospital policies to transport ICH patients directly to CSCs.

Abstract NS2: Enhancing Door-In-Door-Out Times to Achieve Top Third Percentile Ranking for Large Vessel Occlusions Ischemic Stroke at a Primary Stroke Center Through Quality Improvement Initiatives

Abstract NS2: Enhancing Door-In-Door-Out Times to Achieve Top Third Percentile Ranking for Large Vessel Occlusions Ischemic Stroke at a Primary Stroke Center Through Quality Improvement Initiatives

Abstract 143: Remote Telemedicine Based Enrollment in Acute Stroke Studies at Non-Academic Acute Stroke Ready and Primary Stroke Centers: A Multi-Center Experience of the TIMELESS and FASTEST Trials.

Abstract 143: Remote Telemedicine Based Enrollment in Acute Stroke Studies at Non-Academic Acute Stroke Ready and Primary Stroke Centers: A Multi-Center Experience of the TIMELESS and FASTEST Trials.

Abstract 35: Impact of Artificial Intelligence Imaging Decision Support Software on Treatment of Acute Ischemic Stroke in England

Introduction: AI imaging decision support software is recommended by UK and USA stroke guidelines to facilitate identification and transfer of stroke patients eligible for endovascular therapy (EVT) but the impact on thrombectomy delivery is unclear. This prospective observational study evaluated the impact of Brainomix 360 Stroke software in four stroke networks (28 hospitals) in England’s National Health Service (NHS). The primary outcome was percentage of acute stroke patients receiving EVT (the EVT rate); door-in door-out (DIDO) times were assessed as a secondary outcome. Methods: Data were collected prospectively from the Sentinel Stroke National Audit Programme. The impact of Brainomix 360 Stroke software was assessed in two ways: comparison of EVT rates at the 28 evaluation sites and non-evaluation NHS sites before and after implementation (pre-implementation: Jan 2019-Feb 2020; post- : Jan 2022-Feb 2023); comparison of EVT rates and DIDO times at evaluation sites after implementation in patients for whom AI software was used and in those it was not. Multivariate regressions were used to evaluate whether AI use was a predictor of EVT or DIDO time, accounting for other clinical variables (e.g., age, NIHSS, day of week, time of day, time since onset). Results: The dataset included 71,327 patients from 28 evaluation hospitals. Figure 1 shows the change in EVT rates over time in evaluation (blue) and non-evaluation sites (yellow). EVT rate at evaluation sites increased from 2.3% pre-implementation to 4.6% post-implementation (p&lt;.0001). By contrast, EVT rate at non-evaluation sites changed from 1.6% to 2.6% (p&lt;.0001). After implementation, cases that were reviewed with AI software support were more likely to receive EVT (5.9% vs 3.6%; OR=1.6 [95% CI:1.3-1.9, p&lt;.0001). When limiting the analysis to cases admitted to primary stroke centres, the EVT rate was twice as high in cases reviewed with AI software (4.7% vs 2.2%; OR=2.3 [1.8-3.1], p&lt;.0001). DIDO times were shorter for cases reviewed with AI software (median 127 vs. 192 minutes; p&lt;.0001). Conclusions: This prospective study showed that use of AI imaging software was associated with a higher rate of EVT, both by comparing pre- vs. post-implementation data, as well as by comparing cases for whom AI software was versus was not used. Use of AI software was also associated with faster time to transfer. The results support guideline recommendations for AI software to be used in clinical practice.

Abstract WMP54: Outcomes of acute ischemic stroke patients in primary stroke centers versus comprehensive stroke centers: a pre-specified analysis of the Alteplase compared to Tenecteplase trial

Abstract WMP54: Outcomes of acute ischemic stroke patients in primary stroke centers versus comprehensive stroke centers: a pre-specified analysis of the Alteplase compared to Tenecteplase trial

Abstract DP9: Enhancing Nursing Education to Improve Stroke Patient Education and Outcomes

