Assessment of patient mobility improves the risk stratification of triage with the Emergency Severity Index: a prospective cohort study

Predictive value and interrater reliability of mental status and mobility assessment in the emergency department

AIM

To investigate the predictive value of both mental status, assessed with the AVPUC (Alert, responds to Voice, responds to Pain, Unresponsive, and new Confusion) scale, and mobility assessments, and their interrater reliability (IRR) between triage clinicians and a research team.

METHOD

Prospective study of consecutive patients who presented to an ED. Mental status and mobility were assessed by triage clinicians and by a dedicated research team.

RESULTS

4,191 patients were included. After adjustment for age and sex, patients with altered mental status have an odds ratio of 6.55 [4.09-10.24] to be admitted in the ICU and an odds ratio of 21.16 [12.06-37.01] to die within 30 days; patients with impaired mobility have an odds ratio of 7.08 [4.60-11.12] to be admitted in the ICU and an odds ratio of 12.87 [5.93-32.30] to die within 30 days. The kappa coefficient between triage clinicians and the research team for mental status assessment was 0.75, and 0.80 for mobility.

CONCLUSION

Assessment of mental status by the AVPUC scale, and mobility by a simple dichotomous scale are suitable for ED triage. Both altered mental status and impaired mobility are associated with adverse outcomes. Mental status and mobility assessment have good interrater reliability.

Throughput delays: causes, predictors, and outcomes - observational cohort in a Swiss emergency department

BACKGROUND

Optimal throughput times in emergency departments can be adjudicated by emergency physicians. Emergency physicians can also define causes of delays during work-up, such as waiting for imaging, clinical chemistry, consultations, or exit blocks. For adequate streaming, the identification of predictors of delays is important, as the attribution of resources depends on acuity, resources, and expected throughput times.

OBJECTIVE

This observational study aimed to identify the causes, predictors, and outcomes of emergency physician-adjudicated throughput delays.

METHODS

Two prospective emergency department cohorts from January to February 2017 and from March to May 2019 around the clock in a tertiary care centre in Switzerland were investigated. All consenting patients were included. Delay was defined as the subjective adjudication of the responsible emergency physician regarding delay during emergency department work-up. Emergency physicians were interviewed for the occurrence and cause of delays. Baseline demographics, predictor values, and outcomes were recorded. The primary outcome - delay - was presented using descriptive statistics. Univariable and multivariable logistic regression analyses were performed to assess the associations between possible predictors and delays and hospitalization, intensive care, and death with delay.

RESULTS

In 3656 (37.3%) of 9818 patients, delays were adjudicated. The patients with delays were older (59 years, interquartile range [IQR]: 39-76 years vs 49 years, IQR: 33-68 years) and more likely had impaired mobility, nonspecific complaints (weakness or fatigue), and frailty than the patients without delays. The main causes of delays were resident work-up (20.4%), consultations (20.2%), and imaging (19.4%). The predictors of delays were an Emergency Severity Index of 2 or 3 at triage (odds ratio [OR]: 3.00; confidence interval [CI]: 2.21-4.16; OR: 3.25; CI: 2.40-4.48), nonspecific complaints (OR: 1.70; CI: 1.41-2.04), and consultation and imaging (OR: 2.89; CI: 2.62-3.19). The patients with delays had an increased risk for admission (OR: 1.56; CI: 1.41-1.73) but not for mortality than those without delays.

CONCLUSION

At triage, simple predictors such as age, immobility, nonspecific complaints, and frailty may help to identify patients at risk of delay, with the main reasons being resident work-up, imaging, and consultations. This hypothesis-generating observation will allow the design of studies aimed at the identification and elimination of possible throughput obstacles.