Background We performed metabolomic profiling to identify metabolites that correlate with disease progression and death. Methods We performed a study of adults hospitalized with Influenza A(H1N1)pdm09. Cases (n = 32) were defined by a composite outcome of death or transfer to the intensive care unit during the 60-day follow-up period. Controls (n = 64) were survivors who did not require transfer to the ICU. Four hundred and eight metabolites from eight families were measured on plasma sample at enrollment using a mass spectrometry based Biocrates platform. Conditional logistic regression was used to summarize the association of the individual metabolites and families with the composite outcome and its major two components. Results The ten metabolites with the strongest association with disease progression belonged to five different metabolite families with sphingolipids being the most common. The acylcarnitines, glycerides, sphingolipids and biogenic metabolite families had the largest odds ratios based on the composite endpoint. The tryptophan odds ratio for the composite is largely associated with death (OR 17.33: 95% CI, 1.60–187.76). Conclusions Individuals that develop disease progression when infected with Influenza H1N1 have a metabolite signature that differs from survivors. Low levels of tryptophan had a strong association with death. Registry ClinicalTrials.gov Identifier: NCT01056185

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Background Bivariate alternating recurrent event data can arise in longitudinal studies where patients with chronic diseases go through two states that occur repeatedly, e.g., care periods and break periods. However, there was no statistical software that provided tools for the analysis of such data. To meet this software need, we developed , a package for R that contains a set of tools for exploratory, nonparametric and semiparametric regression analysis of bivariate alternating recurrent events. Results The package provides functions for nonparametric estimations for the joint distribution of bivariate gap times () and semiparametric regression methods for evaluating covariate effects on the two types of gap times under the accelerated failure time model framework (). The package also provides exploratory data analysis tools such as a visualization of the gap times by groups. We utilize a subset of the South Verona Psychiatric Case Register (PCR) data to illustrate the use of the package for the reviewed methods. Conclusions We demonstrate ’s capability for data visualization, nonparametric and regression based analysis, as well as data simulation. The package has default methods with satisfactory performance despite the complexity of calculations and fills a gap in software for statistical analysis of bivariate alternating recurrent events. is accessible under the GPL-3 General Public License through CRAN, facilitating its installation.

The purpose of this study was to explore the influence of the COVID-19 pandemic on the management of early pregnancy loss. Additionally, we aimed to identify differences in the use of virtual health care for the management of spontaneous abortions (SABs) before and after the COVID-19 pandemic.

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