Background: Stroke is the fifth leading cause of death in Minnesota and in the United States. Stroke is a leading cause of long-term disabilities and is largely preventable (80%). Evidence indicates that patients receiving care from stroke specialized nurses, including nurse-led stroke prevention, show better outcomes. Stroke program leaders at a Primary Stroke Center identified this as an opportunity to improve stroke program education, quality of nursing care, and stroke patient outcomes. Purpose: The primary focus area was improving stroke-patient education to include individualized stroke risk factors. As our project progressed, the initial goal remained. The secondary focus was to see if the effect of in-person, hands-on (escape room) nurse education would improve individualized stroke-patient education compliance compared to eLearning and poster board education. Methods: All stroke cases from June 2023 - June 2024 were audited for documentation of individualized stroke-patient education. Utilizing stroke guidelines and scientific statements, a standardized practice of individualizing stroke-patient education was developed. Staff learning modules were developed including eLearning, unit poster board, and in-person learning (escape room). Throughout the project, completion rates of individualized stroke-patient education were compared. A nursing survey was conducted to evaluate confidence and knowledge post multimodality learning modules. Results: Prior to initiation of nursing learning modules, 24 hospitalized stroke-patient charts from June and July 2023 were audited for individualized stroke education (40%). After initiation of nursing learning modules, 183 hospitalized stroke-patient charts from August 2023 to June 2024 were audited for individualized stroke education (poster board 44%, eLearning 45%, escape room 75%). The post nursing learning module survey responses demonstrated improved learners’ knowledge for the interactive escape room (poster board average-good 77%, eLearning average-good 83%, escape room good-excellent 89%). Conclusion: By identifying educational gaps, we were able to enhance nursing education; which improved stroke-patient education and outcomes. In conclusion, nursing confidence and knowledge increased with the in-person escape room education. This suggests engaging in-person learning could be more effective in developing nursing knowledge, improving patient care, and positive patient outcomes.

Acute stroke care coordination in the United States: Variation in state laws for Emergency Medical Services and hospitals.

Lack of care coordination between Emergency Medical Services (EMS) and hospitals contributes to delay of acute stroke (AS) treatment. In the United States, states have adopted laws to improve the quality of EMS and hospital care; the degree to which these laws create regulatory incentives to promote care coordination between them is less well known. We examined state variation in attributes of laws that may influence AS care coordination between EMS and hospitals. We selected ten law "dyads" across seven domains of EMS and hospital AS care informed by published risk assessments of critical steps for improved door-to-needle time and door-in-door-out time. We assessed concordance in prescriptiveness (degree to which levels were similar) and in adoption (degree to which laws were adopted concurrently) of the laws in effect between January 2002 and January 2018 in the United States. The proportion of states with prescriptiveness concordance ranged from 47 % (e.g., inter-facility transfer agreements, comprehensive, primary stroke center certification) to 75 % (e.g., Continuous Quality Improvement (CQI) for EMS and hospitals). Adoption concordance ranged from 31 % (e.g., inter-facility transfer agreements, Acute Stroke Ready Hospital certification) to 86 % (e.g., CQI for EMS and hospitals). Laws for EMS triage were less prescriptive than laws for stroke center certification in 22 %-35 % of states adopting both laws, depending on stroke center type. Subsequent policy implementation and impact studies may benefit from assessing concordance and prescriptiveness in policy intervention adoption, particularly as a foundation for evaluating delays in AS treatment due to inefficient care coordination.

Exploring Differences in Stroke Treatment Between Urban and Rural Hospitals: A Thematic Analysis of Practices in Canada.

Treatment of acute ischemic stroke is highly time dependent, which relies heavily on each hospital's ability and capacity. Designated stroke centers have been established across Canada, but there is still a divide between urban and rural hospitals. This study aims to understand the similarities and differences in their stroke treatment process workflow, incorporation of best practices and data collection. Interviews were conducted with clinicians in stroke centers across Canada to identify similarities and differences between provinces and hospital treatment capability. Semi-structured interviews were completed from September 15 to November 3, 2023, with clinicians and stroke coordinators using snowball and purposive sampling techniques. The interviews were analyzed using thematic analysis. Fourteen participants were interviewed with representatives from four primary stroke centers and three comprehensive stroke centers across five provinces. Five primary themes were identified: 1) management of resources, 2) standardization of tasks, 3) data collection, 4) tool integration into workflow and 5) teamwork and experience. Participants in primary centers described limited resources to follow the patient through the entire treatment process, reliance on pre-notification times to prospectively search necessary patient information, using software to aid in calculating National Institute of Health Stroke Scale and being more cautious toward treating thrombolytics. Both center types discussed challenges with complete and accurate data collection. The overall stroke treatment process and information required across primary and comprehensive centers are similar. However, differences occur in the process due to limitations in resources, pre-arrival notification time, completeness and accuracy of data collected and comfort in treating with thrombolytics.

