i STAT CG4+ and CHEM8+ cartridges for point-of-care testing in the emergency department

Study component

Description

Objectives/hypotheses

To study the feasibility and accuracy of a point‑of‑care analyser (i‑STAT with CG4+ cartridge) capable of performing bedside serum lactate measurements to identify emergency department (ED) patients at risk of sepsis, and to determine whether other measurements (pH, base excess) are predictive of mortality. ^a

Study design

Prospective cohort study.

Setting

A tertiary care ED in an urban hospital in the USA. Recruitment dates from May 2006 to March 2007.

Inclusion/exclusion criteria

A convenience sample of adult (age 18 years or older) ED patients with suspected infection during the study period of 1 May 2006 and 15 March 2007 who had a POC lactate measurement obtained with a mandatory confirmatory lactate measurement performed by the hospital's clinical laboratory.

Exclusion criterion: absence of suspected infection.

Primary outcomes

In‑hospital mortality. The AUCs for mortality prediction for parameters including: point‑of‑care lactate, laboratory lactate, pH value, and base excess.

Statistical methods

AUC for ROC curve; Bland–Altman statistics along with a correlation coefficient; relative risk with 95% confidence intervals.

Conclusions

A point‑of‑care testing device provides a reliable and feasible way to measure serum lactate at the bedside. The pH and base excess were less helpful.

Abbreviations: AUC, area under the curve; ED, emergency department; n, number of patients; POC, point of care; ROC, receiver operating characteristic.

^a Serum is not an approved sample type for the i‑STAT CG+ cartridge.

Patients included

n=699 patients, mean age 60.4 years (95% CI 58.9 to 61.2), who were a prospective cohort of a convenience sample of adult (age 18 years or older) ED patients with suspected infection during the study period of 1 May 2006 and 15 March 2007 who had a point‑of‑care lactate measurement obtained (i STAT with CG4+ cartridge) with a mandatory confirmatory lactate measurement performed by the hospital's clinical laboratory.

Primary outcomes

A Bland–Altman plot showed that point‑of‑care lactate measurements were accurate for clinical decision‑making compared with the laboratory lactate test. There was an average bias for point‑of‑care lactate of 0.32 (SD 0.45) mmol/l lower than laboratory lactate, with the limits of agreement ranging from ‑1.1 to 0.50 (the range over which 95% of the differences between the point‑of‑care and laboratory lactate will be contained).

The point‑of‑care lactate was highly correlated with the laboratory lactate (r=0.9)7.

A total of 699 patients were enrolled, 34 (4.9%) of whom died. The mean point‑of‑care lactate value was higher in those who died (3.2 mmol/l; 95% CI 2.05–4.37) than those who lived (1.65 mmol/l; 95% CI 1.56–1.74). Mean laboratory lactate levels also differed between those who died and survivors: 3.83 mmol/l (2.20–5.47) compared with 1.95 mmol/l (1.86–2.04), respectively, as did pH: 7.42 (7.42–7.43) compared with 7.37 (7.33–7.42), respectively. Base excess did not show a statistically significance difference: 1.71 (1.32–2.10) compared with 0.62 (-4.09–2.85), respectively.

The AUCs for mortality prediction: point‑of‑care lactate 0.72, laboratory lactate 0.70, pH measurement 0.60, and base excess 0.60. Bland–Altman showed that mean lactate by the point‑of‑care test was 0.32 (95% CI‑0.35–0.98) lower than that by laboratory test, with agreement Kappa=0.97.

Abbreviations: AUC, area under the curve; CI, confidence interval; ED, emergency department; n, number of patients; SD, standard deviation.

Study component

Description

Objectives/hypotheses

To evaluate the impact of the combination of two strategies, goal‑directed therapy (GDT) and point-of-care blood lactate testing using the i‑STAT CG4+ cartridge, on improving outcomes for babies (younger than 1 month) and young children (younger than 1 year) after congenital heart surgery.

Study design

Before‑and‑after study.

Blood lactate measurements were performed serially for 24 hours after surgery. Post‑operative management of patients was based on serial lactate determinations i.e., based on a lactate value, medical therapy was escalated, diminished or left unchanged. Outcome data were collected prospectively. Mortality at 30 days after surgery was compared for patients undergoing a GDT protocol and a group of historical cohorts. The operative risk for all operations was determined using the RACHS‑1 scoring system.^a The reference value for arterial blood lactate was 0.36–1.25 mmol/l for the i‑STAT analyser.

