bluuebunny/crossref_metadata_2025_split huggingface dataset:
Using the Mercy Method to Estimate Ideal Body Weight in Children – A Description and Validation of a New Methodology
Authors: Wells Mike , Goldstein Lara Nicole 
Published: May 2020

ABSTRACTBackgroundIn obese children, lipophilic medications should be dosed to total body weight (TBW) and hydrophilic medications to ideal body weight (IBW). During emergencies these weights need to be estimated to ensure that urgent drug therapy is accurate and safe. The Mercy method is a well-established weight estimation system that has recently been adapted to provide estimations of body length in children. It was therefore conceivable that this could be further modified to provide estimations of IBW.MethodsA model was developed a priori using the Mercy method’s humeral length segments to predict IBW. The accuracy of this model was then tested in a sample of 13134 children from the National Health and Nutrition Examination Survey (NHANES) datasets. The accuracy of IBW estimation was determined from the percentage of estimations falling within 10% (p10) and 20% (p20) of true IBW. The model was also tested to see the accuracy of detection of obesity in the study sample.ResultsFrom the sample of 13134 children, a subset of 1318 obese children were identified. In this subset, the new Mercy method model achieved an IBW estimation accuracy p10 of 66.9% and a p20 of 95.1%. For the detection of the obese child, the model had a sensitivity of 88.6% and a specificity of 75.8%.ConclusionsThis study established that the Mercy method can be modified to provide a reasonably accurate estimation of IBW in obese children, with very few critical errors. The ability to the model to identify the obese child was also reasonably accurate, on a par with other such predictive methods. While other accurate methods of estimating both TBW and IBW exist, such as the PAWPER XL tape, the modified Mercy method is an acceptable alternative if such other devices are not available.
Incidence of COVID-19 and Connections with Air Pollution Exposure: Evidence from the Netherlands
Authors: Andrée Bo Pieter Johannes 
Published: May 2020

AbstractThe fast spread of severe acute respiratory syndrome coronavirus 2 has resulted in the emergence of several hot-spots around the world. Several of these are located in areas associated with high levels of air pollution. This study investigates the relationship between exposure to particulate matter and COVID-19 incidence in 355 municipalities in the Netherlands. The results show that atmospheric particulate matter with diameter less than 2.5 is a highly significant predictor of the number of confirmed COVID-19 cases and related hospital admissions. The estimates suggest that expected COVID-19 cases increase by nearly 100 percent when pollution concentrations increase by 20 percent. The association between air pollution and case incidence is robust in the presence of data on health-related preconditions, proxies for symptom severity, and demographic control variables. The results are obtained with ground-measurements and satellite-derived measures of atmospheric particulate matter as well as COVID-19 data from alternative dates. The findings call for further investigation into the association between air pollution and SARS-CoV-2 infection risk. If particulate matter plays a significant role in COVID-19 incidence, it has strong implications for the mitigation strategies required to prevent spreading.HighlightsBackgroundResearch on viral respiratory infections has found that infection risks increase following exposure to high concentrations of particulate matter. Several hot-spots of Severe Acute Respiratory Syndrome Coronavirus 2 infections are in areas associated with high levels of air pollution.ApproachThis study investigates the relationship between exposure to particulate matter and COVID-19 incidence in 355 municipalities in the Netherlands using data on confirmed cases and hospital admissions coded by residence, along with local PM2.5, PM10, population density, demographics and health-related pre-conditions. The analysis utilizes different regression specifications that allow for spatial dependence, nonlinearity, alternative error distributions and outlier treatment.ResultsPM2.5 is a highly significant predictor of the number of confirmed COVID-19 cases and related hospital admissions. Taking the WHO guideline of 10mcg/m3 as a baseline, the estimates suggest that expected COVID-19 cases increase by nearly 100% when pollution concentrations increase by 20%.ConclusionThe findings call for further investigation into the association between air pollution on SARS-CoV-2 infection risk. If particulate matter plays a significant role in the incidence of COVID-19 disease, it has strong implications for the mitigation strategies required to prevent spreading, particularly in areas that have high levels of pollution.
Integrating Gut Microbiota and Host Immune Markers for Highly Accurate Diagnosis of Clostridioides difficile Infection
Authors: Ke Shanlin , Pollock Nira R. , Wang Xu-Wen , Chen Xinhua , Daugherty Kaitlyn , Lin Qianyun , Xu Hua , Garey Kevin W. , Gonzales-Luna Anne J. , Kelly Ciarán P. , Liu Yang-Yu 
Published: May 2020

