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Background: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. Methods: A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. Results: 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict “survival”. Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients’ age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. Conclusions: Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration “ClinicalTrials” (clinicaltrials.gov) under NCT04455451.
Radiative transition of an excited baryon to a nucleon with emission of a virtual massive photon converting to dielectron pair (Dalitz decays) provides important information about baryon-photon coupling at low q2 in timelike region. A prominent enhancement in the respective electromagnetic transition Form Factors (etFF) at q2 near vector mesons ρ/ω poles has been predicted by various calculations reflecting strong baryon-vector meson couplings. The understanding of these couplings is also of primary importance for the interpretation of the emissivity of QCD matter studied in heavy ion collisions via dilepton emission. Dedicated measurements of baryon Dalitz decays in proton-proton and pion-proton scattering with HADES detector at GSI/FAIR are presented and discussed. The relevance of these studies for the interpretation of results obtained from heavy ion reactions is elucidated on the example of the HADES results.
In March 2019 the HADES experiment recorded 14 billion Ag+Ag collisions at √sNN = 2.55 GeV as a part of the FAIR phase-0 physics program. In this contribution, we present and investigate our capabilities to reconstruct and analyze weakly decaying strange hadrons and hypernuclei emerging from these collisions. The focus is put on measuring the mean lifetimes of these particles.