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ORIGINAL RESEARCH

Association between emergency departmentlength of stay and outcome of patientsadmitted either to a ward, intensivecare or high dependency unitArthas Flabouris,1,2 Jellsingh Jeyadoss,2,3 John Field4,5 and Tom Soulsby2,6

1Intensive Care, Royal Adelaide Hospital, Adelaide, South Australia, Australia, 2Discipline of Acute Care,School of Medicine, University of Adelaide, Adelaide, South Australia, Australia, 3Department ofAnaesthesia, The Queen Elizabeth Hospital, Adelaide, South Australia, Australia, 4Faculty of HealthScience, University of Adelaide, Adelaide, South Australia, Australia, 5Basil Hetzel Institute, Adelaide,South Australia, Australia, and 6Emergency Department, Royal Adelaide Hospital, Adelaide, SouthAustralia, Australia

Abstract

Objective: To evaluate the association of ED length of stay (EDLOS) and outcome of patients admittedto a ward, intensive care (ICU) or stepdown (high dependency) unit (SDU).

Methods: Design: Retrospective cohort study using linked administrative and clinical data. Setting:650-bed, university-affiliated, tertiary referral hospital, whose ED has approximately60 000 patient presentations per annum. Participants: Adult patients admitted via the ED,to a ward (ED to ward), ICU (ED to ICU) or SDU (ED to SDU), and whose EDLOS was <24 h.Outcome measures: Hospital outcome and LOS.

Results: A total of 43 484 patients over 4 years. Median EDLOS was 2:36 h for ICU, 5:07 h for SDUand 7:19 h for ward (P < 0.01) patients. EDLOS differed significantly, based on hospitaloutcome, for ward (alive, 7:18 h vs died, 7:44 h, P < 0.001), but not SDU or ICU patients. Atan EDLOS of 4 and 8 h, 19.4% and 5.2% of ICU, 52.1% and 15.5% of SDU and 77.9% and32.6% of ward patients remained in the ED. EDLOS was not a significant predictor ofdeath, in comparison with increasing age and admitting unit across all three groups, andhigher triage acuity for ED to ward and ED to ICU.

Conclusions: EDLOS was greater for ED to ward patients, and of the ED to ward patients who died. Atan EDLOS of 4 h there were fewer ICU, in comparison with ward, patients remaining in theED. Future studies that report on EDLOS should differentiate for patients admitted fromthe ED to the ward, ICU or SDU.

Key words: emergency department, intensive care unit, length of stay, mortality.

Correspondence: Associate Professor Arthas Flabouris, Intensive Care, Royal Adelaide Hospital, North Terrace, Adelaide, SA 5000,Australia. Email: [email protected]

Arthas Flabouris, MD, FCICM, FANZCA, Staff Specialist; Jellsingh Jeyadoss, FANZCA, EDIC, Consultant; John Field, PhD, AStat, Researcher;Tom Soulsby, FACEM, Consultant.

doi: 10.1111/1742-6723.12021Emergency Medicine Australasia (2013) 25, 46–54

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© 2012 The AuthorsEMA © 2012 Australasian College for Emergency Medicine and Australasian Society for Emergency Medicine

Introduction

Outcome of critically ill patients is time- and process-dependent.1–3 The duration and interventions beforeintensive care unit (ICU) admission influence patientoutcome.4–6 The ED is a frequent source of patientsadmitted to the ICU,7 and this group of patients mightface a prolonged stay in the ED because of the perform-ance of complicated medical procedures.8

The Australasian College for Emergency Medicine’sdefinition of ED access block incorporates an ED lengthof stay (EDLOS) of greater than 8 h.9 Access block andovercrowding are associated with patient harm, includ-ing a 20–30% increase in mortality, increased inpatientLOS, medical errors and delays in therapy.10–19 Theseassociations have been identified in studies that haveused fixed cut-off values for EDLOS and among mixedpopulations of ED patients, including combined adultand paediatric,10,11 pooled data from three adult hos-pitals,12 and included admitted patients who thenremained physically within the ED.17

Studies of EDLOS specific to patients admitted to anICU have revealed mixed results.4,20–23 An EDLOS ofgreater than 24 h, compared with less than 24 h, didnot alter hospital outcome.20 In contrast, an EDLOS ofgreater than 6 h was associated with a higher ICU andhospital mortality.23 Similarly, studies have shownEDLOS to be an independent predictor of ICU mor-tality, in particular for patients with sepsis,24,25 whereasother studies have found no adverse association withEDLOS.21,22

