Background
MERIT STUDY
• Hospitals are unsafe places
• Most patients who suffer adverse outcomes have
documented deterioration
• Medical Emergency Team system educates and
empowers staff to call a skilled team in response
to specific criteria or if “worried”
Jack Chen MBBS PhDAnnual Health Service Research
• Team is called by group pager and responds
immediately
Meeting, 26-28 June 2005 Boston
MET Calling Criteria
MEDICAL EMERGENCY
TEAM (MET) CONCEPT
• Criteria identifying seriously ill early
• Rapid response to those patients
(similar to a cardiac arrest team)
• Resuscitation and triage
M.E.R.I.T Study
Medical
Early
Response
Intervention AND
Therapy
Terminology
• CAT - Cardiac arrest team
• NFR - Not for resuscitation (DNR, DNAR)
• Events –
–
–
–
Deaths without NFR
Cardiac arrests without NFR
Unplanned ICU admissions
MET and CAT calls independent of above
SRF1
STUDY SAMPLE & SAMPLE
SIZE:
(at design stage)
PRIMARY AIM
• The primary aim of this study was to test the
hypothesis that the implementation of the
hospital-wide MET system will reduce the
aggregate incidence of:
• 23 hospitals with at least 20,000 estimated
admissions per year
• This will provide us with a 90% chance to
detect a 30% reduction in the incidence at
the significant level of 5%
– Unplanned ICU admissions (mainly general
wards)
– Cardiac Arrests (-NFR)
– Unexpected deaths (-NFR)
Kerry & Bland (1998)
SRF2
CLUSTER
RANDOMISED TRIAL
• More complex to design
• More participants to obtain equivalent statistical
power
• Key determinants are number of individual units;
the intracluster correlation; and cluster size
• More complex analysis than ordinary randomised
trial
• Randomised at one time, rather than one at a
time
FRAMEWORK FOR DESIGN,
ANALYSIS & REPORTING
CONSORT STATEMENT:
extension to cluster randomised
trials
BMJ 2004;328:702
Assessed for eligibility (46 hospitals)
Excluded: 9 already had a MET system, 14
declined stating resource limitations
Two months baseline period (23 hospitals)
RANDOMISATION
Randomized (23 hospitals)
Allocated to MET: (12 hospitals) median admission
number at the baseline = 6494, range: 958 - 11026
Allocated to control: (11 hospitals) median
admission number over the baseline =5856; range:
1937 –7845.
Four months implementation of MET with continued
data collection
Four months period with continued data collection
Six months study period with MET system operational
Six months study period
Lost to follow up: none
Analyzed: 12 hospitals, median admission number
over the study period = 18512; range: 2667 - 33115
Lost to follow up: none
Analyzed: 11 hospitals, median admission number
over the study period = 17555; range: 5891 - 22338
• Stratified – blocked randomisation
(4) based on teaching hospital
status
• Independent statistician
Slide 8
SRF1
Design rather than designing
Kerry and Bland rather than Blank?
Simon Finfer, 10/3/2004
Slide 10
SRF2
Designed before this so framework for analysis and reporting - predetermined analysis plan with
additional reporting as suggested by consort statement
Simon Finfer, 10/3/2004
DATA COLLECTION
DATA COLLECTION
•
•
•
•
Log books
Scannable technology
Photocopy forms kept by hospital
Filing of forms and storage in Simpson
Centre
• Web-based tracking data
• 4 databases
• Separate neutral data repository
• 18178 EVENT forms
• 2418 corrections (13.3%)
• Final EVENTS - 13142 after third round data
consistency and logic checking
• In-patients – 750,000
DATA CORRECTION
LOOP
• 10 step standardised data entry
and correction procedure
• Weekly data entry meeting
