The Process of Organ Donation from Non-Living Donors: A Case-Based Journey from Potential Donor Identification to Organ Procurement

Each year, thousands of people worldwide succumb to end-organ failure while awaiting life-saving transplantation procedures. The shortage of organs continues with no signs of easing in the foreseeable future. The availability of organs from living donors continues to be constrained. At the same time, the cumulative knowledge of organ preservation is advancing steadily resulting in an enhanced ability to utilize a growing number of previously unsuitable tissue and organ gifts. Our ability to procure and preserve more organs is accompanied by the increasing use of so-called “expanded criteria” donors, or those whose organs may not have been suitable without modern advances in organ preservation science. Within the overall context of organ donation from non-living donors, the importance of physiologic and end-organ optimization cannot be understated. This chapter discusses our current state of understanding of optimized organ procurement approaches derived from practical experiences and “lessons learned” at a high-performing, community-based tertiary referral hospital

Large-Eddy Simulations of Magnetohydrodynamic Turbulence in Heliophysics and Astrophysics

We live in an age in which high-performance computing is transforming the way we do science. Previously intractable problems are now becoming accessible by means of increasingly realistic numerical simulations. One of the most enduring and most challenging of these problems is turbulence. Yet, despite these advances, the extreme parameter regimes encountered in space physics and astrophysics (as in atmospheric and oceanic physics) still preclude direct numerical simulation. Numerical models must take a Large Eddy Simulation (LES) approach, explicitly computing only a fraction of the active dynamical scales. The success of such an approach hinges on how well the model can represent the subgrid-scales (SGS) that are not explicitly resolved. In addition to the parameter regime, heliophysical and astrophysical applications must also face an equally daunting challenge: magnetism. The presence of magnetic fields in a turbulent, electrically conducting fluid flow can dramatically alter the coupling between large and small scales, with potentially profound implications for LES/SGS modeling. In this review article, we summarize the state of the art in LES modeling of turbulent magnetohydrodynamic (MHD) ows. After discussing the nature of MHD turbulence and the small-scale processes that give rise to energy dissipation, plasma heating, and magnetic reconnection, we consider how these processes may best be captured within an LES/SGS framework. We then consider several special applications in heliophysics and astrophysics, assessing triumphs, challenges,and future directions

Acute myocardial infarction following right coronary artery dissection due to blunt trauma

Despite the frequent occurrence of blunt chest trauma, associated cardiac injuries are relatively rare. The most common presentation of blunt cardiac injury is benign arrhythmia (e.g., sinus tachycardia), followed in decreasing frequency by increasingly severe arrhythmias and finally physically evident injuries to the heart muscle, the conducting system, cardiac valves, and/or coronary vessels. Here we present an unusual case of a patient who sustained a right coronary artery dissection and associated acute myocardial infarction following a motor vehicle crash

Comorbidity polypharmacy score and its clinical utility: A pragmatic practitioner′s perspective

Modern medical management of comorbid conditions has resulted in escalating use of multiple medications and the emergence of the twin phenomena of multimorbidity and polypharmacy. Current understanding of how the polypharmacy in conjunction with multimorbidity influences trauma outcomes is limited, although it is known that trauma patients are at increased risk for medication-related adverse events. The comorbidity-polypharmacy score (CPS) is a simple clinical tool that quantifies the overall severity of comorbidities using the polypharmacy as a surrogate for the "intensity" of treatment necessary to adequately control chronic medical conditions. Easy to calculate, CPS is derived by counting all known pre-injury comorbid conditions and medications. CPS has been independently associated with mortality, increased risk for complications, lower functional outcomes, readmissions, and longer hospital stays. In addition, CPS may help identify older trauma patients at risk of post-emergency department undertriage. The goal of this article was to review and refine the rationale for CPS and to provide an evidence-based outline of its potential clinical applications

Evaluation of a model for glycemic prediction in critically ill surgical patients.

We evaluated a neural network model for prediction of glucose in critically ill trauma and post-operative cardiothoracic surgical patients. A prospective, feasibility trial evaluating a continuous glucose-monitoring device was performed. After institutional review board approval, clinical data from all consenting surgical intensive care unit patients were converted to an electronic format using novel software. This data was utilized to develop and train a neural network model for real-time prediction of serum glucose concentration implementing a prediction horizon of 75 minutes. Glycemic data from 19 patients were used to "train" the neural network model. Subsequent real-time simulated testing was performed in 5 patients to whom the neural network model was naive. Performance of the model was evaluated by calculating the mean absolute difference percent (MAD%), Clarke Error Grid Analysis, and calculation of the percent of hypoglycemic (≤70 mg/dL), normoglycemic (>70 and <150 mg/dL), and hyperglycemic (≥150 mg/dL) values accurately predicted by the model; 9,405 data points were analyzed. The models successfully predicted trends in glucose in the 5 test patients. Clark Error Grid Analysis indicated that 100.0% of predictions were clinically acceptable with 87.3% and 12.7% of predicted values falling within regions A and B of the error grid respectively. Overall model error (MAD%) was 9.0% with respect to actual continuous glucose modeling data. Our model successfully predicted 96.7% and 53.6% of the normo- and hyperglycemic values respectively. No hypoglycemic events occurred in these patients. Use of neural network models for real-time prediction of glucose in the surgical intensive care unit setting offers healthcare providers potentially useful information which could facilitate optimization of glycemic control, patient safety, and improved care. Similar models can be implemented across a wider scale of biomedical variables to offer real-time optimization, training, and adaptation that increase predictive accuracy and performance of therapies