Emergency Department access block, overcrowdingand the consequential increased EDLOS12 reflectbroader health system issues, such as reduced inpatientbed numbers, high hospital occupancy and increased EDpresentations.26 Thus, local quality and resource factorsmight influence the generalisability, and the comparisonof studies that relate EDLOS to patient outcome. Forexample, in the Australian context22 EDLOS for patientsadmitted to the ICU appears to be shorter than the 6 or24 h periods used in the American context.20

The association of EDLOS with outcomes for patientsadmitted to a ward, in contrast to those admitted to theICU or stepdown (high dependency) unit (SDU), withinthe same health setting, has not been explored, nor hasthe use of EDLOS as a continuous measure. Doing thismight better describe the specificity, direction and mag-nitude of effect of EDLOS on patients admitted from theED.

The aim of this study was to compare and contrastthe association between EDLOS and outcome (hospital

outcome, ICU and hospital stay) of patients admittedfrom the ED directly to a ward, an ICU or SDU, usingEDLOS as a continuous measure and at the predefinedcut-off value of 4 (4 h rule) and 8 (access block) hours.

Methods

Setting

The Royal Adelaide Hospital, Adelaide, Australia, a650-bed, university-affiliated, tertiary referral centre.The ED has approximately 60 000 patient presentationsper annum.

Ethics

The Royal Adelaide Hospital Human Research Ethicscommittee approved this study.

Inclusion criteria

Patients with all of the following:• Age >15 years• Admitted from the ED directly to a ward, the ICU or

SDU• EDLOS less than 24 h

Exclusion criteria

Patients with any of the following:• Death while in the ED• Admitted from the ED directly to the operating

theatre, coronary care unit, burns unit or sent home• Admission to the ED short stay ward, including those

subsequently admitted to a ward, ICU or SDU• ED source of admission was another hospital

Outcomes

Hospital outcome (dead or alive), ICU and hospital LOS.

Design

Retrospective cohort study using linkage of administra-tive data, the ED Information System and ICU database(AORTIC). Patients were linked based on all of the fol-lowing identifiers: medical record number, age, sex, dateof hospital admission, and, in addition, for ICU patients:date of ICU admission, source of ICU admission.

ED stay and outcome for ward, ICU and HDU patients

47© 2012 The AuthorsEMA © 2012 Australasian College for Emergency Medicine and Australasian Society for Emergency Medicine

The final dataset included the following variables:patient and illness demographics, mode of arrival to theED, ED arrival and departure date and times, Australa-sian Triage Scale (ATS),27 ED discharge location, ICUand hospital arrival and departure dates, times andoutcomes.

Data analysis and statistics

Patients were divided into the following categories,based on discharge location from the ED:• Ward (direct admission from the ED to a ward)• ICU (direct admission from the ED to the ICU)• SDU (direct admission from the ED to the SDU)

Statistical analysis involved comparison of mediansusing two-sample Kolmogorov–Smirnov and permuta-tion tests. Logistic regression analysis was used toexamine the various ED and demographic factors affect-ing mortality. Non-significant variables in the regres-sion, except EDLOS, were eliminated using backwardsstepwise regression with an Akaike information crite-rion.28 Goodness-of-fit of the final models was testedusing likelihood ratio tests. In-hospital outcome wasassessed using EDLOS with the cut-off of 8 h. Analyseswere carried out using the R statistical language (RFoundation for Statistical Computing, Vienna, Austria,2011).

Results

Patients

From January 2004 to December 2007, there were 43 778patients meeting criteria in the initial dataset. Fromthese 294 (0.7%) records were omitted (20 patients wereexcluded because of mismatch of ICU source of admis-sion and ED discharge destination, 157 records wherethe stated EDLOS was a negative value, 117 ICUpatients whose source of ICU admission was unknown),leaving 43 484 (99.3%) patients for final analysis.

Intensive care and SDU patients were younger andmore likely to be males, arrive by ambulance and havea more urgent ATS, in comparison with ward patients(Table 1).

