1. The Second Patient Report of the National Emergency Laparotomy Audit

2. NELA Aims
To improve the quality of care provided to patients undergoing emergency laparotomy through provision of high quality data
Facilitate local quality improvement
Provide comparative data at hospital level to allow identification of high performing sites
Only reporting at hospital level
emergency laparotomy care is a “team game”
clinician level reporting is not appropriate

3. Year 2 (Dec 2014-Nov 2015) 23000+ patients (70%) from 186 hospitals
Some things have improved
Personal practice
Some have yet to improve
Organisational change
Still considerable variation
70% case ascertainment for year 2
Target for year 3 is 80% 3

4. Improvements seen in: Case ascertainment 70% from 65%
CT scanning & reporting 72% from 68%
Preop risk assessment 64% from 56%
Consultant surgeon and anaesthetist presence in theatre 70% from 65%
High risk patients
Consultant anaesthetists present 82%
Consultant surgeon present 89%
still differences between “in” & “out of hours”.

5. Risk Assessment 56% vs 64%
Assessment, appreciation and communication of risk leads to better standards of care
Improvements seen in risk assessment. The red arrows on these time series graphs mark the time where the 1st results became available, and began to be disseminated to clinicians. Many of these graphs show a reassuring upturn from this point.
Well done!!

6. P-POSSUM calibration
A note about P-POSSUM. It is generally recognised that P-POSSUM overpredicts. This graph shows the degree of correlation between observed outcomes vs Possum predicted outcome. Up to ~15% predicted risk, POSSUM is reasonably accurate. From a clinical perspective, P-POSSUM is therefore still useful for identifying patients who fall in a lower, high or highest risk category, in order to make decisions about the need for resources such as critical care. However we would urge caution in reliance on P-POSSUM when used to guide clinical decision making at high levels of predicted mortality as it overestimates risk of death by a factor of approximately two.

7. Risk of death and observed ONS 30-day & 90-day mortality
Median calculated preoperative P-POSSUM risk of death, and observed ONS 30-day and 90-day mortality by documented preoperative risk category (Year 2 data)
The “not documented” group have mortality profile similar to the “high risk” group

8. Consultant presence & critical care admission according to risk
Intraoperative presence of both a consultant surgeon and consultant anaesthetist, and direct postoperative admission to critical care by documented preoperative risk category (Year 2 data)
The “not documented” group have a lower level of consultant input compared to the “high risk” group.

9. Consultant presence (>5% risk)
Upturn since results disseminated from year 1

10. Critical care admission 83% vs 85%
Interesting dip in Critical Care admissions over winter for the 5-10% group, whereas the higher risk group is more constant

11. Yet to improve Antibiotic administration: 20% did not get pre-op
Delays to theatre: 70% of most urgent patients
Critical care for highest risk patients remained ~85%
Elderly care input only 10%

12. Proportion of hospitals meeting standards
Whilst the number of hospitals rated “green” (ie meeting standards) remains relatively low for some measures, there has been improvement since year 1 as shown on the next graphs

13. Number of standards being met
Number of hospitals rated Green
This shows the number of hospitals meeting 1,2,3 etc standards of care (rated “green”). Y-axis shows number of hospitals. X-axis shows the number of standards met at each hospital. Eg ~42 hospitals met 4 standards. Less than 15 hospitals met 6 standards.
Blue is year 1, red year 2
Number of standards met

14. Number of standards being met
Number of hospitals rated Green
The curve has shifted right in year 2, ie more hospitals meet more standards, although there is still considerable way to go.
Many hospitals are actually rated “yellow” meaning they may be close to meeting standards in many areas of care.
Number of standards met

15. Outcomes

16. ONS 30-day and 90-day mortality by age group (Year 1 and Year 2 data)
Mortality is age related. Almost half the patient are >70 years old.
Given the age-related mortality, there is an argument that the elderly should be admitted to Critical Care regardless of POSSUM risk, especially as POSSUM categorises anyone over 70 as having the same risk.

17. Funnel plot of risk-adjusted ONS 30-day mortality (Year 1 and Year 2 data)
No actual outliers with risk adjusted mortality >3SD above mean. However there are a few who are “alerts” with mortality between 2 & 3 SDs.
From year 3, NELA will be naming outliers and those hospitals above 2SDs for 2 out of 3 years on a rolling basis.

18. Trend in the overall ONS 30-day & 90-day mortality
Trend in the overall ONS 30-day and 90-day mortality percentage rate (split medians denoting the change from Year 1 to Year 2)
Reassuring drop around time year 1 results disseminated. We are hopeful that this trend will continue

19. Length of Stay: £200 million +
Improvements in length of stay equate to ~£200 million savings per year. This is a significant finding, as it means that hospitals should see financial advantages in improving patient care, and can help win the argument that it is worthwhile investing in improved resources.
Mean LOS 18.1 days  16.3 days £22 (€30) million savings

20. Other outcomes Length of stay Return to theatre
Also variation in other outcomes between hospitals
Length of stay
Return to theatre
Unplanned CCU admission

21. Hospital level figures
Suggest inserting Hospital Performance Indicator graphs here as necessary

22. National Emergency Laparotomy Audit?
Quality Improvement Project
Share Best Practice
NELA QI dashboard generates real-time charts that show change over time
The key to improvement is not about waiting to see if things have improved in the next report. Continuous quality improvement is needed at a local level.

23. Summary Thank you for: Risk Assessment drives resources
patients since the audit started
improvement seen at personal level
Risk Assessment drives resources
Need organisational change
Not “National Audit”,
but local Quality Improvement