Time Window Research Articles

Optical memory and counter using a graphene based hybrid plasmonic temporal integrator

This paper presents design and analysis of an optical memory and counter based on ultra-compact temporal integrators (INTs) using a graphene hybrid plasmonic add-drop ring resonator (GHP-ADRR) and pulley-type ring resonator (GHP-PRR) for optical signal processing. Due to the valuable features of graphene hybrid plasmonic technology, the footprint of these INTs is equal to 4 × 3.5 µm2 for GHP-ADRR and 5.4 × 3.6 µm2 for GHP-PRR. Also, the performance of the INTs has been analyzed by the three-dimensional finite-difference time-domain method in the frequency and time domains, and the accuracy of the results has been compared with those of the math counterparts and also key specifications of the first-order temporal INTs including phase jump, insertion loss, 3 dB bandwidth, rise time, integration time window, and energy efficiency have been investigated. Based on the results, both circuits have better performance than the photonic counterparts. Furthermore, the performance of these INTs has been evaluated in detail as a high-speed optical memory and counter. It has been illustrated that due to the greater quality factor of the GHP-PRR, this circuit has more accuracy for realizing the first-order integration, optical memory, and counter than the GHP-ADRR-based INT.

Research on Vehicle Routing Problem with Time Windows Based on Improved Genetic Algorithm and Ant Colony Algorithm

Research on Vehicle Routing Problem with Time Windows Based on Improved Genetic Algorithm and Ant Colony Algorithm

Workload and clinical impact of MRI-based extension of reperfusion therapy time window in acute ischaemic stroke-a prospective single-centre study.

Current European Stroke Organisation (ESO) guidelines recommend extended time window reperfusion therapies (4.5-9h for thrombolysis, 6-24h for thrombectomy) based on advanced imaging. However, the workload and clinical benefit of this strategy on a population basis are not known. To determine the caseload, treatment rates, and outcomes in the extended as compared to the standard time windows. All consecutive ischaemic stroke patients within 24h of last known well between 1st March 2021 and 28th February 2022 were included in a prospective single-centre study. Treatment eligibility in the extended time windows or wake-up strokes recognized within 4h was based on current ESO guideline criteria using MRI DWI-PWI or DWI-FLAIR mismatch. MRI was only available during working hours (8-20h); otherwise, CT/CTA was used. Clinical outcome in treated patients was assessed at three months. Among the 777 admitted patients, 252 (32.4%) had MRI. The thrombolysis rate was 119/304 (39.1%) in standard and 14/231 (6.1%) in the extended time window. The thrombectomy rate was 34/386 (8.8%) in standard and 15/391 (3.8%) in the extended time window. Independent clinical outcomes (mRS ≤ 2) were not statistically different in early and late-treated patients both for thrombolysis (48% vs. 28.6%, p = 0.25) and thrombectomy (38.4% vs. 33.3%, p = 0.99). Even with a limited availability of advanced imaging extending therapeutic time windows resulted in an 11.7% increase in thrombolysis and a 44% increase in thrombectomy with comparable clinical outcomes in early and late-treated patients at the price of a twofold burden in clinical and advanced imaging screening.

From early development to maturity: a phenotypic analysis of the Townes sickle cell disease mice.

Well-characterised mouse models of disease may provide valuable insights into pathophysiology. This study characterises the Townes mouse model of sickle cell disease (SCD) and establishes a time window in which the disease is present but does not progress significantly in terms of severity. We examined Townes mice with the HbAA, HbAS, and HbSS genotypes from young (4 weeks) to mature (5 months) stages of life to assess the disease state at different ages and any progression. We conducted blood tests, histological organ damage evaluations, and metabolic assessments to identify a suitable time frame for study based on welfare considerations. Townes HbSS mice displayed key SCD features such as anaemia, haemolysis, thromboinflammation and organ pathology. Notably, these manifestations remained relatively stable over the study period, indicating a stable phase suitable for conducting intervention studies. Mice with HbAS and HbAA genotypes served as comparative controls, showing minimal to no pathology throughout. These findings are valuable for future research on SCD and may ultimately lead to the development of more effective treatments for this debilitating disease.

