Real World Research Articles

Real-world outcomes in patients with chronic spontaneous urticaria receiving omalizumab: insights from a clinical practice survey.

Objective: Chronic spontaneous urticaria (CSU) is a debilitating inflammatory skin condition, often impacting quality of life. International guidelines recommend omalizumab, an anti-immunoglobulin E antibody, for second-line treatment. Our objective was to understand patient characteristics associated with prescription of omalizumab, and assess real-world outcomes in patients with CSU treated with omalizumab.Methods: We analyzed data from the Adelphi Real World CSU Disease Specific Programme™, a cross-sectional survey with retrospective data collection (December 2020-October 2021) from physicians and patients with CSU in the United States.Results: Data from allergists (n = 45), dermatologists (n = 51), and primary care physicians (PCPs; n = 20) were included. At the time of data collection, one-third of patients were receiving omalizumab (n = 220) and 67% were eligible for but not receiving omalizumab (n = 455). Using logistic regression, the odds of receiving omalizumab were higher in patients whose entire bodies were affected by hives [OR = 2.551; 95% CI 1.502-4.333; p < 0.001] or with deteriorating/unstable prognoses at treatment initiation [OR = 2.219; 95% CI 1.031-4.777; p = 0.042], and lower in patients managed by PCPs [OR = 0.276; 95% CI 0.130-0.584; p < 0.001]. Estimates from an inverse probability weighted regression adjustment model indicated that patients receiving omalizumab had higher treatment satisfaction, improvements in itching, hives, angioedema, insomnia, and anxiety, and lower impact on work productivity, compared with patients not receiving omalizumab.Conclusion: Around two-thirds of patients with CSU considered eligible for omalizumab were not receiving the guideline-recommended therapy. Patients receiving omalizumab had better real-world outcomes compared with patients not receiving omalizumab. Ensuring patients receive the most appropriate treatment could benefit patients with CSU.

AUGMENTED REALITY TECHNOLOGY FOR VISUALIZING DIPANEGARA UNIVERSITY BUILDINGS

Multimedia technology continues to advance, one of which is Augmented Reality (AR) technology, commonly known as the technology for merging virtual objects into the real world. Building brochures, which only display building photos and are visible from only one side, require us to visit the building to view its structure. Therefore, an idea emerged for a design of an application that uses Android-based Augmented Reality technology. This application allows users to view a more realistic physical shape of the building without visiting it directly by directing the camera towards a marker displaying the building's logo and showing a 3D model of the Dipanegara University building. It also provides general information about the building. This application is designed using 3DS Max, Unity, and Vuforia SDK, making it easier for prospective students to view the building's layout. Keywords : 3DS Max, Android, Augmented Reality, Unity, Visualization.

Plasma biomarkers increase diagnostic confidence in patients with Alzheimer’s disease or frontotemporal lobar degeneration

BackgroundThe recent development of techniques to assess plasma biomarkers has changed the way the research community envisions the future of diagnosis and management of Alzheimer’s disease (AD) and other neurodegenerative disorders. This work aims to provide real world evidence on the clinical impact of plasma biomarkers in an academic tertiary care center.MethodsAnonymized clinical reports of patients diagnosed with AD or Frontotemporal Lobar Degeneration with available plasma biomarkers (Aβ42, Aβ42/Aβ40, p-tau181, p-tau231, NfL, GFAP) were independently assessed by two neurologists who expressed diagnosis and diagnostic confidence three times: (T0) at baseline based on the information collected during the first visit, (T1) after plasma biomarkers, and (T2) after traditional biomarkers (when available). Finally, we assessed whether clinicians’ interpretation of plasma biomarkers and the consequent clinical impact are consistent with the final diagnosis, determined after the conclusion of the diagnostic clinical and instrumental work-up by the actual managing physicians who had complete access to all available information.ResultsClinicians assessed 122 reports, and their concordance ranged from 81 to 91% at the three time points. At T1, the presentation of plasma biomarkers resulted in a change of diagnosis in 2% (2/122, p = 1.00) of cases, and in increased diagnostic confidence in 76% (91/120, p < 0.001) of cases with confirmed diagnosis. The change in diagnosis and the increase in diagnostic confidence after plasma biomarkers were consistent with the final diagnosis in 100% (2/2) and 81% (74/91) of cases, respectively. At T2, the presentation of traditional biomarkers resulted in a further change of diagnosis in 13% (12/94, p = 0.149) of cases, and in increased diagnostic confidence in 88% (72/82, p < 0.001) of cases with confirmed diagnosis.ConclusionsIn an academic tertiary care center, plasma biomarkers supported clinicians by increasing their diagnostic confidence in most cases, despite a negligible impact on diagnosis. Future prospective studies are needed to assess the full potential of plasma biomarkers on clinical grounds.

