Database Access Research Articles

A database of chief financial officer turnover and dismissal in S&P 1500 firms, 2000–2022

AbstractResearch SummaryUnderstanding the antecedents and consequences of executive turnover is an important topic in strategy research. Yet, due to limitations in data availability, we know little about the turnover and dismissal of executives beyond the CEO. To address this, we develop an open access database on CFO turnover and dismissal—given the unique interface CFOs form with CEOs as strategic partners—in S&P 1500 firms between 2000 and 2022. We search for media documentation of CFO turnover events and code for various types of departure with supporting evidence. We compare our CFO database with an open access CEO database and show distinct patterns of exit and interdependence between CFO and CEO departure. The results reveal important directions for strategy scholarship. The database is available on the OSF platform (osf.io/fh248/?view_only=2c62d6eb59bd433496ce101689df3adc).Managerial SummaryCFOs are key executives who shape the firm's strategy with CEOs. Consequently, understanding why CFOs leave their jobs and how this impacts the firm is important. Yet, the reasons why CFOs leave their jobs are rarely captured in existing databases. To address this, we develop an open access database documenting various types of CFO turnover and dismissal in S&P 1500 firms from 2000 to 2022. We establish a robust coding protocol and describe how we develop our database. We further explore how our CFO database contributes to the literature by comparing it with an open access CEO database; highlighting new lines of research and questions that it can stimulate. The database is available on the OSF platform (osf.io/fh248/?view_only=2c62d6eb59bd433496ce101689df3adc).

ExoMolHR: A Relational Database of Empirical High-resolution Molecular Spectra

Abstract ExoMolHR is an empirical, high-resolution molecular spectrum calculator for the high-temperature molecular line lists available from the ExoMol molecular database. Uncertainties, where available, in recommended ExoMol data sets are used to select highly accurate spectral lines. These lines largely rely on empirical energy levels generated through the MARVEL procedure, which is being systematically used to improve the energy and transition data provided by the ExoMol database. The freely accessible ExoMolHR database provides line positions with calculated intensities for a user-specified wavenumber/wavelength range and temperature. Spectra can be plotted on the ExoMolHR website https://www.exomol.com/exomolhr/ or downloaded as a .csv file. Cross sections can be calculated using the Python program PyExoCross. The ExoMolHR database currently provides 24,307,135 spectral lines for 33 molecules and 58 isotopologues; these numbers will increase as the ExoMol database is updated.

Protective effects and bioinformatic analysis of narciclasine on vascular aging via cross-talk between inflammation and metabolism through inhibiting skeletal muscle-specific ceramide synthase 1.

The senescence of smooth muscle is one of the independent risk factors in atherosclerosis progression in which the vascular inflammation plays an important role on vascular dysfunction. This study is designed to explore the novel vascular aging biomarkers and screen the potential molecular interventional targets through bioinformatic analysis. Transcriptional analysis was conducted based on the GSE16487 open access database, which included 15 human vascular tissue samples from two groups: young group (≤ 60 years old, n = 8) and aged group (≥ 75 years old, n = 7). There were 275 differential expression genes (119 upregulated and 156 downregulated genes) with minimum 1.5-fold change between two groups. 9 genes were mainly participated in inflammation-related signaling pathways, in which narciclasine was validated as the most effective candidate for modulation the ceramide synthesis. In vitro and animal study demonstrated that narciclasine reversed vascular aging by inhibiting skeletal muscle-specific ceramide synthase 1 (CerS1), reducing the ceramide level derived from CerS1, and improving fat deposition and circulating glycolipid metabolism. Narciclasine attenuates vascular aging and modulates the cross-talk between inflammation and metabolism via inhibiting skeletal muscle-specific ceramide synthase 1.

Communicating with Stakeholders to Identify High-Impact Research Directions for Non-Targeted Analysis.

