The ability to predict patient visits to emergency departments (ED) is crucial for designing strategies aimed at avoiding overcrowding. A good working knowledge of the mathematical models used to predict patient volume and of their results is therefore essential. Articles retrieved by a Medline search were reviewed for studies designed to predict patient attendance at ED or walk-in clinics. Nine studies were identified. Most of the models used to predict patient volume were either linear regression models including calendar variables or time series models. These models explained 31-75% of patient-volume variability. Although the day of the week had the strongest effect, this variable explained only part of the variability. Other causes of this variability are to be defined. However, the performance of the models was good, with errors ranging from 4.2% to 14.4%. Adding meteorological data failed to improve model performance. The mathematical methods developed to predict ED visits have a low rate of error, but the prediction of daily patient visits should be used carefully and therefore does not allow day-to-day adjustments of staff. ED directors or managers should be aware of the model limitations. These models should certainly be used on a larger scale to assess future needs.

clinicaltrials.gov Identifier: NCT02375919.

The combination of single oral doses of an angiotensin I-converting enzyme inhibitor (captopril) and a type 1 angiotensin II receptor antagonist (losartan) has additive effects on blood pressure fall and renin release in sodium-depleted normotensive subjects. We planned the present study to determine whether the magnitude of the hemodynamic and hormonal consequences of renin-angiotensin system blockade by such a combination is larger than that obtained by doubling the dose of the angiotensin-converting enzyme inhibitor given alone. In a single-dose, double-blind, randomized, three-way crossover study, 10 mg enalapril, 20 mg enalapril, and the combination of 50 mg losartan and 10 mg enalapril were administered orally to 12 sodium-depleted normotensive subjects. The area under the time curve from 0 to 24 hours (AUC0-24) of the mean blood pressure fall after losartan-enalapril combination intake (-220 +/- 91 mm Hg.h) was significantly greater than that of either 10 or 20 mg enalapril (-124 +/- 91 and -149 +/- 85 mm Hg.h, respectively, P < .05 vs both doses). The combination significantly increased by 2.3 +/- 1.2-fold the AUC0-24 of plasma active renin compared with either 10 or 20 mg enalapril given alone (P < .05) but had no additive effect on plasma aldosterone fall. The losartan-enalapril combination is more effective in decreasing blood pressure and increasing plasma active renin than doubling of the enalapril dose.

Whereas ED-LOS and EDOU-LOS seem to be directly related to patients' acuity and complexity, notably the need for diagnostic and therapeutic interventions, only EDOU-LOS was significantly associated with age and proposed care pathways. We propose that EDOU-LOS measurement should be made in EDs with an OU.

Objectives: This study investigated whether mathematical models using calendar variables could identify the determinants of emergency department (ED) census over time in geographically close EDs and assessed the performance of long-term forecasts.Methods: Daily visits in four EDs at academic hospitals in the Paris area were collected from 2004 to 2007. First, a general linear model (GLM) based on calendar variables was used to assess two consecutive periods of 2 years each to create and test the mathematical models. Second, 2007 ED attendance was forecasted, based on a training set of data from 2004 to 2006. These analyses were performed on data sets from each individual ED and in a virtual mega ED, grouping all of the visits. Models and forecast accuracy were evaluated by mean absolute percentage error (MAPE). Results:The authors recorded 299,743 and 322,510 ED visits for the two periods, 2004-2005 and 2006-2007, respectively. The models accounted for up to 50% of the variations with a MAPE less than 10%. Visit patterns according to weekdays and holidays were different from one hospital to another, without seasonality. Influential factors changed over time within one ED, reducing the accuracy of forecasts. Forecasts led to a MAPE of 5.3% for the four EDs together and from 8.1% to 17.0% for each hospital.Conclusions: Unexpectedly, in geographically close EDs over short periods of time, calendar determinants of attendance were different. In our setting, models and forecasts are more valuable to predict the combined ED attendance of several hospitals. In similar settings where resources are shared between facilities, these mathematical models could be a valuable tool to anticipate staff needs and site allocation.ACADEMIC EMERGENCY MEDICINE 2010; 17:970-978 ª