A randomised, crossover trial exploring the patient perspective and effectiveness of biosimilar adalimumab transition: IBD reference and biosimilar adalimumab cross over study (iBaSS).

Patient satisfaction has been positively associated with adherence which is expected to impact outcomes. Although vital for successful implementation of biosimilar medicines, little is known about the patient perspective of transition. The aim of this study was to investigate clinical outcomes and patient experience of transitioning between reference adalimumab and a biosimilar (SB5). iBaSS is a phase IV single-centre, prospective, randomised, single-blind, cross-over study in adult subjects with Crohn's disease. Participants, stable on adalimumab before consent, received 24weeks of treatment with both reference adalimumab and SB5. The primary outcome was the proportion of patients maintaining baseline clinical status throughout each treatment period, with patients' perspective of disease control and treatment satisfaction assessed as secondary outcomes. A total of 112 participants, representative of the heterogeneous patient populations encountered in routine clinical practice, were enrolled. A similar proportion of participants maintained baseline clinical status through each treatment period: 81.8% with reference adalimumab and 79.5% with SB5. Patient reported outcomes (IBD-Control questionnaire (SB5: 15.5; reference adalimumab 15) and TSQM), adverse events and therapeutic drug monitoring remained consistent through both treatment periods, although a higher median injection pain VAS score was noted with SB5 (53/100 versus 6/100 with reference adalimumab). The number of switches undertaken in the study did not impact serum drug concentration or immunogenicity. This study, mimicking real world adalimumab transition, demonstrates that patients undertaking brand transition can be expected to have consistent clinical and satisfaction outcomes. Number 2018-004967-30.

Visual Reinforcement Learning for Dynamic Object Detection

Abstract. Object detection is a widely studied task in computer vision. Current methods often focus on images captured from appropriate viewpoints. However, there is a large disparity between objects observed from different viewpoints in the real world. Dynamic Object Detection (DOD) method automatically adjusts the camera viewpoint in a visual scene to sequentially find optimal viewpoints. Currently, the DOD tasks are usually modeled as a sequential decision-making problem and solved using reinforcement learning methods. Existing approaches face challenges with sparse rewards and training instability. To tackle these issues, we proposed a single-step reward function and a lightweight network, respectively. The single-step reward function, which provides timely feedback, gives an efficient training process for DOD tasks. The lightweight network with few parameters can ensure the stability of the training process. To evaluate the effectiveness of our method, we developed a simulation dataset based on UE4, which consists of 1800 training images and 450 testing images. The dataset includes five object categories: vans, cars, trailers, box trucks and SUVs. Experiments demonstrate that our method outperforms SOTA object detectors on our simulation dataset. Specifically, the average precisions(APs) are improved from 89.1% to 96.0% when using the YOLOv8 object detector.

Autonomous Landing Spot Detection for Unmanned Aerial Vehicles Based on Monocular Vision

Abstract. Enhancing the autonomous landing capability of unmanned aerial vehicles (UAVs) is of great significance for improving their operational efficiency and field survival capabilities. To this end, we propose a real-time autonomous landing spot detection method for UAVs. Firstly, the pose of the UAV at any given time and the initial three-dimensional point cloud of the scene are estimated using simultaneous localization and mapping (SLAM) techniques. Then, since the initial point cloud is sparse and cannot be used for terrain analysis, we generate a voxel-based elevation map of the scene, which can establish interconnectivity among the points. Finally, we propose a shift-box strategy to comprehensively analyze various terrain factors in the elevation map, determine the landing spot for UAVs, and update the landing spot in real time. The UAV flight experiments conducted in the real world have demonstrated the effectiveness and real-time performance of the proposed method.

Digital twins in aircraft production and MRO: challenges and opportunities

AbstractThe digital twin (DT) concept, value-adding connecting the real and digital world, has been a rising trend in recent years, while the implementation and observation of challenges are still subject to research. Implementations of holistic Digital Twins of tangible and intangible assets of complex products or processes are often ideal-theoretic; instead, only subsystems and processes are replicated, which digital representations serve specific, meaningful applications. Specifically, with its distinct characteristics, the aviation industry and its production show various future application scenarios, which we use case-driven outline in this work. Therefore, we first summarize common, industry-neutral challenges of implementing Digital Twins and give an overview of aircraft production characteristics. Then, we will outline different fields of utilizing the Digital Twin concept and highlight integrational, organizational, and compliance-related challenges as well as opportunities in the context of aircraft production and Maintenance, Repair, and Overhaul (MRO). The use cases are located at different aircraft life cycle phases, from production system development, production supplying logistics, and Quality Assurance (QA) up to retrofit.