Early Escalation to CentriMag for Acute Myocardial Infarction-Induced Out-of-Hospital Cardiac Arrest With Refractory to Extracorporeal Membrane Oxygen Support.

The mortality rate of acute myocardial infarction (AMI)-related refractory cardiogenic shock (rCS) remains high, particularly in patients experiencing cardiac arrest with extracorporeal cardiopulmonary resuscitation (ECPR). This study aimed to evaluate the outcomes of early escalation to CentriMag for AMI-induced out-of-hospital cardiac arrest (OHCA) with ECPR. Patients with AMI-induced OHCA with refractory to ECMO support after ECPR were enrolled. Clinical data were analyzed to identify predictive factors for mortality and survival benefits. Eighty-nine patients were enrolled, of whom 26 underwent CentriMag implantation. The 1-year survival rate for those with the implantation was 34.6%. In contrast, those without implantation showed a survival rate of 7.9%. The average time from the initiation of ECPR to CentriMag implantation was 22.5 ± 14.6 h. The surgical mortality group exhibited a larger body surface area, longer intervals from CPR to ECPR, shorter duration of CentriMag support, and higher preoperative serum creatinine and postoperative day 1 serum aspartate aminotransferase levels. A prolonged interval from CPR to ECPR was identified as an independent risk factor for mortality. Extended duration of CentriMag support was associated with improved survival outcomes. Early CentriMag implantation rescues patients experiencing AMI-related OHCA with rCS and refractory to ECMO support after ECPR. This intervention provides a critical time window, serving as a safe bridge to decision.

Calibration of parameters and predictive control strategy of a wind turbine for improvement of energy harvesting

Applied mathematical modeling of the various energy resources has contributed widely in their design and operation improvements. Among renewable energy sources, wind energy systems have gained a significant attention for their rapid and continuous growth, which resulted from their cost effectiveness and clean power generation. However, research effort on the industry and university levels is still needed to enhance the characteristics of existing wind turbines and allow larger amounts of their share. This thesis presents an accurate model of a wind turbine (TWT-1.65) system for time-based dynamic simulations. The parameters of the power coefficient have been identified, calibrated and verified using different meta-heuristic optimization techniques, which are the Wild Horse Optimizer (WHO), Whale Optimizer (WO), and Genetic Algorithm (GA) to enhance the model accuracy over the previously related studies. The new version of the parameters has resulted in a higher accuracy while being practically meaningful, with the best achieved MSE of 0.006. The system model has been then integrated with two predictive controllers which are the linear Model Predictive Control (MPC) and Neural Network Predictive Control (NNPC) to regulate the pitch angle trends for the target of maximum energy harvesting. With proper selection of the controllers’ parameters, off-line simulation studies have shown improved production trends of the power output with constrained and safe trajectory of the pitch angle, which can be translated to be an improve in the total average harvested power of 34% and 44.5% over the complete time window of 5760 min using NNPC and Linear MPC respectively.

A Performance-Based Adaptation Index for Automated Insulin Delivery Systems.

Automated insulin delivery (AID) algorithms can benefit from tuning of their aggressiveness to meet individual needs, as insulin requirements vary among and within users. We introduce the Performance-Based Adaptation Index (PAI), a tool designed to enable automatic adjustment of an AID system aggressiveness based on continuous glucose monitoring (CGM) metrics. PAI integrates two CGM-based metrics-one for hypoglycemia and another for hyperglycemia exposure-over a previous time window into a single index (). We propose two methods to compute : one based on time in range (TIR, 70-180 mg/dL), and the other on glycemic risk indices. Using , we developed a multiplicative strategy to adjust the AID system's aggressiveness, accounting for situations where cannot be reliably calculated. The feasibility of this method was assessed in-silico using the UVA/Padova Type 1 Diabetes Simulator and our full closed-loop algorithm (UVA-model predictive control (MPC)) across five scenarios: optimal tuning (baseline), conservative and aggressive tunings, and temporary and permanent changes in insulin needs. Glycemic outcomes were evaluated from the simulated glucose traces. Negligible performance variations were observed in the baseline scenario. For the conservative scenario, adjusting improved TIR (35.1% vs 71.8%) and increased total daily insulin (32.1 U vs 41.2 U). Conversely, for the aggressive scenario, it reduced hypoglycemia exposure (TBR: 2.6% vs 1.4%) and overall insulin usage (45.6 U vs 43.0 U). In-silico results demonstrated the safety and efficacy of using PAI to automatically tune the UVA-MPC controller, achieving TIR values above 70% under fully closed-loop conditions and across various physiological states. Clinical validation of these results is warranted.