AbstractExposure to Clostridioides difficile can result in asymptomatic carriage or infection with symptoms ranging from mild diarrhea to fulminant pseudomembranous colitis. A reliable diagnostic approach for C. difficile infection (CDI) remains controversial. Accurate diagnosis is paramount not only for patient management but also for epidemiology and disease research. Here, we characterized gut microbial compositions and a broad panel of innate and adaptive immunological markers in 243 well-characterized human subjects, who were divided into four phenotype groups: CDI, Asymptomatic Carriage, Non-CDI Diarrhea, and Control. We found that CDI is associated with alteration of many different aspects of the gut microbiota, including overall microbial diversity and microbial association networks. We demonstrated that incorporating both gut microbiome and host immune marker data into classification models can better distinguish CDI from other groups than can either type of data alone. Our classification models display robust diagnostic performance to differentiate CDI from Asymptomatic carriage (AUC~0.916), Non-CDI Diarrhea (AUC~0.917), or Non-CDI that combines all other three groups (AUC~0.929). Finally, we performed symbolic classification using selected features to derive simple mathematic formulas for highly accurate CDI diagnosis. Overall, this study provides evidence supporting important roles of gut microbiota and host immune markers in CDI diagnosis, which may also inform the design of future therapeutic strategies.One Sentence SummaryIncorporating both gut microbiome and host immune marker data into classification models can better distinguish CDI from other groups than can either type of data alone.
Variation among states in rate of coronavirus spread
Authors: Condit Richard 
Published: May 2020

AbstractThe corona virus, COVID-19, has been spreading rapidly across the USA since early March, but at a decreasing rate, where the rate r is defined as the exponential increase. I modeled the way the rate of increase y = ln(er − 1) has declined through time in each of the 51 states with the goal of determining how quickly the rate has declined, whether the decline has changed, and whether states differ. A piecewise linear regression was used, with a single break point. This model can identify whether there was a change in the rate of decline, when the change happened, and which states have shown the greatest improvement in reducing the spread of COVID-19. The piecewise model identified a significant breakpoint on 24 Mar for all states combined, and all states had nearly the same breakpoint. Prior to 24 Mar, the average change in y was −0.013 d−1, meaning a reduction in the rate of spread from 23.5 pct. d−1 to 19.5 pct. d−1; after 24 Mar, the average change in y was −0.070 d−1, a reduction from 19.5 pct. d−1 to 7.5 pct. d−1. Prior to 24 Mar there was no significant variation among states in the decline in y, but after 24 Mar there was substantial variation. Montana, Idaho, and Vermont showed the greatest improvement, while Nebraska, South Dakota, and Iowa the least. The improvement as measured by the reduction after 24 Mar did not correlate with case density in a state, nor state population. The next question is whether it correlates with differences among states in the health measures taken to combat the spread.
Predicting community mortality risk due to CoVID-19 using machine learning and development of a prediction tool
Authors: Das Ashis Kumar , Mishra Shiba , Gopalan Saji Saraswathy 
Published: May 2020

AbstractBackgroundThe recent pandemic of CoVID-19 has emerged as a threat to global health security. There are a very few prognostic models on CoVID-19 using machine learning.ObjectivesTo predict mortality among confirmed CoVID-19 patients in South Korea using machine learning and deploy the best performing algorithm as an open-source online prediction tool for decision-making.Materials and methodsMortality for confirmed CoVID-19 patients (n=3,022) between January 20, 2020 and April 07, 2020 was predicted using five machine learning algorithms (logistic regression, support vector machine, K nearest neighbor, random forest and gradient boosting). Performance of the algorithms was compared, and the best performing algorithm was deployed as an online prediction tool.ResultsThe gradient boosting algorithm was the best performer in terms of discrimination (area under ROC curve=0.966), calibration (Matthews Correlation Coefficient=0.656; Brier Score=0.013) and predictive ability (accuracy=0.987). The best performer algorithm (gradient boosting) was deployed as the online CoVID-19 Community Mortality Risk Prediction tool named CoCoMoRP (https://ashis-das.shinyapps.io/CoCoMoRP/).ConclusionsWe describe the framework for the rapid development and deployment of an open-source machine learning tool to predict mortality risk among CoVID-19 confirmed patients using publicly available surveillance data. This tool can be utilized by potential stakeholders such as health providers and policy makers to triage patients at the community level in addition to other approaches.
COVID-19 Infections and Outcomes in a Live Registry of Heart Failure Patients Across an Integrated Health Care System
Authors: Caraballo Cesar , McCullough Megan , Fuery Michael A. , Chouairi Fouad , Keating Craig , Ravindra Neal , Miller P. Elliott , Malinis Maricar , Kashyap Nitu , Hsiao Allen , Wilson F. Perry , Curtis Jeptha P , Grant Matthew , Velazquez Eric J , Desai Nihar R , Ahmad Tariq 
Published: May 2020

