Background
The identification of risk factors for adverse outcomes and prolonged intensive care unit (ICU) stay in COVID-19 patients is essential for prognostication, determining treatment intensity, ...and resource allocation. Previous studies have determined risk factors on admission only, and included a limited number of predictors. Therefore, using data from the highly granular and multicenter Dutch Data Warehouse, we developed machine learning models to identify risk factors for ICU mortality, ventilator-free days and ICU-free days during the course of invasive mechanical ventilation (IMV) in COVID-19 patients.
Methods
The DDW is a growing electronic health record database of critically ill COVID-19 patients in the Netherlands. All adult ICU patients on IMV were eligible for inclusion. Transfers, patients admitted for less than 24 h, and patients still admitted at time of data extraction were excluded. Predictors were selected based on the literature, and included medication dosage and fluid balance. Multiple algorithms were trained and validated on up to three sets of observations per patient on day 1, 7, and 14 using fivefold nested cross-validation, keeping observations from an individual patient in the same split.
Results
A total of 1152 patients were included in the model. XGBoost models performed best for all outcomes and were used to calculate predictor importance. Using Shapley additive explanations (SHAP), age was the most important demographic risk factor for the outcomes upon start of IMV and throughout its course. The relative probability of death across age values is visualized in Partial Dependence Plots (PDPs), with an increase starting at 54 years. Besides age, acidaemia, low
P
/
F
-ratios and high driving pressures demonstrated a higher probability of death. The PDP for driving pressure showed a relative probability increase starting at 12 cmH
2
O.
Conclusion
Age is the most important demographic risk factor of ICU mortality, ICU-free days and ventilator-free days throughout the course of invasive mechanical ventilation in critically ill COVID-19 patients. pH,
P
/
F
ratio, and driving pressure should be monitored closely over the course of mechanical ventilation as risk factors predictive of these outcomes.
Condensation. In women with a history of preeclampsia skin autofluorescence as marker of tissue AGEs accumulation is increased, supporting a common causal metabolic or vascular link between ...preeclampsia and cardiovascular diseases. Objective. To investigate whether skin autofluorescence (AF), as marker of tissue accumulation of advanced glycation end-products (AGEs), is elevated in women with a 4-year history of severe preeclampsia. Methods. About 17 formerly preeclamptic women and 16 controls were included. Skin AF and several traditional cardiovascular risk factors were recorded. Results. In comparison to controls, formerly preeclamptic women had higher skin AF of the legs, body mass index (BMI), blood pressure, and high-sensitivity C-reactive protein (hsCRP), HbA1C, and triglycerides in serum. Conclusion. Skin AF as well as cardiovascular risk factors is elevated in formerly preeclamptic women. These results suggest a common causal vascular link between preeclampsia and cardiovascular diseases.
Celotno besedilo
Dostopno za:
DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Phys. Rev. D 95, 032003 (2017) The exclusive charmonium production process in $\bar{p}p$ annihilation with
an associated $\pi^0$ meson $\bar{p}p\to J/\psi\pi^0$ is studied in the
framework of QCD ...collinear factorization. The feasibility of measuring this
reaction through the $J/\psi\to e^+e^-$ decay channel with the PANDA
(AntiProton ANnihilation at DArmstadt) experiment is investigated. Simulations
on signal reconstruction efficiency as well as the background rejection from
various sources including the $\bar{p}p\to\pi^+\pi^-\pi^0$ and $\bar{p}p\to
J/\psi\pi^0\pi^0$ reactions are performed with PandaRoot, the simulation and
analysis software framework of the PANDA experiment. It is shown that the
measurement can be done at PANDA with significant constraining power under the
assumption of an integrated luminosity attainable in four to five months of
data taking at the maximum design luminosity.
Simulation results for future measurements of electromagnetic proton form factors at \PANDA (FAIR) within the PandaRoot software framework are reported. The statistical precision with which the ...proton form factors can be determined is estimated. The signal channel \(\bar p p \to e^+ e^-\) is studied on the basis of two different but consistent procedures. The suppression of the main background channel, \(\textit{i.e.}\) \(\bar p p \to \pi^+ \pi^-\), is studied. Furthermore, the background versus signal efficiency, statistical and systematical uncertainties on the extracted proton form factors are evaluated using two different procedures. The results are consistent with those of a previous simulation study using an older, simplified framework. However, a slightly better precision is achieved in the PandaRoot study in a large range of momentum transfer, assuming the nominal beam conditions and detector performance.
The exclusive charmonium production process in \(\bar{p}p\) annihilation with an associated \(\pi^0\) meson \(\bar{p}p\to J/\psi\pi^0\) is studied in the framework of QCD collinear factorization. The ...feasibility of measuring this reaction through the \(J/\psi\to e^+e^-\) decay channel with the PANDA (AntiProton ANnihilation at DArmstadt) experiment is investigated. Simulations on signal reconstruction efficiency as well as the background rejection from various sources including the \(\bar{p}p\to\pi^+\pi^-\pi^0\) and \(\bar{p}p\to J/\psi\pi^0\pi^0\) reactions are performed with PandaRoot, the simulation and analysis software framework of the PANDA experiment. It is shown that the measurement can be done at PANDA with significant constraining power under the assumption of an integrated luminosity attainable in four to five months of data taking at the maximum design luminosity.
PDF
