Extended-Release Versus Immediate-Release Triamcinolone Acetonide in Patients Who Have Knee Osteoarthritis and Type 2 Diabetes Mellitus.

BACKGROUND: Intra-articular (IA) corticosteroid injections may cause hyperglycemia (glucose level > 180 mg/dL). In a phase 2 study of 33 patients who had osteoarthritis of the knee (OAK) and type 2 diabetes mellitus (T2D), triamcinolone acetonide extended-release (TA-ER) was associated with minimal glycemic control disruption compared with triamcinolone acetonide immediate-release (TA-IR). This post hoc analysis characterizes the clinical relevance of these results. METHODS: Patients who had symptomatic OAK for ≥ 6 months, T2D for ≥ 1 year, and hemoglobin A1c ≥ 6.5 and ≤ 9.0% were randomized to receive an IA injection of either TA-ER or TA-IR. Changes in continuous glucose monitor daily glucose level, percentage of time in or above the target glucose range (> 70 to 180 mg/dL), time to glucose level 250 mg/dL and maximum glucose level > 250 mg/dL, and glycemic variability were evaluated. RESULTS: Across postinjection days 1 to 3, the TA-ER group (n = 18) had a lower median change from baseline in maximum glucose level (92.3 versus 169.1 mg/dL), a reduced percentage of time with a glucose level > 250 mg/dL (12 versus 26%), a smaller proportion of patients who had a maximum glucose level > 250 mg/dL (50 versus 93%), and a greater percentage of time in the target glucose range (62 versus 48%) versus the TA-IR group (n = 15). There was less glycemic variability and lower glucose spikes in the TA-ER versus TA-IR group. Median times to glucose level 250 mg/dL (44 versus 6 hours) and maximum glucose level (34 versus 13 hours) were significantly longer in the TA-ER versus TA-IR group. CONCLUSIONS: Use of TA-ER was associated with a clinically meaningful reduction in hyperglycemia versus TA-IR.

Multi-View Human Action Recognition Using Skeleton Based-FineKNN with Extraneous Frame Scrapping Technique.

Human action recognition (HAR) is one of the most active research topics in the field of computer vision. Even though this area is well-researched, HAR algorithms such as 3D Convolution Neural Networks (CNN), Two-stream Networks, and CNN-LSTM (Long Short-Term Memory) suffer from highly complex models. These algorithms involve a huge number of weights adjustments during the training phase, and as a consequence, require high-end configuration machines for real-time HAR applications. Therefore, this paper presents an extraneous frame scrapping technique that employs 2D skeleton features with a Fine-KNN classifier-based HAR system to overcome the dimensionality problems.To illustrate the efficacy of our proposed method, two contemporary datasets i.e., Multi-Camera Action Dataset (MCAD) and INRIA Xmas Motion Acquisition Sequences (IXMAS) dataset was used in experiment. We used the OpenPose technique to extract the 2D information, The proposed method was compared with CNN-LSTM, and other State of the art methods. Results obtained confirm the potential of our technique. The proposed OpenPose-FineKNN with Extraneous Frame Scrapping Technique achieved an accuracy of 89.75% on MCAD dataset and 90.97% on IXMAS dataset better than existing technique.

Computation and memory optimized spectral domain convolutional neural network for throughput and energy-efficient inference.

Conventional convolutional neural networks (CNNs) present a high computational workload and memory access cost (CMC). Spectral domain CNNs (SpCNNs) offer a computationally efficient approach to compute CNN training and inference. This paper investigates CMC of SpCNNs and its contributing components analytically and then proposes a methodology to optimize CMC, under three strategies, to enhance inference performance. In this methodology, output feature map (OFM) size, OFM depth or both are progressively reduced under an accuracy constraint to compute performance-optimized CNN inference. Before conducting training or testing, it can provide designers guidelines and preliminary insights regarding techniques for optimum performance, least degradation in accuracy and a balanced performance-accuracy trade-off. This methodology was evaluated on MNIST and Fashion MNIST datasets using LeNet-5 and AlexNet architectures. When compared to state-of-the-art SpCNN models, LeNet-5 achieves up to 4.2× (batch inference) and 4.1× (single-image inference) higher throughputs and 10.5× (batch inference) and 4.2× (single-image inference) greater energy efficiency at a maximum loss of 3% in test accuracy. When compared to the baseline model used in this study, AlexNet delivers 11.6× (batch inference) and 5× (single-image inference) increased throughput and 25× (batch inference) and 8.8× (single-image inference) more energy-efficient inference with just 4.4% reduction in accuracy.