Prevalence of omalizumab resistant chronic urticaria and real world effectiveness of dupilumab in omalizumab refractory chronic urticaria patients: a single center experience.

Chronic urticaria (CU) is characterized by wheals, angioedema, or both lasting for ≥ 6 weeks with chronic spontaneous urticaria (CSU) being the most common subtype. Omalizumab-resistant CSU cases represent an unmet clinical need. In this study, we aimed to assess the prevalence and predictors of omalizumab failure in a large cohort of CU patients and assess the effectiveness of dupilumab for omalizumab-recalcitrant CU. Of 338 CU patients, 33 received omalizumab. 69.7% (23 patients) were responders and 30.3% (10 patients) non-responders. Bivariate regression demonstrated that female sex (adjusted OR [aOR] = 1.53; 95%CI = 1.14-2.06), higher baseline UAS7 (aOR = 1.05; 95%CI = 1.01-1.09) and older age (controlling for sex) (aOR = 1.00; 95%CI = 1.00, 1.01) were associated with omalizumab failure. Of 10 omalizumab-refractory patients, three were well controlled with cyclosporine (all children), whereas the seven adults failed on average 5.6 ± 2.6 therapies including cyclosporine. All 7 achieved a complete response with dupilumab with time to response varying between 1 to 6 months. While our results suggest a favourable efficacy of dupilumab omalizumab-resistant cases, future confirmatory studies are required.

Automated Determination of Music Genre and Linguistic Categorization

The main objective of the project is to build a system that allows users to predict genre as well as language out of some music . The Automated determination of musical genre and linguistic categorization is a machine learning based prediction model which is used to predict the genres and languages of an audio . Musical genres are categorical labels created by humans to characterize piece of music. A musical genre is characterized by common characteristics typically related to the instrumentation, rhythmic structure and harmonic content of the music. The performance and relative importance of the proposed features are investigated by training the statistical pattern recognition classifiers using real world audio collections and using machine learning algorithm.In this project, we built systems called music genre and language classification and the effects of different feature extraction methods and their various combinations were also investigated. This task is of great interest in music information retrieval and recommendation systems. In recent years, several machine learning algorithms have been developed to tackle this task, using features such spectral, temporal, and tonal characteristics of audio signals. Music genre prediction and language classification is a task that involves automatically predicting a piece of music into a specific genre category as well as language based on its audio features. Keywords-machine-learning,music,music genre and language classification

MMG-Based Knee Dynamic Extension Force Estimation Using Cross-Talk and IGWO-LSTM

Mechanomyography (MMG) is an important muscle physiological activity signal that can reflect the amount of motor units recruited as well as the contraction frequency. As a result, MMG can be utilized to estimate the force produced by skeletal muscle. However, cross-talk and time-series correlation severely affect MMG signal recognition in the real world. These restrict the accuracy of dynamic muscle force estimation and their interaction ability in wearable devices. To address these issues, a hypothesis that the accuracy of knee dynamic extension force estimation can be improved by using MMG signals from a single muscle with less cross-talk is first proposed. The hypothesis is then confirmed using the estimation results from different muscle signal feature combinations. Finally, a novel model (improved grey wolf optimizer optimized long short-term memory networks, i.e., IGWO-LSTM) is proposed for further improving the performance of knee dynamic extension force estimation. The experimental results demonstrate that MMG signals from a single muscle with less cross-talk have a superior ability to estimate dynamic knee extension force. In addition, the proposed IGWO-LSTM provides the best performance metrics in comparison to other state-of-the-art models. Our research is expected to not only improve the understanding of the mechanisms of quadriceps contraction but also enhance the flexibility and interaction capabilities of future rehabilitation and assistive devices.

