Monitoring the performance of residents during training in off-pump coronary surgery.

OBJECTIVE: Control charts (eg, cumulative sum charts) plot changes in performance with time and can alert a surgeon to suboptimal performance. They were used to compare performance of off-pump coronary artery bypass surgery between a consultant and four resident surgeons and to compare performance of off-pump coronary artery bypass surgery and conventional coronary artery bypass grafting within surgeons. METHODS: Data were analyzed for consecutive patients undergoing coronary artery bypass grafting who were operated on by one consultant or one of four residents. Conversions were analyzed by intention to treat. Perioperative death or one or more of 10 adverse events constituted failure. Predicted risks of failure for individual patients were derived from the study population. Variable life-adjusted displays and risk-adjusted sequential probability ratio test charts were plotted. RESULTS: Data for 1372 patients were analyzed; 769 of the procedures were off-pump coronary artery bypass operations (56.0%). The consultant operated on 382 patients (293 off-pump, 76.7%), and the residents operated on 990 (474 off-pump, 47.9%). Patients operated on by residents tended to be older, more obese, more likely to require an urgent operation, and more likely to need a circumflex artery graft but less likely to have triple-vessel disease. There were 7 conversions (consultant 5, residents 2). The overall failure rate was 8.5% (9.2% for consultant's operations and 8.2% for residents' operations), including 10 deaths (0.7%). Predicted and observed risks of failure were similar for all five surgeons. After 100 off-pump coronary artery bypass operations, performance was the same or better for the residents as for the consultant. For all surgeons, performance was the same or better for off-pump as for conventional coronary artery bypass grafting. CONCLUSIONS: Off-pump coronary artery bypass surgery can be safely taught to cardiothoracic residents. Implementation of continuous performance monitoring for residents is practicable

Primary Cardiac Allograft Dysfunction-Validation of a Clinical Definition

Background.Heart transplantation is an established treatment for advanced heart failure. Primary allograft dysfunction (PGD) is reported in up to 40 % of transplants and is associated with a poor outcome.Methods.As part of Heart Evaluation and Retrieval for Transplantation study, an investigation of the assessment of donor hearts for transplantation, we proposed a clinical definition ffecting one or both ventricles accompanied by hypotension, low 72 hours (in the absence of hyper acute rejection and technical mine the prospective application of this definition to 290 heart d non-PGD cases. Results. Ninety-four of 290 transplants the PGD group at 24, 48, and 72 hours after transplantation fo ra o c ty. have acceptable function before organ retrieval may be ex-OriginalClinicalScienceçGeneral30 years.6 This has been attributed to improvements in dono

Approaches to multiplicity in publicly funded pragmatic randomised controlled trials:a survey of clinical trials units and a rapid review of published trials

BACKGROUND: Opinions and practices vary around the issue of performing multiple statistical tests in randomised controlled trials (RCTs). We carried out a study to collate information about opinions and practices using a methodological rapid review and a survey, specifically of publicly funded pragmatic RCTs that are not seeking marketing authorisation. The aim was to identify the circumstances under which researchers would make a statistical adjustment for multiplicity. METHODS: A review was performed extracting information from articles reporting primary analyses of pragmatic RCTs in one of seven high quality medical journals, in January to June (inclusive) 2018. A survey (Survey Monkey) eliciting opinions and practices around multiplicity was distributed to the 47 registered clinical trials units (CTUs) in the UK. RESULTS: One hundred and thirty-eight RCTs were included in the review, and survey responses were received from 27/47 (57%) CTUs. Both the review and survey indicated that adjusting for multiplicity was considered most important for multiple treatment comparisons; adjustment was performed for 11/23 (48%) published trials, and 24/27 (89%) CTU statisticians reported they would consider adjustment. Opinions and practices varied around adjustment for multiplicity arising from multiple primary outcomes and interim analyses. Adjustment was considered less important for multiplicity due to multiple secondary outcomes (adjustment performed for 17/136 [13%] published trials and 3/27 [11%] CTU statisticians would consider adjustment) and subgroup analyses (8/85 [9%] published trials adjusted and 6/27 CTU [22%] statisticians would consider adjustment). CONCLUSIONS: There is variation in opinions about adjustment for multiplicity among both statisticians reporting RCTs and applied statisticians working in CTUs. Further guidance is needed on the circumstances in which adjustment should be considered in relation to primary trial hypotheses, and if there are any situations in which adjustment would be recommended in the context of secondary analyses. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-022-01525-9

