Explainable Boosting Machine approach identifies risk factors for acute renal failure.

Background: Risk stratification and outcome prediction are crucial for intensive care resource planning. In addressing the large data sets of intensive care unit (ICU) patients, we employed the Explainable Boosting Machine (EBM), a novel machine learning model, to identify determinants of acute kidney injury (AKI) in these patients. AKI significantly impacts outcomes in the critically ill.

Patient-Related Factors Associated with Adverse Outcomes Following Weaning from Veno-Arterial Extracorporeal Membrane Oxygenation.

Background: Veno-arterial extracorporeal membrane oxygenation (vaECMO) removal reflects a critical moment and factors of adverse outcomes are incompletely understood. Thus, we studied various patient-related factors during vaECMO removal to determine their association with outcomes.

Methods: A total of 58 patients from a university hospital were included retrospectively.

Corrigendum: Methylene blue dosing strategies in critically ill adults with shock-A retrospective cohort study.

[This corrects the article DOI: 10.3389/fmed.2022.

Methylene blue dosing strategies in critically ill adults with shock-A retrospective cohort study.

Background: Shock increases mortality in the critically ill and the mainstay of therapy is the administration of vasopressor agents to achieve hemodynamic targets. In the past, studies have found that the NO-pathway antagonist methylene blue improves hemodynamics. However, the optimal dosing strategy remains elusive.