Patient groups and their EDLOS

Median EDLOS was least for the ICU, followed by theSDU and ward patients (Table 2). At an EDLOS of >4 hand >8 h, 19.4% and 5.2% of ICU, 52.1% and 15.5% of

SDU and 77.9% and 32.6% of ward patients were still inthe ED. Similarly, 18.3% and 5.3% of ICU, 66.7% and24.6% of SDU and 81.8% and 46.1% of ward patientswhose hospital outcome was died had an EDLOS of>4 h and >8 h (Fig. 1).

Median EDLOS for hospital survivors compared withthose that died differed significantly for ward patients(alive, 7:18 h vs died, 7:44 h, P < 0.001), but not SDU(alive, 5:04 h vs died, 5:46 h, P = 0.07), ICU (alive, 2:40 hvs died, 2:26 h, P = 0.08) or all patients combined (alive,7:12 h vs died, 7:10 h, P = 0.65). Using an EDLOS cut-off<8 h revealed similar findings (Table 2).

The median EDLOS differed among the ATS catego-ries, being least for the most urgent category (ATS 1 –4:45 h, ATS 2 – 6:46 h, ATS 3 – 7:52 h, ATS 4 – 7:19 h,ATS 5 – 6:04 h, P < 0.01). When comparing those whodied with those who survived within each ATS cat-egory, EDLOS was significantly longer for those whodied only within ATS 2, 3 and 4 and only for wardpatients.

EDLOS as a predictor of mortality

Logistic regressions for hospital mortality were fittedseparately for each patient group (Fig. 2). Initial predic-tors were age and EDLOS (continuous variables), sex,admission source, admitting unit, ED arrival time andday of week, ED departure time and ATS (categoricalvariables). Certain categories had no deaths, and so thefollowing were omitted from the regressions, for wardpatients: admitting unit = psychiatry; for ICU patients:admitting unit = psychiatry, ATS = 5; for SDUpatients: admitting unit = psychiatry, palliative care/oncology and orthopaedics, ATS = 5. (Coefficients forthe variables remaining in the regressions are given inTable S1). All regressions showed highly significantgoodness-of-fit tests.

Age and admitting unit were predictors across allthree groups, with risk of death increasing with age.Ward and ICU patients who were an ATS 1 had asignificantly higher risk, whereas ATS was not a sig-nificant predictor for SDU patients. EDLOS was a sta-tistically significant factor for SDU patients only.

A logistic regression model predicting mortalitywas fitted to all three groups (ED to ward, ED to ICUand ED to SDU) combined. The model omitted non-significant variables from the individual patientgroup regressions, but included interactions betweenpatient group and most of the other variables, and aninteraction between age and sex. The terms in theregression were: age ¥ sex, admission source ¥ group,

A Flabouris et al.

48 © 2012 The AuthorsEMA © 2012 Australasian College for Emergency Medicine and Australasian Society for Emergency Medicine

admitting unit ¥ group, triage ¥ group, EDLOS ¥group (Fig. 3). Neither EDLOS, nor its interaction withpatient group, was statistically significant once theother variables and interactions were included in theregression.

The following variables (and standard settings): age= 64 years (the median); sex = male (the largest cat-egory); admission source = self (the largest category);

triage = 1 (the most urgent); admitting unit = medical(the largest category), were used to examine how therisk of mortality changes with EDLOS, according tothe patient group. As EDLOS increases, the riskremains fairly constant for ward, decreases for ICU andincreases for SDU patients (Fig. S1). This patternremained similar across all the other significantfactors.

Table 1. Patient characteristics

Characteristics All patients (n = 43 484) Ward (n = 41 591) ICU (n = 1020) SDU (n = 873)

Age*, median (IQR) (years) 64 (44, 79) 65 (44, 79) 55 (36, 72) 52 (36, 71)Sex (male %) 54.8 54.5 64.2 59.3Admission source (%)

Self 61.9 61.5 69.8 67.7General practitioner 13.9 14.3 3.6 4.6Nursing home 7.0 7.2 2.9 3.4Other† 17.2 17.0 23.7 24.3

Arrival mode (%)Ambulance 61.3 60.1 90.7 83.2Private 34.3 35.4 8.0 13.2Other‡ 4.4 4.5 1.3 3.7

Australasian Triage Scale§ (%)1 5.6 3.8 59.9 28.22 35.5 35.5 27.0 46.93 43.1 44.3 11.4 21.14 14.8 15.2 1.3 3.25 0.9 0.9 0.2 0.1

