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MERIT STUDY

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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MERIT STUDY

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

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

  13. DATA COLLECTION • 18178 EVENT forms • 2418 corrections (13.3%) • Final EVENTS - 13142 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. 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-MET MET Hospitals Number 11 12 Teaching 8 9 Non-teaching 3 3 Median bed size 315 364 (119-630) (88-641)

  19. 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

  20. RESULTS - DIFFERENCE BETWEEN MET & NON-MET HOSPITALSIncidence Rate/1000 admissions

  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 HOSPITALS MET HOSPITALS p 3.1 (1.5-5.8) 8.7 (3.5-16.5) <0.001

  23. 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

  24. 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

  25. 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

  26. 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

  27. 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

  28. 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

  29. 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

  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. 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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