Realistic Data Delays and Alternative Inactivity Definitions in Telecom Churn: Investigating Concept Drift Using a Sliding-Window Approach

Predicting customer churn is essential for telecommunications companies to maintain profitability. However, training models on historical models leads to performance degradation when they are applied to future conditions—a phenomenon known as concept drift. We employ a sliding-window approach that separates the training and testing time windows, creating a future-based “true test”. Using unique real data, we show that a CatBoost classifier model trained on older data can remain relevant when new, unseen intervals are used. A key innovation of our work is the use of 40-day “partial churn” labels; a model trained on these labels accurately predicts 90-day churn by simply adjusting the decision threshold. Out of the six modeled scenarios, in the main realistic scenario, CatBoost retained an accuracy above 0.798 and an F1 of near 0.704, reflecting its robustness even under real-world delays and potential drift. Overall, our findings emphasize that models do not necessarily “expire” with time; rather, their performance varies according to when they are tested. This research underscores the importance of a truly future-based evaluation (instead of artificial splits) and offers practical guidance for earlier churn detection when facing real-world data delays.

Electrochemotherapy in metastatic epidural spinal cord compression: a review and technical update.

This review aims to analyse the safety and clinical efficacy in terms of pain relief and local tumour control, in patients with metastatic epidural spinal cord compression undergoing electrochemotherapy (ECT). Moreover, a recommendation detailing optimal electrodes insertion is proposed with the intent of improving treatment planning. Clinical studies published between 2015 and 2023 were included since this time window is consistent with recent developments in the field of ECT of spinal metastases. In the whole, three articles were included. Results showed that percutaneous image-guided ECT provides effective pain relief with limited procedure-related morbidity, along with local tumour control (complete response 28.5%, 38% partial response, 24% stable disease) at 3- month follow-up in the largest published series so far. In conclusion, ECT should integrate the armamentarium of therapies that are currently being proposed to patients with painful metastatic epidural disease.

Subjective Cognitive Concerns are Associated with Worse Performance on Mobile-App Based Cognitive Assessment: An Observational Study in Cognitively Normal Older Adults.

Subjective cognitive concerns (SCC) may be among the earliest clinical symptoms of dementia. There is growing interest in applying mobile app-based cognitive assessment to remotely screen for cognitive status in preclinical dementia, but the relationship between SCC and relevant mobile assessment metrics is uncertain. We characterized the relationship between SCC and adherence, satisfaction, and performance on mobile-app assessments in cognitively unimpaired older adults. Participants (N=122, Mage=69.85, Meducation=16.52, %female=66.7, %White=86.2) completed 8 assessment days using Mobile Monitoring of Cognitive Change (M2C2), an app-based testing platform, with brief daily sessions within morning, afternoon, and evening time windows (24 total testing sessions). M2C2 includes digital working memory, processing speed, and episodic memory tasks. Participants provided feedback about their satisfaction and motivation related to M2C2 upon study completion. SCC was assessed using the Cognitive Function Instrument (CFI). Regression analyses evaluated the association between SCC and adherence, satisfaction, and performance on M2C2, controlling for age, sex, depression, and loneliness. Linear-mixed effects models evaluated whether SCC predicted M2C2 subtest performance over the 8-day testing period, controlling for covariates. SCC was not associated with app satisfaction or protocol motivation, but it was significantly associated with lower rates of protocol adherence (ß=-0.197, p=.037, 95% CI -0.647, -0.021). Higher SCC endorsement significantly predicted worse overall episodic memory performance (ß=-0.200, p = .020, 95% CI -0.020, -0.002), but not working memory or processing speed. There was a main effect of SCC on working memory performance at day 1 (Est=-1.047, SE=0.47, p=0.031) and a significant interaction between SCC and working memory over the 8-day period (Est=0.048, SE=0.02, p=0.031), such that SCC was associated with initially worse, then progressively better working memory performance. SCCs are associated with worse overall memory performance on mobile-app assessments, patterns of cognitive inefficiency (variable working memory) and mildly diminished adherence across an 8-day assessment period. Findings suggest that mobile app assessments may be sensitive to subtle cognitive changes, with important implications for early detection and treatment for individuals at risk for dementia.