Admitting unit (%)Medicine 44.1 43.8 59.9 42.8Medicine specialty 15.4 15.6 12.4 11.6Palliative care/oncology 3.1 3.2 0.4 0.9Psychiatry 4.2 4.4 0.0 0.0Orthopaedics 8.7 9.0 1.0 1.6Surgery 12.4 12.1 13.3 27.7Surgery specialty 12.1 12.0 13.0 15.4

% Patients remaining in the ED at4 h 85.9 87.7 27.7 67.98 h 41.1 42.3 6.8 20.5

% Patients whose hospital outcomeis dead and EDLOS is

<4 h 19.5 10.1 73.1 24.6<8 h 60.1 53.9 93.1 72.5

ED arrival time of day*, median (IQR) 13:54 (10:09, 18:09) 13:55 (10:12, 18:08) 13:25 (08:07, 18:40) 14:09 (09:55, 18:43)ED departure time of day*, median (IQR) 15:34 (05:40, 19:55) 15:37 (05:40, 19:56) 13:23 (05:51, 19:12) 15:05 (04:52, 19:55)

*P < 0.01 (Comparisons are across all three patient groups: ED to ward; ED to ICU; ED to SDU). †Admission source other: communityhealth, community mental health, police, outpatient. ‡Arrival mode other: police, volunteers escort. §Australasian Triage Scale (ATS). ATS1 – Immediately life-threatening conditions requiring immediate simultaneous assessment and treatment. ATS 2 – Imminently life-threatening conditions requiring assessment and treatment within 10 min. ATS 3 – Potentially life-threatening conditions requiringassessment and treatment within 30 min. ATS 4 – Potentially serious conditions requiring assessment and treatment within 60 min. ATS5 – Less urgent conditions requiring assessment and treatment within 120 min. EDLOS, ED length of stay; ICU, intensive care unit; SDU,stepdown (high dependency) unit.



Discussion

This study explored the association of EDLOS andoutcome of patients admitted from the ED to a ward, theICU or SDU. Patients admitted to the ICU had the short-est EDLOS, followed by those admitted to the SDU and

the ward. EDLOS for patients admitted to the ICU orSDU was not significantly different among survivorsand those who died. In contrast, EDLOS for patientsadmitted to the ward was longer among those who died.When considered in conjunction with other factorspresent at time of presentation to the ED, such as age,

Table 2. Patient outcomes

Characteristics All patients (n = 43 484) Ward (n = 41 591) ICU (n = 1020) SDU (n = 873)

EDLOS (median, IQR)* (h) 7:12 (5:05, 9:50) 7:19 (5:15, 9:57) 2:36 (1:45, 4:19) 5:07 (3:30, 7:24)Mortality, n (%) 1729 (4.0) 1423 (3.4) 242 (23.7) 68 (7.8)EDLOS (median, IQR) (h)

Alive 7:12 (5:06, 9:51) 7:18 (5:14, 9:56) 2:41 (1:49, 4:22) 5:04 (3:27, 7:18)Dead 7:10 (4:41, 9:41) 7:44 (5:33, 10:05) 2:26 (1:38, 4:07) 5:46 (4:04, 8:13)P-value 0.65 <0.01 0.08 0.07

Hospital mortality of patients withand without access block(EDLOS >8 h)

EDLOS �8 h 4.1% 3.2% 23.6% 7.2%EDLOS >8 h 3.9% 3.7% 23.9% 10.6%P-value 0.16 <0.01 0.52 0.09

EDLOS (median, h) and hospitaloutcome by triage category

ATS† 1Alive 4:53 5:52 2:10 4:04Dead 3:45 6:30 2:05 4:28P-value <0.01 0.06 0.37 0.45

ATS 2Alive 6:45 6:50 3:30 5:00Dead 7:06 7:24 3:23 5:49P-value 0.02 <0.01 0.89 0.14

ATS 3Alive 7:50 7:51 5:42 6:37Dead 8:18 8:19 7:19 7:41P-value <0.01 <0.01 0.18 1.00

ATS 4Alive 7:16 7:16 4:20 9:08Dead 8:24 8:22 9:43 9:28P-value <0.01 <0.01 0.11 1.00