Incorporation of adaptive compression into a GPU parallel computing framework for analyzing large-scale vessel trajectories

Automatic Identification System (AIS) offers a wealth of vessel navigation data, which underpins research in maritime data mining, situational awareness, and knowledge discovery within the realm of intelligent transportation systems. The flourishing marine industry has prompted AIS satellites and base stations to generate massive amounts of vessel trajectory data, escalating both data storage and calculation costs. The conventional Douglas-Peucker (DP) algorithm used for trajectory compression sets a uniform threshold, which hampers effective compression. Additionally, compressing and accelerating the computation of large datasets poses a significant challenge in real-world applications. To address these limitations, this paper aims to develop a new Graphics Processing Unit (GPU) parallel computing and compression framework that enables the acceleration of the optimal threshold calculation for each trajectory automatically in maritime big data mining. It achieves this by incorporating a new Adaptive DP with Speed and Course (ADPSC) algorithm, which utilizes the dynamic navigation characteristics of different vessels. It can effectively solve the associated computational time cost concern when using the ADPSC algorithm to compress vast trajectory datasets in the real world. Additionally, this paper proposes a novel evaluation metric for assessing compression efficacy based on the Dynamic Time Warping (DTW) method. Comprehensive experiments encompass vessel trajectory datasets from three representative research areas: Tianjin Port, Chengshan Jiao Promontory, and Caofeidian Port. The experimental results demonstrate that 1) the newly developed ADPSC method outperforms in terms of compression, and 2) the designed GPU parallel computing framework can significantly shorten the compression time for extensive datasets. The GPU-accelerated compression methodology not only minimizes storage and transmission costs for data from both manned and unmanned vessels but also enhances data processing speed, supporting real-time decision-making. From a theoretical perspective, it provides the key to the puzzle of realizing the real-time anti-collision of manned and unmanned ships, particularly in complex waters. It hence makes significant contributions to maritime safety in the autonomous shipping era.

Multi-strategy enhanced Grey Wolf Optimizer for global optimization and real world problems

Multi-strategy enhanced Grey Wolf Optimizer for global optimization and real world problems

The determinants of trump’s defeat: what if the COVID-19 pandemic did not matter?

ABSTRACT While the COVID-19 pandemic significantly impacted the lives of many people worldwide, it is debated whether it led to the defeat of incumbent Donald Trump in the 2020 American presidential election. We argue that the COVID-19 pandemic had a significantly negative impact on Trump’s support due to his conflicting and populist rhetoric, which culminated in contradictory behavior at a time when Americans sought a consistent leader to “rally round the flag.” We use both waves of the ANES 2020 Survey to determine what support for Trump would have looked like if the COVID-19 pandemic had lower influence in citizens’ electoral decision-making compared to real world conditions using regressions and a counterfactual strategy. Our findings suggest that Trump’s electoral defeat depended on multiple factors, with aggregate-level analyzes suggesting that Trump would have received more support had the management of the health crisis mattered less in voting decisions.

Deep Learning-Integrated Crime Detection System Using Convolutional Neural Networks

The work delivered here provides a new deep learning-based approach for crime detection, using Convolutional Neural Networks (CNNs) as a tool. Given the abundance of surveillance data which is becoming more available, the provision of automated systems to the law enforcement agencies ought to help in the identification of crimes seems to be the urgent need of the hour. We aim by this study to build a CNN model that could identify scenes in video simplifying them as criminal or non-criminal. The model is trained on the large dataset of the video clips which are labeled and it can promise the accuracy and efficiency attracted by this model. We test the proposed system on various baseline datasets and then its performance compared with state-of-the-art methods. The experimental results thereby show the effectiveness of our method in the detection of criminal activities, which highlights its potentiality for real world implementation in preventive measures and crime enforcement.

Predictors of bleeding complications during catHeter-dirEcted thrombolysis for peripheral arterial occlusions (POCHET).

The risk of major bleeding complications in catheter directed thrombolysis (CDT) for acute limb ischemia (ALI) remains high, with reported major bleeding complication rates in up to 1 in every 10 treated patients. Fibrinogen was the only predictive marker used for bleeding complications in CDT, despite the lack of high quality evidence to support this. Therefore, recent international guidelines recommend against the use of fibrinogen during CDT. However, no alternative biomarkers exist to effectively predict CDT-related bleeding complications. The aim of the POCHET biobank is to prospectively assess the rate and etiology of bleeding complications during CDT and to provide a biobank of blood samples to investigate potential novel biomarkers to predict bleeding complications during CDT. The POCHET biobank is a multicentre prospective biobank. After informed consent, all consecutive patients with lower extremity ALI eligible for CDT are included. All patients are treated according to a predefined standard operating procedure which is aligned in all participating centres. Baseline and follow-up data are collected. Prior to CDT and subsequently every six hours, venous blood samples are obtained and stored in the biobank for future analyses. The primary outcome is the occurrence of non-access related major bleeding complications, which is assessed by an independent adjudication committee. Secondary outcomes are non-major bleeding complications and other CDT related complications. Proposed biomarkers to be investigated include fibrinogen, to end the debate on its usefulness, anti-plasmin and D-Dimer. The POCHET biobank provides contemporary data and outcomes of patients during CDT for ALI, coupled with their blood samples taken prior and during CDT. Thereby, the POCHET biobank is a real world monitor on biomarkers during CDT, supporting a broad spectrum of future research for the identification of patients at high risk for bleeding complications during CDT and to identify new biomarkers to enhance safety in CDT treatment.

