START was derived using the Emergency Department Data collection registry in New South Wales, Australia. Adult patients (age > or = 16 years) were included if they presented to a Level 5 or 6 Emergency Department in New South Wales, Australia between 2013 and 2014.
The outcome of interest was in-patient admission from the Emergency Department. This included all admissions to short stay and medical assessment units and being transferred out to another hospital. Analyses were performed using logistic regression. Discrimination was assessed using area under curve and derived risk scores were plotted to assess calibration.
Over 1.7 million presentations from twenty three level 5 or 6 hospitals were analysed. Of these 49.38% were male and the mean (sd) age was 49.85 years (22.13). Level 6 hospitals accounted for 47.70% of cases and 40.74% of cases were classified as an in-patient admission based on their mode of separation. The final multivariable model including age, arrival by ambulance triage category previous admission and presenting problem had an AUC of 0.82 (95%CI 0.81, 0.82).
Table 2. Multivariable model of in-patient admission with risk score using derivation set Akiake Information Criterion (intercept only 2326760, intercept and covariates 1768771) Area under Receiver Operator curve for validation dataset 0.82 (95%CI 0.81, 0.82). Hosmer-Lemeshow test statistic p<0.001.
|Variable||Coefficient||Odds ratio (95%CI)||P value||Risk score|
|20-39 yrs||0.19||1.21 (1.19,1.23)||<0.001||+1|
|40-59 yrs||0.61||1.85 (1.82, 1.88)||<0.001||+3|
|60-79yrs||1.20||3.31 (3.25, 3.37)||<0.001||+6|
|≥80 yrs||1.79||6.01 (5.89, 6.13)||<0.001||+9|
|Ambulance arrival||0.77||2.17 (2.15, 2.19)||<0.001||+4|
|1||4.47||87.13 (82.15, 92.60)||<0.001||+24|
|2||2.99||19.84 (19.28, 20.32)||<0.001||+16|
|3||2.08||7.97 (7.80, 8.15)||<0.001||+11|
|4||1.10||3.00 (2.94, 3.07)||<0.001||+5|
|Admission within 30 days||0.66||1.93 (1.90, 1.96)||<0.001||+3|
|Hour of presentation|
|0800-1759||0.21||1.23 (1.22, 1.25)||<0.001||+1|
|1800-2259||0.01||1.01 (1.00, 1.02)||0.06||0|
|Abdominal, gastrointestinal||0.33||1.39 (1.37, 1.41)||<0.001||+2|
|Cardiovascular||-0.71||0.49 (0.46, 0.48)||<0.001||-3|
|Febrile illness||0.65||1.91 (1.87, 1.96)||<0.001||+3|
|Injury||-0.75||0.47 (0.46, 0.49)||<0.001||-4|
|Musculoskeletal||-0.57||0.56 (0.55, 0.57)||<0.001||-3|
|Neurological||-0.25||0.78 (0.77, 0.79)||<0.001||-1|
|Mental health||-0.32||0.72 (0.71, 0.74)||<0.001||-2|
|Toxicological||-0.30||0.74 (0.72, 0.77)||<0.001||-2|
|ENT/eye/head and neck||-1.17||0.31 (0.30, 0.32)||<0.001||-6|
|Administrative||-0.57||0.57 (0.55, 0.59)||<0.001||-3|
|Genitourinary||-0.16||0.85 (0.83, 0.87)||<0.001||-1|
|Social||0.19||1.21 (1.07, 1.38)||0.004||+1|
|Endocrine||-0.03||0.97 (0.91, 1.05)||0.26||0|
|Obstetrics, Gynaecology||-0.55||0.58 (0.56, 0.59)||<0.001||-3|
|Skin, allergy||-0.30||0.74 (0.72, 0.76)||<0.001||-2|
|Other medical||0.99||2.70 (2.40, 3.04)||<0.001||+5|
Fig.1 Mean predicted probability of in-patient admission based on all possible risk score totals
Fig 2. Calibration curve of actual admission rate by predicted mean probability - dots denoting each risk score category (total risk score >40, 30-40, 20-30, 10-20, 1-10, <1). Dotted line denotes perfect calibration
The Emergency Department Data Collection Registry routinely collects patient level data on presentations to all designated Emergency Departments in NSW. Data collection includes, referral source (self referred, General Practice, Specialist, Nursing Home), mode of arrival (self referral, Ambulance), hospital facility, presenting problem, mode of separation (admitted to hospital, discharged or died). Triage categories were defined by the Australasian triage scale (1=immediately life-threatening, 2=potentially life threatening, 3=urgent, 4=semi-urgent and 5=non urgent). Presenting problems allocated by triage nurses and ED diagnoses entered by clinicians were categorised into broad clinical groups (see table1). Full data definitions for the EDDC are located at http://www.cherel.org.au/data-dictionaries#section2
The main driver of this project is to improve ED patient flow and clinical decision making. The translational aspects of this project can therefore be divided into two broad domains
Evaluation study protocol
Investigate whether use of the START score improves ED performance and decreases length of stay in ED
Design - Randomised control trial with unit of randomisation being day of the week
Setting - The study will be conducted in the Emergency Departments of Royal Prince Alfred Hospital (around 75,000 ED presentations per year), Canterbury Hospital, and Concord Hospital (both around 40,000 ED presentations per year). All three hospitals are within the Sydney Local Health District.
