RegulaTome: a corpus of typed, directed, and signed relations between biomedical entities in the scientific literature.

In the field of biomedical text mining, the ability to extract relations from the literature is crucial for advancing both theoretical research and practical applications. There is a notable shortage of corpora designed to enhance the extraction of multiple types of relations, particularly focusing on proteins and protein-containing entities such as complexes and families, as well as chemicals. In this work, we present RegulaTome, a corpus that overcomes the limitations of several existing biomedical relation extraction (RE) corpora, many of which concentrate on single-type relations at the sentence level. RegulaTome stands out by offering 16 961 relations annotated in >2500 documents, making it the most extensive dataset of its kind to date. This corpus is specifically designed to cover a broader spectrum of >40 relation types beyond those traditionally explored, setting a new benchmark in the complexity and depth of biomedical RE tasks. Our corpus both broadens the scope of detected relations and allows for achieving noteworthy accuracy in RE. A transformer-based model trained on this corpus has demonstrated a promising F1-score (66.6%) for a task of this complexity, underscoring the effectiveness of our approach in accurately identifying and categorizing a wide array of biological relations. This achievement highlights RegulaTome's potential to significantly contribute to the development of more sophisticated, efficient, and accurate RE systems to tackle biomedical tasks. Finally, a run of the trained RE system on all PubMed abstracts and PMC Open Access full-text documents resulted in >18 million relations, extracted from the entire biomedical literature.

Patients with cancer and pre-existing severe mental disorder.

Patients with cancer and pre-existing severe mental disorder, which include moderate to severe depression, bipolar disorder and schizophrenia, are known to have reduced life expectancy and are less likely to get recommended cancer treatment. Barriers at patient-, provider- and system level have been identified, e.g. lack of identification of psychiatric comorbidity, shortage of stabilising psychiatric symptoms and fragmentation of the healthcare system. Patient-centered, interdisciplinary and cross-sectorial healthcare interventions have shown a high potential to improve the cancer care, as argued in this review.

STRING-ing together protein complexes: Corpus and methods for extracting physical protein interactions from the biomedical literature.

MOTIVATION: Understanding biological processes relies heavily on curated knowledge of physical interactions between proteins. Yet, a notable gap remains between the information stored in databases of curated knowledge and the plethora of interactions documented in the scientific literature. RESULTS: To bridge this gap, we introduce ComplexTome, a manually annotated corpus designed to facilitate the development of text-mining methods for the extraction of complex formation relationships among biomedical entities targeting the downstream semantics of the physical interaction sub-network of the STRING database. This corpus comprises 1,287 documents with ∼3,500 relationships. We train a novel relation extraction model on this corpus and find that it can highly reliably identify physical protein interactions (F1-score = 82.8%). We additionally enhance the model's capabilities through unsupervised trigger word detection and apply it to extract relations and trigger words for these relations from all open publications in the domain literature. This information has been fully integrated into the latest version of the STRING database. AVAILABILITY AND IMPLEMENTATION: We provide the corpus, code, and all results produced by the large-scale runs of our systems biomedical on literature via Zenodo https://doi.org/10.5281/zenodo.8139716, Github https://github.com/farmeh/ComplexTome_extraction, and the latest version of STRING database https://string-db.org/. SUPPLEMENTARY INFORMATION: Supplementary information are available at Bioinformatics online.

Improving dictionary-based named entity recognition with deep learning.

MOTIVATION: Dictionary-based named entity recognition (NER) allows terms to be detected in a corpus and normalized to biomedical databases and ontologies. However, adaptation to different entity types requires new high-quality dictionaries and associated lists of blocked names for each type. The latter are so far created by identifying cases that cause many false positives through manual inspection of individual names, a process that scales poorly. RESULTS: In this work, we aim to improve block list s by automatically identifying names to block, based on the context in which they appear. By comparing results of three well-established biomedical NER methods, we generated a dataset of over 12.5 million text spans where the methods agree on the boundaries and type of entity tagged. These were used to generate positive and negative examples of contexts for four entity types (genes, diseases, species, and chemicals), which were used to train a Transformer-based model (BioBERT) to perform entity type classification. Application of the best model (F1-score = 96.7%) allowed us to generate a list of problematic names that should be blocked. Introducing this into our system doubled the size of the previous list of corpus-wide blocked names. In addition, we generated a document-specific list that allows ambiguous names to be blocked in specific documents. These changes boosted text mining precision by ∼5.5% on average, and over 8.5% for chemical and 7.5% for gene names, positively affecting several biological databases utilizing this NER system, like the STRING database, with only a minor drop in recall (0.6%). AVAILABILITY AND IMPLEMENTATION: All resources are available through Zenodo https://doi.org/10.5281/zenodo.11243139 and GitHub https://doi.org/10.5281/zenodo.10289360.

