Shock : molecular, cellular, and systemic pathobiological aspects and therapeutic approaches : the official journal the Shock Society, the European Shock Society, the Brazilian Shock Society, the International Federation of Shock Societies
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Background: Patients receiving massive transfusion protocol (MTP) are at risk for posttransfusion hypocalcemia and hyperkalemia. Previous retrospective analysis has suggested the potassium/ionized calcium (K/iCa) ratio as a prognostic indicator of mortality. This prospective study sought to validate the value of the K/iCa ratio as a predictor for mortality in patients receiving MTP. ⋯ Furthermore, it demonstrates that posttransfusion K levels along with iCa levels should be carefully monitored in the MTP setting. Level of Evidence: Level II. Study Type: Prognostic/epidemiological.
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Introduction: The compensatory reserve measurement (CRM) is a continuous noninvasive monitoring technology that provides an assessment of the integrated capacity of all physiological mechanisms associated with responses to a hypovolemic stressor such as hemorrhagic shock. No prior studies have analyzed its use for intraoperative resuscitation guidance. Methods: A prospective observational study was conducted of 23 patients undergoing orthotopic liver transplant. ⋯ XGBoost prediction models showed superior discriminatory capacity of CRM alone compared with the model with SBP and HR and no difference when all three were combined (CRM-HR-SBP). All XGBoost models outperformed equivalent linear regression models. Conclusion: These results demonstrate that CRM can provide an adjunctive clinical tool that can augment early and accurate of hemodynamic compromise and promote goal-directed resuscitation in the perioperative setting.
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Sepsis remains a major challenge that necessitates improved approaches to enhance patient outcomes. This study explored the potential of machine learning (ML) techniques to bridge the gap between clinical data and gene expression information to better predict and understand sepsis. We discuss the application of ML algorithms, including neural networks, deep learning, and ensemble methods, to address key evidence gaps and overcome the challenges in sepsis research. ⋯ Collaborative efforts between clinicians and data scientists are essential for the successful implementation and translation of ML models into clinical practice. Machine learning has the potential to revolutionize our understanding of sepsis and significantly improve patient outcomes. Further research and collaboration between clinicians and data scientists are needed to fully understand the potential of ML in sepsis management.
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Background: Multiple-organ dysfunction syndrome disproportionately contributes to pediatric sepsis morbidity. Humanin (HN) is a small peptide encoded by mitochondrial DNA and thought to exert cytoprotective effects in endothelial cells and platelets. We sought to test the association between serum HN (sHN) concentrations and multiple-organ dysfunction syndrome in a prospectively enrolled cohort of pediatric septic shock. ⋯ Furthermore, sHN was higher among those with high PERSEVERE-mortality risk strata and correlated with platelet counts and several markers of endothelial activation. Conclusion: Future investigation is necessary to validate the association between sHN and sepsis-associated acute kidney injury among children with septic shock. Furthermore, mechanistic studies that elucidate the role of HN may lead to therapies that promote organ recovery through restoration of mitochondrial homeostasis among those critically ill.
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Sepsis is an organ dysfunction caused by a dysregulated host response to infection and remains an ongoing threat to human health worldwide. Septic shock is the most severe subset of sepsis as characterized by abnormalities in cells, circulation, and metabolism. As a time-dependent condition, early recognition allowing appropriate therapeutic measures to be started in a timely manner becomes the most effective way to improve prognosis. ⋯ DDX47 showed preferable diagnostic value in various scenarios, especially in patients with common infections or sepsis and septic shock. Here we also show that hub genes may regulate immune function and immune cell counts through the interaction of different apoptotic pathways and immune checkpoints based on the high correlation. DDX47 is closely associated with B cells according to single-cell sequencing results.