ANZICS S&Q 2014 - Abstract Presentation: Joanne Molloy on How time of day for a rapid response call affect patient outcomes
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Joanne Molloy on how time of day for a rapid response call affect patient outcomes. Recorded at the ANZICS S&Q 2014: Rapid Response Teams.
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ANZICS S&Q 2014 - Abstract Presentation: Joanne Molloy on How time of day for a rapid response call affect patient outcomes
- 1. CRITICAL CARE LIAISON NURSE SERVICE Timing of Rapid Response Team Activations and Patient Outcomes Joanne Molloy RN BN Grad Dip-ICU, Grad Dip Training & Development, MN(Nurse Pract) Naomi Pratt RN BN Grad Cert-ICU, MN(Nurse Pract)
- 2. CRITICAL CARE LIAISON NURSE SERVICE Peninsula Health
- 3. CRITICAL CARE LIAISON NURSE SERVICE CCLN Service • The service commended in 2009 and has expanded greatly over the past 5 years: – 810 patients seen in first 12 months of service – 1736 patients in 2013 • The main referral sources to the service: – ICU patients discharged to the general wards – Rapid Response Calls – Direct referrals of high risk and deteriorating patients • >2000 RRCs attended, coordinated & managed by each of CCLN CNCs during service hours. • Leadership in data management, review and reporting of RRC events.
- 4. CRITICAL CARE LIAISON NURSE SERVICE Literature • 53% of RRC occurred between 1800-0800. (Jones, 2005) • RRT activations were more common during the day (The Medical Emergency Team End-of-Life Care investigators., 2013) • In-hospital cardiac arrests survival rates were substantially lower out of office hours. (Peberdy et al., 2008) • Higher rate of negative outcomes for patients who triggered a RRT call-out at night. (Morris et al., 2013)
- 5. CRITICAL CARE LIAISON NURSE SERVICE Objective The objective of this research project was to investigate the hypothesis that the patients who have had rapid response calls (RRC) during the day time have better outcomes.
- 6. CRITICAL CARE LIAISON NURSE SERVICE Method • Approval for this project was obtained from the Peninsula Health Human Research and Ethics Committee. • A retrospective review of RRCs during 2012 was conducted using existing hospital databases. • The RRCs that occurred from 0800 – 1759 were considered “in-hours” and those that happened 1800-0759 were “out-of-hours”.
- 7. CRITICAL CARE LIAISON NURSE SERVICE Patient Population • Inclusion criteria: RRC on admitted adult inpatients > 18 years of age at Frankston Hospital during 2012 • Exclusion criteria: RRC made for adult patients located within the ICU or emergency department
- 8. CRITICAL CARE LIAISON NURSE SERVICE Intervention • Events were classified as a MET or Respond Blue based upon the interventions performed by the RRC team. • Respond Blue classification: cardiopulmonary arrest chest compression defibrillation advanced airway support +/- endo-tracheal intubation
- 9. CRITICAL CARE LIAISON NURSE SERVICE Data Collection • The data collected included: Patient demographics, Date and time of the RRC, Outcomes immediately after the RRC, at 24 hours and hospital separation (death, discharge and transfer) • Additional data for patients admitted to ICU included: Acute Physiology and Chronic Health Evaluation (APACHE) III score ICU length of stay (LOS) Death in ICU
- 10. CRITICAL CARE LIAISON NURSE SERVICE Results Total RRCs 892 In hours 537 MET 510 Blue 27 Out of hours 355 MET 325 Blue 30
- 11. CRITICAL CARE LIAISON NURSE SERVICE Patient Characteristics In-hours Out-of-hours Number of RRCs 537 (60.2%) 355 (39.8%) Age (SD) 70.80 (17.9) 70.04 (17.9) Sex % Male 247 (46%) 175 (49%) Admitting Unit: Medical 337 (62.7%) 231 (65.1%) Surgical 166 (30.9%) 96 (27.0%) Mental Health 25 (10.2%) 16 (4.5%) Obstetrics 8 (1.5%) 11 (3.1%)
- 12. CRITICAL CARE LIAISON NURSE SERVICE 0 25 50 75 0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 RRCs distribution In hours Out of hours Straight black line = 37 average expected calls over 24-hour period
- 13. CRITICAL CARE LIAISON NURSE SERVICE Respond Blues In-hours (n=537) Out-of-hours (n=355) ρ Values 27/537 (5.0%) 30/355 (8.4%) ρ < 0.05
- 14. CRITICAL CARE LIAISON NURSE SERVICE ICU Admissions In-hours (n=537) Out-of-hours (n=355) Immediate 66/537 (12.3%) 69/355 (19.4%) 24 hours post 75/537 (14.0%) 73/355 (20.6%) APACHE III Mean (SD) 18.17 (6.67) 18.38 (6.56) ICU LOS Mean (SD) 6.09 (9.56) 4.43 (5.82)
- 15. CRITICAL CARE LIAISON NURSE SERVICE RRC Mortality In-hours (n=537) Out-of-hours (n=355) Immediate 7/537 (1.3%) 19/355 (5.4%) 24 hours post 35/537 (6%) 35/355 (9.9%) Hospital Mortality 134/537 (25%) 155/355 (35.5%)
- 16. CRITICAL CARE LIAISON NURSE SERVICE Discussion • Less observation and direct patient care • Less staff both nursing, medical and allied health • Less skilled practitioners, both those calling the RRC and those responding i.e.. consultants, specialist teams, CCLNs etc. • Detection of deterioration may delayed or not detected at all
- 17. CRITICAL CARE LIAISON NURSE SERVICE Conclusion • Patients experiencing an out-of-hours RRC were more likely to have a Code Blue call, have unplanned ICU admissions and were associated with higher mortality rates at all outcome points. • This has significant implications for patient mortality and morbidity The next step: • A prospective study looking in greater detail at patient acuity at time of RRC and the actions of the responders during the RRC.