Current survey of rehabilitation in patients with stroke and comorbidities in primary stroke centers

Current survey of rehabilitation in patients with stroke and comorbidities in primary stroke centers

Identifying Best Practices for Improving the Evaluation and Management of Stroke in Rural Lower-Resourced Settings: A Scientific Statement From the American Heart Association.

Considerable variation exists in the delivery of acute stroke care and stroke outcomes across settings and population groups. This is attributable in part to variation in resources among emergency departments in the United States, most notably in rural regions. Structural constraints of the US health care system, including the geographic distribution of where patients live relative to the location of hospitals and certified stroke centers, will continue to mean that many patients with stroke initially present to community emergency departments that have fewer stroke-related resources. These sites also tend to serve populations in rural areas who experience disparities in care and outcomes. Reducing health disparities related to stroke for populations in rural areas requires investment in these more remote community settings as the anchor of the stroke chain of survival for their respective communities. This scientific statement performs a critical appraisal examining challenges in rural stroke care related to access and variation in stroke-related capabilities for the acute phase of care to inform strategies and propose solutions. The scientific statement considers the value of expansion of Acute Stroke Ready Hospital and Primary Stroke Center certification in rural areas, the role of telehealth and improved transfer processes, as well as increased engagement and mentorship from larger, comprehensive centers to the rural hospitals to which they are connected. Multistakeholder collaboration and policy interventions need to be directed to enhance public awareness, impart staff training, grow infrastructure, enhance access to clinical expertise, streamline data management, and implement quality assessment and improvement programs.

The Certification Ranking of Stroke Treatment Centers Is Unclear to the General Public.

BACKGROUND: Certified stroke treatment centers are classified based on their available resources to treat stroke including an Acute Stroke Ready Hospital (ASRH), a primary stroke center (PSC), a Thrombectomy-Capable Stroke Center (TCSC), and comprehensive stroke centers (CSCs). These hospitals all provide varying levels of care with CSCs being the most able to treat all types and complexities of stroke. This undergraduate nursing-led study explored the public's preference for treatment at different certified stroke treatment centers. METHODS: This study was a prospective nonrandomized observational survey of English-speaking adults at a plasma donation center in the Southwest United States. Subjects completed a 2-minute survey asking whether they would drive to the hospital or call 911 during a suspected stroke, and at which type of hospital they would prefer to be treated. RESULTS: Of 249 respondents, 204 (81.9%) indicated that they would call 911 for hospital transport, and 45 (18.1%) would drive their family member to a hospital. Most respondents (95/248, 38.3%) would prefer treatment at a PSC, 90 (36.3%) preferred an ASRH, 52 (21.0%) preferred a CSC, and 11 (4.4%) preferred a TCSC. There was no association between transportation preference and hospital preference dichotomized as CSC or PSC versus ASRH or TCSC (χ2 = 0.021, P = .885), nor CSC versus other (χ2 = 0.944, P = .331). CONCLUSION: Most respondents did not select CSC as the preferred treatment site, and 1 in 5 would drive rather than call 911. This indicates a knowledge gap regarding stroke center classification that warrants increased education.

Abstract 4119129: PREDICTING 30-DAY ALL-CAUSE READMISSION AFTER STROKE: THE APPLICATION OF MACHINE LEARNING TO LINKED REGISTRY DATA

Background: Thirty-day readmission following hospital discharge for stroke is an important quality measure for US hospitals. Current US prediction models for post stroke readmission based on electronic medical records from single healthcare systems or hospitals have modest discrimination (AUC range 0.64 - 0.74). Aim: To develop 30-day all-cause readmission prediction model using a machine learning (ML) based method trained on linked stroke registry and administrative claims data. Methods: Using probabilistic linking, we matched acute stroke (ICD-10 I61-I63) discharges from 31 hospitals participating in the Michigan Acute Stroke registry between 2016-2020 to multipayer administrative claims data provided by the Michigan Value Collaborative for Medicare and Blue Cross Blue Shield of Michigan commercial beneficiaries. Stroke registry data included patient demographics, clinical characteristics, past medical history, and treatments. Claims data was used to identify readmissions within 30 days of discharge. We used multivariable LASSO logistic regression- a simple ML technique to predict 30-day all-cause-readmission and evaluated the prediction accuracy using a hospital-split internal validation scheme to generate hospital-specific and pooled AUC estimates with 95% confidence intervals (Figure 1). Results: Of 19,382 linked stroke discharges, 2,724 (14.1%) were readmitted within 30-days. Readmitted patients were older, more likely to be male, black, and have higher stroke severity (NIHSS &gt;5). Registry hospitals were either primary (64%) or comprehensive (26%) stroke centers. Hospital-specific 30-day readmission ranged between 9.9%-23.1% ( P &lt;.001) with an average of 14.1% (95% CI:13.6%-14.5%) (Figure 2). Hospital specific AUC estimates ranged between 0.60-0.80 with a pooled AUC of 0.68 (95% CI:0.65-0.70) (Figure 2). Conclusions: ML prediction model fitted to linked registry data can be used to predict hospital-specific and statewide readmissions post stroke.