between statistician, data manager,
IT manager and research
assistants
WEIGHTING AND
ADJUSTMENT
• Weighting: by the number of admissions during
the study period
• Cluster Adjustment for: teaching hospital
status, bed size and baseline outcome variables,
with hospitals weighted by the number of
admissions during the study period
• Multilevel model adjustment for: teaching
hospital status, bed size, age and gender of the
patients
Statistical methods used at cluster level and
individual/multilevel (unadjusted and adjusted analyses)
Types of
analyses
Cluster (Hospital) Level
Individual / Multilevel
Unadjusted
analysis
Weighted t-test
(weighted by hospital admission
number)
Rao-Scott Chi-square
Adjusted t-test;
Adjusted analysis:
Analytically weighted regression
(weighted by the admission number of
the hospital) adjusting for teaching
status, number of bed and baseline
outcome
Multi-level logistic regression
(adjusting for teaching status,
number of bed, age and gender
of the patients)
BASELINE DATA
Non-MET
Hospitals
Number
Teaching
Non-teaching
Median bed size
11
8
3
315
(119-630)
MET
12
9
3
364
(88-641)
SRF3
RESULTS - DIFFERENCE BETWEEN
MET & NON-MET HOSPITALS
Incidence Rate/1000 admissions
BASELINE DATA
Outcomes (incidence rate/
1000 admissions)
Non-MET
MET
Primary Outcome
6.775
6.291
Cardiac arrests (- NFR)
2.606
Unplanned ICU admissions 4.132
Unexpected deaths (- NFR) 1.605
1.597
4.267
1.648
OUTCOMES
NONMET
MET
% AGE
CHANGE
P
Primary outcome
5.860
5.306
10%
0.804
Cardiac arrest – NFR
1.640
1.31
25.1%
0.306
Unplanned ICU
admission
4.683
4.185
12%
0.899
Unexpected deaths (–
NFR)
1.175
1.063
10%
0.564
No significant differences
SRF4
OUTCOME RATES/1000 ADMISSIONS OVER
BASELINE, IMPLEMENTATION AND STUDY PERIODS
Aggregate outcome
Control
Hospitals
1
2
3
4
5
6
7
8
9
10
11
MET
Hospitals
12
13
14
15
16
17
18
19
20
21
22
23
Cardiac arrests
Unplanned ICU admissions
Unexpected deaths
Baseline
Implementation
Study
Baseline
Implementation
Study
Baseline
Implementation
Study
Baseline
Implementation
Study
2.07
3.77
5.10
5.47
5.72
5.98
6.83
7.86
9.04
9.42
13.29
2.65
6.03
4.47
3.55
4.33
2.82
6.28
5.92
10.57
7.82
18.10
3.05
6.32
4.21
2.64
3.53
2.73
5.07
4.72
8.83
7.63
13.92
1.03
1.74
2.29
2.02
3.25
3.76
3.32
1.97
2.85
2.49
3.95
1.45
2.24
1.94
1.31
3.16
1.32
3.29
1.27
1.76
3.41
7.36
2.03
1.93
1.75
1.15
1.47
1.54
1.69
1.11
0.85
1.88
2.65
0.52
1.45
3.31
2.59
2.32
2.22
2.54
6.46
6.66
7.34
10.06
0.24
3.71
2.74
1.85
1.58
1.50
3.29
4.93
8.30
4.97
11.93
0.68
4.49
2.69
1.50
2.39
1.25
3.25
3.61
7.81
5.99
12.07
0.52
1.45
1.15
2.74
1.70
2.22
3.51
1.40
0.95
1.66
0.36
2.41
1.78
1.14
1.31
0.75
1.15
2.39
0.71
1.26
1.00
1.79
1.53
1.31
0.94
0.95
0.60
0.74
1.50
1.02
1.19
1.45
1.72
0.58
1.60
1.85
2.95
3.87
4.26
6.39
6.39
7.29
7.44
13.04
19.83
0.89
3.36
5.80
3.40
3.60
4.19
7.27
4.48
4.98
6.18
6.89
14.43
1.31
4.61
3.42
3.22
2.86
4.66
7.08
4.44
5.90
7.07
5.59
12.75
0.29
0.37
0.46
1.03
2.35
0.82
3.05
4.35
1.56
2.45
1.40
1.04
0.59
1.16
0.67
2.03
1.56
0.75
2.75
2.60
1.70
1.89
1.46
2.96
1.11
0.78
0.45
1.04
1.24
1.49
2.34
1.62
2.05
1.78
1.07
0.75
0.00
1.23
1.39
2.05
1.88
2.46
3.34
2.04
4.17
5.35
11.64
15.66
0.00
2.39
4.46
1.57
2.16
3.60
4.90
2.02
3.01
4.48
5.43
11.10
0.10
4.16
2.38
2.22
1.99
2.87
4.84
2.67
3.16
5.74
4.66
10.87
0.58
0.37
0.46
0.64
0.82
1.48
2.03
2.18
2.86
1.27
1.86
5.22
0.74
0.52
0.67
1.24
0.66
0.92
1.30
1.44
1.05
1.18
1.04
4.07
1.01
0.45
0.89
0.68
0.66
1.49
1.27
1.38
1.37
1.27
0.40
1.88
CALLING RATE/HOSPITAL/1,000
ADMISSIONS
CONTROL HOSPITALS
3.1 (1.5-5.8)
MET HOSPITALS
8.7 (3.5-16.5)
p
<0.001
