Exploring mechanisms of action in clinical trials of complex surgical interventions using mediation analysis.

BACKGROUND: Surgical interventions allow for tailoring of treatment to individual patients and implementation may vary with surgeon and healthcare provider. In addition, in clinical trials assessing two competing surgical interventions, the treatments may be accompanied by co-interventions. AIMS: This study explores the use of causal mediation analysis to (1) delineate the treatment effect that results directly from the surgical intervention under study and the indirect effect acting through a co-intervention and (2) to evaluate the benefit of the surgical intervention if either everybody in the trial population received the co-intervention or nobody received it. METHODS: Within a counterfactual framework, relevant direct and indirect effects of a surgical intervention are estimated and adjusted for confounding via parametric regression models, for the situation where both mediator and outcome are binary, with baseline stratification factors included as fixed effects and surgeons as random intercepts. The causal difference in probability of a successful outcome (estimand of interest) is calculated using Monte Carlo simulation with bootstrapping for confidence intervals. Packages for estimation within standard statistical software are reviewed briefly. A step by step application of methods is illustrated using the Amaze randomised trial of ablation as an adjunct to cardiac surgery in patients with irregular heart rhythm, with a co-intervention (removal of the left atrial appendage) administered to a subset of participants at the surgeon's discretion. The primary outcome was return to normal heart rhythm at one year post surgery. RESULTS: In Amaze, 17% (95% confidence interval: 6%, 28%) more patients in the active arm had a successful outcome, but there was a large difference between active and control arms in the proportion of patients who received the co-intervention (55% and 30%, respectively). Causal mediation analysis suggested that around 1% of the treatment effect was attributable to the co-intervention (16% natural direct effect). The controlled direct effect ranged from 18% (6%, 30%) if the co-intervention were mandated, to 14% (2%, 25%) if it were prohibited. Including age as a moderator of the mediation effects showed that the natural direct effect of ablation appeared to decrease with age. CONCLUSIONS: Causal mediation analysis is a useful quantitative tool to explore mediating effects of co-interventions in surgical trials. In Amaze, investigators could be reassured that the effect of the active treatment, not explainable by differential use of the co-intervention, was significant across analyses

Impact of the Anesthesiologist and Surgeon on Cardiac Surgical Outcomes

ObjectiveTo determine the impact of anesthesiologists, surgeons, and their monthly caseload volume on mortality after cardiac surgery.DesignTen-year audit of prospectively collected cardiac surgical data.SettingLarge adult cardiothoracic hospital.ParticipantsA total of 18,569 cardiac surgical patients in the decade from April 2002 through March 2012, plus 21 consultant surgeons and 29 consultant anesthesiologists.InterventionsMajor risk-stratified cardiac surgical operations.MethodsThe primary outcome was in-hospital death. Random intercept models for the surgeon and anesthesiologist cluster, respectively, were fitted, achieving risk-adjustment through the logistic EuroSCORE. The intraclass correlation coefficient (ICC) subsequently was used to measure the amount of outcome variation due to clustering.Measurements and Main ResultsAfter exclusions (duplicates, very-short-term appointments, and cases performed by more than one consultant), there were 18,426 patients with 581 (3.15%) in-hospital deaths. The overwhelming factor associated with outcome variation was the patient risk profile, accounting for 97.14% of the variation. The impact of the surgeon was small (ICC = 2.78%), and the impact of the anesthesiologist was negligible (ICC = 0.08%). Low monthly surgeon volume of surgery, adjusted for average case mix, was associated with higher risk-adjusted mortality (odds ratio = 0.93, 95% CI 0.87-0.98).ConclusionsOutcome was determined primarily by the patient. There were small but significant differences in outcome between surgeons. The attending anesthesiologist did not affect patient outcome in this institution. Low average monthly surgeon volume was a significant risk factor. In contrast, low average monthly anesthesiologist volume had no effect

The contribution of the anaesthetist to risk-adjusted mortality after cardiac surgery

