Leukocyte depletion improves early postischemic ventricular performance in neonatal models of global myocardial ischemia. However, the rate of leukocyte reaccumulation after cardiopulmonary bypass and its subsequent impact on myocardial function is not known. This laboratory study examined the effect of leukocyte depletion on myocardial performance during the initial 6-hour period after bypass in an in situ, in vivo porcine model of neonatal cardiac surgery. Fifteen 3- to 5-day-old piglets (eight control and seven leukocyte depleted animals) were instrumented by placement of left ventricular short-axis sonomicrometry crystals and an intraventricular micromanometer catheter. Mechanical leukocyte depletion was achieved with Pall RC100 filters (Pall Biomedical, Inc., Fajardo, Puerto Rico) in the cardiopulmonary bypass circuit. Neonatal hearts were subjected to 90 minutes of hypothermic ischemia after a single dose of cold crystalloid cardioplegia. Two control animals died after the operation and were excluded from data analysis. Leukocyte filtration reduced the granulocyte count during initial myocardial reperfusion to 0.8% of control values. However, circulating granulocyte counts increased in leukocyte depleted animals throughout the postoperative period, reaching 68% of control values by 6 hours. Despite this rapid return of circulating granulocytes, animals subjected to leukocyte depletion had significantly better preservation of left ventricular performance (measured by preload recruitable stroke work, p ≤ 0.02), left ventricular systolic function (measured by end-systolic pressure-volume relationship, p ≤ 0.05), and ventricular compliance (p ≤ 0.04) during the experiment. These changes in ventricular function were associated with a significant increase in left ventricular water content (p ≤ 0.02) and tissue myeloperoxidase activity (p ≤ 0.005) in control animals compared with leukocyte depleted animals. This study demonstrates that leukocyte depletion during initial reperfusion results in sustained improvement in postischemic left ventricular function despite the rapid return of granulocytes to the circulation.
Patients with sickle cell abnormalities undergoing surgery are generally considered to be at greater risk for perioperative complications. We present a 25-year-old woman with sickle cell disease (SCD) and severe aortic insufficiency. A minimally invasive, warm, beating heart approach was adopted to try and minimize the risk of sickling due to cardiopulmonary bypass (CPB), low-flow states, cold cardioplegia and aortic cross-clamping. Compared to classical methods, we believe our technique further reduces the risk of systemic and organ hypothermia and thus, sickling.
D. Schattschneider proved that there are exactly eight unilateral and equitransitive tilings of the plane by squares of three distinct sizes. This article extends Schattschneider’s methods to determine a classification of all such tilings by squares of four different sizes. It is determined that there are exactly 39 unilateral and equitransitive tilings by squares of four different sizes.
Abstract Background “Bounce-back” to the intensive care unit (ICU) occurs when patients return to the ICU for critical changes in clinical status within the same hospital admission. Bounce-backs post-cardiac surgery increase resource utilisation, total cost of care, are associated with higher mortality and morbidity. However, prediction of bounce-back has proved to be challenging. Previous work addressed the feasibility of predicting bounce-back, but these models required significant physician input to design and calibrate the predictive variables. Purpose We aimed to develop an automated machine learning model that would identify patients at risk of bounce-back by selecting the most relevant variables from those available before onset of bounce-back. Additionally, we highlight the differences between predictive and causal inference, to demonstrate that purely associative methods of prediction can mislead clinical decision-making. Methods Clinical records of adult cardiac surgery patients between 2011 to 2016 were collected from our institutional Society for Thoracic Surgeons (STS) database and our institutional electronic health record (EHR) system. For bounce-back prediction, an L1 regularised logistic regression model was applied, which also automatically determined important variables with highest prediction effect from the initial 151 variables. For causal inference, the g-computation algorithm was used to compare the differences between causal and predictive regression effects. We quantified the performance of our system on clinically relevant metrics such as specificity, sensitivity, and area under the ROC curve (AUC). Results Of the 6189 patients, 357 (5.7%) bounced back to the ICU. The prediction model achieved an AUC score of 0.75 (0.03) and 22% specificity at 95% sensitivity, Further analysis showed 79% of the false positive patients had faced other severe postoperative complications but none of the false negative patients had downstream complications. Subsequent causal analysis revealed that the actual causal effects of treatments differed from the predictive model estimates, e.g. administration of intra-operative tranexamic acid increased the probability of bounce-back by 13% but its causal effect on bounce-back after removing confounders was negligible (an increase of only 0.5%). Conclusions Our predictive machine-learning model can successfully predict patients at risk of ICU bounce-backs, using linked STS registry data with the comprehensive electronic health record. The prediction model automatically detects important subset of variables. In addition, we note that causal and predictive model estimates of the same parameters differed, indicating that reliance on predictive models for interventional clinical decision-making may not be appropriate. Acknowledgement/Funding National Institutes of Health, Office of Naval Research, Defense Advanced Research Projects Agency