Abstract 189: An Expedited Transfer Protocol Improves Door In Door Out Times

Background Rapid reperfusion with mechanical thrombectomy for ischemic stroke secondary to large vessel occlusion leads to significant reduction in morbidity and mortality. DIDO time is an important metric for stroke centers without on‐site mechanical thrombectomy. Delays in treatment can occur when patients present at a Primary Stroke Center (PSC) then require transfer to the CSC for MT. Our system Comprehensive Stroke Center (CSC) receives patients from 2 of our systems PSC's. In order to expedite care an expedited transfer protocol was implemented.Purpose: The goal of this work is to decrease the time at the PSC, known as door‐in‐door‐out (DIDO) and decrease overall time from patient presentation to treatment with thrombectomy, known as door to groin time (DTG).Methods: Patients are screened at triage by a PSC emergency department provider for the following: aphasia, gaze preference, or neglect with unilateral weakness within 24 hours of last known normal. If these criteria are present, the emergency department provider contacts the CSC transfer center to initiate the expedited transfer process, which begins moving transfer resources to the PSC while clinical work‐up continues. This expedited early transfer process was implemented in January 2021. Transfers for MT during the 12‐month period before and after the implementation of the expedited transfer process were analyzed.Results: Mean DIDO was lower when expedited protocol was utilized (99 min vs. 137 min respectively, (p=.017)). Mean DTG was unchanged (27 min vs. 37 min respectively, (p = 0.067)).Conclusions: Use of the expedited transfer process allows transfer resources to begin moving to the patient with a suspected LVO in parallel with ongoing evaluation, resulting in faster DIDO and times leading to faster treatment aimed at reducing stroke morbidity and mortality.

Abstract 073: ASPECTS decay during interfacility transfers in patients with large vessel occlusion

Background Some patients with large vessel occlusion (LVO) are first evaluated at primary stroke centers (PSCs) before transfer to a comprehensive stroke center (CSC) for Endovascular thrombectomy (EVT). A subgroup of those patients experiences rapid infarction growth during transfer limiting the benefit from intervention. We evaluated the incidence, predictors, and outcomes of imaging decay of patients with LVO transferred for EVT. Methods A retrospective analysis of all patients with anterior circulation LVO transferred for EVT. ASPECTS decay was defined as 2 ASPECTS points decrement at CSC compared to PSC. Primary outcome was 90 day home time. Results 182 (44.5% female) patients met the inclusion criteria. Median (IQR) time between baseline and follow up CTs was 250.5 (163‐324.25) minutes. Out of the 182 patients, 66 patients (36%) had ASPECTS decay, and 32/66 patients (48%) underwent EVT. Collateral score was strongly associated with the ASPECTS decay (OR= 0.29, [0.19‐0.46], p&lt;0.001) in multivariate logistic regression. Patients with ASEPCTS decay have a significant lower rate of 90‐day home‐time (P&lt;0.001) and higher risk of 90‐day mortality (P&lt;0.001), and in hospital death (P=0.03). In patients with ASPECTS decay, performing EVT was not associated with improved 90‐day home time ( β = 0.19, [‐3.2 ‐27.8], p=0.12). Conclusions For patients with LVO transferred for thrombectomy, a third of our patients developed ASPECTS decay. Collateral blood flow was the main determinant of ASPECTS decay during inter‐facility transfers. Decay is strongly associated with poor functional outcomes.

Prehospital computed tomography in a rural district for rapid diagnosis and treatment of stroke.

