Influence of Weekday and Seasonal Trends on Urgency and In-hospital Mortality of Emergency Department Patients

Multimorbidity and adverse outcomes following emergency department attendance: population based cohort study

ABSTRACT

Objectives

To describe the effect of multimorbidity on adverse patient centred outcomes in people attending emergency department.

Design

Population based cohort study.

Setting

Emergency departments in NHS Lothian in Scotland, from 1 January 2012 to 31 December 2019.

Participants

Adults (≥18 years) attending emergency departments.

Data sources

Linked data from emergency departments, hospital discharges, and cancer registries, and national mortality data.

Main outcome measures

Multimorbidity was defined as at least two conditions from the Elixhauser comorbidity index. Multivariable logistic or linear regression was used to assess associations of multimorbidity with 30 day mortality (primary outcome), hospital admission, reattendance at the emergency department within seven days, and time spent in emergency department (secondary outcomes). Primary analysis was stratified by age (<65 v ≥65 years).

Results

451 291 people had 1 273 937 attendances to emergency departments during the study period. 43 504 (9.6%) had multimorbidity, and people with multimorbidity were older (median 73 v 43 years), more likely to arrive by emergency ambulance (57.8% v 23.7%), and more likely to be triaged as very urgent (23.5% v 9.2%) than people who do not have multimorbidity. After adjusting for other prognostic covariates, multimorbidity, compared with no multimorbidity, was associated with higher 30 day mortality (8.2% v 1.2%, adjusted odds ratio 1.81 (95% confidence interval (CI) 1.72 to 1.91)), higher rate of hospital admission (60.1% v 20.5%, 1.81 (1.76 to 1.86)), higher reattendance to an emergency department within seven days (7.8% v 3.5%, 1.41 (1.32 to 1.50)), and longer time spent in the department (adjusted coefficient 0.27 h (95% CI 0.26 to 0.27)). The size of associations between multimorbidity and all outcomes were larger in younger patients: for example, the adjusted odds ratio of 30 day mortality was 3.03 (95% CI 2.68 to 3.42) in people younger than 65 years versus 1.61 (95% CI 1.53 to 1.71) in those 65 years or older.

Conclusions

Almost one in ten patients presenting to emergency department had multimorbidity using Elixhauser index conditions. Multimorbidity was strongly associated with adverse outcomes and these associations were stronger in younger people. The increasing prevalence of multimorbidity in the population is likely to exacerbate strain on emergency departments unless practice and policy evolve to meet the growing demand.

Emergency Department Use among Patients with Mental Health Problems: Profiles, Correlates, and Outcomes

Patients with mental health (MH) problems are known to use emergency departments (EDs) frequently. This study identified profiles of ED users and associated these profiles with patient characteristics and outpatient service use, and with subsequent adverse outcomes. A 5-year cohort of 11,682 ED users was investigated (2012-2017), using Quebec (Canada) administrative databases. ED user profiles were identified through latent class analysis, and multinomial logistic regression used to associate patients' characteristics and their outpatient service use. Cox regressions were conducted to assess adverse outcomes 12 months after the last ED use. Four ED user profiles were identified: "Patients mostly using EDs for accessing MH services" (Profile 1, incident MDs); "Repeat ED users" (Profile 2); "High ED users" (Profile 3); "Very high and recurrent high ED users" (Profile 4). Profile 4 and 3 patients exhibited the highest ED use along with severe conditions yet received the most outpatient care. The risk of hospitalization and death was higher in these profiles. Their frequent ED use and adverse outcomes might stem from unmet needs and suboptimal care. Assertive community treatments and intensive case management could be recommended for Profiles 4 and 3, and more extensive team-based GP care for Profiles 2 and 1.

Forecasting emergency department arrivals using INGARCH models

BACKGROUND

Forecasting patient arrivals to hospital emergency departments is critical to dealing with surges and to efficient planning, management and functioning of hospital emerency departments.

