A simple tool to predict admission at the time of triage

Aim To create and validate a simple clinical score to estimate the probability of admission at the time of triage. Methods This was a multicentre, retrospective, cross-sectional study of triage records for all unscheduled adult attendances in North Glasgow over 2 years. Clinical variables that had significant associations with admission on logistic regression were entered into a mixed-effects multiple logistic model. This provided weightings for the score, which was then simplified and tested on a separate validation group by receiving operator characteristic (ROC) analysis and goodness-of-fit tests. Results 215 231 presentations were used for model derivation and 107 615 for validation. Variables in the final model showing clinically and statistically significant associations with admission were: triage category, age, National Early Warning Score (NEWS), arrival by ambulance, referral source and admission within the last year. The resulting 6-variable score showed excellent admission/discharge discrimination (area under ROC curve 0.8774, 95% CI 0.8752 to 0.8796). Higher scores also predicted early returns for those who were discharged: the odds of subsequent admission within 28 days doubled for every 7-point increase (log odds=+0.0933 per point, p&#60;0.0001). Conclusions This simple, 6-variable score accurately estimates the probability of admission purely from triage information. Most patients could accurately be assigned to ‘admission likely’, ‘admission unlikely’, ‘admission very unlikely’ etc., by setting appropriate cut-offs. This could have uses in patient streaming, bed management and decision support. It also has the potential to control for demographics when comparing performance over time or between departments.</p

Optimal allocation of defibrillator drones in mountainous regions

Responding to emergencies in Alpine terrain is quite challenging as air ambulances and mountain rescue services are often confronted with logistics challenges and adverse weather conditions that extend the response times required to provide life-saving support. Among other medical emergencies, sudden cardiac arrest (SCA) is the most time-sensitive event that requires the quick provision of medical treatment including cardiopulmonary resuscitation and electric shocks by automated external defibrillators (AED). An emerging technology called unmanned aerial vehicles (or drones) is regarded to support mountain rescuers in overcoming the time criticality of these emergencies by reducing the time span between SCA and early defibrillation. A drone that is equipped with a portable AED can fly from a base station to the patient's site where a bystander receives it and starts treatment. This paper considers such a response system and proposes an integer linear program to determine the optimal allocation of drone base stations in a given geographical region. In detail, the developed model follows the objectives to minimize the number of used drones and to minimize the average travel times of defibrillator drones responding to SCA patients. In an example of application, under consideration of historical helicopter response times, the authors test the developed model and demonstrate the capability of drones to speed up the delivery of AEDs to SCA patients. Results indicate that time spans between SCA and early defibrillation can be reduced by the optimal allocation of drone base stations in a given geographical region, thus increasing the survival rate of SCA patients

Modelling and predicting patient recruitment in multi-centre clinical trials

One of the main concerns in multi-centre clinical trials is how to enrol an adequate number of patients during a specific period of time. Accordingly, the sponsors are keen to minimise the recruitment time for cost effectiveness purposes. This research tended to concentrate on forecasting the patients’ accrual time for the pre-arranged number of sample size by simulating an on-going trial. The method was to model the data from the recruitment frequency domain and apply the estimations derived from the frequency domain to predict the time domain. Whereas previous papers did not concentrate on variations of recruiting over centres, this research assumed that patient arrivals followed the Poisson process and let the parameter of the process vary as a Gamma distribution. Consequently, the Poisson-gamma mixed distribution was confirmed as the promising model of the frequency domain. Then with the help of the relationship between the Poisson process and the exponential distribution, accrual time was predicted assuming that the waiting time between patients followed the Gamma-exponential distribution. As the result of the project, a trial was simulated based on the estimated values derived from completed trials. The first part of the prediction estimated the expected average number of patients per centre per month in an on-going trial. The second part, predicted the length of time (in months) to enrol specific number of patients in the simulated trial

A decision support system for demand and capacity modelling of an accident and emergency department

© 2019 Operational Research Society.Accident and emergency (A&E) departments in England have been struggling against severe capacity constraints. In addition, A&E demands have been increasing year on year. In this study, our aim was to develop a decision support system combining discrete event simulation and comparative forecasting techniques for the better management of the Princess Alexandra Hospital in England. We used the national hospital episodes statistics data-set including period April, 2009 – January, 2013. Two demand conditions are considered: the expected demand condition is based on A&E demands estimated by comparing forecasting methods, and the unexpected demand is based on the closure of a nearby A&E department due to budgeting constraints. We developed a discrete event simulation model to measure a number of key performance metrics. This paper presents a crucial study which will enable service managers and directors of hospitals to foresee their activities in future and form a strategic plan well in advance.Peer reviewe

Faecal leukocyte esterase activity is an alternative biomarker in inflammatory bowel disease

Background: Leukocyte cytosolic proteins (e.g., calprotectin) are emerging biomarkers for inflammatory bowel disease. Leukocyte aryl esterase activity has been commonly used for sensitive detection of leukocytes in human body fluids such as urine. Urine test strip results are generally reported in categories. As automated strip readers allow quantitative data to be reported, sensitive quantitative detection of leukocytes in body fluids has become possible. Here, we explored the use of leukocyte esterase as a potential alternative faecal biomarker for inflammatory bowel disease. Methods: We evaluated leukocyte esterase activity in faecal extracts and compared Cobas u 411 (Roche) quantitative reflectance data with calprotectin concentration for 107 routine samples. Stability of leukocyte esterase for trypsin digestion was carried out by adding trypsin to the extract. Incubation occurred at 37 ° C for 24 h or 48 h. Results: Reproducibility of the reflectance signal was good (within-run imprecision: 6.1%; between-run imprecision: 6.2%). Results were linear in the range 10 3 – 10 6 WBC/100 mg faeces. The lower limit of detection was 4 WBC/ μ L and the lower limit of quantification was 5 WBC/ μ L. Stability of LE activity in stool and faecal matrix was good. An adequate correlation was obtained between leukocyte esterase activity and the faecal calprotectin concentration: log(y)  =  4.28 + 0.29log(x). In vitro experiments monitored the digestion of leukocyte esterase and faecal calprotectin. Leukocyte esterase activity was significantly less affected by trypsin activity than calprotectin immunoreactivity. Conclusions: Quantitative leukocyte esterase activity of faecal extracts provides information about the leukocyte count in the gut lumen. Leukocyte esterase is a promising and affordable alternative biomarker for monitoring inflammatory bowel disease

Stroke patients admitted within normal working hours are more likely to achieve process standards and to have better outcomes

Acknowledgements The authors are grateful to David Murphy of the SSCA for providing data and to Lynsey Waugh of ISD Scotland for linking the SSCA data with General Register Office data. The authors also acknowledge the help of all who enter data into SSCA. Funding This study was funded by Chest, Heart and Stroke Scotland (Grant no R14/A156). The SSCA is funded by NHS Scotland via ISD.Peer reviewedPublisher PD