The role of rhythm control in patients with first diagnosed atrial fibrillation in a real world collective

Abstract Funding Acknowledgements Type of funding sources: Private company. Main funding source(s): Daiichi Sankyo Bayer. Background There has been conflicting evidence regarding the importance of rhythm control as opposed to rate control in patients with atrial fibrillation (AF). Thus, no clear recommendation on early rhythm control for risk reduction is given in the current guidelines. However, some evidence suggests that rhythm control is superior to rate control in patients with a recent diagnosis of AF. Purpose We aim to characterize patients with first diagnosed AF and evaluate the effect of a rhythm control strategy at the time of diagnosis on long term cardiovascular outcomes. Methods This is a single center, retrospective observational sub study based as a part of the HERA-FIB project. HERA-FIB included 10.222 patients presenting to the emergency department (ED) of the department of cardiology of a university hospital from June 2009 to March 2020. We consecutively included all patients with a history AF or current AF upon presentation, regardless of the reason for presentation. A structured follow-up regarding survival and major cardiovascular events was performed. Mean follow up was 23 months (IQR 12-35). Patient care was not influenced. The project was performed according to the declaration of Helsinki and was approved by the local ethics committee and registered at ClinicalTrials.gov (Identifier: NCT05995561). Results. After adjudication we included 10.222 patients in this study. Of those 2758 (27%) had no previous history of AF and were first diagnosed with AF upon presentation. There were major differences in baseline characteristics as patients with first diagnosed AF were younger (73 years vs 76 years, p&amp;lt;0.0001), showed a normal left ventricular function more often (47.9% vs 40.2%, p&amp;lt;0.0001) and had fewer histories of coronary artery disease (CAD) (30.2% vs 48.0%, p&amp;lt;0.0001) and fewer histories of stroke or transient ischemic attack (TIA) (9.0% vs 14.0%, p&amp;lt;0.0001). In contrast they had higher heart rates (113 bpm vs 84 bpm, p&amp;lt;0.0001) on admission. Among the patients with first diagnosed AF 24.6% received a cardioversion (either pharmacological of electrical). Patients with first diagnosed AF undergoing rhythm control treatment showed lower rates for all-cause mortality (10.4% vs 18.2%, p&amp;lt;0.0001) and myocardial infarction (2.4% vs. 5.2%, p=0.0045) as compared to patients first diagnosed with AF not recieving a cardioversion. However, no differences for strokes (2.7% vs 3.3%, p=0.6158) or severe bleeding according to ISTH major bleeding criteria (4.0% vs. 5.3%, p=0.2252) could be detected. Conclusions Patients presenting to an ED with first diagnosed AF are younger and show fewer comorbidities compared to patients with pre-existing AF. Patients with first diagnosed AF seem to profit from early rhythm control strategies showing a reduction for the rates of all-cause mortality and myocardial infarction. However, no difference for stroke or major bleeding events could be detected. Additional, prospective studies are required to verify this hypothesis.

Non-line of sight analysis for vehicular visible light communication system: Impact of artificial light source and weather conditions

Visible light communication is seen as a crucial technology within optical wireless communication systems. The technology of vehicular visible light communication holds significant importance in the context of connected vehicles. This technology can serve as a supplementary solution to vehicular systems that are based on radio frequency. In this paper, the authors conduct an analysis of the performance of both line-of-sight and non-line-of-sight vehicle-to-vehicle visible light communication systems under the effect of artificial light source and weather conditions, including clear, hazy, and foggy weather. A practical vehicular laser diode, a street lamp, and an avalanche photodiode are used to design the proposed system model. Performance enhancement for the proposed system is achieved using an optical amplifier at the receiving end. An artificial light source of light-emitting diode Corn-type is used to represent an ambient artificial light source. Different metrics such as quality factor and bit error rate are used to assess the system performance of the non-line-of-sight-vehicular communication system. The proposed line-of-sight model achieves a data rate of 25 Gbps, supporting a distance of 80 m under clear sky and hazy atmospheric conditions. For foggy weather, an attainable link distance of 70 m is achieved. The achieved results emphasize the suitability of the suggested models for vehicular applications in real world environment.

