A Bayesian Network Hybrid Model for Representing Accident and Emergency Waiting Times

The paper introduces a new modeling approach that represents the waiting times in an Accident and Emergency (A&E) Department in a UK based National Health Service (NHS) hospital. The technique uses Bayesian networks to capture the heterogeneity of arriving patients by representing how patient covariates interact to influence their waiting times in the department. Such waiting times have been reviewed by the NHS as a means of investigating the efficiency of A&E departments (Emergency Rooms) and how they operate. As a result activity targets are now established based on the patient total waiting times with much emphasis on trolley waits.

Experimenting with the Coxian Phase-Type Distribution to Uncover Suitable Fits

In the past few decades, Coxian phase-type distributions have become increasingly more popular as a means of representing survival times. In healthcare, they are considered suitable for modelling the length of stay of patients in hospital and more recently for modelling the patient waiting times in Accident and Emergency Departments. The Coxian phase-type distribution has not only been shown to provide a good representation of real survival data, but its interpretation seems reasonably initiative to the medical experts. The drawback, however, is fitting the distribution to the data. There have been many attempts at accurately estimating the Coxian phase-type parameters. This paper wishes to examine the most promising of the approaches reported in the literature to determine the most accurate. Three performance measures are introduced to assess the fitting process of the algorithms along with the likelihood values and AIC to examine the goodness of fit and complexity of the model. Previous research suggests that the fitting process is strongly influenced by the initial parameter estimates and the data itself being quite variable. To overcome this, one experiment in this research paper will use the same initial parameter values for each estimation and perform the fits on the data simulated from a Coxian phase-type distribution with known parameters.

Discrete Conditional Phase-type models utilising classification trees: Application to modelling health service capacities

Discrete Conditional Phase-type (DC-Ph) models consist of a process component (survival distribution) preceded by a set of related conditional discrete variables. This paper introduces a DC-Ph model where the conditional component is a classification tree. The approach is utilised for modelling health service capacities by better predicting service times, as captured by Coxian Phase-type distributions, interfaced with results from a classification tree algorithm. To illustrate the approach, a case-study within the healthcare delivery domain is given, namely that of maternity services. The classification analysis is shown to give good predictors for complications during childbirth. Based on the classification tree predictions, the duration of childbirth on the labour ward is then modelled as either a two or three-phase Coxian distribution. The resulting DC-Ph model is used to calculate the number of patients and associated bed occupancies, patient turnover, and to model the consequences of changes to risk status.

The Utility of Cardiopulmonary Exercise Testing in Difficult Asthma

Background: Unexplained persistent breathlessness in patients with difficult asthma despite multiple treatments is a common clinical problem. Cardiopulmonary exercise testing (CPX) may help identify the mechanism causing these symptoms, allowing appropriate management.

Methods: This was a retrospective analysis of patients attending a specialist-provided service for difficult asthma who proceeded to CPX as part of our evaluation protocol. Patient demographics, lung function, and use of health care and rescue medication were compared with those in patients with refractory asthma. Medication use 6 months following CPX was compared with treatment during CPX.

Results: Of 302 sequential referrals, 39 patients underwent CPX. A single explanatory feature was identified in 30 patients and two features in nine patients: hyperventilation (n = 14), exercise-induced bronchoconstriction (n = 8), submaximal test (n = 8), normal test (n = 8), ventilatory limitation (n = 7), deconditioning (n = 2), cardiac ischemia (n = 1). Compared with patients with refractory asthma, patients without “pulmonary limitation” on CPX were prescribed similar doses of inhaled corticosteroid (ICS) (median, 1,300 µg [interquartile range (IQR), 800-2,000 µg] vs 1,800 µg [IQR, 1,000-2,000 µg]) and rescue oral steroid courses in the previous year (median, 5 [1-6] vs 5 [1-6]). In this group 6 months post-CPX, ICS doses were reduced (median, 1,300 µg [IQR, 800-2,000 µg] to 800 µg [IQR, 400-1,000 µg]; P < .001) and additional medication treatment was withdrawn (n = 7). Patients with pulmonary limitation had unchanged ICS doses post CPX and additional therapies were introduced.