OBJECTIVE

We explore whether past mean values and past observations are useful to forecast daily patient arrivals in an Emergency Department.

MATERIAL AND METHODS

We examine whether an integer-valued generalized autoregressive conditional heteroscedastic (INGARCH) model can yield a better conditional distribution fit and forecast of patient arrivals by using past arrival information and taking into account the dynamics of the volatility of arrivals.

RESULTS

We document that INGARCH models improve both in-sample and out-of-sample forecasts, particularly in the lower and upper quantiles of the distribution of arrivals.

CONCLUSION

Our results suggest that INGARCH modelling is a useful model for short-term and tactical emergency department planning, e.g., to assign rotas or locate staff for unexpected surges in patient arrivals.

Investigating Weekend Effect in the Management of Upper and Lower Extremity Degloving Injuries

Background

Weekend presentation has been associated with adverse outcomes in emergent conditions, including stroke, myocardial infarction, and critical limb ischemia. We examine whether a weekend effect exists in the management of and outcomes after extremity degloving injuries.

Methods

The cohort included adults presenting with open extremity degloving injuries to a tertiary level one trauma center between June 2018 and May 2022. We collected demographics, comorbidities, injury information, interventions, and complications. Propensity score weighting was used to minimize confounding differences between those presenting on weekends (Sat-Sun) versus weekdays (Mon-Fri). Weighted regressions were used to examine differences in interventions by day of presentation. Multivariable weighted regressions accounting for differences in interventions received were used to examine whether weekend presentation was associated with amputation risk, complications, or functional deficits.

Results

Ninety-five patients with 100 open extremity degloving injuries were included. In total, 39% of injuries were weekend-presenting. There was a higher rate of noninsulin-dependent diabetes among patients presenting on weekends (P = 0.03). Weekend-presenting injuries had higher median Injury Severity Scores (P = 0.04). Propensity-weighted regression analysis revealed differences in interventions received on weekends, including lower rates of pedicled and free flaps and bone graft, and increased rates of negative-pressure wound therapy (P ≤ 0.02). Multivariable regression analysis revealed weekend presentation was a significant independent risk factor for amputation of the affected extremity [odds ratio 2.27, 95% CI (1.01-5.33), P = 0.05].

Conclusion

Weekend presentation may impact interventions received and amputation risk in patients presenting with open extremity degloving injuries.

Forecasting Hospital Visits Due to Influenza Based on Emergency Department Visits for Fever: A Feasibility Study on Emergency Department-Based Syndromic Surveillance

This study evaluated the use of chief complaint data from emergency departments (EDs) to detect the increment of influenza cases identified from the nationwide medical service usage and developed a forecast model to predict the number of patients with influenza using the daily number of ED visits due to fever. The National Health Insurance Service (NHIS) and the National Emergency Department Information System (NEDIS) databases from 2015 to 2019 were used. The definition of fever included having an initial body temperature ≥ 38.0 °C at an ED department or having a report of fever as a patient's chief complaint. The moving average number of visits to the ED due to fever for the previous seven days was used. Patients in the NHIS with the International Classification of Diseases-10 codes of J09, J10, or J11 were classified as influenza cases, with a window duration of 100 days, assuming the claims were from the same season. We developed a forecast model according to an autoregressive integrated moving average (ARIMA) method using the data from 2015 to 2017 and validated it using the data from 2018 to 2019. Of the 29,142,229 ED visits from 2015 to 2019, 39.9% reported either a fever as a chief complaint or a ≥38.0 °C initial body temperature at the ED. ARIMA (1,1,1) (0,0,1)₇ was the most appropriate model for predicting ED visits due to fever. The mean absolute percentage error (MAPE) value showed the prediction accuracy of the model. The correlation coefficient between the number of ED visits and the number of patients with influenza in the NHIS up to 14 days before the forecast, with the exceptions of the eighth, ninth, and twelfth days, was higher than 0.70 (p-value = 0.001). ED-based syndromic surveillances of fever were feasible for the early detection of hospital visits due to influenza.