Setting

A 16‑bed cardiac ICU in a 268‑bed free‑standing paediatric hospital in the USA, between June 1995 and June 2003.

Inclusion/exclusion criteria

Inclusion: all patients undergoing congenital heart surgery in the hospital from June 1995 through to July 2003. Exclusion criteria were not specified.

Primary outcomes

Overall mortality at 30 days after surgery; blood lactate level; cardiopulmonary bypass times and aortic cross‑clamp times.

Statistical methods

Chi‑square analysis was used to detected differences in mortality between groups. Mann–Whitney rank sum analysis was used to determine differences in demographic data between groups.

Patients included

Infants (under 1 year of age) and neonates (under 1 month of age) undergoing congenital heart surgery. Group A (June 1995–June 2001 before i‑STAT was introduced): n=851; group B (July 2001–June 2003, after i‑STAT was introduced): n=378.

Patients in group B were smaller and younger than those in group A (median weight 3.8 kg compared with 4.3 kg, p<0.001; median age 42 days compared with 76 days, p=0.02).

Conclusions

The combination of goal‑directed therapy and point‑of‑care testing significantly reduced mortality in patients after congenital heart surgery. This improvement was greatest in the youngest patients and those undergoing higher‑risk surgery.

Abbreviations: GDT, goal‑directed therapy; ICU, intensive care unit; n, number of patients; NS, not (statistically) significant.

^a RACHS‑1, "Risk Adjustment for Congenital Heart Surgery" scoring system. RACHS‑1 scoring was devised to categorise the risk for death associated with various congenital heart operations. RACHS‑1 divides the surgeries into six categories, with category 1 being the simplest surgeries with the lowest mortality and category 6 being the surgeries with the highest mortality.

i‑STAT

Pre i‑STAT

Analysis

Total number of patients

n=378

n=851

Primary outcome ^a

2.4%

6.2%

p reported as " <0.007 "

n=164

4.3%

n=320

12%

p=0.008

n=214

0.9%

n=531

2.6%

p=NS

9%

30%

p=0.03

0.5%

1.5%

p=NS

The turn‑around time for lactate

120 seconds

15 minutes to 2 hours

Not reported

Abbreviations: n, number of patients; NS, not (statistically) significant.

^a 95% confidence intervals not reported.

^b RACHS‑1, "Risk Adjustment for Congenital Heart Surgery" scoring system. RACHS‑1 scoring was devised to categorise the risk for death associated with various congenital heart operations. RACHS‑1 divides the surgeries into six categories, with category 1 being the simplest surgeries with the lowest mortality and category 6 being the surgeries with the highest mortality.

Study component

Description

Objectives/hypotheses

To compare lactate values obtained from multiple central laboratory (plasma‑based assays) and point‑of‑care (whole blood) platforms to determine whether clinically relevant discrepancies might occur if testing is performed on both plasma (central laboratory) and whole blood (point‑of‑care or blood gas analyser) platforms.

Study design

Prospective cohort study.

Three whole blood lactate methods were compared with 2 plasma‑based methods. The Vitros assay was used as the reference method.

The 3 whole blood lactate methods:

The 2 plasma‑based methods:

Whole blood specimens obtained from patients in the ED and ICU (n=90) were analysed on the Radiometer methods, the i‑STAT CG4+, and the Nova analyser within 1–2 minutes of each other. Samples were transported to the laboratory at ambient temperature and all whole blood analysis was completed within 1 hour of draw time. Within 5 minutes of the whole blood analysis, the specimens were centrifuged and plasma separated and kept on ice until testing on the Roche Integra and the Vitros 250 analysers could be completed (within 1 hour of plasma separation). Linearity and precision of each device or assay was also determined using material provided by the individual manufacturers.

It was unclear how the samples or patients were selected.

Setting

Department of Laboratory Medicine and Pathology, Mayo Clinic College of Medicine, Rochester, USA.

Inclusion/exclusion criteria

Not specified.

Primary outcomes

Correlation between lactate methods.

Statistical methods

Results were compared by least squares regression and Bland–Altmann plots and by comparing concordance within clinically relevant lactate ranges. ^a

Conclusions

The negative bias in i‑STAT and Radiometer results may confound the interpretation of patient condition if multiple methods are used within the same institution.