* Excludes patients with prior NFR orders
SRF5
CALLS NOT ASSOCIATED WITH
AN EVENT/1,000 ADMISSIONS
CONTROL
HOSPITALS
1.2
(0-3.3)
194/528 (36.7%)
MET
HOSPITALS
6.3
(2.5-11.2)
1329/1886 (70.5%)
p
<0.001
<0.001
NUMBER OF CALLS/EVENT
(%)
Cardiac
arrests
Unplanned
ICU admissions
Unexpected
deaths
CONTROL
HOSPITALS
236/246 (96%)
MET
HOSPITALS
244/250 (97.6%)
p
0.359
54/568 (9.5%)
209/611 (34.2%)
0.001
5/59
4/48
0.420
(17.2%)
(8.3%)
Slide 20
SRF3
no % change - % difference
Simon Finfer, 10/3/2004
Slide 21
SRF4
same comment as to Rinaldo - too much on this slide
Simon Finfer, 10/3/2004
Slide 23
SRF5
Slides 19 to 27 are not intuitive and will need careful explaining
Simon Finfer, 10/3/2004
EVENTS WHICH HAD MET CRITERIA
BEFOREHAND (<15 min)
Cardiac
arrests
Unplanned ICU
admissions
Unexpected
deaths
CONTROL
HOSPITALS
130/246 (53%)
MET
HOSPITALS
115/250 (46%)
p
0.664
121/568 (21%)
219/611 (36%)
0.090
10/29
12/48
0.473
(35%)
(25%)
CALLS WHEN MET CRITERIA WERE
PRESENT (<15 min before event)
Cardiac
arrests
Unplanned ICU
admissions
Unexpected
deaths
CONTROL
HOSPITALS
124/130 (95%)
MET
HOSPITALS
112/115 (97%)
p
0.545
28/121 (23%)
112/219 (51%)
0.049
4/16
2/12
0.298
(25%)
(17%)
NFR DESIGNATION
Prior NFR/1000 admissions
Prior NFR/Deaths
Non-MET
9.404
1.01
MET
9.434
EVENTS WHICH HAD MET CRITERIA
BEFOREHAND (>15 min)
Cardiac
arrests
Unplanned ICU
admissions
Unexpected
deaths
CONTROL
HOSPITALS
109/246 (44%)
MET
HOSPITALS
76/250 (30%)
p
0.031
314/568 (55%)
313/611 (51%)
0.596
16/29
24/58
0.660
(55%)
(50%)
CALLS WHEN MET CRITERIA WERE
PRESENT (>15 min before event)
Cardiac
arrests
Unplanned ICU
admissions
Unexpected
deaths
CONTROL
HOSPITALS
104/109 (95%)
MET
HOSPITALS
72/76 (95%)
p
0.874
27/314 (9%)
95/313 (30%)
0.009
4/16
2/24
0.231
(25%)
(8%)
NFR ORDERS IN CALLS NOT
ASSOCIATED WITH AN EVENT
1.05
NFR made at time of event/
1000 admissions
0.274
0.799
NFR made at time of event/
1000 events
17.189
38.424
CONTROL
HOSPITALS
6/197 (3%)
MET
HOSPITALS
106/1332 (8%)
p
0.048
SRF6
DIFFERENCES BETWEEN BASELINE
AND STUDY PERIOD/1,000
ADMISSIONS (%)
Primary outcome
Cardiac arrests
Unplanned ICU
admission
Unexpected deaths
-0.85 (13%)
-0.68 (33%)
-0.23 (5%)
p
0.089
0.003
0.577
-0.48 (30%)
0.010
IN SUMMARY
• There was no STATISTICALLY SIGNIFICANT
decrease in the incidence of the primary
outcome in MET hospitals
• There was no STATISTICALLY SIGNIFICANT
decrease in the incidence of the secondary
outcomes in MET hospitals
• WHEN ALL HOSPITALS CONSIDERED
TOGETHER, The incidence of cardiac arrests
and unexpected deaths decreased from
baseline to study period
IN SUMMARY
There was an increase in calls
before ICU admission in MET
hospitals but not before
cardiac arrests or unexpected
deaths
IN SUMMARY
• Randomisation was successful and
appeared balanced
• Call rate was much higher in MET
hospitals mostly due to calls not
associated with events
• More of these event-free calls led to NFR
orders in MET hospitals, but overall NFR
rate was unaffected
IN SUMMARY
If MET criteria were
documented and followed by an
event, only a minority of
patients overall had an actual
MET call made
IN SUMMARY
Less than half of all
events had MET
criteria documented
beforehand
Slide 31
SRF6
Need to make clear this is all hospitals together, these data better presented as both the two groups
seperated and together as in the table in the paper
Simon Finfer, 10/3/2004
IN SUMMARY
IN SUMMARY
36.7% of all cardiac
arrest calls were not in
response to an event
Extreme variability in
event rates amongst
hospitals
Why no significant improvement ?
