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Monitoring Adverse Events in Surgery

Monitoring Adverse Events in Surgery. Steve Gallivan Clinical OR Unit University College London. Principal Collaborators. Jocelyn Lovegrove (Ex-CORU) Chris Sherlaw-Johnson (CORU) Tom Treasure (Cardiac Surgeon) Jaroslav Stark (Paediatric Cardiac Surgeon)

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Monitoring Adverse Events in Surgery

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  1. Monitoring Adverse Events in Surgery Steve Gallivan Clinical OR Unit University College London

  2. Principal Collaborators • Jocelyn Lovegrove (Ex-CORU) • Chris Sherlaw-Johnson (CORU) • Tom Treasure (Cardiac Surgeon) • Jaroslav Stark (Paediatric Cardiac Surgeon) • Marc de Leval (Paediatric Cardiac Surgeon)

  3. Typical CUSUM plot

  4. Factors contributing to model of surgical risk

  5. Cumulative perioperative mortalities 10 Expected mortality (from risk model) 8 Actual mortality Par for the 6 course 4 Net life 2 gain 0 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 Operation number Calculating Net Life Gain based on pre-operative risks

  6. VLAD plot for a single surgeon

  7. Unexpected Net life gain 5 death Surgeon A 4 Surgeon B 3 Surgeon C 2 1 Survivor 0 against -1 the odds -2 -3 -4 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 Operation number Comparing three fictitious surgeons

  8. (%) Net life gain 15 99 95 10 90 75 5 50 0 50 75 -5 90 95 -10 99 -15 50 100 150 200 250 300 350 400 Operation number 6 more survivors than predicted for the case mix. Within 90% interval Displaying probability that divergence a result of chance

  9. Net lives saved VLAD plot for a professor of cardiac surgery

  10. Comparing several surgeons at a single hospital

  11. VARIABLE LIFE ADJUSTED DISPLAY - CABG VLAD plot comparing Bristol with another cardiac centre

  12. Difficulties using VLAD for paediatric cardiac surgery • Many different types of procedure • Operation may involve several procedures • Surgeons perform relatively few operations of the same type • Mortality standards not established • No accepted risk scoring system

  13. Monitor performance + Compare surgeons Change emphasis Early identification of periods of divergent outcome

  14. Mortality rates for 11 paediatric cardiac surgeons (1 year data)

  15. Comparator Centres’ Mortality Rates

  16. Partial risk strata Surgeon’s own mean mortality Surrogate pre-operative ‘risk’ forecast Risk model tailored to surgeon’s own mean mortality rate

  17. Normalised VLAD for a single surgeon

  18. Complexity Category

  19. Complexity Profile 100 80 Tom CumulativePercentage 60 Dick 40 Harry 20 0 1 2 3 4 5 6 Operation Difficulty

  20. 100 80 60 Cumulative percentage 40 Divergent period Rest of period 20 0 1 2 3 4 5 6 Complexity Category (increasing complexity)

  21. Case study 2 A heart transplant centre audits its recent outcome and discovers that there have been 5 deaths out of the most recent 14 operations. Should the service be suspended?

  22. KEY QUESTION What is probability that a binary sequence length M has a sub-string length N with at least K ones? [Probability that i-th bit is one = qi] SURELY FELLER ANSWERED THIS!

  23. N Most recent 6-string Number of ones 1 0 0 2 01 1 3 010 1 4 01001 2 5 010010 2 6 0100101 3 7 01001011 3 8 010010110 3 9 0100101101 4 10 01001011010 3 11 010010110101 4 12 0100101101011 4 13 01001011010111 4 14 010010110101111 5 EVOLUTION OF BINARY STRINGS

  24. Probability of a subsequence length M with K deaths

  25. EXAMPLE FOR 3 DEATHS OUT OF A SUBSEQUENCE OF 4

  26. Recurrence relationship for evolution of probabilities for binary string b from the set HMK f1(b) and f2(b) the two progenitors of b

  27. Probability of a run with 5 deaths out of a subsequence of 14

  28. Probability of poor run when mortality is 16 %

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