Abbreviations: ED, emergency department; ICU, intensive care unit.

^a Results were classified as low risk (lactate result, ≤2.2 mmol/l), intermediate risk (lactate result, 2.3–5.0 mmol/l), or high risk (lactate result, >5 mmol/l) based on available literature relating lactate levels to patient outcome.

Patients included

Patients in the ED and ICU (n=90). No further details were reported. It was unclear how the samples/patients were selected.

Primary outcomes

Correlation between lactate methods was good with slopes of 0.87–1.06 and intercepts of 0.1–0.2 mmol/l of lactate for all 4 methods compared with the Vitros (slopes of 0.87 for the i‑STAT methods, with r²=0.99 or more in each case). Intercepts were between lactate levels of 0.1 and 0.2 mmol/l for all methods.

At high lactate values (>6 mmol/l), the Radiometer and i‑STAT assays exhibited negative bias (relative to the Vitros), and the Radiometer and i‑STAT methods reported lower lactate results compared with the Vitros and Integra.

Among the 90 samples analysed on the Vitros, there were 29, 30, and 31 samples in the low-, intermediate-, and high‑risk categories respectively. ^a

The percentage of concordance (percentage of all samples that fell in the same risk category as the Vitros result): the Radiometer and i‑STAT had 85 (94%) of 90 samples concordant with the Vitros result; for the Integra, 89 (99%) of 90 samples fell within the same risk category as the Vitros value; the Nova demonstrated 90% concordance (81/90) with the Vitros.

Abbreviations: ED, emergency department; ICU, intensive care unit; n, number of patients.

^a Results were classified as low risk (lactate result, ≤2.2 mmol/l), intermediate risk (lactate result, 2.3–5.0 mmol/l), or high risk (lactate result, >5 mmol/l) based on available literature relating lactate levels to patient outcome.

Study component

Description

Objectives/hypotheses

To evaluate the "measure of treatment agreement" – the number of standard clinical laboratory arterial blood gas measurements that prompted changes in mechanical ventilator support therapy compared with number of portable device measurements that would have prompted the same or different changes.

Study design

Prospective cohort study.

Treatment decisions made with arterial blood measurements by:

were compared with the recommended treatment from paired arterial blood measurements by laboratory Radiometer ABL‑725: _SaCO₂, PO₂, pH, PCO₂.

Setting

A shock‑trauma ICU at a level 1 trauma centre in the USA between 23 September 2002 and 13 November 2003.

Inclusion/exclusion criteria

Intubated and ventilated adult patients admitted to the shock‑trauma ICU were eligible for inclusion if they had an indwelling arterial catheter and if portable bedside measurements could be performed at the time of the first arterial blood gas sample.

Patients were excluded if any measurements were missing from any of the study devices (Nonin, Novametrix, i‑STAT, and Radiometer ABL 725) or if the arterial blood gas had been inadvertently run on another bench‑top blood gas analyser other than the Radiometer ABL 725.

Primary outcomes

Association between portable and laboratory blood gas measurements.

Statistical methods

Regression scatter plots, Bland–Altman statistics.

Conclusions

The i‑STATPO₂ and PCO₂ portable device measurements were acceptable as surrogates to standard clinical laboratory blood gas measurements in guiding protocol‑directed ventilator management. The "measure of treatment agreement," based on standardised decisions and measurement thresholds of a protocol, provides a simple method for assessing clinical validity of surrogate measurements.

Abbreviations: ICU, intensive care unit.

Patients included

446 intubated adult ICU patients, mean age 48 (SD 19) years, males 57%.

Admission category: infectious 11%, medical 17%; neurological 5%, psychological <1%, respiratory 11%, surgical 20%, and trauma 32%.

Mean injury severity scores: 28 (SD 12).

Hospital mortality: 18%.

Primary outcomes

Except for the Novametrix‑610 ETCO₂ (r²=0.460), correlation coefficients between portable and laboratory measurements were high (r²≥0.755).

Testing for equivalence, the Nonin SpO₂, i‑STAT PO₂, i‑STAT pH, and i‑STAT PCO₂ were deemed "equivalent" surrogates to paired laboratory measurements.

