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MERIT STUDY Jack Chen MBBS PhD Annual Health Service Research Meeting, 26-28 June 2005 Boston.

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Presentation on theme: "MERIT STUDY Jack Chen MBBS PhD Annual Health Service Research Meeting, 26-28 June 2005 Boston."— Presentation transcript:

1 MERIT STUDY Jack Chen MBBS PhD Annual Health Service Research Meeting, June 2005 Boston

2 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

3 MEDICAL EMERGENCY TEAM (MET) CONCEPT Criteria identifying seriously ill early Rapid response to those patients (similar to a cardiac arrest team) Resuscitation and triage

4 MET Calling Criteria

5 M.E.R.I.T Study Medical Early Response Intervention AND Therapy

6 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

7 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)

8 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)

9 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

10 FRAMEWORK FOR DESIGN, ANALYSIS & REPORTING CONSORT STATEMENT: extension to cluster randomised trials BMJ 2004;328:702

11 Assessed for eligibility (46 hospitals) Excluded: 9 already had a MET system, 14 declined stating resource limitations Randomized (23 hospitals) Two months baseline period (23 hospitals) Allocated to MET: (12 hospitals) median admission number at the baseline = 6494, range: Allocated to control: (11 hospitals) median admission number over the baseline =5856; range: 1937 –7845. Lost to follow up: none Analyzed: 12 hospitals, median admission number over the study period = 18512; range: Lost to follow up: none Analyzed: 11 hospitals, median admission number over the study period = 17555; range: Four months implementation of MET with continued data collection Four months period with continued data collection Six months study period with MET system operationalSix months study period

12 RANDOMISATION Stratified – blocked randomisation (4) based on teaching hospital status Independent statistician

13 DATA COLLECTION EVENT forms 2418 corrections (13.3%) Final EVENTS after third round data consistency and logic checking In-patients – 750,000

14 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

15 DATA CORRECTION LOOP 10 step standardised data entry and correction procedure Weekly data entry meeting between statistician, data manager, IT manager and research assistants

16 Types of analyses Cluster (Hospital) LevelIndividual / 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) Statistical methods used at cluster level and individual/multilevel (unadjusted and adjusted analyses)

17 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

18 BASELINE DATA Non-METMET Hospitals Number Teaching 8 9 Non-teaching 3 3 Median bed size ( ) (88-641)

19 BASELINE DATA Outcomes (incidence rate/Non-MET MET 1000 admissions) Primary Outcome Cardiac arrests (- NFR) Unplanned ICU admissions Unexpected deaths (- NFR) No significant differences

20 RESULTS - DIFFERENCE BETWEEN MET & NON-MET HOSPITALS Incidence Rate/1000 admissions OUTCOMESNON- MET MET% AGE CHANGE P Primary outcome %0.804 Cardiac arrest – NFR %0.306 Unplanned ICU admission %0.899 Unexpected deaths (– NFR) %0.564

21 OUTCOME RATES/1000 ADMISSIONS OVER BASELINE, IMPLEMENTATION AND STUDY PERIODS * Excludes patients with prior NFR orders

22 CALLING RATE/HOSPITAL/1,000 ADMISSIONS CONTROL HOSPITALSMET HOSPITALS p 3.1 ( ) 8.7 ( ) <0.001

23 CALLS NOT ASSOCIATED WITH AN EVENT/1,000 ADMISSIONS CONTROL MET HOSPITALS HOSPITALS p 1.2 (0-3.3)6.3 ( )< /528 (36.7%)1329/1886 (70.5%)<0.001

24 NUMBER OF CALLS/EVENT (%) CONTROL MET HOSPITALSHOSPITALSp 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

25 EVENTS WHICH HAD MET CRITERIA BEFOREHAND (<15 min) CONTROL MET HOSPITALSHOSPITALSp Cardiac 130/246 (53%)115/250 (46%)0.664 arrests Unplanned ICU121/568 (21%)219/611 (36%)0.090 admissions Unexpected 10/29 (35%)12/48 (25%)0.473 deaths

26 EVENTS WHICH HAD MET CRITERIA BEFOREHAND (>15 min) CONTROL MET HOSPITALSHOSPITALSp Cardiac 109/246 (44%)76/250 (30%)0.031 arrests Unplanned ICU314/568 (55%)313/611 (51%)0.596 admissions Unexpected 16/29 (55%)24/58 (50%)0.660 deaths

27 CALLS WHEN MET CRITERIA WERE PRESENT (<15 min before event) CONTROL MET HOSPITALSHOSPITALSp Cardiac 124/130 (95%)112/115 (97%)0.545 arrests Unplanned ICU28/121 (23%)112/219 (51%)0.049 admissions Unexpected 4/16 (25%)2/12 (17%)0.298 deaths

28 CALLS WHEN MET CRITERIA WERE PRESENT (>15 min before event) CONTROL MET HOSPITALSHOSPITALSp Cardiac 104/109 (95%)72/76 (95%)0.874 arrests Unplanned ICU27/314 (9%)95/313 (30%)0.009 admissions Unexpected 4/16 (25%)2/24 (8%)0.231 deaths

29 NFR DESIGNATION Non-MET MET Prior NFR/1000 admissions Prior NFR/Deaths NFR made at time of event/ 1000 admissions NFR made at time of event/ 1000 events

30 NFR ORDERS IN CALLS NOT ASSOCIATED WITH AN EVENT CONTROL MET HOSPITALS HOSPITALS p 6/197 (3%)106/1332 (8%) 0.048

31 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

32 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

33 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

34 IN SUMMARY If MET criteria were documented and followed by an event, only a minority of patients overall had an actual MET call made

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

36 IN SUMMARY Less than half of all events had MET criteria documented beforehand

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

38 IN SUMMARY Extreme variability in event rates amongst hospitals

39 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

40 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

41 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

42 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)

43 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

44 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


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