Design and Implementation of an Open-Source SCADA System for a Community Solar-Powered Reverse Osmosis System.

Design and implementation of an open-source-based supervisory control and data acquisition (SCADA) system for a community solar-powered reverse osmosis are presented in this paper. A typical SCADA system available on the market is proprietary and has a high initial and maintenance cost. Aside from that, there is no SCADA system with an alert system available to give users updates and status information concerning the system. The objective of this study is to develop a comprehensive SCADA design that takes advantage of open-source technology to address the world's most pressing problem, access to clean water. The designed reverse Osmosis system also uses renewable energy-based power sources. In this system, all data is stored and analyzed locally, which ensures the data is secure and allows the user to make data-driven decisions based on the collected data. Among the main components of this system are the field instrument devices (FIDs), the remote terminal unit (RTU), the main terminal units (MTUs), the web-based programming software, and the data analytics software. The Node-Red programming and dashboard tool, Grafana for data analytics, and InfluxDB for database management run on the main terminal unit having Debian operating system. Data is transmitted from the FIDs to the RTU, which then redirects it to the MTU via serial communication. Node-Red displays the data processed by the MTU on its dashboard as well, as the data is stored locally on the MTU and is displayed by means of Grafana, which is also installed on the same MTU. Through the Node-Red dashboard, the system is controlled, and notifications are sent to the community.

Decoding the meaning of unconsciously processed words using fMRI-based MVPA.

Does the human brain elicit patterns of activity associated with the meaning of words in the absence of conscious awareness? Do such non-conscious semantic representations generalize across languages? This study aimed to address these questions using fMRI-based multivariate pattern analysis (MVPA) in a masked word paradigm. Animal and non-animal words were visually presented in two different languages (i.e. Spanish and Basque). Words were presented very briefly and were masked. On each trial, participants identified the semantic category and provided a visibility rating of the word. A support vector machine (SVM) was used to decode word category from multivoxel patterns of BOLD responses in seven canonical semantic regions of a left-lateralized network that were prespecified based on a previous meta-analysis. We show that the semantic category of non-conscious words (i.e. associated with null visual experience and chance-level discrimination performance) can be significantly decoded from BOLD response patterns. For Spanish, such discriminative patterns of BOLD responses were consistently found in inferior parietal lobe, dorsomedial prefrontal cortex, inferior frontal gyrus and posterior cingulate gyrus. While for Basque, these were found in ventromedial temporal lobe and posterior cingulate gyrus. All of the areas identified have previously been associated with semantic processing in studies involving animals-tools and animals-artifacts contrasts. In conscious trials, such patterns were found to be distributed over all seven regions of the semantic network in both Spanish and Basque. However, we found no evidence of across-language generalization. These results demonstrate that even in the absence of conscious awareness and lack of behavioural sensitivity to the words, putative semantic brain areas carry information related to the meanings of the words. The generalization of semantic representations across languages, however, may require deeper conscious semantic access.

Classification of Overt and Covert Speech for Near-Infrared Spectroscopy-Based Brain Computer Interface.