Correction: The effectiveness and efficiency of using normative messages to reduce waste: A real world experiment

Correction: The effectiveness and efficiency of using normative messages to reduce waste: A real world experiment

Validation of A New Scoring Method to Assess the Efficacy of Rapid Initiation and Titration of Combination Pharmacotherapy for Patients Hospitalized with Acute Decompensated Heart Failure with Reduced and Mildly Reduced Ejection Fraction

Background: Despite the encouragement of early initiation and titration of guideline-di-rected medical therapy (GDMT) for the treatment of heart failure (HF), most patients do not receive an adequate type and dose of pharmacotherapy in the real world. Objectives: This study aimed to determine the efficacy of titrating composite GDMT in patients with HF with reduced and mildly reduced ejection fraction and to identify patient conditions that may benefit from titration of GDMT. Methods: This was a two-center, retrospective study of consecutive patients hospitalized with acute decompensated heart failure (ADHF). Patients were classified into two groups according to a scoring scale determined by combination and doses of four types of HF agents (ACEis/ARBs/ARNis, BBs, MRAs, and SGLT2is) at discharge. A score of 5 or greater was defined as titrated GDMT, and a score of 4 or less was regarded as sub-optimal medical therapy (MT). Results: A total of 979 ADHF patients were screened. After 553 patients were excluded based on exclusion criteria, 426 patients (90 patients in the titrated GDMT group and 336 patients in the sub-optimal MT group) were enrolled for the analysis. The median follow-up period was 612 (453–798) days. Following statistical adjustment using the propensity score weighting method, the 2-year composite endpoint (composite of cardiac death and HF rehospitalization) rate was significantly lower in the titrated GDMT group, at 19%, compared with the sub-optimal MT group: 31% (score 3–4 points) and 43% (score 0–2 points). Subgroup analysis indicated a marked benefit of titrated GDMT in particular patient subgroups: age &lt; 80 years, BMI 19.0–24.9, eGFR &gt; 20 mL/min/1.73 m2, and serum potassium level ≤ 5.5 mmol/L. Conclusions: Prompt initiation and dose adjustment of multiple HF medications, with careful monitoring of the patient’s physiologic and laboratory values, is a prerequisite for improving the prognosis of patients with heart failure.

Endovascular treatment versus standard medical treatment in patients with established large infarct: a cohort study.

Previous trials confirmed the benefit of endovascular treatment (EVT) in acute large core stroke, but the effect of EVT on outcomes in these patients based on non-contrast computed tomography (NCCT) in real-world clinical practice was unclear. The aim of this study was to explore the effect of EVT versus standard medical treatment (SMT) in patients with large ischemic core stroke defined as Alberta Stroke Program Early CT Score (ASPECTS)≤5 based on NCCT alone. Patients with acute large core stroke at 38 Chinese centers between November 2021 and February 2023 were reviewed from prospectively maintained databases. The primary outcome was favorable functional outcome (modified Rankin Scale score [mRS], 0-3) at 90 days. Safety outcomes included 48-hour symptomatic intracerebral hemorrhage (sICH) and 90-day mortality. Of 745 eligible patients recruited at 38 stroke centers between November 2021 and February 2023, 490 were treated with EVT and 255 with SMT alone. One hundred and eighty-one (36.9%) in the EVT group achieved favorable functional independence versus 48 (18.8%) treated with SMT only (adjusted risk ratio [RR], 1.86; 95% CI, 1.43 to 2.42, P<0.001; adjusted risk difference [RD], 13.77; 95% CI, 7.40 to 20.15, P<0.001). The proportion of sICH was significantly higher in patients undergoing EVT (13.3% vs. 2.4%; adjusted RR, 5.17; 95% CI, 2.17 to 12.32, P<0.001; adjusted RD, 10.10; 95% CI, 6.12 to 14.09, P<0.001). No significant difference of mortality between the groups was observed (41.8% vs. 49.0%; adjusted RR, 0.91; 95% CI, 0.77 to 1.07, P=0.24; adjusted RD, -5.91; 95% CI, -12.91 to 1.09, P=0.1). Among patients with acute large core stroke based on NCCT in real world, EVT is associated with better functional outcomes at 90 days despite of higher risk of sICH. Rates of procedure-related complications were high in the EVT group.