Need for critical care in gynaecology: a population-based analysis

INTRODUCTION: The purpose of this study was to note potential gynaecological risk factors leading to intensive care and to estimate the frequency, costs and outcome of management. MATERIALS AND METHODS: In a cross-sectional study of intensive care admissions in Kuopio from March 1993 to December 2000, 23 consecutive gynaecological patients admitted to a mixed medical-surgical intensive care unit (ICU) were followed. We recorded demographics, admitting diagnoses, scores on the Acute Physiological and Chronic Health Evaluation (APACHE) II, clinical outcome and treatment costs. RESULTS: The overall need for intensive care was 2.3 per 1000 women undergoing major surgery during the study period. Patients were 55.4 ± 16.9 (mean ± SD) years old, with a mean APACHE II score of 14.07 (± 5.57). The most common diagnoses at admission were postoperative haemorrhage (43%), infection (39%) and cardiovascular disease (30%). The duration of stay in the ICU was 4.97 (± 9.28) (range 1–42) days and the mortality within 6 months was 26%, although the mortality in the ICU was 0%. The total cost of intensive care was approximately US$7044 per patient. CONCLUSIONS: Very few gynaecological patients develop complications requiring intensive care. The presence of gynaecological malignancy and pre-existing medical disorders are clinically useful predictors of eventual outcome, but many cases occur in women with a low risk and this implies that the risk is relevant to all procedures. Further research is needed to determine effective preventive approaches

On Light and Heavy Traffic Approximations of Balanced Fairness

International audienceFlow level analysis of communication networks with multiple shared resources is generally difficult. A recently introduced sharing scheme called balanced fairness has brought these systems within the realm of tractability. While straightforward in principle, the numerical evaluation of practically interesting performance metrics like per-flow throughput is feasible for limited state spaces only, besides some specific networks where the results are explicit. In the present paper , we study the behaviour of balanced fairness in light and heavy traffic regimes and show how the corresponding performance results can be used to approximate the flow throughput over the whole load range. The results apply to any network, with a state space of arbitrary dimension. A few examples are explicitly worked out to illustrate the concepts

Improving Multicast Tree Construction in Static Ad Hoc Networks

The multicast tree problem Ad hoc networks are likely to support applications where considerable amount of data is delivered to several destinations at the same time. In some settings the energy resources are scarce and difficult to replace. There is a need for well designed multicast trees to efficiently use the resources. • Problem statement: Select a set of sequential transmissions which connect a source to a set of receivers so that the sum of the transmission energy costs is minimised. • Transmissions are omni-directional and have variable power. • Previous work by Wieselthier et al. [1] – Multicast Incremental Power (MIP) algorithm. ISPT-algorithm • Constructs a multicast tree in two phases – Tree initialisation – Grafting (repeated for all receivers) • Initial tree is an arbitrary subtree originating at the source node. • Each grafting step consists of adding a multicast receiver (selected by the grafting order) to the tree using the path that yields the lowest incremental path cost, see Figure 1. • Incremental path cost is the additional energy needed to reach the destination from the tree. • Worst case complexity O(N 3), where N is the number of nodes. Performance analysis Comparison with MIP • Two versions of ISPT, both of which use the grafting order “lowest cost first”, but with initial trees as follows: – The source node itself (ISPT1). – The shortest path to the furthermost receiver (ISPT2, Fig. 1). • Transmission cost is rα, 2 ≤ α ≤ 4, where r is the distance. • Comparison by relative difference x of the tree costs (alg1 and alg2), averaging over 1000 samples of 100 node networks, x

A Packet Marking Algorithm for Congestion Pricing

In the resource pricing concept, developed by Kelly et al. [1], the network marks packets with appropriate price signals to direct the users' actions. The theoretically correct congestion dependent prices, however, are not all known at the time when the marking occurs at a network resource. In this paper, we propose a packet marking algorithm which maximally utilises the information available at the time of marking. The known prices are marked directly on the packets, while the unknown ones are replaced by their expected values. Calculation of the expected values depends on a suitable system model. We derive these prices for a simple M/M/1/K resource model and generalise the result for GI/GI/1/K models using an approximate diffusion approach

Flow Level Performance Analysis of Wireless Data Networks: A Case Study

Abstract — We give an example of flow level performance analysis of data traffic in wireless networks by studying a scenario where two base stations with link adaptation serve in a coordinated fashion downloading users on a road or street between the stations. Due to the dynamic nature of such systems, a detailed flow level analysis is challenging and conventional methods run into computational difficulties. We motivate the detailed analysis by studying the system under different operational goals such as maximum throughput, max-min fairness and balanced fairness, concluding that the performance under these dynamic policies differ significantly from the performance under more tractable static policies. We discuss how the corresponding numerical analyses can be facilitated by applying the notion of balanced fairness and, in particular, introduce a novel approximation method referred to as value extrapolation. Value extrapolation can be applied to approximate any performance measure expressed as the expected value of a random variable which is a function of the system state. The idea of the value extrapolation is to consider the system in the MDP (Markov Decision Processes) setting and to solve the expected value from the Howard equations written for a truncated state space. Instead of a simple truncation, the relative values of states just outside the truncated state space are estimated using a polynomial extrapolation based on the states inside. This leads to a closed system and, unless the system is heavily loaded, allows one to obtain accurate results with remarkably small truncated state spaces. I