Mechanisms of stochastic onset and termination of atrial fibrillation studied with a cellular automaton model.

Mathematical models of cardiac electrical excitation are increasingly complex, with multiscale models seeking to represent and bridge physiological behaviours across temporal and spatial scales. The increasing complexity of these models makes it computationally expensive to both evaluate long term (more than 60 s) behaviour and determine sensitivity of model outputs to inputs. This is particularly relevant in models of atrial fibrillation (AF), where individual episodes last from seconds to days, and interepisode waiting times can be minutes to months. Potential mechanisms of transition between sinus rhythm and AF have been identified but are not well understood, and it is difficult to simulate AF for long periods of time using state-of-the-art models. In this study, we implemented a Moe-type cellular automaton on a novel, topologically equivalent surface geometry of the left atrium. We used the model to simulate stochastic initiation and spontaneous termination of AF, arising from bursts of spontaneous activation near pulmonary veins. The simplified representation of atrial electrical activity reduced computational cost, and so permitted us to investigate AF mechanisms in a probabilistic setting. We computed large numbers (approx. 105) of sample paths of the model, to infer stochastic initiation and termination rates of AF episodes using different model parameters. By generating statistical distributions of model outputs, we demonstrated how to propagate uncertainties of inputs within our microscopic level model up to a macroscopic level. Lastly, we investigated spontaneous termination in the model and found a complex dependence on its past AF trajectory, the mechanism of which merits future investigation.

Performing or not performing: what's in a target?

This paper analyses how providers have coped with the 4-hour target over the past 7 years. To do this, we used publicly available data from NHS Digital to track how long patients remain in accident and emergency (A&E) departments and their 'attendance disposal method'. Using this tool, we compared two A&E departments with similar arrival patterns and age profiles and that perform equally well against the target in a specific year. However, these hospitals exhibit very different underlying behaviour. Over 7 years, both exhibit a general increase in length of stay, increasing number of patients being admitted in the 20 minutes preceding the 4-hour target, and rising numbers of patients that breach the target. Despite the two hospitals having similar input profiles there is a 12 percentage point difference in the number of patients who leave the A&E department in the last 20 minutes. This operational information is not visible simply by monitoring the single existing metric. We conclude that the 4-hour target in isolation is an inadequate measure and we reflect on the difference between selecting measures for policy-level review, and for operational management. A link to download the graphs for each A&E in England is available.

A Stochastic Individual-Based Model of the Progression of Atrial Fibrillation in Individuals and Populations.

Models that represent the mechanisms that initiate and sustain atrial fibrillation (AF) in the heart are computationally expensive to simulate and therefore only capture short time scales of a few heart beats. It is therefore difficult to embed biophysical mechanisms into both policy-level disease models, which consider populations of patients over multiple decades, and guidelines that recommend treatment strategies for patients. The aim of this study is to link these modelling paradigms using a stylised population-level model that both represents AF progression over a long time-scale and retains a description of biophysical mechanisms. We develop a non-Markovian binary switching model incorporating three different aspects of AF progression: genetic disposition, disease/age related remodelling, and AF-related remodelling. This approach allows us to simulate individual AF episodes as well as the natural progression of AF in patients over a period of decades. Model parameters are derived, where possible, from the literature, and the model development has highlighted a need for quantitative data that describe the progression of AF in population of patients. The model produces time series data of AF episodes over the lifetimes of simulated patients. These are analysed to quantitatively describe progression of AF in terms of several underlying parameters. Overall, the model has potential to link mechanisms of AF to progression, and to be used as a tool to study clinical markers of AF or as training data for AF classification algorithms.

Simulating the use of re-prioritisation as a wait-reduction strategy in an emergency department.

BACKGROUND/AIM: Simulation modelling has proven a useful approach for capturing the dynamic nature of emergency departments (EDs) and informing improvements to clinical and operational processes alike. However, few models have simulated the impact of the UK Department of Health's 4 h operational standard, which arguably has placed pressure to improve standards and performance, promoting the use of wait-reduction strategies to cope with the target in practice. The aim of this study was to determine the impact a re-prioritisation strategy has on the 4 h target by simulating the operation of an ED using a model that represents the flow of patients through the department. METHODS: This study was based on a district general hospital in West London. To ascertain patients' length of stay, the hospital's historical records and staff rotas were used to obtain data on activities, timeframes and resources on three separate representative weeks and included all patients' arrival time, mode of arrival, whether the patient was referred to minors, majors, paediatrics or the resuscitation unit, and whether the patient was admitted or discharged, and at what time. RESULTS: The close correlation (r=0.98) in distributions between actual length of stay and simulated length of stay demonstrates that the model of the ED accurately replicates the 4 h peak caused by the use of re-prioritisation. CONCLUSION: The model accurately reproduced the use of a dominant wait-reduction strategy to identify patients approaching the breach and re-prioritise them to expedite treatment and remove them from the department by the 4 h target.

Meeting the four-hour deadline in an A&E department.

PURPOSE: Accident and emergency (A&E) departments experience a secondary peak in patient length of stay (LoS) at around four hours, caused by the coping strategies used to meet the operational standards imposed by government. The aim of this paper is to build a discrete-event simulation model that captures the coping strategies and more accurately reflects the processes that occur within an A&E department. DESIGN/METHODOLOGY/APPROACH: A discrete-event simulation (DES) model was used to capture the A&E process at a UK hospital and record the LoS for each patient. Input data on 4,150 arrivals over three one-week periods and staffing levels was obtained from hospital records, while output data were compared with the corresponding records. Expert opinion was used to generate the pathways and model the decision-making processes. FINDINGS: The authors were able to replicate accurately the LoS distribution for the hospital. The model was then applied to a second configuration that had been trialled there; again, the results also reflected the experiences of the hospital. PRACTICAL IMPLICATIONS: This demonstrates that the coping strategies, such as re-prioritising patients based on current length of time in the department, employed in A&E departments have an impact on LoS of patients and therefore need to be considered when building predictive models if confidence in the results is to be justified. ORIGINALITY/VALUE: As far as the authors are aware this is the first time that these coping strategies have been included within a simulation model, and therefore the first time that the peak around the four hours has been analysed so accurately using a model.

Evaluating telemedicine: a focus on patient pathways.

Evaluations of telemedicine have sought to assess various measures of effectiveness (e.g., diagnostic accuracy), efficiency (e.g., cost), and engagement (e.g., patient satisfaction) to determine its success. Few studies, however, have looked at evaluating the organizational impact of telemedicine, which involves technology and process changes that affect the way that it is used and accepted by patients and clinicians alike. This study reviews and discusses the conceptual issues in telemedicine research and proposes a fresh approach for evaluating telemedicine. First, we advance a patient pathway perspective, as most of the existing studies view telemedicine as a support to a singular rather than multiple aspects of a health care process. Second, to conceptualize patient pathways and understand how telemedicine impacts upon them, we propose simulation as a tool to enhance understanding of the traditional and telemedicine patient pathway.