Conclusions: In difficult asthma, CPX can confirm that persistent exertional breathlessness is due to asthma but can also identify other contributing factors. Patients with nonpulmonary limitation are prescribed inappropriately high doses of steroid therapy, and CPX can identify the primary mechanism of breathlessness, facilitating steroid reduction.

Changes in muscle sympathetic nerve activity and vascular responses evoked in the spinotrapezius muscle of the rat by systemic hypoxia.

Responses evoked in muscle sympathetic nerve activity (MSNA) by systemic hypoxia have received relatively little attention. Moreover, MSNA is generally identified from firing characteristics in fibres supplying whole limbs: their actual destination is not determined. We aimed to address these limitations by using a novel preparation of spinotrapezius muscle in anaesthetised rats. By using focal recording electrodes, multi-unit and discriminated single unit activity were recorded from the surface of arterial vessels. This had cardiac- and respiratory-related activities expected of MSNA, and was increased by baroreceptor unloading, decreased by baroreceptor stimulation and abolished by autonomic ganglion blockade. Progressive, graded hypoxia (breathing sequentially 12, 10, 8% O2 for 2 min each) evoked graded increases in MSNA. In single units, mean firing frequency increased from 0.2 ± 0.04 in 21% O2 to 0.62 ± 0.14 Hz in 8% O2, while instantaneous frequencies ranged from 0.04–6 Hz in 21% O2 to 0.09–20 Hz in 8% O2. Concomitantly, arterial pressure (ABP), fell and heart rate (HR) and respiratory frequency (RF) increased progressively, while spinotrapezius vascular resistance (SVR) decreased (Spinotrapezius blood flow/ABP), indicating muscle vasodilatation. During 8% O2 for 10 min, the falls in ABP and SVR were maintained, but RF, HR and MSNA waned towards baselines from the second to the tenth minute. Thus, we directly show that MSNA increases during systemic hypoxia to an extent that is mainly determined by the increases in peripheral chemoreceptor stimulation and respiratory drive, but its vasoconstrictor effects on muscle vasculature are largely blunted by local dilator influences, despite high instantaneous frequencies in single fibres.

Using simulation to assess cardiac first-responder schemes exhibiting stochastic and spatial complexities

A Monte-Carlo simulation-based model has been constructed to assess a public health scheme involving mobile-volunteer cardiac First-Responders. The scheme being assessed aims to improve survival of Sudden-Cardiac-Arrest (SCA) patients, through reducing the time until administration of life-saving defibrillation treatment, with volunteers being paged to respond to possible SCA incidents alongside the Emergency Medical Services. The need for a model, for example, to assess the impact of the scheme in different geographical regions, was apparent upon collection of observational trial data (given it exhibited stochastic and spatial complexities). The simulation-based model developed has been validated and then used to assess the scheme's benefits in an alternative rural region (not a part of the original trial). These illustrative results conclude that the scheme may not be the most efficient use of National Health Service resources in this geographical region, thus demonstrating the importance and usefulness of simulation modelling in aiding decision making.

An On Demand Queue Management Architecture for a Programmable Traffic Manager

A queue manager (QM) is a core traffic management (TM) function used to provide per-flow queuing in access andmetro networks; however current designs have limited scalability. An on-demand QM (OD-QM) which is part of a new modular field-programmable gate-array (FPGA)-based TM is presented that dynamically maps active flows to the available physical resources; its scalability is derived from exploiting the observation that there are only a few hundred active flows in a high speed network. Simulations with real traffic show that it is a scalable, cost-effective approach that enhances per-flow queuing performance, thereby allowing per-flow QM without the need for extra external memory at speeds up to 10 Gbps. It utilizes 2.3%–16.3% of a Xilinx XC5VSX50t FPGA and works at 111 MHz.