Does the anaesthesiologist make a difference? - Andrew Klein - SSAI2017
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A talk by Andrew Klein at the 2017 meeting of the Scandinavian Society of Anaestesiology and Intensive Care Medicine. All of the conference content can be found here: https://scanfoam.org/ssai2017/ Developed in collaboration between scanFOAM, SSAI and SFAI.
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Does the anaesthesiologist make a difference? - Andrew Klein - SSAI2017
- 1. Death after surgery Does the anaesthesiologist make a difference? Andrew Klein Papworth Hospital, Cambridge University, UK andrew.klein@nhs.net
- 2. • Conflicts of interest: – Unrestricted educational grants/honoraria from CSL Behring, Brightwake Ltd, Vifor Pharma, Fisher and Paykel and Pharmacosmos – Editor-in-Chief of Anaesthesia
- 3. • Impact Factor 4.7 • Turnaround on decisions: • < 2 weeks for 90% of submissions • < 1 week for 50% of submissions • Friendly editors who correspond directly with you • Online Early e-publication for rapid publication
- 4. Outline • What do we know about death after surgery? • What factors are associated with mortality? • Does the surgeon matter? • Does the anaesthetist matter?
- 6. Death after surgery • Variable • Causes: – Patient – Healthcare system (country) – Surgeon – Anaesthetist – Hospital – Day of the week
- 7. Patient • Risk factors and risk scores • EuroSCORE Age Sex COPD Arterial disease Creatinine >200 endocarditis Critical state unstable angina LV function recent MI Emergency multiple procedures Neurological dysfunction Previous cardiac surgery Pulmonary hypertension
- 10. Mortality after surgery in Europe a 7 day cohort study • One week in 2011 • 498 hospitals across 28 European nations • 46 539 patients • 1855 (4%) died before hospital discharge. • Crude mortality rates varied widely between countries: from 1·2% [95% CI 0·0–3·0] for Iceland to 21·5% [16·9–26·2] for Latvia Lancet 2012; 380: 1059–65
- 12. Hospitals • Mortality rates reported as varying widely • According to Dr Foster, 2 Trusts in the UK have higher than expected deaths after surgery and 2 Trusts have lower than expected
- 13. Day of the week
- 14. • Routinely collected hospital administrative data from 2008-09 to 2010-11 for English public hospitals • comorbidity score assigned based on weights specific for England using information from secondary diagnosis fields and an area level socioeconomic deprivation score • Elective surgery
- 17. • What surgery is done at the weekend? • Risk scoring very basic and not validated independently • Selection bias • What about days cases? • Length of stay 9 days always includes a weekend
- 18. Surgeons • Mortality after cardiac surgery first published in the UK in 2005 • UK National Cardiac Surgery Audit supported by charitable funding
- 19. Surgical outcome data • Outcome – in-hospital mortality (in the hospital the surgery took place) • Risk adjustment • Missing data • Defining unacceptable variation • Notification and publication • Professional revalidation
- 20. Bridgewater et al. BMJ 2013;346:f1139
- 22. What about the anaesthetist? • Indirect effect • Direct effect • Not much data
- 28. Surgeon effects on risk-adjusted mortality after cardiac surgery have been established. Anaesthetist effects on mortality were previously overlooked. Operator and Centre effects on the postoperative length-of- hospital stay (LOS) unknown. Aims Quantify the variation in risk-adjusted three-month mortality between cardiac anaesthetists. Should individual anaesthetists’ mortality be published in the public domain? What is the safe minimum annual caseload?
- 29. Description of the data Prospectively collected consecutive case series data on 110,721 cases, 193 consultant anaesthetists and 124 surgeons from 10 Centres during 10 years (2002-2012).
- 30. Description of the data Case-mix risk-adjustment: logistic-EuroSCORE of each patient. Exclusions: providers with caseloads≤10 cases.
- 32. Statistical Methodology Modelling approach: Random effects regression analysis. Explicitly accounts for grouped data structure (patients grouped within anaesthetists and surgeons, grouped within Centres).
- 33. Random effects models Operator results can be generalised to the whole population, in contrast to fixed effects models, which restrict results to the sample of operators available. Traditional regression methods underestimate the standard errors of the regression coefficients possible overestimation of statistical significance. Separate estimation of operator effects and the effects of operator- level covariates. Measure of the proportion of variation in outcome attributed to Centre, surgeons, anaesthetists and patients Intra-Class Correlation coefficient (ICC).