Reproducibility and Repeatability in Focus: Evaluating LVEF Measurements with 3D Echocardiography by Medical Technologists.

Three-dimensional echocardiography (3DE) is currently the preferred method for monitoring left ventricular ejection fraction (LVEF) in cancer patients receiving potentially cardiotoxic anti-neoplastic therapy. In Denmark, however, the traditional standard for LVEF monitoring has been rooted in nuclear medicine departments utilizing equilibrium radionuclide angiography (ERNA). Although ERNA remains a principal modality, there is an emerging trend towards the adoption of echocardiography for this purpose. Given this context, assessing the reproducibility of 3DE among non-specialized medical personnel is crucial for its clinical adoption in such departments. To assess the feasibility of 3DE for LVEF measurements by technologists, we evaluated the repeatability and reproducibility of two moderately experienced technologists. They performed 3DE on 12 volunteers over two sessions, with a collaborative review of the results from the first session before the second session. Two-way intraclass correlation values increased from 0.03 to 0.77 across the sessions. This increase in agreement was mainly due to the recognition of false low measurements. Our findings underscore the importance of incorporating reproducibility exercises in the context of 3DE, especially when operated by technologists. Additionally, routine control of the acquisitions by physicians is deemed necessary. Ensuring these hurdles are adequately managed enables the adoption of 3DE for LVEF measurements by technologists.

Electron beam radiotherapy for the management of squamous cell carcinoma of the anal margin.

PURPOSE AND OBJECTIVE: Squamous cell carcinoma of the anal margin (SCCAM) is an uncommon lesion that comprises one-third to a quarter of all anal squamous cell carcinoma. Treatment involves surgery or exclusive radiotherapy for small tumours, whereas the preferred treatment for larger tumours is chemoradiotherapy. In our department, selected patients with SCCAM are treated with electron beam radiotherapy using one perineal field. The present study evaluates this strategy. MATERIAL AND METHODS: All consecutive patients with SCCAM and treated with electron beam radiotherapy from 2012 to 2022 were included. Data were retrospectively extracted from the medical records and analysed descriptively. Local control (LC) and overall survival (OS) were analysed using Kaplan-Meier statistics. RESULTS: Forty patients were evaluated. Primary radiotherapy was delivered in 35 (87.5%) patients. Five (12.5%) patients had postoperative radiotherapy. Median prescription dose was 60.0 (range 45.0-60.2) Gy in 28 (range 10-30) fractions delivered with 8 (range 4-18) MeV using a standard circular aperture and bolus. At a median follow-up of 73 (range 9-135) months, 7 (17.5%) patients were diagnosed with local recurrences. The 5-year LC rate was 84.3% (95% CI: 71.4%-97.2%). Analysis of LC according to T-stage revealed a 5-year LC of 100% (95% CI: 100%-100%) in T1 tumours compared to 57.0% (95% CI: 27.4%-86.6%) in T2 tumours (p < 0.001). 5-year OS was 91.6% (95% CI: 83.0%-100%). Late grade 3 toxicity included ulceration in the skin and subcutis in 2 (5.0%) patients. INTEPRETATION: Electron beam radiotherapy enables the delivery of 'eye-guided' radiotherapy directly to the tumour. LC is good in patients with T1 tumours. Patients with T2 tumours have less satisfactory LC and should be treated with chemoradiotherapy. Electron beam radiotherapy enables the delivery of "eye-guided" RT directly to the tumour. LC is excellent in patients with T1 tumours. Patients with T2 tumours have less satisfactory LC and should be treated with chemoradiotherapy.

TIN-X version 3: update with expanded dataset and modernized architecture for enhanced illumination of understudied targets.