Non-targeted analysis (NTA) using high-resolution mass spectrometry without defined chemical targets has the potential to expand and improve chemical monitoring in many fields. Despite rapid advancements within the research community, NTA methods and data remain underutilized by many potential beneficiaries. To better understand barriers toward widespread adoption, the Best Practices for Non-Targeted Analysis (BP4NTA) working group conducted focus group meetings and follow-up surveys with scientists (n = 61) from various sectors (e.g., drinking water utilities, epidemiologists, n = 9) where NTA is expected to provide future value. Meeting participants included producers and end-users of NTA data with a wide range of familiarity with NTA methods and outputs. Discussions focused on identifying specific barriers that limit adoption and on setting NTA product development priorities. Stated priorities fell into four major categories: 1) education and training materials; 2) QA/QC frameworks and study design guidance; 3) accessible compound databases and libraries; and 4) NTA data linkages with chemical fate and toxicity information. Based on participant feedback, this manuscript proposes research directions, such as standardization of training materials, that BP4NTA and other institutions can pursue to expand NTA use in various application scenarios and decision contexts.

Rainfall Intensity–Duration–Frequency Curves Dataset for Brazil

This is a database containing rainfall intensity–duration–frequency equations (IDF equations) for 6550 pluviographic and pluviometric stations in Brazil. The database was compiled from 370 different publications and contains the following information: station identification, geographic position, size and period of the rainfall series used, parameters of the IDF equations, and literature references. The database is available on Mendeley Data (DOI: 10.17632/378bdcmnc8.1) in the form of spreadsheets and vector files. Since the launch of the Pluvio 2.1 software in 2006, which included 549 IDF equations obtained in the country, this is the largest and most accessible database of IDF equations in Brazil. The data provided may be useful, among other purposes, for designing hydraulic structures, controlling water erosion, planning land use, and water resource planning and management.

Deep learning-enabled electrocardiogram assessment of valvular heart disease

Abstract Background Valvular heart disease is a highly prevalent condition that contributes significantly to cardiovascular morbidity and mortality. Echocardiography is the gold-standard for valvular disease evaluation; however, diagnosis may be delayed due to limited resources or expertise. Deep learning analysis of electrocardiography (ECG) has demonstrated the ability to identify subtle structural and functional abnormalities within the cardiovascular system. Purpose The aim of this study was to evaluate the accuracy of a deep learning algorithm to identify valvular heart disease from a standard 12-lead ECG. Methods From 2008 to 2019, a total of 27,689 patients who were admitted to the Medical Center with an echocardiogram and ECG performed within 14 days of each other were included in the study. We trained a convolutional neural network to identify the presence of moderate or severe aortic stenosis (AS), aortic regurgitation (AR), mitral regurgitation (MR), and/or tricuspid regurgitation (TR) using a 12-lead ECG. Model performance was assessed using area under the receiver-operating characteristic (AUROC). Patients were randomly divided into development (n = 22,151; 80%) and independent validation sets (n = 5,538; 20[AN1] %). Data used for the study was collected from two open access databases, MIMIC-IV-ECG and MIMIC-IV-Note. Results Mean age of the cohort was 65.9 ± 16.6 years old and 51% were female. On the independent validation dataset, AS was present in 126 (2.3%) of patients, AR in 51 (1%), MR in 337 (6.1%), and TR in 450 (8.1 %) patients. During validation, the model accurately identified AS, AR, MR, and TR with AUROCs of 0.80 (95% CI 0.77-0.83), 0.74 (95% CI 0.69-0.80), 0.86 (95% CI 0.84-0.88), and 0.85 (95% CI 0.83-0.87), respectively (Table 1). Test characteristics were dependent on underlying prevalence and selected sensitivity levels. For a composite of any of AS, AR, MR, or TR, the model achieved a positive predictive value of 28% with 79.4% sensitivity. Conclusion A deep learning-enabled ECG demonstrates robust performance to identify patients living with valvular heart disease. This model may serve as a simple and promising tool for improving the early detection of valvular heart disease, showcasing the potential for an early development valvular disease screening program.