- 34. Statistical Methodology Fitted Models Three-level cross-classification model adjusting simultaneously for Centre, Surgeon and Anaesthetist random effects. Two distinct three-level models to establish the individual Surgeon and Anaesthetist effects, controlling for Centre used for comparisons. Outcome: i. In-hospital mortality up to 3 months post-operatively ii. Length-of-Stay(LOS) up to 3 months post-operatively
- 35. Results – In hospital mortality
- 36. Results – In hospital mortality
- 37. Results – Centre 1
- 38. Results – Centre 1
- 40. Conclusions Study conducted on consecutive case series data of more than 110000 patients, 124 surgeons and 193 anaesthetists from 10 out of 36 specialist UK Centres. Anaesthetists did not have an effect on mortality after adjusting for surgeon effects. Centre does not affect mortality after adjustment for surgeon and anaesthetist. Surgeon effects on mortality were identified as also previously noted in the literature.
- 41. Laws of the House of God • Our study has validated Law number 4: • The patient is the one with the disease
- 42. • Validates current UK specialist training and practice in cardiothoracic anaesthesia as fit for purpose, at least as far as it affects patient mortality. • No apparent effect of caseload and centre • Surgeon has a small but statistically significant effect on mortality
- 46. Morbidity is the next step • Cardiac • Respiratory • Renal • Neurological • Patient-reported outcomes • Pain • Nausea and vomiting
- 47. What about non-cardiac surgery? • Vascular? • #NOF • Emergency laparotomy?
- 48. Summary • What do we know about death after surgery? • What factors are associated with mortality? • Does the surgeon matter? • Does the anaesthetist matter?
Editor's Notes
- It is widely known that risk-adjusted mortality after cardiac surgery is affected by the operating surgeon. However, very little effort has been put into establishing the respective effects of the operating anaesthetist, which in the past, were the most part ignored. Only two relatively small studies published over 20 years ago have suggested a potential impact of the anaesthetist as a risk factor for cardiac surgical outcomes since when the topic has received scant attention. Merry: CABG only, 1301 consecutive cases, composite outcome of in-hospital death or AST-D1 greater than 100 u litre-1 Slogoff: CABG only, 1023 cases – 75% of all in the period June 1981-May 1982, outcome PMI (Perioperative Myocardial infarction) Glance2015: CABG only -7920 NY registry 2009-2010, 91 anaesthetists, 97 surgeons, 23 Centres, composite outcome of in-hospital mortality or major in-hospital complication (Q-wave MI, renal failure or stroke). Moreover, the effects of the surgeon and anaesthetist, as well as of the Centre they operate in on LOS are also unexplored. The aims of our analysis were to firstly, quantify the variation in the risk-adjusted three-month mortality between cardiac anaesthetists and secondly, establish the impact of both Operators as well as of the Centre they operate in on the LOS.
- The data studied were consecutive case series, prospectively collected for the period starting April 2002 through March 2012 (with the exception of Centre 4, where data were available through March 2013 and Centre 8, where data were available from April 2004 through August 2013). The median cluster size was for the Centre, Surgeon and Anaesthetist levels respectively 9978 (mean=11216, range: 6625 - 18 426 patients), 844.5 (mean=904.5, range: 9 - 2384 patients) and 496.5 (mean=582.5, range: 8 -2268 patients) patients. The median number of surgeons per Centre was 12 (mean: 12.4, range: 6-18) and for anaesthetists, 17 (mean=19.3, range: 8-35). NB - ONLY IF ASKED: Recall that we had a small amount of missing values for the Discharge Date and hence, the LOS models were fitted in somewhat smaller datasets (1% reduction in patients). Due to the small percentage of missing entries for the anaesthetist, models including an anaesthetist random intercept were fitted for both outcomes on slightly smaller datasets (1% reduction in patients) than the models adjusting solely for surgeon effects. NB – ONLY IF ASKED: Note that in 5 of 10 Centres, there were re-operations during the same hospital admission. In such cases, the patient information for the first procedure was linked to the final outcome after the second procedure, so that we considered second procedures as a consequence of the first. In all Centres, patients with multiple operations during the study period, taking place however, at independent admissions, were treated as independent episodes. Any duplicated cases were removed.
- Risk-adjustment was achieved using the logistic EuroSCORE, a very well established risk score, constructed to be used as a risk predictor for in-hospital death after cardiac operations. It is based on 17 cardiac, operation and patient related factors and it is used for risk assessment in many countries. (Note that in Centre 6, an older version of the score, the Additive EuroSCORE was used instead.) Note that since this risk score was created for in-hospital death, it may not capture risk as effectively for the LOS outcome. The data included all major cardiac operations at each Centre. Cardiac transplants, pulmonary endarterectomy procedures and, other procedures for which EuroSCORE is not appropriate were excluded. There were no pronounced differences in the percentage of in-hospital deaths between Centres, with the mean percentage being 3.1%. Likewise, we see from the figure that the differences in median LOS are minimal, with the average stay being 10.4 days. (The average age in the full dataset is 66.4 years (s.d. 11.3) and 72.77% of the patients were male.)