It is widely accepted that the performance of the operating surgeon affects outcomes, and this has led to the publication of surgical results in the public domain. However, the effect of other members of the multidisciplinary team is unknown. We studied the effect of the anaesthetist on mortality after cardiac surgery by analysing data collected prospectively over ten years of consecutive cardiac surgical cases from ten UK centres. Casemix-adjusted outcomes were analysed in models that included random-effects for centre, surgeon and anaesthetist. All cardiac surgical operations for which the EuroSCORE model is appropriate were included, and the primary outcome was in-hospital death up to three months postoperatively. A total of 110 769 cardiac surgical procedures conducted between April 2002 and March 2012 were studied, which included 127 consultant surgeons and 190 consultant anaesthetists. The overwhelming factor associated with outcome was patient risk, accounting for 95.75% of the variation for in-hospital mortality. The impact of the surgeon was moderate (intra-class correlation coefficient 4.00% for mortality), and the impact of the anaesthetist was negligible (0.25%). There was no significant effect of anaesthetist volume above ten cases per year. We conclude that mortality after cardiac surgery is primarily determined by the patient, with small but significant differences between surgeons. Anaesthetists did not appear to affect mortality. These findings do not support public disclosure of cardiac anaesthetists' results, but substantially validate current UK cardiac anaesthetic training and practice. Further research is required to establish the potential effects of very low anaesthetic caseloads and the effect of cardiac anaesthetists on patient morbidity

Effect of individual patient risk, centre, surgeon and anaesthetist on length of stay in hospital after cardiac surgery: Association of Cardiothoracic Anaesthesia and Critical Care (ACTACC) consecutive cases series study of 10 UK specialist centres.

OBJECTIVES: To determine the relative contributions of patient risk profile, local and individual clinical practice on length of hospital stay after cardiac surgery. DESIGN: Ten-year audit of prospectively collected consecutive cardiac surgical cases. Case-mix adjusted outcomes were analysed in models that included random effects for centre, surgeon and anaesthetist. SETTING: UK centres providing adult cardiac surgery. PARTICIPANTS: 10 of 36 UK specialist centres agreed to provide outcomes for all major cardiac operations over 10 years. After exclusions (duplicates, cases operated by more than one consultant, deaths and procedures for which the EuroSCORE risk score for cardiac surgery is not appropriate), there were 107 038 cardiac surgical procedures between April 2002 and March 2012, conducted by 127 consultant surgeons and 190 consultant anaesthetists. MAIN OUTCOME MEASURE: Length of stay (LOS) up to 3 months postoperatively. RESULTS: The principal component of variation in outcomes was patient risk (represented by the EuroSCORE and remaining patient heterogeneity), accounting for 95.43% of the variation for postoperative LOS. The impact of the surgeon and centre was moderate (intra-class correlation coefficients ICC=2.79% and 1.59%, respectively), whereas the impact of the anaesthetist was negligible (ICC=0.19%). Similarly, 96.05% of the variation for prolonged LOS (>11 days) was attributable to the patient, with surgeon and centre less but still influential components (ICC=2.12% and 1.66%, respectively, 0.17% only for anaesthetists). Adjustment for year of operation resulted in minor reductions in variation attributable to surgeons (ICC=2.52% for LOS and 2.23% for prolonged LOS). CONCLUSIONS: Patient risk profile is the primary determinant of variation in LOS, and as a result, current initiatives to reduce hospital stay by modifying consultant performance are unlikely to have a substantial impact. Therefore, substantially reducing hospital stay requires shifting away from a one-size-fits-all approach to cardiac surgery, and seeking alternative treatment options personalised to high-risk patients

Assessment of learning curves in complex surgical interventions: a consecutive case-series study