Mapping Soil Organic Matter in Black Soil Cropland Areas Using Remote Sensing and Environmental Covariates

The accurate prediction of soil organic matter (SOM) content is important for sustainable agriculture and effective soil management. This task is particularly challenging due to the variability in factors influencing SOM distribution across different cultivated land types, as well as the site-specific responses of SOM to remote sensing data and environmental covariates, especially in the black soil region of northeastern China, where SOM exhibits significant spatial variability. This study evaluated the variations on the importance of different remote sensing imagery and environmental covariates in different cultivated land zones. A total of 180 soil samples (0–20 cm) were collected from Youyi County, Heilongjiang Province, China, and multi-year synthetic bare soil images from 2014 to 2022 (focusing on April and May) were acquired using Google Earth Engine. Combining three types of environmental covariates such as drainage, climate and topography, the study area was categorized into dry field and paddy field. Then, the SOM prediction model was constructed using random forest regression method and the accuracy of different strategies was evaluated by 10-fold cross-validation. The findings indicated that, (1) in the overall regression analysis, combining drainage and climate variables and multi-year synthetic remote sensing images of May could attain the highest prediction accuracy, and the importance of environmental covariates was ranked as follows: remote sensing (RS) > climate (CLI) > drainage (DN) > Topography (TP). (2) Zonal regression analysis was conducted with a high degree of precision, as evidenced by an R2 of 0.72 and an impressively low RMSE of 0.73%. The time window for remote monitoring of SOM was different for dry field and paddy field. More specifically, the optimal time frames for SOM prediction in dryland were identified as April and May, while those for paddy fields were concentrated in May. (3) In addition, the importance of diverse environmental covariates was observed to vary with the cultivated land types. In regions characterized by intricate topography, such as dry fields, the contributions of remote sensing images and climate variables assumed a heightened importance. Conversely, in paddy fields featuring flat terrain, the roles of climate and drainage variables played a more substantial role in influencing the outcomes. These findings underscore the importance of selecting appropriate environmental inputs for improving SOM prediction accuracy.

Doppler velocity of m=1 high-latitude inertial mode over the last five sunspot cycles

A variety of solar global modes of oscillation in the inertial frequency range have been identified in maps of horizontal flows derived from GONG and HMI data. Among these, the high-latitude mode with azimuthal order m=1 (HL) has the largest amplitude and plays a role in shaping the Sun's differential rotation profile. We aim to study the evolution of the HL mode parameters, utilizing Dopplergrams from the Mount Wilson Observatory (MWO), GONG, and HMI, covering together five solar cycles since 1967. We calculated the averages of line-of-sight Doppler signals over longitude, weighted by the sine of longitude with respect to the central meridian, as a proxy for zonal velocity at the surface. We measured the mode's power and frequency from these zonal velocities at high latitudes in sliding time windows of three years. The HL mode is easily observed in the maps of zonal velocity at latitudes above 50 degrees, especially during solar minima. The mode parameters measured from the three independent data sets are consistent during their overlapping periods and agree with previous findings using HMI ring-diagram analysis. We find that the amplitude of the mode undergoes very large variations, taking maximum values at the start of solar cycles 21, 22, and 25, and during the rising phases of cycles 23 and 24. The mode amplitude is anticorrelated with the sunspot number (textrm corr =-0.50) but not correlated with the polar field strength. Over the period 1983--2022 the mode amplitude is strongly anticorrelated with the rotation rate at latitude $60^∘$ (textrm corr =-0.82), that is, with the rotation rate near the mode's critical latitude. The mode frequency variations are small and display no clear solar cycle periodicity above the noise level (∼ ± 3 nHz). Since about 1990, the mode frequency follows an overall decrease of ∼ 0.25 nHz/year, consistent with the long-term decrease of the angular velocity at $60^∘$ latitude. We have shown that the amplitude and frequency of the HL mode can be measured over the last five solar cycles, together with the line-of-sight projection of the velocity eigenfunction. We expect that these very long time series of the mode properties will be key to understand the dynamical interactions between the high-latitude modes, differential rotation, and (possibly) magnetic activity.