Early diagnosis and triage of patients with ischemic stroke is essential for rapid reperfusion therapy. The prehospital delay may be substantial and patients from rural districts often arrive at their local hospital too late for disability-preventing thrombolytic therapy due to prolonged transport times. Hallingdal District Medical Centre (HDMC) is located in a rural area of Norway and is equipped with a computed tomography (CT) scanner. We established emergency pathways of CT imaging and thrombolytic treatment of patients with acute ischemic stroke at HDMC. During office hours these pathways were managed by a radiographer and a general physician supported by videoconference from the Primary Stroke Centre. Outside office hours we remotely controlled the CT exam and supported telestroke guided paramedics handling and examining the patients. With a primary aim of demonstrating the feasibility of this de novo concept we enrolled patients in the period 2017-2021 into a comparative cohort observational study. We compared patients treated at HDMC (the Rural CT group) to patients from two other rural regions in Norway with similar distances to their local hospital but without access to a rural CT scanner (the Reference group). A total of 86 patients were included in the Rural CT group (mean age 74, 52% male, 43% stroke mimics), and 69 patients were included in the Reference group (mean age 70, 42% male, 28% stroke mimics). Median time from onset of symptoms to completed CT examination was 93 min in the Rural CT group as compared to 240 min in the Reference group (p < 0.05). In patients receiving intravenous thrombolysis time from onset of symptoms to treatment was median 124 min in the Rural CT group and 213 min in the Reference group, p < 0.05. The frequency of thrombolysis for ischemic stroke did not significantly differ between the two groups. Combining prehospital rural CT examination with telestroke guided diagnosis and thrombolytic treatment by paramedics may facilitate earlier initiation of thrombolysis for patients with ischemic stroke.

Streamlining Acute Stroke Processes and Data Collection: A Narrative Review

(1) Background: Acute ischemic stroke treatment has been thoroughly studied to identify strategies to reduce treatment times. However, many centers still struggle to achieve fast treatment times. Additionally, studies primarily focus on larger, more advanced centers; yet, smaller centers often face longer treatment times. (2) Objectives: The aim of this study is to analyze the existing literature reviewing stroke treatment processes in primary and comprehensive stroke centers that investigated or reduced treatment times. The articles identified were categorized based on the focus areas and approaches used. (3) Results: Three main categories of improvements were identified in the literature: (1) standardization of processes, (2) resource management, and (3) data collection. Both primary and comprehensive stroke centers were able to reduce treatment times through standardization of the processes. However, challenges such as variations in hospital resources and difficulties incorporating data collection software into workflow were highlighted. Additionally, many strategies to optimize resources and data collection that can benefit primary stroke centers were only conducted in comprehensive stroke centers. (4) Conclusions: Many existing strategies to improve stroke treatment times, such as pre-notification and mass stroke team alerts, have been implemented in both primary and comprehensive stroke centers. However, tools such as simulation training are understudied in primary stroke centers and should be analyzed. Additionally, while data collection and feedback are recognized as crucial for process improvement, challenges persist in integrating consistent data collection methods into clinical workflow. Further development of easy-to-use software tailored to clinician needs can help improve stroke center capabilities to provide feedback and improve treatment processes.

Clinical and Imaging Features Associated With Fast Infarct Growth During Interhospital Transfers of Patients With Large Vessel Occlusions.

Acute ischemic stroke patients with a large vessel occlusion (LVO) who present to a primary stroke center (PSC) often require transfer to a comprehensive stroke center (CSC) for thrombectomy. Not much is known about specific characteristics at the PSC that are associated with infarct growth during transfer. Gaining more insight into these features could aid future trials with cytoprotective agents targeted at slowing infarct growth. We aimed to identify baseline clinical and imaging characteristics that are associated with fast infarct growth rate (IGR) during interhospital transfer. We included patients from the CT Perfusion to Predict Response to Recanalization in Ischemic Stroke Project, a prospective multicenter study. Patients with an anterior circulation LVO who were transferred from a PSC to a CSC for consideration of thrombectomy were eligible if imaging criteria were fulfilled. A CT perfusion (CTP) needed to be obtained at the PSC followed by an MRI at the CSC, before consideration of thrombectomy. The interhospital IGR was defined as the difference between the infarct volumes on MRI and CTP, divided by the time between the scans. Multivariable logistic regression was used to determine characteristics associated with fast IGR (≥5 mL/h). A total of 183 patients with a median age of 74 years (interquartile range 61-82), of whom 99 (54%) were male and 82 (45%) were fast progressors, were included. At baseline, fast progressors had a higher NIH Stroke Scale score (median 16 vs 13), lower cerebral blood volume index (median 0.80 vs 0.89), more commonly poor collaterals on CT angiography (35% vs 13%), higher hypoperfusion intensity ratios (HIRs) (median 0.51 vs 0.34), and larger core volumes (median 11.80 mL vs 0.00 mL). In multivariable analysis, higher HIR (adjusted odds ratio [aOR] for every 0.10 increase 1.32 [95% CI 1.10-1.59]) and larger core volume (aOR for every 10 mL increase 1.54 [95% CI 1.20-2.11]) remained independently associated with fast IGR. Fast infarct growth during interhospital transfer of acute stroke patients is associated with imaging markers of poor collaterals on baseline imaging. These markers are promising targets for patient selection in cytoprotective trials aimed at reducing interhospital infarct growth.

