REDCapDM: An R package with a set of data management tools for a REDCap project.

BACKGROUND: Research Electronic Data CAPture (REDCap) is a web application for creating and managing online surveys and databases. Clinical data management is an essential process before performing any statistical analysis to ensure the quality and reliability of study information. Processing REDCap data in R can be complex and often benefits from automation. While there are several R packages available for specific tasks, none offer an expansive approach to data management. RESULTS: The REDCapDM is an R package for accessing and managing REDCap data. It imports data from REDCap to R using either an API connection or the files in R format exported directly from REDCap. It has several functions for data processing and transformation, and it helps to generate and manage queries to clarify or resolve discrepancies found in the data. CONCLUSION: The REDCapDM package is a valuable tool for data scientists and clinical data managers who use REDCap and R. It assists in tasks such as importing, processing, and quality-checking data from their research studies.

Impact of a prospective audit and feedback antimicrobial stewardship programme on carbapenem consumption: a quasi-experimental study (IPANEMA study).

OBJECTIVES: To test the hypothesis that a prospective audit and feedback (PAF) intervention combined with electronic tools will reduce carbapenem use without negatively affecting patient outcomes. METHODS: A quasi-experimental, pre-intervention and intervention study was performed conducted in the urology department of a university hospital. The intervention involved implementing a PAF within an antimicrobial stewardship programme with the aid of an electronic tool. The primary outcome was carbapenem use, assessed by DDD/100 patient-days (PD). Secondary outcomes included evaluating the effect of the intervention on overall antibiotic use measured by DDD/100 PD and days of therapy (DOT)/100 PD, as well as patient safety. The chi-squared test or t-test was used, and the Poisson model was employed to assess the association between the intervention and outcomes. RESULTS: A 9% decrease in carbapenem DDD/100 PD was observed during the intervention period (IR = 0.91; 95% CI = 0.85-0.97, P = 0.007). The proportion of patients who received carbapenem treatment dropped from 17.8% to 16.5% [incidence ratio (IR) = 0.95; 95% CI = 0.86-2.05, P = 0.31]. Carbapenem DOT/100 PD decreased from 12.4 to 11.0 (IR = 0.89; 95% CI = 0.83-0.94, P < 0.001). Overall antibiotic DDD/100 PD decreased by 3% (IR = 0.97; 95% CI = 0.94-0.99, P = 0.001) and DOT/100 PD by 7% (IR = 0.93; 95% CI = 0.91-0.95, P < 0.001). The incidence of infections caused by carbapenemase-producing microorganisms, Enterococcus faecium bacteraemia and Clostridioides difficile-associated diarrhoea episodes was similar in the pre-intervention and intervention periods. ESBL incidence rate decreased, but the differences were not statistically significant (3.94/1000 PD versus 2.88/1000 PD, P = 0.111). Length of hospital stay, in-hospital all-cause mortality, and 30 day readmission incidence remained unchanged. CONCLUSIONS: The implementation of PAF combined with an electronic tool was an effective and safe intervention for reducing carbapenem use.

Use of phytosterol-fortified foods to improve LDL cholesterol levels: A systematic review and meta-analysis.

AIMS: The main objective was to assess if foods fortified with phytosterols (PS), including plant sterols and plant stanols, reduce low-density lipoprotein cholesterol (LDL-C) concentrations. The secondary objective was to determine the impact of different factors related to PS administration. DATA SYNTHESIS: The search was carried out in MEDLINE, EMBASE, Web of Science, Scopus and The Cochrane Central Register of Controlled Trials (CENTRAL) databases up to March 2023. The meta-analysis was registered in the PROSPERO database (CRD42021236952). From a total of 223 studies, 125 were included. On average, PS lowered LDL-C 0.55 mmol/L [95% confidence interval (CI) = 10.82-12.67], and this decrease was significantly maintained for all analysed subgroups. A greater reduction in LDL-C levels was detected in relation to a higher daily PS dosage. The food format "Bread, biscuits, cereals", conditioned a lower decrease of 0.14 mmol/L (95%CI -8.71 to -2.16) in LDL-C levels, compared to the predominant food format group of "butter, margarine, spreads". No significant differences were detected with the other subgroups (treatment duration, intake pattern, number of daily intakes and concomitant statin treatment). CONCLUSION: The present meta-analysis supported that the use of PS-fortified foods had a beneficial effect on LDL-C lowering. In addition, it was observed that the factors that influence a decline LDL-C levels were PS dose as well as the food format in which they were consumed.

Accentuated aging associated with HIV in a Mediterranean setting occurs mainly in persons aged>70 years: a comparative cohort study (Over50 cohort).