People suffering from neuromuscular disorders such as locked-in syndrome (LIS) are left in a paralyzed state with preserved awareness and cognition. In this study, it was hypothesized that changes in local hemodynamic activity, due to the activation of Broca's area during overt/covert speech, can be harnessed to create an intuitive Brain Computer Interface based on Near-Infrared Spectroscopy (NIRS). A 12-channel square template was used to cover inferior frontal gyrus and changes in hemoglobin concentration corresponding to six aloud (overtly) and six silently (covertly) spoken words were collected from eight healthy participants. An unsupervised feature extraction algorithm was implemented with an optimized support vector machine for classification. For all participants, when considering overt and covert classes regardless of words, classification accuracy of 92.88 ± 18.49% was achieved with oxy-hemoglobin (O2Hb) and 95.14 ± 5.39% with deoxy-hemoglobin (HHb) as a chromophore. For a six-active-class problem of overtly spoken words, 88.19 ± 7.12% accuracy was achieved for O2Hb and 78.82 ± 15.76% for HHb. Similarly, for a six-active-class classification of covertly spoken words, 79.17 ± 14.30% accuracy was achieved with O2Hb and 86.81 ± 9.90% with HHb as an absorber. These results indicate that a control paradigm based on covert speech can be reliably implemented into future Brain⁻Computer Interfaces (BCIs) based on NIRS.

A VELOUR post hoc subset analysis: prognostic groups and treatment outcomes in patients with metastatic colorectal cancer treated with aflibercept and FOLFIRI.

BACKGROUND: The VELOUR study demonstrated a survival benefit for FOLFIRI + aflibercept versus FOLFIRI + placebo in metastatic colorectal cancer (mCRC) patients who progressed on oxaliplatin-based chemotherapy. Continued divergence of overall survival (OS) curves in the intension to treat (ITT) population, with the survival advantage persisting beyond median survival time, suggested subpopulations might have different magnitudes of survival gain. Additionally, 10% of patients within VELOUR had recurrence during or within 6 months of completing oxaliplatin-based adjuvant therapy (adjuvant fast relapsers)--previously identified as having poorer survival outcomes. METHODS: To determine which patients received the greatest benefit from FOLFIRI-aflibercept, a post hoc multivariate analysis of the VELOUR ITT population was conducted. Prognostic factors identified were applied to the ITT population, excluding adjuvant fast relapsers, to derive OS prognostic profiles. RESULTS: The better efficacy subgroup was identified as patients within VELOUR exclusive of adjuvant fast relapsers and had performance status (PS) 0 with any number of metastatic site or PS 1 with <2 metastatic site. A significant improvement in efficacy outcome was observed with aflibercept in the better efficacy subgroup. Median OS for FOLFIRI-aflibercept and FOLFIRI-placebo:16.2 and 13.1 months (adjusted Hazard Ratio [HR] = 0.73; 95% confidence interval [CI]: 0.61-0.86); median progression free survival (PFS): 7.2 and 4.8 months (adjusted HR = 0.68; 95% CI: 0.57-0.80); and objective response rate (ORR): 24% versus 11% respectively. Poorer efficacy subgroup comprised of adjuvant fast relapsers or patients with PS2 or PS1 with ≥ 2 metastatic sites. In poorer efficacy subgroup, no benefit was seen with aflibercept. Median OS for FOLFIRI-aflibercept and FOLFIRI-placebo: 10.4 and 9.6 months (adjusted HR = 0.97; 95% CI: 0.78-1.21) respectively with no improvement in PFS or ORR. CONCLUSION: This analysis suggests that within VELOUR, patients in the better efficacy subgroup may derive enhanced benefit from treatment with FOLFIRI-aflibercept. These prognostic criteria may guide practitioners toward optimal use of targeted biologicals in appropriate second-line mCRC patients.

Privacy-preserving record linkage across disparate institutions and datasets to enable a learning health system: The national COVID cohort collaborative (N3C) experience.

