Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New Background With appropriate algorithms, computers can learn to detect patterns and associations in large data sets. The authors’ goal was to apply machine learning to arterial pressure waveforms and create an algorithm to predict hypotension. The algorithm detects early alteration in waveforms that can herald the weakening of cardiovascular compensatory mechanisms affecting preload, afterload, and contractility. Methods The algorithm was developed with two different data sources: (1) a retrospective cohort, used for training, consisting of 1,334 patients’ records with 545,959 min of arterial waveform recording and 25,461 episodes of hypotension; and (2) a prospective, local hospital cohort used for external validation, consisting of 204 patients’ records with 33,236 min of arterial waveform recording and 1,923 episodes of hypotension. The algorithm relates a large set of features calculated from the high-fidelity arterial pressure waveform to the prediction of an upcoming hypotensive event (mean arterial pressure < 65 mmHg). Receiver-operating characteristic curve analysis evaluated the algorithm’s success in predicting hypotension, defined as mean arterial pressure less than 65 mmHg. Results Using 3,022 individual features per cardiac cycle, the algorithm predicted arterial hypotension with a sensitivity and specificity of 88% (85 to 90%) and 87% (85 to 90%) 15 min before a hypotensive event (area under the curve, 0.95 [0.94 to 0.95]); 89% (87 to 91%) and 90% (87 to 92%) 10 min before (area under the curve, 0.95 [0.95 to 0.96]); 92% (90 to 94%) and 92% (90 to 94%) 5 min before (area under the curve, 0.97 [0.97 to 0.98]). Conclusions The results demonstrate that a machine-learning algorithm can be trained, with large data sets of high-fidelity arterial waveforms, to predict hypotension in surgical patients’ records.

Background: Respiratory arterial pulse pressure variations (PPV) are the best predictors of fluid responsiveness in mechanically ventilated patients during general anesthesia.

Respiratory variation in aortic blood flow peak velocity was the only variable shown to predict fluid responsiveness in children. Static variables did not predict fluid responsiveness in children, which was consistent with evidence in adults. Dynamic variables based on arterial blood pressure did not predict fluid responsiveness in children, but the evidence for dynamic variables based on plethysmography was inconclusive.

PVI, an automatic and continuous monitor of DeltaPOP, can predict fluid responsiveness non-invasively in mechanically ventilated patients during general anaesthesia. This index has potential clinical applications.

IntroductionSeveral studies have demonstrated that perioperative hemodynamic optimization has the ability to improve postoperative outcome in high-risk surgical patients. All of these studies aimed at optimizing cardiac output and/or oxygen delivery in the perioperative period. We conducted a survey with the American Society of Anesthesiologists (ASA) and the European Society of Anaesthesiology (ESA) to assess current hemodynamic management practices in patients undergoing high-risk surgery in Europe and in the United States.MethodsA survey including 33 specific questions was emailed to 2,500 randomly selected active members of the ASA and to active ESA members.ResultsOverall, 368 questionnaires were completed, 57.1% from ASA and 42.9% from ESA members. Cardiac output is monitored by only 34% of ASA and ESA respondents (P = 0.49) while central venous pressure is monitored by 73% of ASA respondents and 84% of ESA respondents (P < 0.01). Specifically, the pulmonary artery catheter is being used much more frequently in the US than in Europe in the setup of high-risk surgery (85.1% vs. 55.3% respectively, P < 0.001). Clinical experience, blood pressure, central venous pressure, and urine output are the most widely indicators of volume expansion. Finally, 86.5% of ASA respondents and 98.1% of ESA respondents believe that their current hemodynamic management could be improved.ConclusionsIn conclusion, these results point to a considerable gap between the accumulating evidence about the benefits of perioperative hemodynamic optimization and the available technologies that may facilitate its clinical implementation, and clinical practices in both Europe and the United States.

BackgroundPerioperative fluid therapy remains a highly debated topic. Its purpose is to maintain or restore effective circulating blood volume during the immediate perioperative period. Maintaining effective circulating blood volume and pressure are key components of assuring adequate organ perfusion while avoiding the risks associated with either organ hypo- or hyperperfusion. Relative to perioperative fluid therapy, three inescapable conclusions exist: overhydration is bad, underhydration is bad, and what we assume about the fluid status of our patients may be incorrect. There is wide variability of practice, both between individuals and institutions. The aims of this paper are to clearly define the risks and benefits of fluid choices within the perioperative space, to describe current evidence-based methodologies for their administration, and ultimately to reduce the variability with which perioperative fluids are administered.MethodsBased on the abovementioned acknowledgements, a group of 72 researchers, well known within the field of fluid resuscitation, were invited, via email, to attend a meeting that was held in Chicago in 2011 to discuss perioperative fluid therapy. From the 72 invitees, 14 researchers representing 7 countries attended, and thus, the international Fluid Optimization Group (FOG) came into existence. These researches, working collaboratively, have reviewed the data from 162 different fluid resuscitation papers including both operative and intensive care unit populations. This manuscript is the result of 3 years of evidence-based, discussions, analysis, and synthesis of the currently known risks and benefits of individual fluids and the best methods for administering them.ResultsThe results of this review paper provide an overview of the components of an effective perioperative fluid administration plan and address both the physiologic principles and outcomes of fluid administration.ConclusionsWe recommend that both perioperative fluid choice and therapy be individualized. Patients should receive fluid therapy guided by predefined physiologic targets. Specifically, fluids should be administered when patients require augmentation of their perfusion and are also volume responsive. This paper provides a general approach to fluid therapy and practical recommendations.

In this study, ▵Vpeak was the most appropriate variable to predict fluid responsiveness.

Editor's key points † Effects of different vasopressor agents on cerebral oxygenation have been unclear. † Ephedrine and phenylephrine, used for intraoperative hypotension, were investigated in a cross-over design study. † Phenylephrine, but not ephedrine, decreased cardiac output (CO) and brain oxygenation. † This study highlights the importance of CO in preserving brain oxygenation during management of intraoperative hypotension. Background. How phenylephrine and ephedrine treatments affect global and regional haemodynamics is of major clinical relevance. Cerebral tissue oxygen saturation (Sct O 2)-guided management may improve postoperative outcome. The physiological variables responsible for Sct O 2 changes induced by phenylephrine and ephedrine bolus treatment in anaesthetized patients need to be defined.