In an ongoing Mediterranean cohort, we compared age-related conditions between 208 HIV-infected persons and 104 matched controls. ≥3 comorbidities were found in 31.0% of HIV-infected patients and 8.7% of controls. Conditions significantly more frequent among the HIV-infected population were: lipid abnormalities, cancer, osteopenia/osteoporosis, liver disease, sexual dysfunction, hearing deficit, sleep disorders, falls, cognitive complaints, being single, living alone, and being elderly at risk. HIV-infected patients aged >70 years had a significantly higher number of cardiovascular risk factors (CVRF) and comorbidities than controls. HIV-infected persons who had never smoked had a higher prevalence of CVRFs, ≥3 comorbidities, liver disease, cancer, and cognitive complaints compared to controls. Factors associated with frailty were being a man who has sex with men, ≥3 CVRFs, nadir CD470 years. The multidisciplinary assessment also revealed concerning findings in social, cognitive, and functional variables among HIV-infected individuals, with a higher prevalence of elderly at risk than among controls.

COVID19-world: a shiny application to perform comprehensive country-specific data visualization for SARS-CoV-2 epidemic.

BACKGROUND: Data analysis and visualization is an essential tool for exploring and communicating findings in medical research, especially in epidemiological surveillance. RESULTS: Data on COVID-19 diagnosed cases and mortality, from January 1st, 2020, onwards is collected automatically from the European Centre for Disease Prevention and Control (ECDC). We have developed a Shiny application for data visualization and analysis of several indicators to follow the SARS-CoV-2 epidemic using ECDC data. A country-specific tool for basic epidemiological surveillance, in an interactive and user-friendly manner. The available analyses cover time trends and projections, attack rate, population fatality rate, case fatality rate, and basic reproduction number. CONCLUSIONS: The COVID19-World online web application systematically produces daily updated country-specific data visualization and analysis of the SARS-CoV-2 epidemic worldwide. The application may help for a better understanding of the SARS-CoV-2 epidemic worldwide.

Bayesian spatio-temporal analysis of the COVID-19 pandemic in Catalonia.

In this study, we modelled the incidence of COVID-19 cases and hospitalisations by basic health areas (ABS) in Catalonia. Spatial, temporal and spatio-temporal incidence trends were described using estimation methods that allow to borrow strength from neighbouring areas and time points. Specifically, we used Bayesian hierarchical spatio-temporal models estimated with Integrated Nested Laplace Approximation (INLA). An exploratory analysis was conducted to identify potential ABS factors associated with the incidence of cases and hospitalisations. High heterogeneity in cases and hospitalisation incidence was found between ABS and along the waves of the pandemic. Urban areas were found to have a higher incidence of COVID-19 cases and hospitalisations than rural areas, while socio-economic deprivation of the area was associated with a higher incidence of hospitalisations. In addition, full vaccination coverage in each ABS showed a protective effect on the risk of COVID-19 cases and hospitalisations.

Easy-Amanida: An R Shiny application for the meta-analysis of aggregate results in clinical metabolomics using Amanida and Webchem.

Meta-analysis is a useful tool in clinical research, as it combines the results of multiple clinical studies to improve precision when answering a particular scientific question. While there has been a substantial increase in publications using meta-analysis in various clinical research topics, the number of published meta-analyses in metabolomics is significantly lower compared to other omics disciplines. Metabolomics is the study of small chemical compounds in living organisms, which provides important insights into an organism's phenotype. However, the wide variety of compounds and the different experimental methods used in metabolomics make it challenging to perform a thorough meta-analysis. Additionally, there is a lack of consensus on reporting statistical estimates, and the high number of compound naming synonyms further complicates the process. Easy-Amanida is a new tool that combines two R packages, "amanida" and "webchem", to enable meta-analysis of aggregate statistical data, like p-value and fold-change, while ensuring the compounds naming harmonization. The Easy-Amanida app is implemented in Shiny, an R package add-on for interactive web apps, and provides a workflow to optimize the naming combination. This article describes all the steps to perform the meta-analysis using Easy-Amanida, including an illustrative example for interpreting the results. The use of aggregate statistics metrics extends the use of Easy-Amanida beyond the metabolomics field.

High incidence of clinical fragility fractures in postmenopausal women with rheumatoid arthritis. A case-control study.