TIN-X (Target Importance and Novelty eXplorer) is an interactive visualization tool for illuminating associations between diseases and potential drug targets and is publicly available at newdrugtargets.org. TIN-X uses natural language processing to identify disease and protein mentions within PubMed content using previously published tools for named entity recognition (NER) of gene/protein and disease names. Target data is obtained from the Target Central Resource Database (TCRD). Two important metrics, novelty and importance, are computed from this data and when plotted as log(importance) vs. log(novelty), aid the user in visually exploring the novelty of drug targets and their associated importance to diseases. TIN-X Version 3.0 has been significantly improved with an expanded dataset, modernized architecture including a REST API, and an improved user interface (UI). The dataset has been expanded to include not only PubMed publication titles and abstracts, but also full-text articles when available. This results in approximately 9-fold more target/disease associations compared to previous versions of TIN-X. Additionally, the TIN-X database containing this expanded dataset is now hosted in the cloud via Amazon RDS. Recent enhancements to the UI focuses on making it more intuitive for users to find diseases or drug targets of interest while providing a new, sortable table-view mode to accompany the existing plot-view mode. UI improvements also help the user browse the associated PubMed publications to explore and understand the basis of TIN-X's predicted association between a specific disease and a target of interest. While implementing these upgrades, computational resources are balanced between the webserver and the user's web browser to achieve adequate performance while accommodating the expanded dataset. Together, these advances aim to extend the duration that users can benefit from TIN-X while providing both an expanded dataset and new features that researchers can use to better illuminate understudied proteins.

Lung cancer among outpatients with COPD: a 7-year cohort study.

Introduction: Lung cancer (LC) is the most common cause of cancer-related deaths worldwide, and its prognosis upon metastasis remains poor. Patients with COPD face a significantly elevated LC risk, up to six times greater than those with normal lung function. We aimed to investigate LC prevalence and stage distribution among COPD outpatients. Furthermore, we aimed to outline the COPD-related variables associated with referral for LC examination. Methods: We conducted a retrospective analysis encompassing the period from 1 January 2012 to 31 December 2018 on all outpatients with COPD and LC and individuals referred for LC examinations. Results: Among all COPD outpatients, 2231 patients (18%) were referred for LC examinations and 565 (4.6%) were diagnosed with LC. LC patients with COPD were more likely to be stage I-II, in contrast to the non-COPD LC population (46% versus 26%, p<0.001 for all). Patients referred for LC examinations exhibited higher use of COPD-related medications, reported more severe dyspnoea (69% versus 66% with Medical Research Council dyspnoea score >2) and experienced a greater frequency of exacerbations (30% versus 24% with two or more exacerbations). Conclusion: Our study revealed a notably high LC incidence among COPD outpatients. LC patients with COPD were diagnosed at earlier stages, and outpatients with more pronounced COPD symptoms were more inclined to undergo LC diagnostics. The overrepresentation of LC cases among COPD outpatients emphasises the importance of tailoring specific screening initiatives for this demographic.

Real-world Effectiveness of Fecal Microbiota Transplantation for First or Second Clostridioides difficile Infection.

BACKGROUND & AIMS: Clostridioides difficile infection (CDI) is associated with high mortality. Fecal microbiota transplantation (FMT) is an established treatment for recurrent CDI, but its use for first or second CDI remains experimental. We aimed to investigate the effectiveness of FMT for first or second CDI in a real-world clinical setting. METHODS: This multi-site Danish cohort study included patients with first or second CDI treated with FMT from June 2019 to February 2023. The primary outcome was cure of C. difficile-associated diarrhea (CDAD) 8 weeks after the last FMT treatment. Secondary outcomes included CDAD cure 1 and 8 weeks after the first FMT treatment and 90-day mortality following positive C. difficile test. RESULTS: We included 467 patients, with 187 (40%) having their first CDI. The median patient age was 73 years (interquartile range [IQR], 58-82 years). Notably, 167 (36%) had antibiotic-refractory CDI, 262 (56%) had severe CDI, and 89 (19%) suffered from fulminant CDI. Following the first FMT treatment, cure of CDAD was achieved in 353 patients (76%; 95% confidence interval [CI], 71%-79%) at week 1. At week 8, 255 patients (55%; 95% CI, 50%-59%) maintained sustained effect. In patients without initial effect, repeated FMT treatments led to an overall cure of CDAD in 367 patients (79%; 95% CI, 75%-82%). The 90-day mortality was 10% (95% CI, 8%-14%). CONCLUSION: Repeated FMT treatments demonstrate high effectiveness in managing patients with first or second CDI. Forwarding FMT in CDI treatment guidelines could improve patient survival. CLINICALTRIALS: gov, Number: NCT03712722.

Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning.

Imputation techniques provide means to replace missing measurements with a value and are used in almost all downstream analysis of mass spectrometry (MS) based proteomics data using label-free quantification (LFQ). Here we demonstrate how collaborative filtering, denoising autoencoders, and variational autoencoders can impute missing values in the context of LFQ at different levels. We applied our method, proteomics imputation modeling mass spectrometry (PIMMS), to an alcohol-related liver disease (ALD) cohort with blood plasma proteomics data available for 358 individuals. Removing 20 percent of the intensities we were able to recover 15 out of 17 significant abundant protein groups using PIMMS-VAE imputations. When analyzing the full dataset we identified 30 additional proteins (+13.2%) that were significantly differentially abundant across disease stages compared to no imputation and found that some of these were predictive of ALD progression in machine learning models. We, therefore, suggest the use of deep learning approaches for imputing missing values in MS-based proteomics on larger datasets and provide workflows for these.

High-Performance Dendrite-Free Lithium Metal Anode Based on Metal-Organic Framework Glass.

The performance of lithium metal batteries is severely hampered by uncontrollable dendrite growth and volume change within the anode. This work addresses these obstacles by introducing a novel strategy: applying an isotropic and internal grain-boundary-free layer, specifically, a metal-organic framework (MOF) glass layer with nano-porosity onto the electrochemically plated lithium metal anode. Both ab initio and classical molecular dynamics simulations indicate that the MOF glass layer makes the lithium transport smooth and uniform via its internal monolithic and interfacial advantages. This MOF glass layer with the fast and more uniform lithium diffusion in the monolithic interior and its interface enables dendrite-free lithium plating and stripping through surface confinement effect and interfacial effect. When employed in symmetric batteries, the achieved Li metal anode can operate over 300 h at 1 mA cm-2. The full batteries matched with LiFePO4 exhibit high capacity (148 mAh g-1), excellent rate performance (61 mAh g-1 at 5 C), and outstanding cycling stability (with capacity retention of ≈90% after 1000 cycles). The full batteries matched with high-voltage LiCoO2 also show superior performances. Therefore, the strategy of utilizing a MOF glass layer enables the development of high-performance lithium metal anodes.

Missingness mechanisms and generalizability of patient reported outcome measures in colorectal cancer survivors - assessing the reasonableness of the "missing completely at random" assumption.

BACKGROUND: Patient-Reported Outcome Measures (PROM) provide important information, however, missing PROM data threaten the interpretability and generalizability of findings by introducing potential bias. This study aims to provide insight into missingness mechanisms and inform future researchers on generalizability and possible methodological solutions to overcome missing PROM data problems during data collection and statistical analyses. METHODS: We identified 10,236 colorectal cancer survivors (CRCs) above 18y, diagnosed between 2014 and 2018 through the Danish Clinical Registries. We invited a random 20% (2,097) to participate in a national survey in May 2023. We distributed reminder e-mails at day 10 and day 20, and compared Initial Responders (response day 0-9), Subsequent Responders (response day 10-28) and Non-responders (no response after 28 days) in demographic and cancer-related characteristics and PROM-scores using linear regression. RESULTS: Of the 2,097 CRCs, 1,188 responded (57%). Of these, 142 (7%) were excluded leaving 1,955 eligible CRCs. 628 (32%) were categorized as initial responders, 418 (21%) as subsequent responders, and 909 (47%) as non-responders. Differences in demographic and cancer-related characteristics between the three groups were minor and PROM-scores only marginally differed between initial and subsequent responders. CONCLUSION: In this study of long-term colorectal cancer survivors, we showed that initial responders, subsequent responders, and non-responders exhibit comparable demographic and cancer-related characteristics. Among respondents, Patient-Reported Outcome Measures were also similar, indicating generalizability. Assuming Patient-Reported Outcome Measures of subsequent responders represent answers by the non-responders (would they be available), it may be reasonable to judge the missingness mechanism as Missing Completely At Random.

