Data in brief
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Chest pain is a common clinical condition in the emergency department. A high sensitive (hs) troponin test assay may help to identify patients with acute coronary syndrome earlier compared to conventional tests but also entails the risk of a high proportion of positive test results in patients without cardiac disease. We assessed the impact of the introduction of the hs-troponin test in clinical practice in an emergency department. ⋯ Between-group differences were estimated with 95% confidence intervals. All analyses were performed with the statistical software R for windows [1]. Interpretation of this data can be found in a research article titled Impact of the introduction of high-sensitive troponin assay on the evaluation of chest pain patients in the emergency department: a retrospective study [2]).
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The database here described contains data of integrated surveillance for the "Coronavirus disease 2019" (abbreviated as COVID-19 by the World Health Organization) in Italy, caused by the novel coronavirus SARS-CoV-2. The database, included in a main folder called COVID-19, has been designed and created by the Italian Civil Protection Department, which currently manages it. The database consists of six folders called 'aree' (containing charts of geographical areas interested by containment measures), 'dati-andamento-nazionale' (containing data relating to the national trend of SARS-CoV-2 spread), 'dati-json' (containing data that summarize the national, provincial and regional trends of SARS-CoV-2 spread), 'dati-province' (containing data relating to the provincial trend of SARS-CoV-2 spread), 'dati-regioni' (containing data relating to the regional trend of SARS-CoV-2 spread) and 'schede-riepilogative' (containing summary sheets relating to the provincial and regional trends of SARS-CoV-2 spread). ⋯ Thus, the database is subject to daily updates and integrations. The database is freely accessible (CC-BY-4.0 license) at https://github.com/pcm-dpc/COVID-19. This database is useful to provide insight on the spread mechanism of SARS-CoV-2, to support organizations in the evaluation of the efficiency of current prevention and control measures, and to support governments in the future prevention decisions.
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The novel Coronavirus disease (COVID-19) was first identified in Wuhan, China in December 2019 but later spread to other parts of the world. The disease as at the point of writing this paper has been declared a pandemic by the World Health Organization (WHO). The application of mathematical models, artificial intelligence, big data, and similar methodologies are potential tools to predict the extent of the spread and effectiveness of containment strategies to stem the transmission of this disease. ⋯ Nonetheless, we propose an online forecasting mechanism that streams data from the Nigeria Center for Disease Control to update the parameters of an ensemble model which in turn provides updated COVID-19 forecasts every 24 hours. The ensemble combines an Auto-Regressive Integrated Moving Average model (ARIMA), Prophet - an additive regression model developed by Facebook, and a Holt-Winters Exponential Smoothing model combined with Generalized Autoregressive Conditional Heteroscedasticity (GARCH). The outcomes of these efforts are expected to provide academic thrust in guiding the policymakers in the deployment of containment strategies and/or assessment of containment interventions in stemming the spread of the disease in Nigeria.
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Ingestion of toxic alcohols other than ethanol (ethylene glycol, methanol, isopropanol, and propylene glycol) can cause life-threatening complications including altered level of consciousness, respiratory depression, and organ damage from metabolites. Many hospitals lack the ability to specifically analyze these compounds using gas chromatography, gas chromatography/mass spectrometry, or by enzymatic assays for ethylene glycol. Consequently, the presence of these compounds in blood is often ascertained indirectly by laboratory testing for acid-base status, osmolal gap, and anion gap. ⋯ The analyzed data is provided in the supplementary tables included in this article. Bias plots of osmolal gap estimations are included in a figure. The dataset reported is related to the research article entitled "Correlation of Osmolal Gap with Measured Concentrations of Acetone, Ethylene Glycol, Isopropanol, Methanol, and Propylene Glycol in Patients at an Academic Medical Center" [1].
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The use of tranexamic acid (TXA) has recently gained popularity as a treatment modality for epistaxis in the emergency department. Data are presented on the efficacy of the topical use of the intravenous formulation of TXA versus the vasoconstrictor oxymetazoline applied topically in achieving hemostasis in patient presenting to the emergency department with anterior epistaxis. The original article "Comparative Effectiveness of Topically Administered TXA Versus Topical Oxymetazoline Spray for Achieving Hemostasis in Epistaxis" [1] provides complete interpretation of the data. The dataset regarding these treatment modalities has clinical significance toward preventing an avoidable need for escalation of treatment that could potentially increase patient discomfort and prolong emergency department throughput time.