An annotated heterogeneous ultrasound database

Ultrasound is a primary diagnostic tool commonly used to evaluate internal body structures, including organs, blood vessels, the musculoskeletal system, and fetal development. Due to challenges such as operator dependence, noise, limited field of view, difficulty in imaging through bone and air, and variability across different systems, diagnosing abnormalities in ultrasound images is particularly challenging for less experienced clinicians. The development of artificial intelligence (AI) technology could assist in the diagnosis of ultrasound images. However, many databases are created using a single device type and collection site, limiting the generalizability of machine learning models. Therefore, we have collected a large, publicly accessible ultrasound challenge database that is intended to significantly enhance the performance of AI-assisted ultrasound diagnosis. This database is derived from publicly available data on the Internet and comprises a total of 1,833 distinct ultrasound data. It includes 13 different ultrasound image anomalies, and all data have been anonymized. Our data-sharing program aims to support benchmark testing of ultrasound disease diagnosis in multi-center environments.

Abordaje del Ciberbullying y Grooming en adolescentes mexicanos: Una revisión sistemática de la literatura

Given the lack of systematic reviews in Mexico that explore the phenomena of Cyberbullying and Grooming, types of digital violence that mainly affect children and adolescents, this research was conducted with the aim of identifying the approach that has been given to the phenomenon within the country between 2000-2022. The PRISMA method was used and publications indexed in the Open Access databases SciELO, Redalyc, Dialnet, ScienceDirect and PubMed were consulted. Thirty-five studies were analyzed between 2000-2022, finding a high prevalence of quantitative works that mainly explore the nature and dynamics of Cyberbullying; most of the studies are concentrated in the center-north of the country, with more than two authors per article. The almost null research on Grooming in Mexico stands out, as well as studies on validation of instruments and intervention programs against Cyberbullying; in future reviews it is suggested to explore non-indexed sources that broaden the scope that has been had of the phenomenon.

Harnessing national data systems to understand circumstances surrounding veteran suicide: linking Department of Veterans Affairs and National Violent Death Reporting System Data

BackgroundVeterans are at elevated risk for suicide compared to non-Veteran U.S. adults. Data sources and analyses to inform prevention efforts, especially for those who do not use Department of Veterans Affairs (VA) healthcare services, are needed. This study aimed to link VA and CDC’s National Violent Death Reporting System (NVDRS) data to create a novel data source to characterize the circumstances precipitating and preceding suicide among Veterans, including among those who did not use VA healthcare.MethodsMulti-variable, multi-stage, deterministic linkage of VA-Department of Defense (DoD) Mortality Data Repository (MDR) and NVDRS-Restricted Access Database suicide and undetermined intent mortality records within 189 state-year strata (42 states, 2012–2018). Three linkage stages: (1) exact (matched on: age, sex, death date, underlying cause of death, day of month of birth, first initial of last name); (2) probable (all but one variable matched); (3) possible (all but 2 variables matched). Linkage success and accuracy of NVDRS-documented military history were assessed.ResultsAcross all state-years, 22,019 matches (89.20% of 24,685 MDR Veteran records) were identified (65.47% exact). When high missingness (2 + matching variables in > 10% of records; n = 23) or incomplete reporting (n = 12) state-years were excluded, match rate increased to 94.29% (77.15% exact). NVDRS-documented military history (ever served) was accurate for 87.79% of matched records, with an overall sensitivity of 84.62%. Sensitivity was lower for female (61.01%) and younger (17–39 years; 77.51%) Veterans.ConclusionsAccurate linkage of VA-DoD and NVDRS data is feasible and offers potential to improve understanding of circumstances surrounding suicide among Veterans.

Huaier inhibits the proliferation and migration of gastrointestinal stromal tumors by regulating the JAK2 / STAT3 signaling pathway.