Identification of patients' smoking status using an explainable AI approach: a Danish electronic health records case study.

BACKGROUND: Smoking is a critical risk factor responsible for over eight million annual deaths worldwide. It is essential to obtain information on smoking habits to advance research and implement preventive measures such as screening of high-risk individuals. In most countries, including Denmark, smoking habits are not systematically recorded and at best documented within unstructured free-text segments of electronic health records (EHRs). This would require researchers and clinicians to manually navigate through extensive amounts of unstructured data, which is one of the main reasons that smoking habits are rarely integrated into larger studies. Our aim is to develop machine learning models to classify patients' smoking status from their EHRs. METHODS: This study proposes an efficient natural language processing (NLP) pipeline capable of classifying patients' smoking status and providing explanations for the decisions. The proposed NLP pipeline comprises four distinct components, which are; (1) considering preprocessing techniques to address abbreviations, punctuation, and other textual irregularities, (2) four cutting-edge feature extraction techniques, i.e. Embedding, BERT, Word2Vec, and Count Vectorizer, employed to extract the optimal features, (3) utilization of a Stacking-based Ensemble (SE) model and a Convolutional Long Short-Term Memory Neural Network (CNN-LSTM) for the identification of smoking status, and (4) application of a local interpretable model-agnostic explanation to explain the decisions rendered by the detection models. The EHRs of 23,132 patients with suspected lung cancer were collected from the Region of Southern Denmark during the period 1/1/2009-31/12/2018. A medical professional annotated the data into 'Smoker' and 'Non-Smoker' with further classifications as 'Active-Smoker', 'Former-Smoker', and 'Never-Smoker'. Subsequently, the annotated dataset was used for the development of binary and multiclass classification models. An extensive comparison was conducted of the detection performance across various model architectures. RESULTS: The results of experimental validation confirm the consistency among the models. However, for binary classification, BERT method with CNN-LSTM architecture outperformed other models by achieving precision, recall, and F1-scores between 97% and 99% for both Never-Smokers and Active-Smokers. In multiclass classification, the Embedding technique with CNN-LSTM architecture yielded the most favorable results in class-specific evaluations, with equal performance measures of 97% for Never-Smoker and measures in the range of 86 to 89% for Active-Smoker and 91-92% for Never-Smoker. CONCLUSION: Our proposed NLP pipeline achieved a high level of classification performance. In addition, we presented the explanation of the decision made by the best performing detection model. Future work will expand the model's capabilities to analyze longer notes and a broader range of categories to maximize its utility in further research and screening applications.

Early integration of basic palliative care in cancer: scoping review of cross-sectorial models - components, facilitators, barriers.

BACKGROUND: Shared care between oncology specialists and general practice regarding the delivery of palliative care (PC) is necessary to meet the demands for a cohesive PC. The primary objective of this study is to investigate models of cross-sectorial integration between primary care and oncology specialists that have been developed to promote early and basic PC and factors influencing the process. METHOD: A scoping review was conducted using publications dated up until April 2023. Searches were conducted in MEDLINE, CINAHL, Embase, Web of Science and ProQuest Dissertations and Theses. Complementary searches were performed via reference lists and grey literature. Explicit early PC models aimed at patients with cancer aged ≥18 years with healthcare professionals from primary care and oncology constituted the inclusion criteria. The screening of the papers was performed independently by two reviewers. The reporting adheres to the extension for scoping reviews of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. RESULTS: The search provided 5630 articles of which six met the eligibility criteria, each describing a different model of early and cross-sectorial, integrated PC. 12 active components were identified. Education of staff as well as good communication and cooperation skills are essential factors to succeed with integrated, early PC. CONCLUSION: Integration of PC between general practice and oncology specialists has potential. The components of basic PC have been established. Factors known to influence the process are trust, communication and a common goal. Further research is required into strategies for approaching different levels of integration.

Opportunities and barriers in omics-based biomarker discovery for steatotic liver diseases.

