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MERIT STUDY. Jack Chen MBBS PhD. Annual Health Service Research Meeting, 26-28 June 2005 Boston. Background. Hospitals are unsafe places Most patients who suffer adverse outcomes have documented deterioration

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slide1

MERIT STUDY

Jack Chen MBBS PhD

Annual Health Service Research Meeting, 26-28 June 2005 Boston

background
Background
  • 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”
  • Team is called by group pager and responds immediately
medical emergency team met concept
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
M.E.R.I.T Study

Medical

Early

Response

Intervention AND

Therapy

terminology
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
primary aim
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:
      • Unplanned ICU admissions (mainly general wards)
      • Cardiac Arrests (-NFR)
      • Unexpected deaths (-NFR)
study sample sample size at design stage
STUDY SAMPLE & SAMPLE SIZE: (at design stage)
  • 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%

Kerry & Bland (1998)

cluster randomised trial
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

FRAMEWORK FOR DESIGN, ANALYSIS & REPORTING

CONSORT STATEMENT: extension to cluster randomised trials

BMJ 2004;328:702

slide11

Assessed for eligibility (46 hospitals)

Excluded: 9 already had a MET system, 14 declined stating resource limitations

Two months baseline period (23 hospitals)

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

randomisation
RANDOMISATION
  • Stratified – blocked randomisation (4) based on teaching hospital status
  • Independent statistician
data collection
DATA COLLECTION
  • 18178 EVENT forms
  • 2418 corrections (13.3%)
  • Final EVENTS - 13142 after third round data consistency and logic checking
  • In-patients – 750,000
data collection1
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
data correction loop
DATA CORRECTION LOOP
  • 10 step standardised data entry and correction procedure
  • Weekly data entry meeting between statistician, data manager, IT manager and research assistants
slide16

Statistical methods used at cluster level and individual/multilevel (unadjusted and adjusted analyses)

weighting and adjustment
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
baseline data

BASELINE DATA

Non-MET MET

Hospitals

Number 11 12

Teaching 8 9

Non-teaching 3 3

Median bed size 315 364

(119-630) (88-641)

baseline data1

BASELINE DATA

Outcomes (incidence rate/Non-MET MET

1000 admissions)

Primary Outcome6.775 6.291

Cardiac arrests (- NFR) 2.606 1.597 Unplanned ICU admissions4.132 4.267

Unexpected deaths (- NFR)1.605 1.648

No significant differences

results difference between met non met hospitals incidence rate 1000 admissions
RESULTS - DIFFERENCE BETWEEN MET & NON-MET HOSPITALSIncidence Rate/1000 admissions
outcome rates 1000 admissions over baseline implementation and study periods
OUTCOME RATES/1000 ADMISSIONS OVER BASELINE, IMPLEMENTATION AND STUDY PERIODS

* Excludes patients with prior NFR orders

calling rate hospital 1 000 admissions

CALLING RATE/HOSPITAL/1,000 ADMISSIONS

CONTROL HOSPITALS MET HOSPITALS p

3.1 (1.5-5.8) 8.7 (3.5-16.5) <0.001

calls not associated with an event 1 000 admissions

CALLS NOT ASSOCIATED WITH AN EVENT/1,000 ADMISSIONS

CONTROL MET HOSPITALS HOSPITALS p

1.2 (0-3.3) 6.3 (2.5-11.2) <0.001

194/528 (36.7%) 1329/1886 (70.5%) <0.001

number of calls event

NUMBER OF CALLS/EVENT (%)

CONTROL MET

HOSPITALS HOSPITALS p

Cardiac 236/246 (96%) 244/250 (97.6%) 0.359 arrests

Unplanned 54/568 (9.5%) 209/611 (34.2%) 0.001 ICU admissions

Unexpected 5/59 (17.2%) 4/48 (8.3%) 0.420 deaths

events which had met criteria beforehand 15 min

EVENTS WHICH HAD MET CRITERIA BEFOREHAND (<15 min)

CONTROL MET

HOSPITALS HOSPITALS p

Cardiac 130/246 (53%) 115/250 (46%) 0.664 arrests

Unplanned ICU 121/568 (21%) 219/611 (36%) 0.090 admissions

Unexpected 10/29 (35%) 12/48 (25%) 0.473 deaths

events which had met criteria beforehand 15 min1

EVENTS WHICH HAD MET CRITERIA BEFOREHAND (>15 min)

CONTROL MET

HOSPITALS HOSPITALS p

Cardiac 109/246 (44%) 76/250 (30%) 0.031 arrests

Unplanned ICU 314/568 (55%) 313/611 (51%) 0.596 admissions

Unexpected 16/29 (55%) 24/58 (50%) 0.660 deaths

calls when met criteria were present 15 min before event

CALLS WHEN MET CRITERIA WERE PRESENT (<15 min before event)

CONTROL MET

HOSPITALS HOSPITALS p

Cardiac 124/130 (95%) 112/115 (97%) 0.545 arrests

Unplanned ICU 28/121 (23%) 112/219 (51%) 0.049 admissions

Unexpected 4/16 (25%) 2/12 (17%) 0.298 deaths

calls when met criteria were present 15 min before event1

CALLS WHEN MET CRITERIA WERE PRESENT (>15 min before event)

CONTROL MET

HOSPITALS HOSPITALS p

Cardiac 104/109 (95%) 72/76 (95%) 0.874 arrests

Unplanned ICU 27/314 (9%) 95/313 (30%) 0.009 admissions

Unexpected 4/16 (25%) 2/24 (8%) 0.231 deaths

nfr designation

NFR DESIGNATION

Non-MET MET

Prior NFR/1000 admissions 9.404 9.434

Prior NFR/Deaths 1.01 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

nfr orders in calls not associated with an event

NFR ORDERS IN CALLS NOT ASSOCIATED WITH AN EVENT

CONTROL MET

HOSPITALS HOSPITALS p

6/197 (3%) 106/1332 (8%) 0.048

differences between baseline and study period 1 000 admissions

DIFFERENCES BETWEEN BASELINE AND STUDY PERIOD/1,000 ADMISSIONS (%)

p

Primary outcome -0.85 (13%) 0.089

Cardiac arrests -0.68 (33%) 0.003

Unplanned ICU -0.23 (5%) 0.577

admission

Unexpected deaths -0.48 (30%) 0.010

in summary

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 summary1

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 summary2

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 summary3

IN SUMMARY

There was an increase in calls before ICU admission in MET hospitals but not before cardiac arrests or unexpected deaths

in summary4

IN SUMMARY

Less than half of all events had MET criteria documented beforehand

in summary5

IN SUMMARY

36.7% of all cardiac arrest calls were not in response to an event

in summary6

IN SUMMARY

Extreme variability in event rates amongst hospitals

in summary7

IN SUMMARY

23 hospitals – needed >100 to show a difference

Estimated primary outcome incidence 3% - actual rate 0.57%

Between hospital variability high

Intra-class correlation co-efficient high

why no significant improvement
Why no significant improvement ?
  • The MET may be ineffective;
  • The implementation is less optimal;
  • The participating hospitals are unrepresentative;
  • We studied wrong outcome;
  • 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

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
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)

merit study1
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

participating hospitals investigators research nurses
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

PARTICIPATING HOSPITALS, INVESTIGATORS & RESEARCH NURSES
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