Search

The impact of the coronavirus disease 2019 (COVID-19) pandemic on admission patterns and outcomes at a burn center is still largely unknown. The aim of this study was to determine how the COVID-19 pandemic affected the epidemiology of burn admissions at a major metropolitan burn center. This retrospective cohort study examined how the COVID-19 pandemic affected burn volumes and time to presentation. All burn admissions were included from January 20 to August 31 for the years 2020, 2019, and 2018. The COVID-19 pandemic group included admissions from January 20, 2020 to August 31, 2020 and was compared to the nonpandemic group comprised of admissions from January 20 to August 31 in 2018 and 2019. Subgroup analysis was performed according to meaningful dates during the COVID-19 pandemic including the first U.S. COVID-19 case, shelter-in-place, and state reopening orders. Admission volumes were 403 patients in the COVID-19 pandemic group compared to a mean of 429 patients in the nonpandemic group, which correlated to a 5.8% decrease in volume during the pandemic. The pandemic group showed an increase in time to presentation of 1 day (P < .0001). Subgroup analysis demonstrated stable admission volumes and an increase in time to presentation of 1 day (P < .0001) at each time point. During shelter-in-place orders, there were higher rates of second/third-degree burns and operative burns (94.7 vs 56.3% and 45.6 vs 27%, P < .0001, P = .013). During the pandemic, there were stable admission volumes, delayed time to admission, and an increase in operative burns during shelter-in-place orders. This reinforces the need to maintain appropriate burn center staffing and resources during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Background In the presence of inflammation, the serum or plasma ferritin concentration ("ferritin" hereafter) transiently increases, confounding its interpretation as an iron status marker. The extent to which adiposity-related inflammation may influence ferritin interpretation is uncertain. Objectives We describe relationships between weight status, inflammation, and ferritin among nonpregnant women of reproductive age (WRA; 15–49 years) and preschool-age children (PSC; 6–59 months) with normal weight to overweight or obesity (OWOB) in differing geographic settings. Methods Cross-sectional data were separately analyzed from 18 surveys (WRA) and 25 surveys (PSC) from the Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia (BRINDA) project, excluding observations with underweight, wasting, pregnancy, or malaria. Relationships were assessed between BMI (in WRA) or BMI-for-age z-score (BAZ; in PSC), inflammatory biomarkers of C-reactive protein (CRP) and/or α-1-acid glycoprotein (AGP), ferritin by linear regression, and potential mediation by CRP and/or AGP in relationships between BMI or BAZ and ferritin with structural equation modeling. Regression and mediation models accounted for complex survey designs. Results were grouped by World Bank income classifications. Results In 5 of 6 surveys among WRA from upper-middle and high-income countries, ferritin was significantly positively associated with BMI, and this relationship was partially (or fully, in the United States) mediated by CRP and/or AGP. Mediation was present in 4 of 12 surveys for WRA in low- and lower-middle income countries. Among PSC, ferritin was positively associated with CRP and/or AGP in all surveys, but there were no significant CRP- or AGP-mediated relationships between ferritin and BAZ, except a negative relationship in the Philippines. Conclusions Where having OWOB is common among WRA, measurements of inflammatory biomarkers and their uses in interpreting ferritin may improve iron status assessments. While these relationships were inconsistent among PSC, inflammation was common and should be measured to interpret iron status. Included Kenyan trial data are registered at clinicaltrials.gov as NCT01088958. [ABSTRACT FROM AUTHOR]

Background Practice-based experiences documenting development and implementation of nutrition and health surveillance systems are needed. Objectives To describe processes, methods, and lessons learned from developing and implementing a population-based household nutrition and health surveillance system in Guatemala. Methods The phases and methods for the design and implementation of the surveillance system are described. Efforts to institutionalize the system in government institutions are described, and illustrative examples describing different data uses, and lessons learned are provided. Results After initial assessments of data needs and consultations with officials in government institutions and partners in the country, a population-based nutrition surveillance system prototype with complex sampling was designed and tested in 5 Guatemalan Highland departments in 2011. After dissemination of the prototype, government and partners expanded the content, and multitopic nutrition and health surveillance cycles were collected in 2013, 2015, 2016, 2017/18, and 2018/19 providing nationally representative data for households, women of reproductive age (15–49 y), and children aged 0–59 mo. For each cycle, data were to be collected from 100 clusters, 30 households in each, and 1 woman and 1 child per household. Content covered ∼25 health and nutrition topics, including coverage of all large-scale nutrition-specific interventions; the micronutrient content of fortifiable sugar, salt, and bread samples; anthropometry; and biomarkers to assess annually, or at least once, ∼25 indicators of micronutrient status and chronic disease. Data were collected by 3–5 highly trained field teams. The design was flexible and revised each cycle allowing potential changes to questionnaires, population groups, biomarkers, survey design, or other changes. Data were used to change national guidelines for vitamin A and B-12 interventions, among others, and evaluate interventions. Barriers included frequent changes of high-level government officials and heavy dependence on US funding. Conclusions This system provides high-quality data, fills critical data gaps, and can serve as a useful model for others. [ABSTRACT FROM AUTHOR]