Interoperable Models for Identifying Critically Ill Children at Risk of Neurologic Morbidity

Decreasing mortality in the field of pediatric critical care medicine has shifted practicing clinicians' attention to preserving patients' neurodevelopmental potential as a main objective. Earlier identification of critically ill children at risk for incurring neurologic morbidity would facilitate heightened surveillance that could lead to timelier clinical detection, earlier interventions, and preserved neurodevelopmental trajectory. To develop machine-learning models for identifying acquired neurologic morbidity in hospitalized pediatric patients with critical illness and assess correlation with contemporary serum-based, brain injury-derived biomarkers. This prognostic study used data from all children admitted to a quaternary pediatric intensive care unit in a large, freestanding children's hospital in Western Pennsylvania between January 1, 2010, and December 31, 2022. External model validation used data from children admitted between January 1, 2018, and December 31, 2023, to a quaternary pediatric intensive care unit in a large, freestanding children's hospital that serves as a referral center for the 5-state region of Washington, Wyoming, Alaska, Montana, and Idaho. Critical illness. The outcome was neurologic morbidity, defined according to a computable, composite definition at the development site or an order for neurocritical care consultation at the validation site. Models were developed using varying time windows for temporal feature engineering and varying censored time horizons between the last feature and the identified neurologic morbidity. A generalizable model created at the development site was optimized and assessed at an external validation site. Correlation was assessed between development site model predictions and measurements of brain biomarkers from a convenience cohort. After exclusions, there were 18 568 encounters from 2010 to 2022 in the development site generalizable model cohort (median age, 70 [IQR, 18-161] months; 8325 [45%] female). There were 6825 encounters from 2018 to 2021 at the external validation site (median age, 96 [IQR 18-171] months; 3159 [46%] female). A generalizable extreme gradient boosted model with a 24-hour time horizon and 48-hour feature engineering window demonstrated an F1 score of 0.37 (95% CI, 0.33-0.40), area under the receiver operating characteristics curve of 0.81 (95% CI, 0.78-0.83), and number needed to alert of 4 at the validation site. After recalibration at the validation site, the Brier score was 0.04. Serum levels of the brain injury biomarker glial fibrillary acidic protein significantly correlated with model output (rs = 0.34; P = .007). This prognostic study of prediction models for detecting neurologic morbidity in critically ill children demonstrated a well-performing ensemble of models with biomolecular corroboration. Prospective assessment and refinement of biomarker-coupled risk models in pediatric critical illness are warranted.

Code and Data Repository for A Branch-and-Price Algorithm for Robust Drone-Vehicle Routing Problem with Time Windows

The goal of this repository is to share data and results related to the Robust Drone-Vehicle Routing Problem with Time Windows (RDVRRPTW), solved using our solution approaches.

Global Awareness and Response to Early Symptoms of Acute Stroke: A Systematic Literature Review.

Stroke is a condition that leavespersistent disability and causes high mortality worldwide. Knowledge of recognizing early symptoms along with awareness of immediate response to acute stroke among the general population has proven insufficient. Pre-attendance delays adversely affect the time window from symptoms onset to needle time, therefore the effect of recanalization treatments. Although 35% of patients are potentially eligible for these treatments such as intravenous thrombolysis only about about 8-10% of them manage to receive. The study aimed to investigate the association between knowledge of early symptoms in the general population and proper response to acute stroke through prompt activation of emergency medical services (EMS) independently of sociodemographic factors. A systematic review was conducted to identify relevant studies that assessed the general population's knowledge of early symptom recognition and awareness of immediate response to acute stroke. Two investigators reviewed articles published in the PubMed database, from December 1, 2023, to February 29, 2024. The search yielded a total of 340 articles. After the 340 articles were screened, 293 reports were excluded due to title and abstract, irrelevant studies, or when the full text was not available. Finally, a total of 10 articles were included in this systematic review. Out of ten studies included in this review, seven studies wereconducted in Asia, in one study the origin continent is Oceania, while the remaining two studies come from America. All studies were designed and conducted as cross-sectional studies. The most commonly recognizable early symptoms of a stroke were found to be difficulty in speaking or understanding speech, weakness on one side of the body including the face, plus sudden dizziness. Regarding responding to early stroke symptoms, the EMS call option gathered the most preferences of the respondents by 80%. Enlightenment campaigns are needed to highlight acute stroke symptoms unknown to the general public such as facial asymmetry.