The rising prevalence of liver diseases related to obesity and excessive use of alcohol is fuelling an increasing demand for accurate biomarkers aimed at community screening, diagnosis of steatohepatitis and significant fibrosis, monitoring, prognostication and prediction of treatment efficacy. Breakthroughs in omics methodologies and the power of bioinformatics have created an excellent opportunity to apply technological advances to clinical needs, for instance in the development of precision biomarkers for personalised medicine. Via omics technologies, biological processes from the genes to circulating protein, as well as the microbiome - including bacteria, viruses and fungi, can be investigated on an axis. However, there are important barriers to omics-based biomarker discovery and validation, including the use of semi-quantitative measurements from untargeted platforms, which may exhibit high analytical, inter- and intra-individual variance. Standardising methods and the need to validate them across diverse populations presents a challenge, partly due to disease complexity and the dynamic nature of biomarker expression at different disease stages. Lack of validity causes lost opportunities when studies fail to provide the knowledge needed for regulatory approvals, all of which contributes to a delayed translation of these discoveries into clinical practice. While no omics-based biomarkers have matured to clinical implementation, the extent of data generated has enabled the hypothesis-free discovery of a plethora of candidate biomarkers that warrant further validation. To explore the many opportunities of omics technologies, hepatologists need detailed knowledge of commonalities and differences between the various omics layers, and both the barriers to and advantages of these approaches.

Functional, Structural and Proteomic Effects of Ageing in Resistance Arteries.

The normal ageing process affects resistance arteries, leading to various functional and structural changes. Systolic hypertension is a common occurrence in human ageing, and it is associated with large artery stiffening, heightened pulsatility, small artery remodeling, and damage to critical microvascular structures. Starting from young adulthood, a progressive elevation in the mean arterial pressure is evidenced by clinical and epidemiological data as well as findings from animal models. The myogenic response, a protective mechanism for the microcirculation, may face disruptions during ageing. The dysregulation of calcium entry channels (L-type, T-type, and TRP channels), dysfunction in intracellular calcium storage and extrusion mechanisms, altered expression of potassium channels, and a change in smooth muscle calcium sensitization may contribute to the age-related dysregulation of myogenic tone. Flow-mediated vasodilation, a hallmark of endothelial function, is compromised in ageing. This endothelial dysfunction is related to increased oxidative stress, lower nitric oxide bioavailability, and a low-grade inflammatory response, further exacerbating vascular dysfunction. Resistance artery remodeling in ageing emerges as a hypertrophic response of the vessel wall that is typically observed in conjunction with outward remodeling (in normotension), or as inward hypertrophic remodeling (in hypertension). The remodeling process involves oxidative stress, inflammation, reorganization of actin cytoskeletal components, and extracellular matrix fiber proteins. Reactive oxygen species (ROS) signaling and chronic low-grade inflammation play substantial roles in age-related vascular dysfunction. Due to its role in the regulation of vascular tone and structural proteins, the RhoA/Rho-kinase pathway is an important target in age-related vascular dysfunction and diseases. Understanding the intricate interplay of these factors is crucial for developing targeted interventions to mitigate the consequences of ageing on resistance arteries and enhance the overall vascular health.

A quantitative and site-specific atlas of the citrullinome reveals widespread existence of citrullination and insights into PADI4 substrates.

Despite the importance of citrullination in physiology and disease, global identification of citrullinated proteins, and the precise targeted sites, has remained challenging. Here we employed quantitative-mass-spectrometry-based proteomics to generate a comprehensive atlas of citrullination sites within the HL60 leukemia cell line following differentiation into neutrophil-like cells. We identified 14,056 citrullination sites within 4,008 proteins and quantified their regulation upon inhibition of the citrullinating enzyme PADI4. With this resource, we provide quantitative and site-specific information on thousands of PADI4 substrates, including signature histone marks and transcriptional regulators. Additionally, using peptide microarrays, we demonstrate the potential clinical relevance of certain identified sites, through distinct reactivities of antibodies contained in synovial fluid from anti-CCP-positive and anti-CCP-negative people with rheumatoid arthritis. Collectively, we describe the human citrullinome at a systems-wide level, provide a resource for understanding citrullination at the mechanistic level and link the identified targeted sites to rheumatoid arthritis.

Empagliflozin to elderly and obese patients with increased risk of developing heart failure: Study protocol for the Empire Prevent trial program.