Motivation Data processing is a key bottleneck for 1H NMR-based metabolic profiling of complex biological mixtures, such as biofluids. These spectra typically contain several thousands of signals, corresponding to possibly few hundreds of metabolites. A number of binning-based methods have been proposed to reduce the dimensionality of 1 D 1H NMR datasets, including statistical recoupling of variables (SRV). Here, we introduce a new binning method, named JBA ("pJRES Binning Algorithm"), which aims to extend the applicability of SRV to pJRES spectra. Results The performance of JBA is comprehensively evaluated using 617 plasma 1H NMR spectra from the FGENTCARD cohort. The results presented here show that JBA exhibits higher sensitivity than SRV to detect peaks from low-abundance metabolites. In addition, JBA allows a more efficient removal of spectral variables corresponding to pure electronic noise, and this has a positive impact on multivariate model building Availability and implementation The algorithm is implemented using the MWASTools R/Bioconductor package. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Summary: MWASTools is an R package designed to provide an integrated pipeline to analyse metabonomic data in large-scale epidemiological studies. Key functionalities of our package include: quality control analysis; metabolome-wide association analysis using various models (partial correlations, generalized linear models); visualization of statistical outcomes; metabolite as- signment using statistical total correlation spectroscopy (STOCSY); and biological interpretation of metabolome-wide association studies results. [ABSTRACT FROM AUTHOR]

Summary: MetaboSignal is an R package that allows merging metabolic and signaling pathways reported in the Kyoto Encyclopaedia of Genes and Genomes (KEGG). It is a network-based approach designed to navigate through topological relationships between genes (signaling- or metabolic-genes) and metabolites, representing a powerful tool to investigate the genetic landscape of metabolic phenotypes. [ABSTRACT FROM AUTHOR]

Introduction The current and long-term impact of the coronavirus disease 2019 (COVID-19) global pandemic on our healthcare system is still unknown. When healthcare resources were being diverted to only the most critical of needs, emergent surgical and burn care remained essential. Currently, no data exist on the impact of a global pandemic on a burn center. Our aim for this study was to understand how the COVID-19 pandemic affected admissions, hospital course, and discharges at a major metropolitan burn center. Methods This was a retrospective cohort study of admissions to our burn center. Our institution's medical record was reviewed from 1/1–8/31 for years 2020, 2019, & 2018. We included all thermal, chemical, and electrical burn inpatient admissions over these time periods. Non-burn wound admissions and vulnerable patient populations were excluded. Our population included 1,358 patients. These patients were grouped by year 2020 (n=425), 2019 (n=470), and 2018 (n=463). The medical record was queried for admission, hospital course, and discharge variables. SAS 9.4 statistical software was used to compare the pre-pandemic 2018/2019 groups against the 2020 group. Group means were compared using two-sample two-tailed t-tests, and categorical variables were compared using Chi-Square analysis. Results In 2020 the burn center had 425 admissions compared to 470 and 463 in 2019 and 2018 respectively. On admission, there were no differences in age, gender, pediatric admissions, burn etiology, total body surface area (TBSA), TBSA >20%, work-related injuries, or suspected abuse related injuries. Of note, the mean days from injury to admission for the groups were (2020 2.5±4.9 vs 2019 1.4±4.3, p=0.001, vs 2018 1.5±4.3, p=0.0017). Groups were similar in respect to burns requiring surgery and mean OR visits. 2019 and 2018 had increased ICU admissions compared to the 2020 cohort (ICU: 2020 60-(14%) vs 2019 91-(19.4%), p=0.041, vs 2018 108-(23.3%), p=0.033). Inpatient mortality was lower in the 2020 cohort compared to the pre-pandemic cohorts (2020 2 (0.6%) vs 2019 9 (2.5%), p=0.04, vs 2018 14 (4.2%) p=0.0017). Conclusions Volume at our burn center remains high during the pandemic. There is an increased lag time from burn injury to hospital admission in the 2020 cohort. We hypothesize this is due to patients' avoiding the hospital due to fear of contracting COVID-19. TBSA is similar across groups, but ICU admissions are down in the 2020 cohort. Further work is needed to understand whether the increased lag time has affected outcomes and whether the decreased ICU admissions are due to yearly variation or the pandemic. [ABSTRACT FROM AUTHOR]