IN SUMMARY
•The MET may be ineffective;
23 hospitals – needed >100 to
show a difference
•The implementation is less optimal;
• Estimated primary outcome incidence
3% - actual rate 0.57%
• Between hospital variability high
• Intra-class correlation co-efficient high
•We studied wrong outcome;
•The participating hospitals are unrepresentative;
•The documentation of the vital signs is poor;
•The calling rate is low given the existing calling criteria;
•The contamination;
•The low statistical power
CONCLUSIONS
• First large hospital system change trial ever
conducted according to rigorous principles of
design and statistical analysis
• It encompassed close to 750,000 admissions
• Although we did not demonstrate a significant
difference in the primary outcome, the study
produced a large body of useful data on patient
care, documentation and outcomes, which will
hopefully illuminate future studies
MERIT STUDY
CONDUCTED BY:
Simpson Centre for Health Services
Research
ANZICS Clinical Trials Group
FUNDED BY:
NHMRC
Australian COUNCIL FOR Quality and Safety
in Health Care (AQSHC)
SRF9
PARTICIPATING HOSPITALS,
INVESTIGATORS & RESEARCH NURSES
MERIT STUDY
MANAGEMENT COMMITTEE
Prof. Ken Hillman (Chair)
Prof. Rinaldo Bellomo
Mr. Daniel Brown
Dr. Jack Chen
Dr. Michelle Cretikos
Dr. Gordon Doig
Dr. Simon Finfer
Dr. Arthas Flabouris
•
•
•
•
•
•
•
•
•
•
•
Bendigo – John Edington, Kath Payne
Box Hill – David Ernest, Angela Hamilton
Broken Hill – Coral Bennet, Linda Peel,
Mathew Oliver, Russell Schedlich,
Sittampalam Ragavan, Linda Lynott
Calvery – Marielle Ruigrok, Margaret
Willshire,
Canberra – Imogen Mitchell, John
Gowardman, David Elliot, Gillian Turner,
Carolyn Pain
Flinders – Gerard O’Callaghan, Tamara Hunt
Geelong – David Green, Jill Mann, Gary
Prisco
Gosford – Sean Kelly, John Albury
John Hunter – Ken Havill, Jane O’Brien
Mackay – Kathryn Crane, Judy Struik
Monash – Ramesh Nagappan, Laura Lister
•
•
•
•
•
•
•
•
•
•
•
•
Prince of Wales – Yahya Shahabi, Harriet
Adamsion
Queen Elizabeth – Sandy Peake, Jonathan
Foote
Redcliffe – Neil Widdicombe, Matthys Campher,
Sharon Ragou, Raymond Johnson
Redland – David Miller, Susan Carney
Repatriation General – Gerard O’Callaghan,
Vicki Robb
Royal Adelaide – Marianne Chapman, Peter
Sharley, Deb Herewane, Sandy Jansen
Royal North Shore - Simon Finfer, Simeon Dale
St. Vincent’s – John Santamaria, Jenny Holmes
Townsville – Michael Corkeron, Michelle
Barrett, Sue Walters
Wangaratta – Chris Giles, Deb Hobijn
Wollongong - Sunny Rachakonda, Kathy
Rhodes
Wyong – Sean Kelly, John Albury
Slide 43
SRF9
Listing should generally be alphabetical after the chair, if we are putting titles then Rinaldo is a
Professor
Simon Finfer, 10/3/2004