AbstractBackgroundPatients with comorbid conditions have a higher risk of mortality with SARS-CoV-2 (COVID-19) infection, but the impact on heart failure patients living near a disease hotspot is unknown. Therefore, we sought to characterize the prevalence and outcomes of COVID-19 in a live registry of heart failure patients across an integrated health care system in Connecticut.MethodsIn this retrospective analysis, the Yale Heart Failure Registry (NCT04237701) that includes 26,703 patients with heart failure across a 6-hospital integrated health care system in Connecticut was queried on April 16th, 2020 for all patients tested for COVID-19. Sociodemographic and geospatial data as well as, clinical management, respiratory failure, and patient mortality were obtained via the real-time registry. Data on COVID-19 specific care was extracted by retrospective chart review.ResultsCOVID-19 testing was performed on 900 symptomatic patients, comprising 3.4% of the Yale Heart Failure Registry (N=26,703). Overall, 206 (23%) were COVID-19+. As compared to COVID-19-, these patients were more likely to be older, black, have hypertension, coronary artery disease, and were less likely to be on renin angiotensin blockers (P<0.05, all). COVID-19- patients tended to be more diffusely spread across the state whereas COVID-19+ were largely clustered around urban centers. 20% of COVID-19+ patients died, and age was associated with increased risk of death [OR 1.92 95% CI (1.33–2.78); P<0.001]. Among COVID-19+ patients who were ≥85 years of age rates of hospitalization were 87%, rates of death 36%, and continuing hospitalization 62% at time of manuscript preparation.ConclusionsIn this real-world snapshot of COVID-19 infection among a large cohort of heart failure patients, we found that a small proportion had undergone testing. Patients found to be COVID-19+ tended to be black with multiple comorbidities and clustered around lower socioeconomic status communities. Elderly COVID-19+ patients were very likely to be admitted to the hospital and experience high rates of mortality.
Cardiometabolic risk factors and preclinical target organ damage among adults in Ghana: Findings from a national study
Authors: Li Jie , Owusu Isaac Kofi , Geng Qingshan , Folson Aba Ankomaba , Zheng Zhichao , Adu-Boakye Yaw , Dong Xinran , Wu Wen-Chih , Agyekum Francis , Fei Hongwen , Ayetey Harold , Deng Mulan , Adomako-Boateng Fred , Jiang Zuxun , Abubakari Braimah Baba , Xian Zhao , Fokuoh Forster Nketiah , Appiah Lambert Tetteh , Liu Simin , Lin Chunying 
Published: May 2020