Background: Surgical interventions are complex, which complicates their rigorous assessment through randomised clinical trials. An important component of complexity relates to surgeon experience and the rate at which the required level of skill is achieved, known as the learning curve. There is considerable evidence that operator performance for surgical innovations will change with increasing experience. Such learning effects complicate evaluations; the start of the trial might be delayed, resulting in loss of surgeon equipoise or, if an assessment is undertaken before performance has stabilised, the true impact of the intervention may be distorted. Methods: Formal estimation of learning parameters is necessary to characterise the learning curve, model its evolution and adjust for its presence during assessment. Current methods are either descriptive or model the learning curve through three main features: the initial skill level, the learning rate and the final skill level achieved. We introduce a fourth characterising feature, the duration of the learning period, which provides an estimate of the point at which learning has stabilised. We propose a two-phase model to estimate formally all four learning curve features. Results: We demonstrate that the two-phase model can be used to estimate the end of the learning period by incorporating a parameter for estimating the duration of learning. This is achieved by breaking down the model into a phase describing the learning period and one describing cases after the final skill level is reached, with the break point representing the length of learning. We illustrate the method using cardiac surgery data. Conclusions: This modelling extension is useful as it provides a measure of the potential cost of learning an intervention and enables statisticians to accommodate cases undertaken during the learning phase and assess the intervention after the optimal skill level is reached. The limitations of the method and implications for the optimal timing of a definitive randomised controlled trial are also discussed

Does the "Weekend Effect" for Postoperative Mortality Stand Up to Scrutiny? Association for Cardiothoracic Anesthesia and Critical Care Cohort Study of 110,728 Cardiac Surgical Patients.

OBJECTIVE: Ongoing debate focuses on whether patients admitted to the hospital on weekends have higher mortality than those admitted on weekdays. Whether this apparent "weekend effect" reflects differing patient risk, care quality differences, or inadequate adjustment for risk during analysis remains unclear. This study aimed to examine the existence of a "weekend effect" for risk-adjusted in-hospital mortality after cardiac surgery. DESIGN: Retrospective analysis of prospectively collected cardiac registry data. SETTING: Ten UK specialist cardiac centers. PARTICIPANTS: A total of 110,728 cases, undertaken by 127 consultant surgeons and 190 consultant anesthetists between April 2002 and March 2012. INTERVENTIONS: Major risk-stratified cardiac surgical operations. MEASUREMENTS AND MAIN RESULTS: Crude in-hospital mortality rate was 3.1%. Multilevel multivariable models were employed to estimate the effect of operative day on in-hospital mortality, adjusting for center, surgeon, anesthetist, patient risk, and procedure priority. Weekend elective cases had significantly lower mortality risk compared to Monday elective cases (odds ratio [OR] 0.64, 95% confidence interval [CI] 0.42, 0.96) following risk adjustment by the logistic European System for Cardiac Operative Risk Evaluation (EuroSCORE) and procedure priority; differences between weekend and Monday for urgent and emergency/salvage cases were not significant (OR 1.12, 95% CI 0.73, 1.72, and 1.07, 95% CI 0.79, 1.45 respectively). Considering only the logistic EuroSCORE but not procedure priority yielded 29% higher odds of death for weekend cases compared to Monday operations (OR 1.29, 95% CI 1.08, 1.54). CONCLUSIONS: This study suggests that undergoing cardiac surgery during the weekend does not affect negatively patient survival, and highlights the importance of comprehensive risk adjustment to avoid detecting spurious "weekend effects.

International Fragility Fracture Network Delphi consensus statement on the principles of anaesthesia for patients with hip fracture

Globally, the number of hip fractures is expected to double between 2017 and 2050, from ~2.2 million to ~4.5 million. For the purposes of analgesia and remobilisation, ~ 99% of hip fractures should be fixed surgically, requiring anaesthesia. Surgery for hip fracture has become increasingly standardised, but peri‐operative medical and anaesthetic care varies considerably. Peri‐operative morbidity and mortality remain high. Guidelines exist for the anaesthetic management of patients with hip fracture, but are specific to the healthcare systems of Western nations. This consensus statement (advises basic standards of anaesthetic care that hip fracture patients should expect to receive in any country, regardless of resources. On behalf of the Fragility Fracture Network (FFN), the Anaesthesia Working Group (SW) invited internationally recognised experts in hip fracture anaesthesia and national professional leaders to contribute to a Consensus Com-mittee