ATS 5Alive 6:02 6:03 4:29 4:25Dead 6:51 6:51 – –P-value 0.70 0.69 – –

Hospital LOS (days)* (median, IQR) 4.0 (1.9, 8.1) 3.9 (1.8, 7.9) 7.4 (3.2, 14.4) 6.8 (3.3, 13.3)Correlation with EDLOS R = 0.049, P < 0.01 R = 0.070, P < 0.01 R = 0.057, P = 0.07 R = 0.085, P = 0.01ICU/SDU LOS (days)* (median, IQR) – – 1.6 (0.8,3.6) 0.9 (0.5,1.7)Correlation with EDLOS – – R = -0.030, P = 0.34 R = -0.018, P = 0.59

*P < 0.01 (Comparisons are across all three patient groups: ED to ward; ED to ICU; ED to SDU). †ATS, Australian Triage Scale. ATS1 – Immediately life-threatening conditions requiring immediate simultaneous assessment and treatment. ATS 2 – Imminently life-threatening conditions requiring assessment and treatment within 10 min. ATS 3 – Potentially life-threatening conditions requiringassessment and treatment within 30 min. ATS 4 – Potentially serious conditions requiring assessment and treatment within 60 min. ATS5 – Less urgent conditions requiring assessment and treatment within 120 min. EDLOS, ED length of stay; ICU, intensive care unit; SDU,stepdown (high dependency) unit.

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triage category, sex and admission source, EDLOS wasnot significant as a predictor of mortality.

The absence of an association of EDLOS on ED toICU patients is in keeping with the findings from a large

Australian and New Zealand ICU patient dataset.22

A possible explanation for this is that the ED wasoperating as it should, that is prioritising for assess-ment, treatment and admission, those with the highest

Figure 1. Cumulative mortality, and rate of discharge, for the ED to ward, ED to intensive care unit (ICU) and ED to stepdown (highdependency) unit (SDU) groups with increasing ED length of stay. ED to ICU total; ED to ICU dead; ED to SDU total;

ED to SDU dead; ED to ward total; ED to ward dead.

Ward

P-value

0.0 0.2 0.4 0.6 0.8

ED leave

Day

ED arrive

EDLOS

Sex

Admission source

Age

Triage

Admitting unit

ICU

P-value

0.0 0.2 0.4 0.6 0.8

ED leave

Sex

Day

EDLOS

Admission source

ED arrive

Admitting unit

Triage

Age

SDU

P-value

0.0 0.2 0.4 0.6 0.8

ED leave

ED arrive

Triage

Sex

Admission source

Day

EDLOS

Admitting unit

Age

Figure 2. Predictors for mortality for ED patients admitted to the ward, intensive care unit (ICU) or stepdown (high dependency) unit(SDU). Each panel shows the P-value for the variable in the regression. These P-values represent the significance level associated withthat variable, given that all other variables are already included as predictors. The dotted vertical line represents P = 0.05, and the mostsignificant predictors are at the bottom of each plot. EDLOS, ED length of stay.



severity of illness, for which our only surrogate measurewas the ATS and the need for an ICU or SDU admission.In support of this explanation was the finding that forward patients with the most urgent ATS, EDLOS wasnot significantly different for survivors and those whodied, and was shorter in comparison with less urgentATS. Similarly, if we apply the premise that patientsadmitted to a SDU are of a level of acuity between thatof ICU and ward-level care, we found that SDU patientshad an EDLOS between that of ICU and ward patients.

The majority of the ED patients were wardpatients, with a lower urgency ATS (ATS 3 and 4), andthus, presumed severity of illness. These patients werealso those with the longest EDLOS. Possible explana-tions for these findings are that the accessibility of criti-cal care beds is better than for ward beds, that there isselection of patients on ED discharge with those ofhigher acuity taking preference during times of low bedavailability, or higher-acuity patients within the EDcontribute in delays to assessment and treatment oflower-acuity patients.29

Our finding of an association with mortality forEDLOS for ward patients is in keeping with some,12

but not all, prior studies. Some of these studies exam-ined primarily ED access block or overcrowding,10,11

which in themselves are associated with increasedEDLOS,12 or incorporated a dichotomous EDLOSmeasure.9 We used EDLOS as a continuous variable aswell as with a cut-off set at 8 h (access block). Doingso enabled us to discover the different patterns ofEDLOS between ward (higher EDLOS among thosewho died) and ICU patients (no difference for survi-vors and those who died, although patients who diedtended to have a shorter EDLOS). However, when allof our study patients were grouped together, EDLOSwas not a significant factor for mortality. This high-lights the specificity of effect of EDLOS on differentpatient groups, which might not be evident whenexamining a diverse ED patient population. This is animportant consideration when attempting to generalisefindings of past and future studies that incorporateEDLOS.