The measure of treatment agreement between the portable and paired laboratory blood gas measurements were Nonin-SpO₂ (68%), i‑STAT _SaO₂ (73%), i‑STAT PO₂ (97%), i‑STAT pH (88%), i‑STAT PCO₂ (95%), and Novametrix ETCO₂ (60%). Based on a minimum of ≥95% measure of treatment agreement, only the i‑STAT PO₂ and the i‑STAT PCO₂ were considered acceptable surrogates to the laboratory PO₂ and PCO₂.

Abbreviations: ETCO₂, end‑tidal CO₂; ICU, intensive care unit; n, number of patients; SD, standard deviation.

Study component

Description

Objectives/hypotheses

To assess the effects of bedside POC lactate measurement (using the i‑STAT CG4+ cartridge) on the time to administration of IV fluids and antibiotics in adult ED patients with suspected sepsis.

Study design

Before‑and‑after study.

Bedside lactate measurement (using the i‑STAT CG4+ cartridge) in a convenience sample of 80 patients presenting to the ED between January and September 2013 who met the study inclusion criteria, was compared with laboratory lactate measurement in the first 80 consecutive patients presenting to the ED12 months prior to introduction of the bedside lactate testing (starting from 1 calendar year prior to study initiation).

Setting

A suburban, academic tertiary care medical centre with annual ED attendance of approximately 90,000 people.

Inclusion/exclusion criteria

Inclusion criteria

In the 'before' group: the first 80 consecutive patients presenting to the ED12 months prior to the introduction of bedside lactate testing who also had an initial lactate level of ≥2 mmol/l (starting from 1 calendar year prior to study initiation).

In the 'after' group: following the introduction of bedside lactate testing, patients attending the ED between January and November 2011 with suspected infection and at least 2 of the clinical criteria for the SIRS (including a temperature of ≥38°C, a temperature of ≤35°C, a heart rate of ≥90 beats per minute, a respiratory rate of ≥20 per minute, a systolic blood pressure < 90 mmHg, or an acute change in mental status) and with a bedside lactate level of at least 2 mmol/l.

Exclusion criteria

Patients who could not give consent or for whom consent could not be obtained from a legal guardian were excluded. Patient who received an intravenous antibiotic for suspected sepsis within the last 12 hours were also excluded.

Primary outcomes

Time from ED triage to iv fluids and antibiotic administration.

Statistical methods

Binary data were compared between groups with Χ² or Fischer's exact tests. Continuous data were compared with t‑tests and Mann Whitney U tests as appropriate. A sample size calculation determined 80 patients in each of the study periods. The agreement between bedside POC and central lab lactates was analysed with scatterplots, correlation coefficients and Bland Altman analysis.

Patients included

n=80 in each group. Respiratory infection as the source of infection: 50% in the before group and 29% in the after group (p=0.01). There were no statistically significant differences in other baseline demographic and clinical characteristics.

Conclusions

Implementation of bedside POC lactate measurement in adult ED patients with suspected sepsis reduces time to test results and time to administration of IV fluids but not antibiotics. A significant reduction in mortality and ICU admissions was also demonstrated, which is likely to be due, at least in part, to POC testing.

Abbreviations: ED, emergency department; ICU, intensive care unit; IV, intravenous; POC, point‑of‑care; SIRS, systemic inflammatory response syndrome.

After

(i‑STAT CG4+)

Before

(laboratory)

Analysis

Number analysed

n=80

n=80

Primary outcomes

Time to iv fluids (minutes)

55 (34–83)

71 (42–110)

p=0.03

Time to iv antibiotics (minutes) ^b

89 (54–156)

97 (55–160)

p=0.59

Secondary outcomes

Test turnaround time (minutes)

34 (26–55)

122 (82–149)

p<0.001

Time from arrival to standard central laboratory results

71 (53–101) ^c

122 (82–149)

p<0.001

Time from order to standard central laboratory results

38 (26–53)

71 (53–91)

p<0.001

ICU admits, n (%) ^d

26 (33%)

41 (51%)

p=0.02

Total ED length of stay

352 (246–457)

326 (249–436)

p=0.50

ICU length of stay, days

3 (2–6)

4 (2–6)

p=0.90

Hospital length of stay, days ^e

7 (3–13)

8 (4–13)

p=0.27

Mortality

5 (6%)

15 (19%)

p=0.02

Abbreviations: ED, emergency department; ICU, intensive care unit; IV, intravenous; POC, point‑of‑care; n, number of patients.