Gastrointestinal stromal tumor (GIST) is the most common mesenchymal tumor of the digestive tract, often accompanied by a high risk of recurrence and drug resistance. Huaier (Trametes robiniophila Murr), a traditional Chinese medicinal fungus, has demonstrated potent anticancer properties and is widely used as an adjuvant treatment for liver, breast, gastric, colon, and non-small cell lung cancers. However, its effects and molecular mechanisms in GIST remain unclear. This study aims to explore the inhibitory effects and underlying mechanisms of Huaier on GIST through network pharmacology and experimental validation. Initially, we utilized a publicly accessible database to identify the core targets and principal pathways associated with Huaier's therapeutic effects on gastrointestinal stromal tumors. To further evaluate its biological impact, cell viability, proliferation, migration, and invasion were assessed through CCK-8 and EdU assays, wound healing tests, and Transwell experiments. Apoptotic cell death was quantified using flow cytometry analysis. Additionally, the influence of Huaier extract on the expression levels of JAK2 and STAT3 proteins was examined via Western blotting. Finally, a subcutaneous xenograft mouse model was employed to investigate the anti-tumor efficacy of Huaier in vivo. In this study, GAPDH, TNF, STAT3, ESR1, EGFR, IL6, CCND1, PTGS2, BCL2L1, and MAPK3 were identified as shared molecular targets, with the JAK/STAT signaling pathway recognized as the pivotal regulatory mechanism. Experimental findings demonstrated that Huaier exerted inhibitory effects on the proliferation, migration, and invasion of GIST-T1 and GIST-882 cells, exhibiting both dose- and time-dependent responses. Furthermore, Huaier was found to promote apoptosis in these cells. Western blot analysis revealed that treatment with Huaier extract significantly decreased the phosphorylation levels of JAK2 and STAT3, thereby suppressing the activation of the JAK2 / STAT3 signaling cascade. In vivo experiments further substantiated these findings, showing that Huaier treatment markedly reduced tumor size and inhibited tumor progression. Our results suggest that Huaier may inhibit the growth of GIST cells by inhibiting the JAK2/STAT3 signaling pathway, reduce cell proliferation, induce apoptosis, reduce cell migration and invasion, and show anti-tumor effects in vivo and in vitro.

Use of adjuvant treatment options in tobacco and nicotine counseling: results of an anonymous survey

Counseling and treatment of tobacco and nicotine users include classical behavioral therapy and cognitive-behavioral therapy methods. More than half of the quitters after an intervention relapsed into old behavior after 12 months. Discussions with experts have shown that evidence-based interventions are individually supplemented with treatment options without scientific evidence or with inconsistent efficacy. This study examined the use of adjuvant treatment options in the area of smoking cessation such as aromatherapy.The sample for our anonymous online survey (Sosci survey) consisted of 351 experts. All of them had registered with their contact data in a freely accessible database (https://www.anbieter-raucherberatung.de/anbieter.php). A total of 88 experts took part in the survey. Respondents were on average 53 years old, and 71% were female. They reported on average 14 years of experience in smoking cessation counseling. More than half of the participants (66.3%; n=57) had employed at least one adjuvant treatment option. Exercise (49.1%), hypnosis (34.5%) and acupuncture (27.3%) were mentioned most often.Adjuvant treatment options appear to be important in the counseling of tobacco and nicotine users and there should be more discussion on these options among experts. These should also considered in the development of curricula for counsellors on tobacco and nicotine cessation.

The use of public spatial databases in risk analysis: A US-oriented tutorial.

This tutorial focuses on opportunities and challenges associated with using six large, publicly accessible spatial databases published during the last decade by US federal agencies. These databases provide opportunities for researchers to risk-inform policy by comparing community asset, demographic, economic, and social data, along with anthropogenic and natural hazard data at multiple geographic scales. The opportunities for data analysis come with challenges, including data accuracy, variations in the shape and size of data cells, spatial autocorrelation, and other issues endemic to spatial datasets. If ignored, these issues can lead to misleading results. This article briefly reviews the six databases and how agencies use them. It then focuses on the data and its limitations. Examples are provided, as are summaries of the debates surrounding these databases, followed by paths forward for improving their use. We end with a checklist that users should consider when they access any of the six spatial databases or others. We believe that these new resources can be effectively used with appropriate caution to answer user-generated questions about hazards and risks-questions that are important to both community groups and government decision-makers.