Patient population - Eligible adult (age>16 years) patients will be consecutive patients presenting at these hospital Emergency Departments between 1000 and 1400. Exclude planned representations, immediately life-threatening presentations (trauma calls, stroke calls, LifeNet (acute myocardial infarction) and cardiac arrest calls), transfers from other hospitals, expected admissions and those brought in by police
Intervention - The START score will be scored by a designated trained investigator at the point of triage. The investigator will observe each nursing triage encounter and enter relevant data fields (see START risk scoring sheet) that is being routinely collected by the triage nurse. The score will be calculated using a paper scoring checklist or mobile app calculator if available. Risk scores and probabilities were determined using a previous derivation and validation paper.
The recommendation based on the score will range from very likely admission, likely admission, uncertain, likely discharge and very likely discharge.
For patients allocated to the intervention group a copy of the paper START risk scoring checklist will be attached to the clinical notes and used by the Nurse Unit Manager or patient flow Navigator in ED together with the treating clinician to assist with disposition decisions.
These decisions may include allocation of in-patient or short stay unit beds, or streaming to fast track units. The patient will not be interviewed, approached or contacted for any information and no specific patient information will be recorded on the scoring app.
Control - Presentations allocated to control group will receive standard management in ED by clinicians without the assistance of the risk score tool. The study investigator will still score the triage encounter using the risk tool but the results of the risk scoring will not be made known to the clinician or included in the clinical notes.
Group allocation - Consecutive days of the week will be randomly allocated using a computer generated number sequence in an opaque sealed envelope drawn at the time of triage (OR START OF DAY?) by the study investigator.
Consent - Triage nurses and Nurse Unit Managers in ED will be asked to read a study information sheet and consent during regular weekly nursing in-service sessions. As the information does not directly affect patient care and will not involve direct interaction with ED patients, patient consent will not be obtained. Patients will be informed with signage at the triage office that a study investigator will be observing the triage process and collecting information that is being entered by the triage nurses.
Primary Outcome - The primary outcomes are proportion of patients with length of stay less than four hours and total length of stay in ED. These are routinely collected using existing patient information systems (FirstNet, Cerner Millenium) and reported by the ED data manager Ms Sook Lee Chai.
Secondary Outcome - Disposition time, (time that admission ready or discharge ready icons were activated on patient information system) Representation within 3 days of initial presentations, did not wait and hospital length of stay. These are also routinely collected and reported by the ED data manager.
The hypothesis will be that patients allocated to the intervention groups are associated with reduced length of stay in ED. Descriptive statistics will be used to compare proportions and means between groups and (multi-level modelling used to account for day of week randomisation)
Pre specified subgroups will be admitted or discharged patients
Based on length of stay in the derivation study, the mean (sd) for admitted patients was 7.2(12) and 3.3(16) for discharges. A 2 hour decrease is considered a clinically meaningful and it is estimated that around 500 patients in each arm. A total of 1200 presentations will need to be analysed assuming exclusions are factored.
This will provide enough power to detect a 10% improvement in proportion of patients staying in ED less than four hours with a power of 0.80 and a two tailed alpha of 0.05. Assuming around 5 presentations are allocated and studied each hour at each site, an estimated six months of active recruitment is required (two days a week at four hours a day).
An application will be made to the NSW Population and Health Services Research Ethics Committee with RPA Ethics committee acting as the lead site for all three single site applications. The trial will also be submitted for registration in the ANZ Clinical Trials Registry
All risk tool forms and data collection sheets will be stored in paper form in a locked cabinet with the offices of the ED Executive. Patient details contained on data collection sheets will not be used for data analysis.
The study will be limited to patients presenting during the hours of 1000 to 1400 on weekdays. This is seen as a necessary step to ensure study protocols are met prior to wider implementation across all hours.