^a The data reported for 'time to', 'time from', and 'length of stay' days were in median (interquartile).

^b Discrepancy between the data reported in the abstract and that in the table 2 of the paper.

^c All patients in the prospective arm receiving a POC lactate test result also had their serum lactate levels measured in the central laboratory to assess the performance of the POC lactate assay compared with the standard of care. Treatment was initiated based on the POC result; it was not delayed or contingent on the value or the availability of the central lab serum lactate result.

^d patients in whom the initial lactate level was 4 mmol/l or greater were transferred the critical care area for further evaluation and management.

^e Excludes deaths.

Study component

Description

Objectives/hypotheses

The authors hypothesised that nurse‑led triage in the ED may not be the most efficient method of initiating care. The study assessed the impact of introducing a consultant‑supported point‑of‑care rapid assessment model and point‑of‑care testing on the length of time patients spend in the ED.

Study design

A before‑and‑after study consisting of two consecutive phases: phase 1 during which patients were assessed and treated using a nurse‑led triage model; phase 2 during which patients were assessed using a rapid assessment model. The rapid assessment model used point‑of‑care testing for full blood counts, renal function (i‑STAT CHEM8+) and blood gases (i‑STAT CG4+).

Setting

An ED in a district general hospital (major trauma unit) in the UK with an annual number of ED attendances of approximately 65,000.

Phase 1: between 1 April 2013 and 24 May 2013.

Phase 2: between 30 September 2013 and 18 October 2013.

Inclusion/exclusion criteria

Not specified.

Primary outcomes

Time from the patient arriving in the ED to the point in time when all ED care is complete and the patients is deemed ready to move to the next destination of care.

Statistical methods

Chi square test; interpretations were based on α=0.05 and β=0.8.

A 2‑tailed sample size calculation estimated that 497 patients were required in both phases.

Patients included

Phase 1: n=3835, male 51.8%, mean age 42 years.

Phase 2: n=787, male 50.2%, mean age 45 years.

There was no statistically significant differences between the population characteristics examined, including age, gender, full blood counts, renal functions, blood gases, and proportion of arrived by ambulance and triage category.

Conclusions

The study demonstrates that a consultant‑supported rapid assessment model using POCT significantly shortens the time patients spend in the ED.

Abbreviations: ED, emergency department; n, number of patients; POCT, point‑of‑care testing.

i‑STAT CHEM8+/CG4+

Nurse-led

Analysis

Number analysed

n=787

n=3835

Primary outcomes

Median time from patients arriving in the ED to be declared "ED ready"

76 minutes

129 minutes

Median reduction=53 minutes or 41.1% (95% CI 39.7%–42.3%; p<0.0001)

Median time from arrival to the commencement of an assessment by a member of clinical staff (doctor or nurse)

4 minutes

12 minutes

Median reduction=8 minutes or 66.7% (95% CI 65.0%–68.3%; p<0.0001)

Median time from arrival in the ED to assessment by an ED physician

24 minutes

96 minutes

Median reduction=72 minutes or 75.0% (95% CI 74.6%–75.3%; p<0.0001)

Abbreviations: CI, confidence interval; ED, emergency department; n, number of patients.

Study component

Description

Objectives/hypotheses

To quantify the impact of introducing point‑of‑care testing for renal function on the length of time patients spend in the ED.

Study design

A before‑and‑after study. It consisted of two consecutive phases: phase 1 during which renal function was tested using the hospital's centralised laboratory analyser and phase 2 during which renal function analysis was tested using the bedside i‑STAT CHEM8+ cartridge.

Setting

An ED in a district general hospital (major trauma unit) in the UK with an annual number of ED attendances of approximately 65,000.

Phase 1: between 1 April 2013 and 24 May 2013.

Phase 2: between 28 May 2013 and 29 September 2013.

Inclusion/exclusion criteria

All patients attending the ED within the study period that were identified as requiring renal function analysis and did not have a minor injury were included in the data analysis. patients who presented with a minor injury were excluded.

Primary outcomes

Time for patients to be declared ready to leave the ED.

Statistical methods

Wilcoxon rank sum tests; interpretations were based on α=0.05 and β=0.8.

A 2‑tailed sample size calculation estimated that 155 patients were required in both phases.

Patients included

Phase 1: n=3835, male 51.8%, age 42 years (unclear whether mean or median).