Multi-omics dataset of individual variations in growth performance of large yellow croaker

Large yellow croaker (Larimichthys crocea) is a highly economically important marine fish species in China. However, substantial individual variations in growth performance have emerged as a limiting factor for the sustainable development of the large yellow croaker industry. Gut microbiota plays a crucial role in fish growth and development by regulating metabolic processes. To explore these dynamics, we employed metagenomics, transcriptomics, and untargeted metabolomics to comprehensively analyze the structure of the intestinal microbiome and its relationship with intestinal metabolism and host gene expression. We constructed association models for “gut microbiota–differentially expressed genes”, “differentially expressed genes–metabolites,” and “gut microbiota–metabolites.” Sequencing data and LC–MS/MS raw data have been deposited in NCBI and MetaboLights databases for public access. Our findings offer critical insights into the molecular mechanisms underlying growth variations in L. crocea and provide valuable data for the selective breeding of improved strains.

Single-cell and bulk RNA sequencing analysis reveals CENPA as a potential biomarker and therapeutic target in cancers.

Cancer remains one of the most significant public health challenges worldwide. A widely recognized hallmark of cancer is the ability to sustain proliferative signaling, which is closely tied to various cell cycle processes. Centromere Protein A (CENPA), a variant of the standard histone H3, is crucial for selective chromosome segregation during the cell cycle. Despite its importance, a comprehensive pan-cancer bioinformatic analysis of CENPA has not yet been conducted. Data on genomes, transcriptomes, and clinical information were retrieved from publicly accessible databases. We analyzed CENPA's genetic alterations, mRNA expression, functional enrichment, association with stemness, mutations, expression across cell populations and cellular locations, link to the cell cycle, impact on survival, and its relationship with the immune microenvironment. Additionally, a prognostic model for glioma patients was developed to demonstrate CENPA's potential as a biomarker. Furthermore, drugs targeting CENPA in cancer cells were identified and predicted using drug sensitivity correlations and protein-ligand docking. CENPA exhibited low levels of gene mutation across various cancers. It was found to be overexpressed in nearly all cancer types analyzed in TCGA, relative to normal controls, and was predominantly located in the nucleus of malignant cells. CENPA showed a strong association with the cancer cell cycle, particularly as a biomarker for the G2 phase. It also emerged as a valuable diagnostic and prognostic biomarker across multiple cancer types. In glioma, CENPA demonstrated reliable prognostic potential when used alongside other prognostic factors. Additionally, CENPA was linked to the immune microenvironment. Drugs such as CD-437, 3-Cl-AHPC, Trametinib, BI-2536, and GSK461364 were predicted to target CENPA in cancer cells. CENPA serves as a crucial biomarker for the cell cycle in cancers, offering both diagnostic and prognostic value.

A Review of 25 Spontaneous and Dynamic Facial Expression Databases of Basic Emotions

Abstract Most prior research on basic emotions has relied upon posed, static displays that do not accurately reflect the facial behavior seen in everyday life. To address this gap, the present paper aims to highlight existing facial expression databases (FEDBs) that feature spontaneous and dynamic displays of the six basic emotions. To assist readers in their decisions about stimulus selection, we comprehensively review 25 FEDBs in terms of three key dimensions: (a) conceptual features which reflect thematic approaches in database construction and validation, i.e., emotional content and elicitation procedures, encoder demographics, measurement and elicitation techniques; (b) technical features which concern technological aspects in stimulus development, i.e., stimulus numbers and duration, frame rate, and resolution; and (c) practical features which entail information about database access and potential ethical restrictions. Finally, we outline some of the remaining challenges in stimulus generation and make recommendations for future research.

The Expert System Application to Diagnose Computer Damage Using UML Model (Unified Modeling Language)

The expert system application are developed in line with the existence of information technology. The development of expert systems aims to be a means of assistance to provide solutions in our lives. This expert system can help technicians get solutions quickly and can save time. Expert systems use computer technology to integrate, manipulate and display information or characteristics. Expert systems can also help in making better solutions. With the very rapid technological advances today, an idea or idea has emerged from the author to try to implement one of the expert system application programs into the quality of service activities of computer technicians. The author tries to build an application that will help to facilitate the provision of solutions to computer damage to hardware so that it can make it easier for users or technicians to get solutions quickly. The system to be created is "Designing an Expert System for Diagnosing Computer Problems Using Visual Basic" This system will use the prototype method and tools for modeling using UML (Unified Modeling Language). This system is built using the Visual Basic 6.0 application to process the Microsoft Access database.