AbstractBackgroundSub-Saharan Africa (SSA) has the highest prevalence of cardiovascular diseases (CVD). Nevertheless, very few studies have directly examined the development of and risk factors for CVD among Africans.ObjectiveTo examine CVD risk factors and outcomes particularly in the early stage of CVD development among adults in Ghana.MethodsUsing a stratified multistage random sampling method, 1,106 participants were recruited as a nationally representative sample of the general population ≥18 years in Ghana from 2016 to 2017. For each participant, we measured CVD risk factors and preclinical target organ damage (TOD) for CVD development.ResultsThe prevalence of CVD risk factors was 21.1% for obesity, 10.8% for diabetes, 55.4% for hypertension, 37.3% for dyslipidemia, 12.8% for hyperuricemia, and 39.3% for hsCRP>3 mg/L in the recruited population. The prevalence of preclinical TOD was 8.6% for peripheral artery disease (PAD), 14.7% for carotid thickening, 5.9% for left ventricular hypertrophy (LVH), and 4.4% for chronic kidney disease (CKD). Three CVD risk factors appeared to play most prominent roles in TOD development, including obesity for PAD (OR 1.88, 95% CI 1.13–3.09), hypertension for carotid thickening (OR 1.57, 95% CI 0.99–2.54) and LVH (OR 6.25, 95% CI 2.98–14.50), and hyperuricemia for CKD (OR 5.56, 95% CI 2.79–11.15).ConclusionsThe prevalence of CVD risk factors and early outcomes have reached epidemic proportions among Ghanaian adults. The distinct patterns of risk factors in the development of TOD presents important challenges and opportunities for interventions to improve cardiometabolic health among adults in Ghana.
Mental Health Status of the General Population during the COVID-19 Pandemic: A Cross-Sectional National Survey in Japan
Authors: Ueda Michiko , Stickley Andrew , Sueki Hajime , Matsubayashi Tetsuya 
Published: May 2020

AbstractThe ongoing COVID-19 pandemic may have detrimental mental health consequences. However, as yet, there is limited understanding of its impact on the mental health of the general population. The aim of this study is to examine the mental health of the Japanese general population by conducting the first systematic survey during the pandemic (N=1,000), with a particular focus on identifying the most vulnerable groups. Results from logistic regression analyses showed that the mental health of young and middle-aged individuals was significantly worse than that of older individuals during the pandemic. There was also some indication that individuals who were not currently working were significantly more likely to report a high level of anxiety and depressive symptoms. Part-time and temporary contract-based workers were also more likely to suffer from anxiety disorder. Our results highlight that monitoring the mental health of younger and economically vulnerable individuals may be especially important. In addition, they also indicate that population mental health might not only be affected by the direct health consequences of COVID-19, but also by the economic ramifications of the pandemic.
Clinical features and outcomes of inpatients with neurological disease and COVID-19
Authors: Benussi Alberto , Pilotto Andrea , Premi Enrico , Libri Ilenia , Giunta Marcello , Agosti Chiara , Alberici Antonella , Baldelli Enrico , Benini Matteo , Bonacina Sonia , Brambilla Laura , Caratozzolo Salvatore , Cortinovis Matteo , Costa Angelo , Piccinelli Stefano Cotti , Cottini Elisabetta , Cristillo Viviana , Delrio Ilenia , Filosto Massimiliano , Gamba Massimo , Gazzina Stefano , Gilberti Nicola , Gipponi Stefano , Imarisio Alberto , Invernizzi Paolo , Leggio Ugo , Leonardi Matilde , Liberini Paolo , Locatelli Martina , Masciocchi Stefano , Poli Loris , Rao Renata , Risi Barbara , Rozzini Luca , Scalvini Andrea , di Cola Francesca Schiano , Spezi Raffaella , Vergani Veronica , Volonghi Irene , Zoppi Nicola , Borroni Barbara , Magoni Mauro , Pezzini Alessandro , Padovani Alessandro 
Published: May 2020

AbstractObjectiveTo report the clinical and laboratory characteristics, as well as treatment and clinical outcomes of patients admitted for neurological diseases with COVID-19 in a Neuro-COVID unit compared to patients without COVID-19.MethodsIn this retrospective, single centre cohort study, we included all adult inpatients with confirmed COVID-19, who had been discharged or died by April 5, 2020. Demographic, clinical, treatment, and laboratory data were extracted from medical records.Results173 patients were included in this study, of whom 56 resulted positive for COVID-19 and 117 resulted negative for COVID-19. Patients with COVID-19 were older, had a different distribution regarding admission diagnoses, including cerebrovascular disorders, and had a higher quick Sequential Organ Failure Assessment (qSOFA) score on admission (all p<0.05).In-hospital mortality rates and incident delirium were significantly higher in the COVID-19 group (all p<0.05). COVID-19 and non-COVID patients with stroke had similar baseline characteristics but patients with COVID-19 had higher modified Rankin scale scores at discharge (p<0.0001), with a significantly lower number of patients with a good outcome (p<0.0001).Multivariable regressions showed increasing odds of in-hospital death associated with higher qSOFA scores (odds ratio 4.47, 95% CI 1.21-16.5; p=0.025), lower platelet count (0.98, 0.97-0.99; p=0.005) and higher lactate dehydrogenase (1.01, 1.00-1.03; p=0.009) on admission.ConclusionsCOVID-19 patients admitted with neurological disease, including stroke, have a significantly higher in-hospital mortality, incident delirium and higher disability than patients without COVID-19.
The Chronic Kidney Disease and Acute Kidney Injury Involvement in COVID-19 Pandemic: A Systematic Review and Meta-analysis
Authors: Liu Ya-Fei , Zhang Zhe , Pan Xiao-Li , Xing Guo-Lan , Zhang Ying , Liu Zhang-Suo , Tu Sheng-Hao 
Published: May 2020

