Jason Imperato

The Effect of an Ambulance Diversion Ban on Emergency Department Length of Stay and Ambulance Turnaround Time

STUDY OBJECTIVE Massachusetts became the first state in the nation to ban ambulance diversion in 2009. It was feared that the diversion ban would lead to increased emergency department (ED) crowding and ambulance turnaround time. We seek to characterize the effect of a statewide ambulance diversion ban on ED length of stay and ambulance turnaround time at Boston-area EDs. METHODS We conducted a retrospective, pre-post observational analysis of 9 Boston-area hospital EDs before and after the ban. We used ED length of stay as a proxy for ED crowding. We compared hospitals individually and in aggregate to determine any changes in ED length of stay for admitted and discharged patients, ED volume, and turnaround time. RESULTS No ED experienced an increase in ED length of stay for admitted or discharged patients or ambulance turnaround time despite an increase in volume for several EDs. There was an overall 3.6% increase in ED volume in our sample, a 10.4-minute decrease in length of stay for admitted patients, and a 2.2-minute decrease in turnaround time. When we compared high- and low-diverting EDs separately, neither saw an increase in length of stay, and both saw a decrease in turnaround time. CONCLUSION After the first statewide ambulance diversion ban, there was no increase in ED length of stay or ambulance turnaround time at 9 Boston-area EDs. Several hospitals actually experienced improvements in these outcome measures. Our results suggest that the ban did not worsen ED crowding or ambulance availability at Boston-area hospitals.

Acute Coronary Syndrome Screening and Diagnostic Practice Variation

BACKGROUND In the absence of the existing acute coronary syndrome (ACS) guidelines directing the clinical practice implementation of emergency department (ED) screening and diagnosis, there is variable screening and diagnostic clinical practice across ED facilities. This practice diversity may be warranted. Understanding the variability may identify opportunities for more consistent practice. METHODS This is a cross-sectional clinical practice epidemiology study with the ED as the unit of analysis characterizing variability in the ACS evaluation across 62 diverse EDs. We explored three domains of screening and diagnostic practice: 1) variability in criteria used by EDs to identify patients for an early electrocardiogram (ECG) to diagnose ST-elevation myocardial infarction (STEMI), 2) nonuniform troponin biomarker and formalized pre-troponin risk stratification use for the diagnosis of non-ST-elevation myocardial infarction (NSTEMI), and 3) variation in the use of noninvasive testing (NIVT) to identify obstructive coronary artery disease or detect inducible ischemia. RESULTS We found that 85% of EDs utilize a formal triage protocol to screen patients for an early ECG to diagnose STEMI. Of these, 17% use chest pain as the sole criteria. For the diagnosis of NSTEMI, 58% use intervals ≥4 hours for a second troponin and 34% routinely risk stratify before troponin testing. For the diagnosis of noninfarction ischemia, the median percentage of patients who have NIVT performed during their ED visit is 5%. The median percentage of patients referred for NIVT in hospital (observation or admission) is 61%. Coronary CT angiography is used in 66% of EDs. Exercise treadmill testing is the most frequently reported first-line NIVT (42%). CONCLUSION Our results suggest highly variable ACS screening and clinical practice.

Using markedly abnormal vital signs in the emergency department to anticipate needs for intensive care unit admission

Objective: To assess the utility and relative strength of markedly abnormal vital signs thresholds (triggers) in prediction of the needs for intensive care unit (ICU) admission from the emergency department (ED). Methods: A retrospective cohort study performed in a 37 000 annual visit, urban, community teaching ED. All adult patient encounters from July 10, 2011 to July 9, 2013 were eligible for inclusion. The primary outcome was ICU admission from the ED. We collected the incidence of trigger vital signs (heart rate>130 bpm, heart rate<40 bpm, respiratory rate>30 breaths per minute, respiratory rate< breaths 8 per minute, oxygen saturation<90%, systolic blood pressure<90 mmHg) as binary variables for each patient enrolled. Univariate and multi-variable logistic regression models were created to determine the ability of the trigger vital signs to predict ICU admission. Results: Total of 68 554 patient encounters were included in the analysis. Of these, 2 355 [3.4%, 95% confidence intervals (CI) 3.3%-3.6%] patients exhibited trigger vital signs, and 1 135 (1.7%, 95% CI 1.6%-1.8%) patients were admitted to ICU. All trigger vital signs were strongly associated with admission to the ICU and demonstrated higher odds of ICU admission with HR<40 (odds ratio 5.2, with 95% CI 2.7-10.1) being the best predictor among the studied covariates. The likelihood of ICU admission increased in a linear fashion with the number of trigger vital signs exhibited. Conclusions: Trigger vital signs serve as predictors that an ED patient may need admission to the ICU and may serve as a tool to expedite disposition of these resource-intensive patients.