Saturated hydraulic conductivity and steady-state infiltration rate database for Brazilian soils

ABSTRACT Soil saturated hydraulic conductivity (Ksat) and steady-state infiltration rate (SSIR) are essential and necessary soil properties for different geoscience applications. Values of these hydraulic properties for the Brazilian territory are difficult to access and are dispersed in research efforts carried out around the country. This study developed an easy-to-manipulate, freely accessible database of soil saturated hydraulic conductivity, comprising field and laboratory analyses, and steady-state infiltration rates for Brazilian soils. This database was named Ksat-SSIR-DB. One analysis of the Ksat-SSIR-DB aimed to evaluate its coverage in Brazilian territory and in different soil groups. Average values of these hydraulic properties were also presented for textural classes, with values compared to those reported in international literature, and for other groupings, such as soil class, land use class, and porosity class. The variability of Ksat data in these groupings and in their combinations were also analyzed. The Ksat-SSIR-DB showed broad national coverage, comprising a total of 2,579 records, corresponding to 409 sampling sites, with Ksat and/or SSIR data and other associated soil information. A significant difference was observed between Ksat values for the vast majority of Brazilian clayey and very clayey soils compared to soils from the same textural groups from temperate regions. The two groupings that presented the lowest variability in terms of Ksat standard deviation values were the combination of textural classes with soil classes at the second category level of SiBCS (Brazilian Soil Classification System), and porosity classes with soil classes at the second category level of SiBCS. The Ksat-SSIR-DB has enormous potential for developing and testing Ksat pedotransfer functions in Brazilian soils, serving as a reference source for different geoenvironmental applications and, in particular, for modeling land surface processes. It is open access and can be accessed at https://www.sgb.gov.br/ksat-ssir-dbbase-de-dados-de-condutividade-hidraulica-saturada-e-de-taxa-de-infiltracao-basica-emsolos-brasileiros , which also includes the python script for data analysis.

Inherited or Immunological Thrombocytopenia: The Complex Nature of Platelet Disorders in 22q11.2 Deletion Syndrome.

22q11.2 deletion syndrome (22q11.2DS) is one of the most common congenital malformation syndromes resulting from disrupted embryonic development of pharyngeal pouches. The classical triad of symptoms described by Angelo DiGeorge is frequently accompanied by hematological and immune disorders. While it is well-established that patients with 22q11.2DS have an increased risk of recurrent autoimmune cytopenias, including immune thrombocytopenia, the platelet abnormalities in this population are more complex and multifaceted. Given this issue, we conducted a comprehensive literature review on platelet disorders in 22q11.2DS using accessible databases (PubMed and Scopus). We aimed to outline previous studies limitations and most urgent challenges concerning thrombocytopenia in these patients. One characteristic finding frequently observed in 22q11.2DS is mild macrothrombocytopenia caused presumably by the loss of one GP1BB allele, encoding the element of the GPIb-IX-V complex. This structure plays a central role in thrombocyte adhesion, aggregation, and subsequent activation. Recent studies suggest that defective megakaryopoiesis and impaired vasculogenesis may strongly influence platelet and hemostasis disorders in 22q11.2DS. Furthermore, the phenotypic manifestation may be modulated by epigenetic factors and gene expression modifiers located outside the deletion region. Although the final hemorrhagic phenotype is typically mild, these patients may require more frequent transfusions following major surgical procedures. Despite the risk of thrombocytopenia and thrombocytopathy, there is a lack of large-scale research on hematological anomalies in 22q11.2DS, and the available results are often inconclusive. Given the complexity of hemostatic disorders, it is essential to establish specific recommendations for perioperative management and autoimmune cytopenias treatment within this population.