Editor's Notes
- We will present the Background to our study and some of the relevant literature. We present our objective, methods and results. And then discuss this findings and our conclusions.
- Frankston Hospital has 336 beds which services a population of 300K, which can increase by a 100K over the summer holiday period. Greater than average population over the age of 65 with 21% (state average 14%) It does no provide cardiac surgery, neurosurgery or major trauma service. The ED is one of the states busiest, with close to 60 000 Presenations. Frankston Hospital Intensive Care Unit (ICU) is a 15-bed Level-III ICU with capacity for 9 ICU equivalent. There were over 1100 admissions in 2013.
- The CCLN service at Peninsula health was first established in 2009 and has developed and expanded over the past 5 years. The CCLN team provides a consistent service bring corporate knowledge to the bedside with an understanding of the hospital and ward environments including identifying the best environment for deteriorating patients within the organisation. We work and communicate across all skill levels from graduates to consultants. At PH the medical emergency team or MET and respond blue teams answers calls made by frontline medical and nursing staff in response to clinical triggers. The team comprises of a senior ICU and medical registrar and Critical care trained nurse. The CCLN attends the RRC during their service hours and provides clinical leadership. As a result over the past 5 years, Naomi and I have attended, coordinated and managed > 2000 RRCs between us. We have also responsible for maintaining the RRC database, monthly quality reporting and the identification of cases for review.
- While routinely evaluating our RRS data at PH, we observed differences between patient outcomes out-of-hours With this in mind we went looking for current research evidence and found limited studies looking at the variability of patient outcomes according to time of day
- After we received ethics approval, we conducted a retrospective review of RRCs during 2012 using existing hospital databases. RRC data is collected at the time of the RRC by clinicians attending the event using a duplicate form. A master log is maintained and this data is entered into an ACCESS database. We also used other hospital databases including ipm and the ICU ANZICS database. The RRCs that occurred from 0800 – 1759 were considered “in-hours” and those that happened 1800-0759 were “out-of-hours”.
- These inclusion/exclusion criteria and events classification were used to match patient population characteristics of other published RRC studies.
- Advanced airway support included use of oral-pharyngeal or naso-pharyngael airways and the use of a bag-valve mask circuit.
- Using a customised data collection form, and entered into an excel spread sheet. This was then analysed using statistics software by Dr Ravi Tiruvoipati, using students t-test and chai square to obtain results.
- Total of 892 calls that met the inclusion/exclusion criteria. 60% occurred during in-hours which represents only 40% of the 24 hours So we can already see an uneven distribution of RRC throughout the 24 hrs. which is demonstrated in a later graph.
- Two groups are quite homogenous in regards to age, sex and admitting unit There was no statistical significance btw the groups
- Activation of RRC was not uniform over the 24 hour cycle. The average hourly activation of RRC was 37 calls per hour over the 24 hr period for the 12months. We found that a total of 60% of the RRC occurred during daylight hours. This coincides with findings published this year 2013 by the “MET end of life care investigator’s” who collected data on all RRC calls in 1 month during 2009 from 7 different institutions (Australia, Canada and Sweden) who also found a peaks first thing in the morning, with RRC less common overnight. REFERENCE
- There were 57 blues during 2012 with 47% in-hours and 53% occurring out-of-hours There was a significant difference in this with the increased likelihood that a RRC which occured out of hours would be a BLUE this achieved a p value of < 0.05
- From the 892 RRCs in 2012, 135 patients transferred to ICU immediately after RRC – 12.3% in hours – and 19.4% out of hours. You were more likely to be require transfer to ICU either immediately or within the first 24hrs after a RRC if your call occurred out-of-hours. Overall if you had a RRC out of hours you were more likely to be transferred to ICU with 20% of RRC transferred to ICU. There was no significant differences noted between APACHE, ICU LOS or ICU mortality between the two groups. This may be due to the sample size. Mortality ICU – Not significant between two groups – however trend towards higher in out of hours group with 23% dying in ICU and 16% in hour did not reach significance –likely due to sample size.
- The results from our analysis demonstrated that the RRC that occurred out-of-hours were associated with a significantly worse mortality rate – This was significant irrespective of the time point used for analysis – with significant difference both immediately, at 24hr and whether the patient survived to hospital discharge. Hospital LOS – Mean – In hours – 15.65 days, out of hours – 16.00 days Combined data for all RRC during 2012 Overall mortality rate of 32.4% Immediate mortality rate – 3%, 24hr – 7.8% Hospital mortality rate at Frankston Hospital is close to 2%
- Looking at our results – What is different out-of-hours? Are the patients different? Our data showed a homogenous patient group Vital signs spaced more widely overnight – peak levels of cardiac arrest detection corresponded to overnight nursing routine Not just less staff itself, tend to be covering staff – staff not familiar with the patient Characteristics of RRC team Delayed MET activation has been independently associated with greater risk of unplanned ICU admissions and hospital mortality (Calzavacca et al., 2010) Delayed activation of MET team strongest independent predictor in mortality of patients receiving a RRT review (Calzavacca et al., 2008) Out of hours RRC – found to have higher mortality rate at all outcome points, more likely to be a Code Blue call, and be admitted to ICU.
- Staffing and resource allocation should be reviewed in the light of these findings. Utilisation of high acuity or observing areas (HDU/ICU) As well as skill level of both the activator and responder