Phase 2: n=7033, male 52%, age 45 years (unclear whether mean or median).

Conclusions

The study demonstrates that using POCT for renal function in the ED was significantly quicker than using a centralised hospital laboratory. The use of a bedside POCT device enables clinicians to make informed clinical decisions in a timelier manner.

Abbreviations: ED, emergency department; CI, confidence interval; n, number of patients; POCT, point‑of‑care testing.

i‑STAT CHEM8+

Laboratory

Analysis

Number analysed

n=7033

n=3835

Primary outcomes

Median time from patients arriving in the ED to be declared "ED ready"

109 minutes

129 minutes

Median reduction=20 minutes or 15.5% (95% CI 14.8%–16.2%; p=0.0025)

Median time from arrival to the commencement of an assessment by a member of clinical staff (doctor or nurse)

7 minutes

10 minutes

Median reduction=3 minutes or 30% (95% CI 29.1%–30.86%; p=0.0025)

Median time from arrival in the ED to assessment by an ED physician

80 minutes

90 minutes

Median reduction=16 minutes or 16.7% (95% CI 16.0%–17.4%: p=0.0025)

Abbreviations: CI, confidence interval; ED, emergency department; n, number of patients.

Authors

Outline

Hsiao et al (2007)

In the study it was not specified which POCT devices were used, nor were the use of i‑STAT and cartridges specified (one specialist commentator suggested that this study should be included in the briefing).

The study was a randomised controlled trial comparing the effect of POCT with traditional laboratory methods on patient length of stay in a paediatric ED. A total of 225 patients presenting to a tertiary hospital ED in the US were included, 114 were in the POCT group and 111 in the routine laboratory analysis group.

Time intervals were analysed including time spent in the waiting room, time waiting for first physician contact, and time waiting for blood draw.

Similar waiting periods were noted in both groups for time spent in the waiting room, time waiting for first physician contact, and time waiting for blood draw. Statistically significantly less time was required in the POCT group compared with the laboratory group for results to become available to physicians (65.0 minutes; p<0.001) and in overall length of stay (38.5 minutes, p<0.001).

Gilkar et al (2013)

This article reported a project that implemented POCT to test the hypothesis that interfacing 'on‑line' POCT devices to a clinical electronic order communications system reduces patient waiting times in an NHS A&E in 2012. The devices selected for evaluation initially comprised the Sysmex XS 1000i haematology analyser and the i‑STAT chemistry analyser (i‑STAT cartridges were unspecified but the manufacturer claims it was the CHEM8+ cartridge).

Patient waiting time (presumably, it was defined as from time of arrival to time of discharge) and the time to produce test results (turnaround time, i.e. from requesting a test and receiving the results) were assessed in total of 217 cases associated with POCT tests only, and were compared with that in 229 controls who were randomly selected from the clinical laboratory database.

Study period was not specified. No further details on sampling process for both groups.

The time to produce test results was 23 minutes for the POCT tests and 60 minutes for the laboratory tests. The patient waiting time was 167 minutes for the POCT group and 208 minutes for the clinical laboratory group, a difference of 31 minutes.

Webb and Campbell (2014)

This article described a project setting up an Emergency Multidisciplinary Unit in the Oxford region in the UK. The i‑STAT was used but cartridges were unspecified (although specified that it would give a full biochemical profile).

Giles et al (2015)

A press‑release published on an online in‑house journal by Step Communication. It reported a project that integrated POCT and evidence‑based lean service redesign at an NHS hospital (year unclear) to provide emergency medical patients with efficient and high quality care. The i‑STAT analyser (cartridges unspecified) and Emerald CEL‑DYN full blood count analyser were used for a 3‑month pilot period, coupled with service redesign. Performance data were reported for each of the 3 months, including total patients, average patients per day, mean length of stay, median length of stay, and same‑day discharge rate. The authors stated that for the patient cohort the length of stay reduced from 1.04 to 0.8 bed‑days (reduced by 40.8% from an established baseline of 250 minutes). There was an 8.22% increase (188 patients) in the number of same‑day discharges (zero length of stay admission), with an associated decrease of 8.93% in "1, 2 and 3 day length of stay patient admissions – equating to 59 saved bed days during the pilot period."

Abbreviations: A&E, accident and emergency department; ED, emergency department; POCT, point‑of‑care testing.