Anesthesiology
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This review focuses on the use of veno-venous extracorporeal membrane oxygenation for respiratory failure across all blood flow ranges. Starting with a short overview of historical development, aspects of the physiology of gas exchange (i.e., oxygenation and decarboxylation) during extracorporeal circulation are discussed. The mechanisms of phenomena such as recirculation and shunt playing an important role in daily clinical practice are explained. ⋯ In the latter context, extracorporeal carbon dioxide removal plays an emerging role in the treatment of chronic obstructive pulmonary disease patients during acute exacerbations. Both applications of extracorporeal lung support raise important ethical considerations, such as likelihood of ultimate futility and end-of-life decision-making. The review concludes with a brief overview of potential technical developments and persistent challenges.
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Overactivation of ryanodine receptors and the resulting impaired calcium homeostasis contribute to Alzheimer's disease-related pathophysiology. This study hypothesized that exposing neuronal progenitors derived from induced pluripotent stems cells of patients with Alzheimer's disease to dantrolene will increase survival, proliferation, neurogenesis, and synaptogenesis. ⋯ Dantrolene ameliorated the impairment of neurogenesis and synaptogenesis, in association with restoring intracellular Ca homeostasis and physiologic autophagy, cell survival, and proliferation in induced pluripotent stem cells and their derived neurons from sporadic and familial Alzheimer's disease patients.
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Although prediction of hospital readmissions has been studied in medical patients, it has received relatively little attention in surgical patient populations. Published predictors require information only available at the moment of discharge. The authors hypothesized that machine learning approaches can be leveraged to accurately predict readmissions in postoperative patients from the emergency department. Further, the authors hypothesize that these approaches can accurately predict the risk of readmission much sooner than hospital discharge. ⋯ A machine learning approach to predicting postoperative readmission can produce hospital-specific models for accurately predicting 30-day readmissions via the emergency department. Moreover, these predictions can be confidently calculated at 36 h after surgery without consideration of discharge-level data.
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Making good decisions in the era of Big Data requires a sophisticated approach to causality. We are acutely aware that association ≠ causation, yet untangling the two remains one of our greatest challenges. This realization has stimulated a Causal Revolution in epidemiology, and the lessons learned are highly relevant to anesthesia research. ⋯ We also illustrate how controlling for the wrong variables can introduce, rather than eliminate, bias; and how directed acyclic graphs can help us diagnose this problem. This is a rapidly evolving field, and we cover only the most basic elements. The true promise of these techniques is that it may become possible to make robust statements about causation from observational studies-without the expense and artificiality of randomized controlled trials.