- Since our data structure is hierarchical, with patients grouped within surgeons or, anaesthetists respectively, a natural choice for the method to use is random effects models which explicitly model the clustering/grouping in the data. The structure of our data is naturally grouped (patients nested within surgeons/anaesthetists), thus we expect to have correlation among observations within a group (here represented by each Centre/surgeon/anaesthetist). In standard regression modelling, we assume all patients are independent. However, when there may be differences due to surgeon (or, anaesthetist), patients operated upon by the same surgeon (anaesthetist) may have more similar outcomes than patients who are operated on by different surgeons (anaesthetists). Therefore to address this, we include terms in the model, called “random effects”, which represent the surgeons (anaesthetists).
- We prefer random effects models over ordinary regression models as they treat the anaesthetists (and surgeons) as a random sample from the population of all cardiac anaesthetists (and surgeons), allowing the generalisation of the results to the whole population of anaesthetists (and surgeons), in contrast to fixed effects models, which restrict results only to the sample of anaesthetists (surgeons) available. (The distribution of their results would have been similar and thus provides us with generalizable estimates). Furthermore, failure to take the dependency between each anaesthetist’s results into account during analysis can lead to bias in the estimated surgeon/anaesthetist and covariate effects and, inaccuracy in the standard errors and p-values for these effects. Furthermore, using these models we can estimate both the operator effects as well as the effects of covariates associated to the operators such caseload volume or, operator age. Finally, they provide us with a measure of the variation in outcome which can be attributed to each of the groups is the data, known as the ICC . (Such a measure is essential in order to appropriately adjust the sample size in trials where there is clustering in the data.)
- We started off by fitting two distinct three-level random intercept models to establish the individual surgeon and anaesthetist effects on the patient outcome, controlling for Centre effects and case-mix risk. We additionally wanted to fit a model controlling for both operator groupings simultaneously, to avoid the risk of wrongly attributing more variation than that due to one of the professionals. Hence, to investigate the combined effect of surgeon and anaesthetist, we fitted a three-level cross-classified model assuming an additive, individual contribution from each of type of provider, nested with Centres. The models were fitted using two outcome measures,... .
- Recall we were mainly interested in the impact of the anaesthetist on the in-hospital mortality. From the forest plots we again see that, adjusting for Centre and case-mix risk, the variation in the anaesthetist effects is small, quantified by the ICC at 0.71% and once we also adjust for the surgeon, it reduces significantly to 0.25% and, no anaesthetist is now significantly different than average. NB. The difference in the probability of event between the “best” and “worst” anaesthetist reduced from 1.5% to about 0.5%.
- In contrast, after adjusting for Centre and case-mix risk, we can see there is significant variation in surgeon effects, with the respective ICC being 4.06%. Moreover, additionally adjusting for the anaesthetist effects resulted in only a minimal reduction in the surgeon variation to 4%, indicating that the surgeon effects are much more influential than anaesthetist effects. (We now notice a considerable amount of surgeons remains significantly different than average.) NB: The difference in the event probability between the surgeons at the extremes remains the same, at about 4%. We must point out that after adjusting for surgeon effects in our model, there were no remaining centre effects suggesting most of the variation in outcome was principally due to patient risk, followed by the surgeons.
- Finally, we look at the anaesthetist and surgeon effects on in-hospital mortality within a single Centre. It is clear that anaesthetist effects are negligible, with essentially no variation amongst them; everyone is on a flat line.
- In contrast, 2.58% of the variation in in-hospital mortality can be attributed to the surgeons and we can see that three of them appear to perform below average. Adjusting also for the anaesthetist effects did not alter the ICC nor the forest plot, indicating that their effects were negligible. NB. The difference between the two surgeons at the extremes in the event probability remained stable at approximately 1.5%.
- We pursued our study on.... In summary, both in-hospital mortality and LOS after cardiac surgery are mainly attributed to the patient risk profile. (alternative: the overwhelming factor associated with outcome variation is the patient risk profile) We further deduced that after adjusting for surgeon effects, anaesthetists did not have an effect on mortality or LOS. Likewise, the Centre did not affect mortality but, it did have an impact on LOS after adjustment for surgeon and anaesthetist. We identified that the most influential component on mortality was the surgeon, in agreement to what previously found in the literature. And finally, we determined that the most influential component on the LOS was again the surgeon.