Introduction: Research driven by real-world clinical data is increasingly vital to enabling learning health systems, but integrating such data from across disparate health systems is challenging. As part of the NCATS National COVID Cohort Collaborative (N3C), the N3C Data Enclave was established as a centralized repository of deidentified and harmonized COVID-19 patient data from institutions across the US. However, making this data most useful for research requires linking it with information such as mortality data, images, and viral variants. The objective of this project was to establish privacy-preserving record linkage (PPRL) methods to ensure that patient-level EHR data remains secure and private when governance-approved linkages with other datasets occur. Methods: Separate agreements and approval processes govern N3C data contribution and data access. The Linkage Honest Broker (LHB), an independent neutral party (the Regenstrief Institute), ensures data linkages are robust and secure by adding an extra layer of separation between protected health information and clinical data. The LHB's PPRL methods (including algorithms, processes, and governance) match patient records using "deidentified tokens," which are hashed combinations of identifier fields that define a match across data repositories without using patients' clear-text identifiers. Results: These methods enable three linkage functions: Deduplication, Linking Multiple Datasets, and Cohort Discovery. To date, two external repositories have been cross-linked. As of March 1, 2023, 43 sites have signed the LHB Agreement; 35 sites have sent tokens generated for 9 528 998 patients. In this initial cohort, the LHB identified 135 037 matches and 68 596 duplicates. Conclusion: This large-scale linkage study using deidentified datasets of varying characteristics established secure methods for protecting the privacy of N3C patient data when linked for research purposes. This technology has potential for use with registries for other diseases and conditions.

Techno-economic modelling of hybrid energy system to overcome the load shedding problem: A case study of Pakistan.

This paper demonstrates the application of hybrid energy system (HES) that comprises of photovoltaic (PV) array, battery storage system (BSS) and stand-by diesel generator (DGen) to mitigate the problem of load shedding. The main work involves techno-economic modelling to optimize the size of HES such that the levelized cost of electricity (LCOE) is minimized. The particle swarm optimization (PSO) algorithm is used to determine the optimum size of the components (PV, BSS). Simulations are performed in MATLAB using real dataset of irradiance, temperature and load shedding schedule of the small residential community situated in the city of Quetta, Pakistan. The LCOE for the HES system under study is 8.32 cents/kWh-which is lower than the conventional load shedding solution, namely the uninterruptable power supply (UPS) (13.06 cents/kWh) and diesel and generator system (29.19 cents/kWh). In fact, the LCOE of the HRES is lower than the grid electricity price of Pakistan (9.3 cents/kWh). Besides that, the HES alleviates the grid burden by 47.9% and 13.1% compared to the solution using the UPS and generator, respectively. The outcomes of the study suggests that HES is able to improve reliability and availability of electric power for regions that is affected by the load shedding issue.

Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow.

Current development in precision agriculture has underscored the role of machine learning in crop yield prediction. Machine learning algorithms are capable of learning linear and nonlinear patterns in complex agro-meteorological data. However, the application of machine learning methods for predictive analysis is lacking in the oil palm industry. This work evaluated a supervised machine learning approach to develop an explainable and reusable oil palm yield prediction workflow. The input data included 12 weather and three soil moisture parameters along with 420 months of actual yield records of the study site. Multisource data and conventional machine learning techniques were coupled with an automated model selection process. The performance of two top regression models, namely Extra Tree and AdaBoost was evaluated using six statistical evaluation metrics. The prediction was followed by data preprocessing and feature selection. Selected regression models were compared with Random Forest, Gradient Boosting, Decision Tree, and other non-tree algorithms to prove the R2 driven performance superiority of tree-based ensemble models. In addition, the learning process of the models was examined using model-based feature importance, learning curve, validation curve, residual analysis, and prediction error. Results indicated that rainfall frequency, root-zone soil moisture, and temperature could make a significant impact on oil palm yield. Most influential features that contributed to the prediction process are rainfall, cloud amount, number of rain days, wind speed, and root zone soil wetness. It is concluded that the means of machine learning have great potential for the application to predict oil palm yield using weather and soil moisture data.

Neurocognitive mechanisms supporting the generalization of concepts across languages.

The neurocognitive mechanisms that support the generalization of semantic representations across different languages remain to be determined. Current psycholinguistic models propose that semantic representations are likely to overlap across languages, although there is evidence also to the contrary. Neuroimaging studies observed that brain activity patterns associated with the meaning of words may be similar across languages. However, the factors that mediate cross-language generalization of semantic representations are not known. We here identify a key factor: the depth of processing. Human participants were asked to process visual words as they underwent functional MRI. We found that, during shallow processing, multivariate pattern classifiers could decode the word semantic category within each language in putative substrates of the semantic network, but there was no evidence of cross-language generalization in the shallow processing context. By contrast, when the depth of processing was higher, significant cross-language generalization was observed in several regions, including inferior parietal, ventromedial, lateral temporal, and inferior frontal cortex. These results are in keeping with distributed-only views of semantic processing and favour models based on multiple semantic hubs. The results also have ramifications for existing psycholinguistic models of word processing such as the BIA+, which by default assumes non-selective access to both native and second languages.

