Patients Referred to a Norwegian Trauma Centre: effect of transfer distance on injury patterns, use of resources and outcomes

Background Triage and interhospital transfer are central to trauma systems. Few studies have addressed transferred trauma patients. This study investigated transfers of variable distances to OUH (Oslo University Hospital, Ullevål), one of the largest trauma centres in Europe. Methods Patients included in the OUH trauma registry from 2001 to 2008 were included in the study. Demographic, injury, management and outcome data were abstracted. Patients were grouped according to transfer distance: ≤20 km, 21-100 km and > 100 km. Results Of the 7.353 included patients, 5.803 were admitted directly, and 1.550 were transferred. The number of transfers per year increased, and there was no reduction in injury severity during the study period. Seventy-six per cent of the transferred patients were severely injured. With greater transfer distances, injury severity increased, and there were larger proportions of traffic injuries, polytrauma and hypotensive patients. With shorter distances, patients were older, and head injuries and injuries after falls were more common. The shorter transfers less often activated the trauma team: ≤20 km -34%; 21-100 km -51%; > 100 km -61%, compared to 92% of all directly admitted patients. The mortality for all transferred patients was 11%, but was unequally distributed according to transfer distance. Conclusion This study shows heterogeneous characteristics and high injury severity among interhospital transfers. The rate of trauma team assessment was low and should be further examined. The mortality differences should be interpreted with caution as patients were in different phases of management. The descriptive characteristics outlined may be employed in the development of triage protocols and transfer guidelines

Design Constraints on a Synthetic Metabolism

A metabolism is a complex network of chemical reactions that converts sources of energy and chemical elements into biomass and other molecules. To design a metabolism from scratch and to implement it in a synthetic genome is almost within technological reach. Ideally, a synthetic metabolism should be able to synthesize a desired spectrum of molecules at a high rate, from multiple different nutrients, while using few chemical reactions, and producing little or no waste. Not all of these properties are achievable simultaneously. We here use a recently developed technique to create random metabolic networks with pre-specified properties to quantify trade-offs between these and other properties. We find that for every additional molecule to be synthesized a network needs on average three additional reactions. For every additional carbon source to be utilized, it needs on average two additional reactions. Networks able to synthesize 20 biomass molecules from each of 20 alternative sole carbon sources need to have at least 260 reactions. This number increases to 518 reactions for networks that can synthesize more than 60 molecules from each of 80 carbon sources. The maximally achievable rate of biosynthesis decreases by approximately 5 percent for every additional molecule to be synthesized. Biochemically related molecules can be synthesized at higher rates, because their synthesis produces less waste. Overall, the variables we study can explain 87 percent of variation in network size and 84 percent of the variation in synthesis rate. The constraints we identify prescribe broad boundary conditions that can help to guide synthetic metabolism design

A probabilistic neural network as the predictive classifier of out-of-hospital defibrillation outcomes

Introduction: Although modern defibrillators are nearly always successful in terminating ventricular fibrillation (VF), multiple defibrillation attempts are usually required to achieve return of spontaneous circulation (ROSC). This is potentially deleterious as cardiopulmonary resuscitation (CPR) must be discontinued during each defibrillation attempt which causes deterioration in the heart muscle and reduces the chance of ROSC from later defibrillation attempts. In this work defibrillation outcomes are predicted prior to electrical shocks using a neural network model to analyse VF time series in an attempt to avoid defibrillation attempts that do not result in ROSC. Methods: The 198 pre-shock VF ECG episodes from 83 cardiac arrest patients with defibrillation conversions to different outcomes were selected from the Oslo ambulance service database. A probabilistic neural network model was designed for training and testing with a cross validation method being used for the better generalisation performance. Results: We achieved an accuracy of 75% in overall prediction with a sensitivity of 84% and a specificity of 65% using VF ECG time series of an order of 1s in length. Conclusion: Pre-shock VF ECG time series can be classified according to the defibrillation conversion to a return of spontaneous circulation (ROSC) or No-ROSC