P-value

0.00 0.02 0.04 0.06 0.08

EDLOS

PatientGroup * EDLOS

Age * Sex

AdmissionSrce * PatientGroup

PatientGroup * Triage

PatientGroup * AdmittingUnit

Sex

AdmissionSrce

Age

PatientGroup

Triage

AdmittingUnit

Figure 3. Single regression model fitted to all three groups combined (i.e. ED to ward plus ED to intensive care unit plus ED tostepdown [high dependency] unit). The most significant variables are at the bottom, the least significant at the top. EDLOS, ED lengthof stay.

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In our regression analysis, EDLOS was not asignificant predictor of hospital mortality, in contrastto increasing age, more urgent ATS, ED source ofadmission and admitting medical unit, all of which weresignificant predictors of hospital mortality. These find-ings are in keeping with prior studies.12 In contrast toEDLOS, age, triage urgency and source of admission arelargely predetermined and fixed patient factors, presentat time of ED presentation. Thus, although EDLOS ispotentially modifiable, doing so might only have asmall, if any, impact on patient outcome. Our findingsmight, in part, help to explain as to why interventionsthat have targeted EDLOS and ED overcrowding havenot had the expected outcome benefit30,31 and supportthe alternative pursuit of system-wide changes thatfocus on ED patient disposition and ED and post-EDphase of care.26

Ours is the first study to specifically differentiate forSDU patients. EDLOS for SDU patients was quantita-tively between that of ICU and ward patients, as was theproportion that were in the highest triage acuity orarrived by ambulance. SDU patients who died, com-pared with survivors, did not have a statistically sig-nificant longer EDLOS. In contrast to ward and ICUpatients, EDLOS was a significant independent predic-tor, in addition to age, admitting unit and triage, ofoutcome for SDU patients. Based on our available data,we could not account for this definite finding andfurther evaluation is warranted.

There are important time-based harm minimisationand performance measures set for patients admittedfrom the ED. These are an EDLOS of less than 4 h,which is rapidly being adopted, but for which evidenceis lacking,30 and access block, defined as an EDLOS ofgreater than 8 h.9 We exclusively related EDLOS, asa continuous measure, to hospital outcome. In thatcontext, our findings suggest that these time periods arenot generalisable across the ED to ICU, ED to SDU andED to ward groups. Of patients who did not survivetheir hospital admission, the vast majority of ICUpatients had an EDLOS of less than 4 h, in contrast toSDU and ward patients, most of whom were still withinthe ED at 4 h. These findings would suggest that the useof a 4 h target, as a meaningful performance measure, isless relevant for ICU than for SDU and ward patients.Similarly, in relation to the definition of access block,almost no ICU and a minority of SDU patients remainedin the ED after 8 h, in contrast to just under half of theward patients.

The strength of our study is that it examined EDLOSacross groups of patients admitted from a single

acute hospital ED, thus controlling for health systemfactors among the patient groups. We also usedEDLOS as a continuous variable within a large hospitaladministrative dataset, with a high linkage rate. Indoing so, we also inherited the limitations of beingrestricted to the available variables, and as such, ourfindings might have been influenced by the presence ofsignificant confounders, data for which were not avail-able to us. Also, despite some of our findings havingsimilarities with other studies, our findings might not begeneralisable to health systems different to ours.

In conclusion, EDLOS, in comparison with age, triagecategory, sex and admission source, is not as a signifi-cant predictor of mortality. EDLOS is longest for wardand SDU patients, who are elderly, have a less urgentATS and require admission to general medical or sur-gical units and should be the focus for strategies toreduce EDLOS. Future studies that report on EDLOS, aswell as use EDLOS as a performance measure, must doso by differentiating for ward, ICU and SDU patients.

Competing interests

None declared.

Accepted 24 October 2012

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Supporting Information

Additional Supporting information may be found in theonline version of this article:

Figure S1. Change in risk of death for ED to ward, EDto ICU and ED to SDU groups with the change inEDLOS. Calculated based on a standard setting ofage = 64 years; sex = male; admission source = self;triage = 1; admitting unit = medical. Grey zone repre-sents the 95% CI.Table S1. Coefficients for variables remaining inlogistic regressions for mortality.

A Flabouris et al.
