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Recent work has demonstrated that the formation of platelet neutrophil complexes (PNCs) affects inflammatory tissue injury. Vasodilator-stimulated phosphoprotein (VASP) is crucially involved into the control of PNC formation and myocardial reperfusion injury. Given the clinical importance of hepatic IR injury we pursued the role of VASP during hepatic ischemia followed by reperfusion. We report here that VASP−/− animals demonstrate reduced hepatic IR injury compared to wildtype (WT) controls. This correlated with serum levels of lactate dehydrogenase (LDH), aspartate (AST) and alanine (ALT) aminotransferase and the presence of PNCs within ischemic hepatic tissue and could be confirmed using repression of VASP through siRNA. In studies employing bone marrow chimeric mice we identified hematopoietic VASP to be of crucial importance for the extent of hepatic injury. Phosphorylation of VASP on Ser153 through Prostaglandin E1 or on Ser235 through atrial natriuretic peptide resulted in a significant reduction of hepatic IR injury. This was associated with a reduced presence of PNCs in ischemic hepatic tissue. Taken together, these studies identified VASP and VASP phosphorylation as crucial target for future hepatoprotective strategies.
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.