A Checklist to Improve Acute Stroke Evaluation and Treatment in the Emergency Department.

Narrow therapeutic time windows and delays in assessing acute ischemic stroke patients limit the access to and effectiveness of reperfusion therapies. A 2-year quality improvement project codesigned and tested a checklist for quicker evaluation of suspected stroke cases in 2 emergency departments (EDs). Utility, feasibility, and implementation barriers were assessed through semistructured interviews. The impact on stroke quality metrics was analyzed using bivariate and multivariate regression models with data from the American Heart Association's Get With the Guidelines registry. Implementing the checklist was significantly associated with higher odds of receiving intravenous thrombolytics within 60 minutes of ED arrival (odds ratio: 6.4, 95% confidence interval: 1.1-68.7, P = 0.03). Users felt the checklist improved the standardization of stroke care and promoted teamwork, especially in a time of higher staff turnover. An ED-based stroke checklist resulted in timelier stroke care for acute ischemic stroke patients, meriting further testing in larger, more diverse settings.

Drone‐enabled material handling in smart manufacturing

AbstractAmidst labor shortages, increasing land prices, and other aspects of the post‐pandemic world, our logistics chain must consider new technological alternatives. Automated material handling equipment often requires valuable floor space and specialized layouts. Uncrewed aerial vehicles, popularly called drones, offer a flexible and cost‐effective alternative. To explore the potential benefits and challenges of such systems, we used a version of the multi‐trip vehicle routing problem with time windows to model two realistic manufacturing environments as use cases. We provide the mixed integer linear programming formulation of this problem and compare results with those of a human‐only based system in a detailed simulated environment. Furthermore, as a physical proof‐of‐concept, we outfitted a commercially available drone with pick‐up and carry capabilities. We found that, even with limited drone carrying capacity, there are economic benefits realized from time savings, compared to ground‐based material handling systems. Besides these time savings, drones are sustainable as they operate on battery power and do not impair the air quality or the traffic experienced on the ground.

Multi-Objective Airport Slot Allocation with Demand-Side Fairness Considerations

Airport slot allocation is a key short-term solution to address airport capacity constraints, and it has long been a focus of research in the field of air traffic management. The existing studies primarily consider constraints such as airport capacity and flight operations, optimizing the slot allocation of arrival and departure flights to maximize the utilization of airport resources. This study proposes an airline fairness index based on a demand-side value system and addresses the problem of flight slot allocation by developing a tri-objective model. The model simultaneously considers the maximum slot deviation, total slot deviation, and airline fairness. Additionally, dynamic capacity constraints using rolling time windows and constraints on slot migration during peak periods are incorporated. The ε-constraint method is employed in conjunction with a large-neighborhood search heuristic to solve a two-stage optimization process, yielding an efficient allocation scheme. The experimental results show that the introduction of rolling capacity constraints effectively resolves the issue of continuous overcapacity that arises when only a fixed capacity is considered. Additionally, the proposed airline fairness index, based on a demand-side value system, can significantly improve fairness during the slot allocation process. By sacrificing at most 16% of the total displacement, it is possible to reduce the unfairness index by nearly 80%.

Abstract 10: Does the use of CT perfusion (CTP) increase treatment rate of endovascular thrombectomy (EVT) in acute ischemic stroke patients?

