Estimating Nosocomial Infection and its Outcomes in Hospital Patients in England with a Diagnosis of COVID-19 Using Machine Learning

BACKGROUND: COVID-19 nosocomial infections (NIs) may have played a significant role in the dynamics of the pandemic in England, but analysis of their impact at the national scale has been lacking. Our aim was to provide a comprehensive account of NIs, identify their characteristics and outcomes in patients with a diagnosis of COVID-19 and use machine learning modelling to refine these estimates. METHODS: From the Hospital Episodes Statistics database all adult hospital patients in England with a diagnosis of COVID-19 and discharged between March 1st 2020 and March 31st 2021 were identified. A cohort of suspected COVID-19 NIs was identified using four empirical methods linked to hospital coding. A random forest classifier was designed to model the relationship between acquiring NIs and the covariates: patient characteristics, comorbidities, frailty, trust capacity strain and severity of COVID-19 infections. FINDINGS: In total, 374,244 adult patients with COVID-19 were discharged during the study period. The four empirical methods identified 29,896 (8.0%) patients with NIs. The random forest classifier estimated a mean NI rate of 10.5%, with a peak close to 18% during the first wave, but much lower rates thereafter and around 7% in early spring 2021. NIs were highly correlated with longer lengths of stay, high trust capacity strain, greater age and a higher degree of patient frailty. NIs were also found to be associated with higher mortality rates and more severe COVID-19 sequelae, including pneumonia, kidney disease and sepsis. INTERPRETATION: Identification of the characteristics of patients who acquire NIs should help trusts to identify those most at risk. The evolution of the NI rate over time may reflect the impact of changes in hospital management practices and vaccination efforts. Variations in NI rates across trusts may partly reflect different data recording and coding practice

Complex variations in X-ray polarization in the X-ray pulsar LS V +44 17/RX J0440.9+4431

We report on Imaging X-ray polarimetry explorer (IXPE) observations of the Be-transient X-ray pulsar LS V +44 17/RX J0440.9+4431 made at two luminosity levels during the giant outburst in January- February 2023. Considering the observed spectral variability and changes in the pulse profiles, the source was likely caught in supercritical and subcritical states with significantly different emission-region geometry, associated with the presence of accretion columns and hot spots, respectively. We focus here on the pulse-phase-resolved polarimetric analysis and find that the observed dependencies of the polarization degree and polarization angle (PA) on the pulse phase are indeed drastically different for the two observations. The observed differences, if interpreted within the framework of the rotating vector model (RVM), imply dramatic variations in the spin axis inclination, the position angle, and the magnetic colatitude by tens of degrees within the space of just a few days. We suggest that the apparent changes in the observed PA phase dependence are predominantly related to the presence of an unpulsed polarized component in addition to the polarized radiation associated with the pulsar itself. We then show that the observed PA phase dependence in both observations can be explained with a single set of RVM parameters defining the pulsar s geometry. We also suggest that the additional polarized component is likely produced by scattering of the pulsar radiation in the equatorial disk wind

Rotating Stars in Relativity

Rotating relativistic stars have been studied extensively in recent years, both theoretically and observationally, because of the information one could obtain about the equation of state of matter at extremely high densities and because they are considered to be promising sources of gravitational waves. The latest theoretical understanding of rotating stars in relativity is reviewed in this updated article. The sections on the equilibrium properties and on the nonaxisymmetric instabilities in f-modes and r-modes have been updated and several new sections have been added on analytic solutions for the exterior spacetime, rotating stars in LMXBs, rotating strange stars, and on rotating stars in numerical relativity.Comment: 101 pages, 18 figures. The full online-readable version of this article, including several animations, will be published in Living Reviews in Relativity at http://www.livingreviews.org

Estimating nosocomial infection and its outcomes in hospital patients in England with a diagnosis of COVID-19 using machine learning

Our aim was to provide a comprehensive account of COVID-19 nosocomial infections (NIs) in England and identify their characteristics and outcomes using machine learning. From the Hospital Episodes Statistics database, 374,244 adult hospital patients in England with a diagnosis of COVID-19 and discharged between March 1, 2020, and March 31, 2021, were identified. A cohort of suspected COVID-19 NIs was identified using four empirical methods linked to hospital coding. A random forest classifier was designed to model the characteristics of these infections. The model estimated a mean NI rate of 10.5%, with a peak close to 18% during the first wave, but much lower rates (7%) thereafter. NIs were highly correlated with longer lengths of stay, high trust capacity strain, greater age and a higher degree of patient frailty, and associated with higher mortality rates and more severe COVID-19 sequelae, including pneumonia, kidney disease and sepsis. Identification of the characteristics of patients who acquire NIs should help trusts to identify those most at risk. The evolution of the NI rate over time may reflect the impact of changes in hospital management practises and vaccination efforts. Variations in NI rates across trusts may partly reflect different data recording and coding practise

Soluble P-selectin levels during normal pregnancy: a longitudinal study.

Objective To investigate soluble P-selectin (sP-selectin) levels and platelet parameters in normal pregnant women compared with non-pregnant control subjects. Design A longitudinal case–control study. Setting Obstetric outpatient clinic in the Jubilee Maternity Hospital, Belfast. Population One hundred and twenty normal pregnant women and 41 non-pregnant age-matched control subjects. Methods The plasma concentration of sP-selectin in pregnant women sampled at 12, 20 and 35 weeks of gestation, and, in a subgroup at three days postpartum, and non-pregnant controls sampled in parallel, was determined using a commercial quantitative sandwich immunoassay kit. Platelet parameters on each blood sample were also recorded using a SYSMEX SE 9500 analyser. Main outcome measures Plasma sP-selectin as a measure of platelet activation in normal pregnancy. Results Soluble P-selectin was significantly higher in pregnant women than in non-pregnant control subjects at 20 and 35 weeks of gestation, (P < 0.01, and P < 0.001, respectively). Correlation analyses showed positive correlation between sP-selectin and platelet count in pregnant women at 20 and 35 weeks of gestation (r = 0.247, P < 0.05 and r = 0.360, P < 0.001, respectively). Soluble P-selectin concentration per platelet was also significantly higher in pregnant women than in non-pregnant control subjects at 20 and 35 weeks of gestation (P < 0.001). Conclusions Our results show that sP-selectin concentration is significantly higher in the second and third trimester of pregnancy when compared with non-pregnant control subjects sampled in parallel. This finding clarifies previous conflicting results on platelet activation in normal pregnancy, and is in agreement with those earlier studies which reported, using other methods, increased platelet activation in normal pregnancy