Analytical challenges in estimating the effect of exposures that are bounded by follow-up time: experiences from the Blood Stream Infection—Focus on Outcomes study

Abstract Objective To illustrate the challenges of estimating the effect of an exposure that is bounded by duration of follow-up on all-cause 28-day mortality, whilst simultaneously addressing missing data and time-varying covariates. Study design and methods BSI-FOO is a multicentre cohort study with the primary aim of quantifying the effect of modifiable risk factors, including time to initiation of therapy, on all-cause 28-day mortality in patients with bloodstream infection. The primary analysis involved two Cox proportional hazard models, first one for non-modifiable risk factors and second one for modifiable risk factors, with a risk score calculated from the first model included as a covariate in the second model. Modifiable risk factors considered in this study were recorded daily for a maximum of 28 days after infection. Follow-up was split at daily intervals from day 0 to 28 with values of daily collected data updated at each interval (i.e., one row per patient per day). Analytical challenges Estimating the effect of time to initiation of treatment on survival is analytically challenging since only those who survive to time t can wait until time t to start treatment, introducing immortal time bias. Time-varying covariates representing cumulative counts were used for variables bounded by survival time e.g. the cumulative count of days before first receipt of treatment. Multiple imputation using chained equations was used to impute missing data, using conditional imputation to avoid imputing non-applicable data e.g. ward data after discharge. Conclusion Using time-varying covariates represented by cumulative counts within a one row per day per patient framework can reduce the risk of bias in effect estimates. The approach followed uses established methodology and is easily implemented in standard statistical packages

Delivering successful randomized controlled trials in surgery:methods to optimize collaboration and study design

Randomized controlled trials in surgery are notoriously difficult to design and conduct due to numerous methodological and cultural challenges. Over the last 5 years, several UK-based surgical trial-related initiatives have been funded to address these issues. These include the development of Surgical Trials Centers and Surgical Specialty Leads (individual surgeons responsible for championing randomized controlled trials in their specialist fields), both funded by the Royal College of Surgeons of England; networks of research-active surgeons in training; and investment in methodological research relating to surgical randomized controlled trials (to address issues such as recruitment, blinding, and the selection and standardization of interventions). This article discusses these initiatives more in detail and provides exemplar cases to illustrate how the methodological challenges have been tackled. The initiatives have surpassed expectations, resulting in a renaissance in surgical research throughout the United Kingdom, such that the number of patients entering surgical randomized controlled trials has doubled

Type A aortic dissection: Has surgical outcome improved with time?

ObjectiveThe study objective was to determine whether developments in surgical, anesthetic, and perfusion techniques in the treatment of type A aortic dissection have resulted in improved clinical outcome.MethodsA consecutive series of 165 patients undergoing surgical repair of type A aortic dissection performed between April of 1992 and March of 2006 in a single center were analyzed. Operations were grouped in 2 time frames of equal length (before April of 1999 vs from April of 1999 onward).ResultsThere were 30 in-hospital deaths (18.2%), and the death rate was similar in the 2 time periods. Patients who underwent operation in the recent era compared with the earlier era were older (median 62 years [interquartile range 51–68] vs 59 years [45–68], P = .18), with a significantly higher incidence of concomitant coronary artery disease (13 [18%] vs 5 [7%], P = .03]) and significantly worse (moderate to poor) left ventricular function (33 [40%] vs 13 [18%], P = .002). The duration of circulatory arrest was shorter in the recent era (median 31 minutes [interquartile range 26.5–39] vs 37.5 minutes [31–45], P = .009), with a higher incidence of concomitant procedures (19 [21%] vs 10 [14%], P = .22). Except for total hospital stay, which increased over time, there were no significant differences in postoperative outcome.ConclusionDespite the adoption of techniques to improve outcome for patients with type A dissection, mortality remains unchanged. A deteriorating risk profile and factors relating to the disease process itself may explain this observation