Rare Case of Multiple Intradural Extramedullary Spinal Schwannomas With Intramedullary Extension.

Spinal schwannomas are benign WHO grade I nerve sheath tumors that account for nearly 30% of all spinal neoplasm. Typically, these lesions are intradural extramedullary in location and are composed entirely of well-differentiated eosinophilic Schwann cells. Intramedullary schwannomas, however, are extremely rare due to the lack of Schwan cells in the normal spinal cord and represent 1% of all the spinal schwannoma population. The presence of such an intramedullary component makes diagnosis challenging as imaging features may resemble other intramedullary neoplastic entities. Here, we describe a case of a 56-year-old male patient who presented with an 18-month history of intermittent right-sided mid-thoracic pain secondary to multiple intradural extramedullary spinal schwannoma with intramedullary extensions. We also review the literature pertaining to the condition.

Lead encephalopathy in an adult opioid abuser.

A 38-year-old man presented at the emergency department with abdominal pain, vomiting, generalised weakness and altered consciousness. He had been ingesting opioids for over 5 years and had several past hospital admissions for abdominal pain. His investigations showed deranged liver function tests, anaemia and basophilic stippling on the peripheral blood smear. Further investigations revealed a significant increase in the serum lead level. We started chelation with peroral penicillamine 250 mg every 6 hours for 2 days and switched to intramuscular dimercaprol 4 mg/kg every 12 hours and intravenous calcium ethylenediamine tetraacetic acid 50 mg/kg in two divided doses daily for the next 5 days. We then discharged him home; he had become clinically stable by that time. We repeated his lead level and followed him up in the clinic. In this report, we emphasise the consideration of lead toxicity in opioid abusers and bring to attention a rare way of lead chelation in resource-limited settings.

Predominantly Solid Hemangioblastoma Presenting as an Extra-Axial Cerebellopontine Angle Lesion.

Hemangioblastomas (HBs) are typically intra-axial, highly vascular tumors of the central nervous system and account for up to 2.5% of all intracranial tumors and up to 12% of posterior fossa neoplasms. Extra-axial HBs are rarely described in the literature. The radiological appearances of cerebellopontine angle (CPA) extra-axial HB can lead to a diagnostic conundrum as they may mimic the appearance of dural metastasis, vestibular schwannoma, or meningioma. Here, we describe a patient who presented with an extra-axial CPA HB and explore the literature of the condition.

Socio-economic and corporate factors and COVID-19 pandemic: a wake-up call.

The novel coronavirus 2019 (COVID-19) emerges from the Chinese city Wuhan and its spread to the rest of the world, primarily affected economies and their businesses, leading to a global depression. The explanatory and cross-sectional regression approach assesses the impact of COVID-19 cases on healthcare expenditures, logistics performance index, carbon damages, and corporate social responsibility in a panel of 77 countries. The results show that COVID-19 cases substantially increase healthcare expenditures and decrease corporate social responsibility. On the other hand, an increase in the coronavirus testing capacity brings positive change in reducing healthcare expenditures, increased logistics activities, and corporate social responsibility. The cost of carbon emissions increases when corporate activities begin to resume. The economic affluence supports logistics activities and improves healthcare infrastructure. It linked to international cooperation and their assistance to supply healthcare logistics traded equipment through mutual trade agreements. The greater need to enhance global trade and healthcare logistics supply helps minimize the sensitive coronavirus cases that are likely to provide a safe and healthy environment for living.

The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. MATERIALS AND METHODS: The Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. RESULTS: Organized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. CONCLUSIONS: The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19.

Correction to 'Decoding and encoding models reveal the role of mental simulation in the brain representation of meaning'.

[This corrects the article DOI: 10.1098/rsos.192043.].

Decoding and encoding models reveal the role of mental simulation in the brain representation of meaning.