INTRODUCTION: Sodium-glucose cotransporter 2 (SGLT2) inhibitors have previously demonstrated cardioprotective properties in patients with type 2 diabetes, suggesting a preventive effect on heart failure (HF). The Empire Prevent trial program investigates the therapeutic potential for HF prevention by evaluating the cardiac, metabolic, and renal effects of the SGLT2 inhibitor empagliflozin in patients with increased risk of developing HF, but without diabetes or established HF. METHODS: The Empire Prevent trial program is an investigator-initiated, double-blind, randomized clinical trial program including elderly and obese patients (60-84 years, body mass index >28 kg/m2) with at least one manifestation of hypertension, cardiovascular or chronic kidney disease, but no history of diabetes or HF. The aims are to investigate the effects of empagliflozin on 1) physical capacity and left ventricular and atrial structural changes with peak oxygen consumption and left ventricular mass as primary endpoints (Empire Prevent Cardiac), and 2) cardiac-adipose tissue interaction and volume homeostasis with primary endpoints of changes in epicardial adipose tissue and estimated extracellular volume (Empire Prevent Metabolic). At present, 138 of 204 patients have been randomized in the Empire Prevent trial program. Patients are randomized 1:1 to 180 days treatment with empagliflozin 10 mg daily or placebo, while undergoing a comprehensive examination program at baseline and follow-up. DISCUSSION: The Empire Prevent trial program will mark the first step towards elucidating the potential of SGLT2 inhibition for HF prevention in an outpatient setting in elderly and obese patients with increased risk of developing HF, but with no history of diabetes or established HF. Furthermore, the Empire Prevent trial program will supplement the larger event-driven trials by providing mechanistic insights to the beneficial effects of SGLT2 inhibition. TRIAL REGISTRATION: Both parts of the trial program have been registered on September 13th 2021 (Clinical Trial Registration numbers: NCT05084235 and NCT05042973) before enrollment of the first patient. All patients will provide oral and written informed consent. The trial is approved by The Regional Committee on Health Research Ethics and the Danish Medicines Agency. Data will be disseminated through scientific meetings and peer-reviewed journals irrespective of outcome.

Safety and Efficacy of Midline vs Peripherally Inserted Central Catheters Among Adults Receiving IV Therapy: A Randomized Clinical Trial.

Importance: Midline catheters (MCs) are widely used, but safety and efficacy compared with peripherally inserted central catheters (PICCs) has not been adequately evaluated. Objective: To compare the safety and efficacy of MCs with PICCs among adult patients with an anticipated intravenous therapy lasting from 5 to 28 days. Design, Setting, and Participants: This parallel, 2-group, open-label, randomized clinical trial (RCT) was conducted in Denmark from October 2018 to February 2022 at a single academic tertiary care center. Adult inpatients and outpatients were consecutively randomized. Intervention: Patients were randomized in a 1:1 ratio to either the MC group or the PICC control group. Main Outcomes and Measures: The primary outcome was catheter-related bloodstream infection (CRBSI), analyzed using the Fisher exact test. Secondary outcomes were symptomatic catheter-related thrombosis and catheter failure, including mechanical cause, phlebitis, infiltration, pain in relation to drug or fluid administration, and leaking from the puncture site. Incidence rate ratios (IRRs) were calculated to assess between-group failure rates over device dwell time using Poisson regression. An intention-to-treat analysis was performed. Results: A total of 304 patients (mean [SD] age, 64.6 [13.5] years; 130 [42.8%] female) were included in the analysis, and 152 patients were allocated to each catheter group. The incidence of CRBSI was low, with 0 in the MC group and 1 in the PICC control group (P > .99). The MC group had a higher catheter-related complication rate (20 [13.2%] vs 11 [7.2%]), and an IRR of 2.37 (95% CI, 1.12-5.02; P = .02) for complications compared with the PICC control group. In a post hoc analysis stratified by catheter dwell time, no significant difference in complication rate (IRR, 1.16; 95% CI, 0.50-2.68; P = .73) was found between the 2 groups for catheters used less than 16 days. Conclusions and Relevance: In this RCT with patients who received medium- to long-term intravenous therapy, the incidence of CRBSI was low, with no difference between MCs and PICCs. The use of MCs resulted in a higher incidence of catheter-related complications compared with use of PICCs. This finding should be balanced in the decision of type of catheter used at the individual patient level. Trial Registration: ClinicalTrials.gov Identifier: NCT04140916.