Introduction: CTP is used to distinguish core infarct from penumbral tissue in the 6-24 hour time window from last known well time (LKW). In addition, routine use of CTP inside of 6 hours may increase the sensitivity of detecting medium vessel occlusion (MeVO) as compared with CTA alone. Therefore, some centers apply a strategy of using CTP/CTA routinely in the first 6 hours from LKW while others reserve CTP for the 6-24 hour delayed time window. We hypothesized that use of CTP/CTA inside of 6 hours from LKW is associated with increased EVT especially for MeVO. Methods: This is a retrospective analysis of acute ischemic stroke (AIS) patients from a multi-state stroke registry between Jan 2018 and Mar 2024 who presented within 6 hours of LKW. The incidence of EVT by triage imaging modality (CTA or CTA/CTP) was analyzed overall and also by occlusion location (Large vessel occlusion (LVO), MeVO) through linear mixed models (LMM). Multivariable models were used to adjust for confounding factors. A two-tailed p value of <0.05 was considered statistically significant. Statistical analyses were performed using the statistical software R (version 4.2.3). Results: Of the 13,778 AIS patients analyzed, 43% were triaged with CTP/CTA and CTA alone was used for 57%. The unadjusted EVT treatment rate was 9.8% in patients triaged with CTA alone and 22% for patients triaged with CTP/CTA. LMMs adjusted for age at diagnosis, sex, race and ethnicity, NIHSS at admit and comorbidities show a higher statistically significant likelihood of receiving EVT in the CTA/CTP triaged patients (OR 1.6, 95%CI 1.3 to 1.8, p <0.001). For LVO, there was no difference in the likelihood of receiving EVT in the CTA/CTP triaged patients (OR 1.3, 95%CI 0.7 to 2.3, p = .4). However, for MeVO there was a higher likelihood of receiving EVT in the CTA/CTP triaged patients (OR 3.2, 95%CI 2.0 to 5.0, p <0.001) ( Fig 1 ) Conclusions: The results here show that in a large system of stroke hospitals that AIS patients presenting within 6 hours of LKWT who received CTP/CTA were more likely to undergo EVT than those who received CTA alone. This increased utilization of EVT was accounted for by increased EVT for MEVO but not for LVO. The increased utilization of EVT in MEVO may reflect increased sensitivity of detecting MeVO with the addition of CTP. If MEVO EVT treatment proves effective in ongoing randomized clinical trials, our data argue for more routine use of CTP as a “MEVO detector” in the early time window.

Abstract TMP73: Endovascular therapy for acute ischemic stroke beyond 24 hours after onset: Japan Stroke Data Bank

Background: The therapeutic time window of endovascular therapy (EVT) for acute ischemic stroke (AIS) has been expanded up to 24 hours of onset. Patients with AIS beyond 24 hours may also benefit from EVT, but the actual status of this clinical issue is unknown. Purpose: To evaluate the real-world status of EVT for AIS beyond 24 hours of onset using a nation-wide stroke registry, Japan Stroke Data Bank (JSDB). JSDB is an ongoing hospital-based multicenter prospective registry of hospitalized patients with acute stroke or transient ischemic attack within 7 days of onset. Methods: From the JSDB dataset, patients with AIS who underwent EVT from 1996 through 2021 were analyzed. Patients were divided into 3 groups according to time from onset to hospital arrival (early: <4.5 hour; late: 4.5–24 hour; and very late: >24 hour). Outcomes included the favorable outcome (mRS 0–2 or return to pre-stroke mRS at discharge) and in-hospital mortality. Results: Among the 256,293 stroke patients, 91,907 AIS patients available for analysis were included in the present study. EVT was implemented in 6.9% (6,356/91,907) (median age 78 years; 41.6% women; median NIHSS score 16). The EVT rate by time window groups was 11.8% (4,258/36,044) for the early, 5.3% (1,676/31,328) for the late, and 1.7% (422/24,535) for the very late group. In the very late group with EVT, baseline NIHSS score was lowest (median 17 points in the early group, 14 points in the late group, and 5 points in the very late group), cardioembolism was least common (67.2%, 53.5%, and 22.0%, respectively), and large artery atherosclerosis was most common (17.9%, 30.8%, and 55.7%, respectively). Favorable outcome was more frequently achieved in the very late group (52.9%) than in the early (41.6%) and late (36.5%) groups. Symptomatic intracranial hemorrhage was more frequently seen in the very late group (6.0%) compared to the early (2.9%) and late (3.8%) groups, but in-hospital mortality was comparable among the time window groups (8.1% in the early group, 7.1% in the late group, and 6.6% in the very late group). Conclusions: Outcomes after EVT for patients with AIS beyond 24 hours were not necessarily worse than for patients with AIS up to 24 hours, but the patient profile in the very late group with EVT differed significantly from that in the early and late groups; developing optimal patient selection strategies for very late AIS may be required.