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Embedded
Research
Quality
Improvement
Initiative
Amith Shetty
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Objectives
Embedded research
Shoe stringing
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Background
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Background
Research to Evidence based practice – delays
Capturing the effects of practice change – are we really doing better
Quality initiatives –
Usually very focused
System targets
Intended and unintended consequences
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SEPSIS KILLS program:reduce preventable harm to patients with sepsis
RECOGNISE:
Risk factors, signs and symptoms of sepsis and inform senior clinician
RESUSCITATE:
With rapid antibiotics and IV fluids within one hour
REFER:
To specialist care and initiate retrieval if needed
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Sepsis Bundle
– Oxygen
– Lactate
– Monitor
– Empirical Antibiotics
– Blood Cultures
– Intravenous Fluids
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Sepsis Kills
Embedded research
Shoe stringing
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Sepsis Pathways
Pathways guide clinicians to THINK about sepsis
NOT prescriptive ……clinical judgement is KEY
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SEPSIS KILLS results
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NSW hospital sepsis mortality
10
12
14
16
18
20
22
2009-2011 2012 2013 2014 2015
De
ath
s w
ith
an
d w
ith
ou
t A
uto
psy
(%
)
Principal only P+4 Comor P+5 Comor P+25 Comor P+50 Comor MJA - Comor 1-5
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SMEDSA
– Sydney Multicentre Emergency Department Sepsis Archive
– Retrospective chart review populated sepsis registry approved at 5
Western Sydney EDs patients placed on the sepsis pathway
– Patients identified through clinician reported EMR alert for sepsis
based on CEC SIRS criteria or senior clinician suspicion
– Collects all SIRS, investigative and in-hospital outcome data for
identified patients
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What we can already do!
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Track and trigger
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Self reported Time to antibiotics
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Data reports
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Research outcomes
At state level – CEC sepsis register – Broad coarse system level data
At district level – Multicentre data-rich Sepsis archive
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Lactate in Suspected sepsis –
CEC sepsis register
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ED Lactate levels risk
stratificationLactate group (mmol/L) Age median (IQR) Total, n (Died n/%)
[p]*
AE
n (%) [p]*
0 to <1 66.7 (48.1-79.4) 847 (37/4.37)
[NA]
54 (6.38)
[NA]
1 to <2 72.1 (57-82.1) 3531 (181/5.13)
[0.36]
244 (6.91)
[0.58]
2 to <3 73.1 (60.3-83) 1922 (145/7.54)
[0.0003]
198 (10.3)
[<0.0001]
3 to <4 74.3 (61.9-83.5) 897 (105/11.71)
[0.0003]
135 (15.05)
[0.0003]
≥ 4 74.1 (60.9-84) 1113 (283/25.43)
[<0.0002]
352 (31.63)
[<0.0002]
Total 72.6 (58.1-82.6) 8310 (751/9.04) 983 (11.83)
*p-values calculated for proportion difference against group below lactate group
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Data learning to guideline
translation
State Level
– Time to antibiotics target extended to 120 minutes
– Lactate trigger for high degree of adverse outcome risk ≥ 2 mmol/L included
Registry data
– SIRS algorithms performance
– Broad spectrum antibiotic usage and AMS initiatives
– Multicentre data validation of qSOFA and SOFA sepsis definitions
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What more have we done?
– Large dataset evaluation of sepsis algorithms in state-wide datasets
– Over 4 million events in NSW
– Cerner alert, Severe sepsis alert and qSOFA – sensitivity and specificity compared
– Multicentre ED data-sharing for validation and improvement of qSOFA
– 12555 events across multiple EDs in Australia and the Netherlands
– qSOFA – sensitivity 47.6% Specificity 89.1%
– LqSOFA(2) – sensitivity 65.5% Specificity 81.5%
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Why do QI Research
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Lessons learnt
– Clinician leadership locally critical
– Engagement carrots!
– Sustainability crucial
– Reproducibility
– DATA DATA DATA
– Implementation science – guidelines, knowledge generation, reflection,
adaptation and reimplementation + monitoring
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Future challenges
– Clinician decision support versus Clinician Automation
– How do we track clinicians’ behaviour and suspected infection cohorts?
– Triaging patients in ED
– Tracking clinician test ordering
– What is acceptable test performance statistics?
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Acknowledgements
– Dr Harvey Lander, Malcolm Green, Mary Fullick and CEC Sepsis kills team
– All NSW ED QI and staff – data in data out
– ED clinician leads at various sites and many others