Death incidents following transcatheter edge-to-edge repair (TEER) with the MitraClip device: analysis of 10-years post-marketing death reports from the Manufacturer and User Facility Device Experience (MAUDE) registry

BackgroundThe transcatheter edge-to-edge repair (TEER) technique, facilitated by the MitraClip device, is a minimally invasive intervention designed for high-risk patients with mitral regurgitation (MR). This study conducts a retrospective analysis of death events associated with MitraClip implantation over a ten-year decade, utilizing data from the FDA’s Manufacturer and User Facility Device Experience (MAUDE) database to evaluate trends in safety outcomes.MethodsA comprehensive search of the publicly accessible MAUDE database was conducted to retrieve reports of deaths and injuries related to MitraClip implantation from October 2013 to September 2023. Duplicate reports and records from unrelated sources were excluded. The Cochran-Armitage test was performed to evaluate trends in the proportion of fatal events over time.ResultsDuring the 10-year period following FDA approval, the MAUDE database recorded a total of 927 death reports and 9,211 injury reports associated with MitraClip. After excluding duplicates and irrelevant reports, 592 death cases were analyzed. The most commonly reported complications were MR (26.69%), tissue damage (24.16%), and hypotension (22.13%). The most frequent device-related issues were incomplete coaptation (14.70%), difficulty removing the divice (6.42%), and failure to adhere or bond/positioning failure(4.90%). Notably, 76.94% of deaths occurred within one year of implantation. The proportion of fatal events demonstrated a gradual decline, from 15.9% in 2014–2015 to 3.5% in 2020–2021 (p < 0.0001).ConclusionsThis analysis of the MAUDE database indicates a gradual decline in the proportion of fatal events associated with MitraClip implantation, which may be attributed to growing operator experience and advancements in device design. Nonetheless, persistent focus is required on managing complications and addressing potential risks to further enhance device performance and optimize its clinical utility.

Contribution of Open Access Databases to Intensive Care Medicine Research: Scoping Review.

Intensive care units (ICUs) handle the most critical patients with a high risk of mortality. Due to those conditions, close monitoring is necessary and therefore, a large volume of data is collected. Collaborative ventures have enabled the emergence of large open access databases, leading to numerous publications in the field. The aim of this scoping review is to identify the characteristics of studies using open access intensive care databases and to describe the contribution of these studies to intensive care research. The research was conducted using 3 databases (PubMed-MEDLINE, Embase, and Web of Science) from the inception of each database to August 1, 2022. We included original articles based on 4 open databases of patients admitted to ICUs: Amsterdam University Medical Centers Database, eICU Collaborative Research Database, High time resolution ICU dataset, Medical Information Mart for Intensive Care (II to IV). A double-blinded screening for eligibility was performed, first on the title and abstract and subsequently on the full-text articles. Characteristics relating to publication journals, study design, and statistical analyses were extracted and analyzed. We observed a consistent increase in the number of publications from these databases since 2016. The Medical Information Mart for Intensive Care databases were the most frequently used. The highest contributions came from China and the United States, with 689 (52.7%) and 370 (28.3%) publications respectively. The median impact factor of publications was 3.8 (IQR 2.8-5.8). Topics related to cardiovascular and infectious diseases were predominant, accounting for 333 (25.5%) and 324 (24.8%) articles, respectively. Logistic regression emerged as the most commonly used statistical model for both inference and prediction questions, featuring in 396 (55.5%) and 281 (47.5%) studies, respectively. A majority of the inference studies yielded statistically significant results (84.0%). In prediction studies, area under the curve was the most frequent performance measure, with a median value of 0.840 (IQR 0.780-0.890). The abundance of scientific outputs resulting from these databases, coupled with the diversity of topics addressed, highlight the importance of these databases as valuable resources for clinical research. This suggests their potential impact on clinical practice within intensive care settings. However, the quality and clinical relevance of these studies remains highly heterogeneous, with a majority of articles being published in low-impact factor journals.