OBJECTIVES: To estimate the incidence of clinical fragility fractures in postmenopausal women with rheumatoid arthritis (RA) and analyze risk factors for fracture. METHODS: Incidence of clinical fragility fractures in 330 postmenopausal women with RA was compared to that of a control population of 660 age-matched postmenopausal Spanish women. Clinical fractures during the previous five years were recorded. We analyzed associations with risk factors for fracture in both populations and with disease-related variables in RA patients. RESULTS: Median age of RA patients was 64 years; median RA duration was eight years. Sixty-nine percent were in remission or on low activity. Eighty-five percent had received glucocorticoids (GCs); 85 %, methotrexate; and 40 %, ≥1 biologic DMARD. Fifty-four patients and 47 controls had ≥1 major osteoporotic fracture (MOF). Incidence of MOFs was 3.55 per 100 patient-year in patients and 0.72 in controls (HR: 2.6). Risk factors for MOFs in RA patients were age, previous fracture, parental hip fracture, years since menopause, BMD, erosions, disease activity and disability, and cumulative dose of GCs. Previous fracture in RA patients was a strong risk for MOFs (HR: 10.37). CONCLUSION: Of every 100 postmenopausal Spanish women with RA, 3-4 have a MOF per year. This is more than double that of the general population. A previous fracture poses a high risk for a new fracture. Other classic risk factors for fracture, RA disease activity and disability, and the cumulative dose of GCs are associated with fracture development.

[COVID19-Tracker: a shiny app to analise data on SARS-CoV-2 epidemic in Spain]. / COVID19-Tracker: una aplicación Shiny para analizar datos de la epidemia de SARS-CoV-2 en España.

Data visualization is an important tool for exploring and communicating findings in medical research, and specially in epidemiological surveillance. The COVID19-Tracker web application systematically produces daily updated data visualization and analysis of SARS-CoV-2 epidemic in Spain. It collects automatically daily data on COVID-19 diagnosed cases and mortality, from February 24th, 2020 onwards. Three applications have already been developed: 1) to analyze data trends and estimating short-term projections; 2) to estimate the case fatality rate; and 3) to assess the effect of the lockdowns on the data trends. The application may help for a better understanding of the SARS-CoV-2 epidemic data in Spain.

COVID19-Tracker: una aplicación Shiny para analizar datos de la epidemia de SARS-CoV-2 en España / COVID19-Tracker: a shiny app to analise data on SARS-CoV-2 epidemic in Spain

La visualización de datos es una herramienta relevante para explorar y comunicar resultados en la investigación médica, en especial cuando se trata de vigilancia epidemiológica. La aplicación web COVID19-Tracker analiza y produce de forma sistemática visualizaciones diarias de los datos de la epidemia de COVID-19 de casos diagnosticados y fallecimientos desde el 24 de febrero de 2020 en adelante. Se han desarrollado tres aplicaciones para: 1) análisis de la tendencia y proyecciones a corto plazo; 2) estimación de la tasa de letalidad; y 3) efecto del estado de alarma sobre la tendencia de datos incidentes. La aplicación online puede ser de utilidad para un mejor conocimiento de la epidemia de SARS-CoV-2 en España

Data visualization is an important tool for exploring and communicating findings in medical research, and specially in epidemiological surveillance. The COVID19-Tracker web application systematically produces daily updated data visualization and analysis of SARS-CoV-2 epidemic in Spain. It collects automatically daily data on COVID-19 diagnosed cases and mortality, from February 24th, 2020 onwards. Three applications have already been developed: 1) to analyze data trends and estimating short-term projections; 2) to estimate the case fatality rate; and 3) to assess the effect of the lockdowns on the data trends. The application may help for a better understanding of the SARS-CoV-2 epidemic data in Spain

COVID19-Tracker: una aplicación Shiny para analizar datos de la epidemia de SARS-CoV-2 en España / COVID19-Tracker: a shiny app to analise data on SARS-CoV-2 epidemic in Spain

La visualización de datos es una herramienta relevante para explorar y comunicar resultados en la investigación médica, en especial cuando se trata de vigilancia epidemiológica. La aplicación web COVID19-Tracker analiza y produce de forma sistemática visualizaciones diarias de los datos de la epidemia de COVID-19 de casos diagnosticados y fallecimientos desde el 24 de febrero de 2020 en adelante. Se han desarrollado tres aplicaciones para: 1) análisis de la tendencia y proyecciones a corto plazo; 2) estimación de la tasa de letalidad; y 3) efecto del estado de alarma sobre la tendencia de datos incidentes. La aplicación online puede ser de utilidad para un mejor conocimiento de la epidemia de SARS-CoV-2 en España

Data visualization is an important tool for exploring and communicating findings in medical research, and specially in epidemiological surveillance. The COVID19-Tracker web application systematically produces daily updated data visualization and analysis of SARS-CoV-2 epidemic in Spain. It collects automatically daily data on COVID-19 diagnosed cases and mortality, from February 24th, 2020 onwards. Three applications have already been developed: 1) to analyze data trends and estimating short-term projections; 2) to estimate the case fatality rate; and 3) to assess the effect of the lockdowns on the data trends. The application may help for a better understanding of the SARS-CoV-2 epidemic data in Spain