A data envelopment analysis model for optimizing transfer time of ischemic stroke patients under endovascular thrombectomy

This study applies Data Envelopment Analysis (DEA) to optimize transfer times and futile transfers of eligible ischemic stroke patients receiving Endovascular Thrombosis (EVT) in Primary Stroke Centers (PSC) in Nova Scotia. The study aims to assess healthcare delivery in Nova Scotia over two periods. It seeks to improve stroke care for rural populations by examining nine inputs, including age and distance between PSCs and the Comprehensive Stroke Centre (CSC) that provided EVT treatment, concerning a single output variable: whether EVT is performed or not. In the first phase, 115 patients were treated as Decision-Making Units (DMUs) for ten PSCs by applying an input-oriented Variable Returns to Scale (VRS) assisted by super-efficiency analysis using the Python-based PyDEA tool. This tool is known for its unrestricted capacity to handle DMUs, inputs, and outputs. In the second phase, eight PSCs with low patient numbers were merged into four DMUs, each consisting of two PSCs. These two merged PSCs have limited patients, and the selected PSCs are also geographically close. Two PSCs have been kept separate because they had sufficient patient volume. In the first phase, VRS generated more reasonable efficiency scores for evaluation, while in the second phase, Constant Returns to Scale (CRS) outperformed VRS, yielding better results. In the initial stage of the second phase, ten PSCs were considered as six DMUs using the input-oriented CRS and VRS for 115 patients. Super-efficiency measures were applied in this stage to improve the evaluation process further. In the second part of the second phase, a comparison between the first period (2018–2019) and the second period (2020–2021) was conducted using the Malmquist Productivity Index (MPI), considering CRS and VRS to evaluate the relative efficiency and productivity change of six DMUs over time.

Utilization of Artificial Intelligence to Improve Door-In Door-Out Times for Mechanical Thrombectomy-Eligible Patients at a Hub-and-Spoke Community-Based Comprehensive Stroke Center: A Single Case Study Presentation

Background: Delays in the transfer of patients with hyperacute stroke may lead to treatment ineligibility due to the degree of cerebral hypoxic injury suffered. Cloud-based artificial intelligence applications may improve transfer times and expand access to advanced therapies. One case between a community-based comprehensive stroke center (CSC) and a primary stroke center (PSC) using a third-party telemedicine service and shared cloud-based artificial intelligence application may provide insight in opportunities to improve stroke systems of care. Case Presentation: A 62-year-old female with a past medical history of hypertension, current everyday tobacco smoker, and marijuana user presented to an outlying emergency department (ED) with dense left-sided hemiplegia affecting the arm and leg, right-sided gaze preference, and severe dysarthria. Her last known well (LKW) time was 0900 hours. CNS imaging revealed a right middle cerebral artery occlusion, visible to members of the CSC stroke team through the use of a cloud-based artificial intelligence cell phone application. The patient was treated with intravenous thrombolytics at the PSC, and she was transferred to the CSC, where she underwent a diagnostic cerebral arteriogram with carotid artery stenting. Later, Magnetic Resonance Imaging (MRI) of the brain revealed a 3.5 cm x 2.5 cm hemorrhagic lesion in the right frontal lobe and diffusion restriction in the right frontal and right posterior temporal lobes. The patient’s hospital stay was three days and, at the time of discharged, her modified Rankin score and NIHSS were zero. She was discharged on dual antiplatelet therapy, statin therapy, and nicotine replacement. Utilization of Artificial Intelligence: Transfer delays are complicated by organizing care at PSC and CSC and can be lengthy when communication across different facilities and subspecialties. Implementing cloud-based AI image sharing in stroke systems of care has reduced DIDO times by providing rapid imaging interpretation, streamlining communication, and enhancing coordination between PSCs and CSCs. Conclusions: Our case presentation showed how a hub-and-spoke model combined with cloud-based AI utilization can improve DIDO times and enhance stroke systems of care.