ABSTRACTAimThe aim of this study was to uncover whether kidney diseases were involved in COVID-19 pandemic from a systematic review.MethodsThe studies reported the kidney outcomes in different severity of COVID-19 were included in this study. Standardized mean differences or odds ratios were calculated by employing Review Manager meta-analysis software.ResultsThirty-six trials were included in this systematic review with a total of 6395 COVID-19 patients. The overall effects indicated that the comorbidity of chronic kidney disease (CKD) (OR = 3.28), complication of acute kidney injury (AKI) (OR = 11.02), serum creatinine (SMD = 0.68), abnormal serum creatinine (OR = 4.86), blood urea nitrogen (SMD = 1.95), abnormal blood urea nitrogen (OR = 6.53), received continuous renal replacement therapy (CRRT) (OR = 23.63) was significantly increased in severe group than that in nonsevere group. Additionally, the complication of AKI (OR = 13.92) and blood urea nitrogen (SMD = 1.18) were remarkably elevated in critical group than that in severe group.ConclusionCKD and AKI are susceptible to occur in patients with severe COVID-19. CRRT is applied frequently in severe COVID-19 patients than that in nonsevere COVID-19 patients. The risk of AKI is higher in critical group than that in severe group.
Excess mortality during COVID-19 in five European countries and a critique of mortality data analysis
Authors: Félix-Cardoso J. , Vasconcelos H. , Pereira Rodrigues P. , Cruz-Correia R. 
Published: May 2020

AbstractINTRODUCTIONThe COVID-19 pandemic is an ongoing event disrupting lives, health systems, and economies worldwide. Clear data about the pandemic’s impact is lacking, namely regarding mortality. This work aims to study the impact of COVID-19 through the analysis of all-cause mortality data made available by different European countries, and to critique their mortality surveillance data.METHODSEuropean countries that had publicly available data about the number of deaths per day/week were selected (England and Wales, France, Italy, Netherlands and Portugal). Two different methods were selected to estimate the excess mortality due to COVID19: (DEV) deviation from the expected value from homologue periods, and (RSTS) remainder after seasonal time series decomposition. We estimate total, age- and gender-specific excess mortality. Furthermore, we compare different policy responses to COVID-19.RESULTSExcess mortality was found in all 5 countries, ranging from 10.6% in Portugal (DEV) to 98.5% in Italy (DEV). Furthermore, excess mortality is higher than COVID-attributed deaths in all 5 countries.DISCUSSIONThe impact of COVID-19 on mortality appears to be larger than officially attributed deaths, in varying degrees in different countries. Comparisons between countries would be useful, but large disparities in mortality surveillance data could not be overcome. Unreliable data, and even a lack of cause-specific mortality data undermine the understanding of the impact of policy choices on both direct and indirect deaths during COVID-19. European countries should invest more on mortality surveillance systems to improve the publicly available data.
A Model of Workflow in the Hospital During a Pandemic to Assist Management
Authors: Garbey Marc , Joerger Guillaume , Furr Shannon , Fikfak Vid 
Published: May 2020

AbstractWe present a computational model of workflow in the hospital during a pandemic. The objective is to assist management in anticipating the load of each care unit, such as the ICU, or ordering supplies, such as personal protective equipment, but also to retrieve key parameters that measure the performance of the health system facing a new crisis. The model was fitted with good accuracy to France’s data set that gives information on hospitalized patients and is provided online by the French government. The goal of this work is both practical in offering hospital management a tool to deal with the present crisis of COVID-19 and offering a conceptual illustration of the benefit of computational science during a pandemic.