How the brain representation of conceptual knowledge varies as a function of processing goals, strategies and task-factors remains a key unresolved question in cognitive neuroscience. In the present functional magnetic resonance imaging study, participants were presented with visual words during functional magnetic resonance imaging (fMRI). During shallow processing, participants had to read the items. During deep processing, they had to mentally simulate the features associated with the words. Multivariate classification, informational connectivity and encoding models were used to reveal how the depth of processing determines the brain representation of word meaning. Decoding accuracy in putative substrates of the semantic network was enhanced when the depth processing was high, and the brain representations were more generalizable in semantic space relative to shallow processing contexts. This pattern was observed even in association areas in inferior frontal and parietal cortex. Deep information processing during mental simulation also increased the informational connectivity within key substrates of the semantic network. To further examine the properties of the words encoded in brain activity, we compared computer vision models-associated with the image referents of the words-and word embedding. Computer vision models explained more variance of the brain responses across multiple areas of the semantic network. These results indicate that the brain representation of word meaning is highly malleable by the depth of processing imposed by the task, relies on access to visual representations and is highly distributed, including prefrontal areas previously implicated in semantic control.

Understanding Knowledgeable Workers' Behavior Toward COVID-19 Information Sharing Through WhatsApp in Pakistan.

Using social media through mobile has become a major source of disseminating information; however, the motivations that impact social media users' intention and actual information-sharing behavior need further examination. To this backdrop, drawing on the uses and gratifications theory, theory of prosocial behavior, and theory of planned behavior, we aim to examine various motivations toward information-sharing behaviors in a specific context [coronavirus disease 2019 (COVID-19)]. We collected data from 388 knowledgeable workers through Google Forms and applied structural equation modeling to test the hypotheses. We noted that individuals behave seriously toward crisis-related information, as they share COVID-19 information on WhatsApp not only to be entertained and seek status or information but also to help others. Further, we noted norms of reciprocation, habitual diversion, and socialization as motivators that augment WhatsApp users' positive attitude toward COVID-19 information-sharing behavior.

Cost-effectiveness of rivaroxaban versus apixaban for the initial treatment of venous thromboembolism and extended prevention of recurrences in the UK.

Aim: To evaluate the relative cost-effectiveness of using rivaroxaban vs apixaban for the initial treatment plus extended prevention of venous thromboembolism (VTE) in the UK. Extended prevention was assessed using a 10-mg rivaroxaban dose, as the 20-mg dose has already been evaluated. Methods: A Markov model compared the health outcomes and costs of treating VTE patient cohorts with either rivaroxaban (15 mg twice daily for 3 weeks, followed by 20 mg once daily for 6 months, then extended prevention with 10 mg once daily) or apixaban (10 mg twice daily for 1 week, followed by 5 mg twice daily for 6 months, then extended prevention with 2.5 mg twice daily) over a lifetime horizon. The model included an initial acute treatment and prevention phase (0-6 months) and an extended prevention phase (6-18 months). Efficacy and safety data were derived from two network meta-analyses. Reference treatment comparators were derived from the EINSTEIN-Pooled study and EINSTEIN-CHOICE trial. Healthcare costs and utility data were derived from published literature. Results: The rivaroxaban regimen was associated with increased quality-adjusted life years (QALYs) and slightly lower total costs compared with apixaban over a lifetime horizon. Deterministic and probabilistic sensitivity analyses demonstrated that rivaroxaban remained a cost-effective alternative to apixaban over a wide range of parameters. Incremental cost-effectiveness ratio estimates were below the £20,000 per QALY threshold in 74.1% of 2,000 model simulations. Scenario analyses further supported that rivaroxaban is a cost-effective alternative to apixaban. Limitations: Clinical and safety inputs were derived from network meta-analysis, which are subject to inherent limitations whereby small differences between study designs may severely impact efficacy and safety outcomes. Furthermore, these inputs were based on data from clinical trials, which may not reflect real-world data. Conclusions: Rivaroxaban was associated with a slightly lower total cost and increased QALYs compared with apixaban for VTE management in the UK over a lifetime horizon.