Laparoscopic radical prostatectomy outcome data: how should surgeon's performance be reported? A retrospective learning curve analysis of two surgeons.

Sarah Mason,Ashish Chandra,Christian Brown,Mieke Van Hemelrijck,Declan Cahill

doi:10.3332/ecancer.2016.651

Abstract

ObjectiveTo document the learning curve for the laparoscopic radical prostatectomy (LRP) procedure and discuss the optimal usage of prospectively documented outcome data for reporting a surgeon’s performance.Materials and methodsUsing prospectively collected data from the first series of patients to undergo LRP by two surgeons in the same institution, linear and logistic regression multivariate analyses per 25 patients were carried out to graphically represent the surgical learning curve for operative time, blood loss, complications, length of stay (LOS), and positive margins. Surgeon A carried out 275 operations between 2003–2009; Surgeon B carried out 225 between 2008–2012.ResultsLearning curves showing continuous improvement of each of the above outcomes were demonstrated for both cohorts. For surgeon A, a plateau was observed for LOS and T2 positive margins after 100 and 150 surgeries respectively. No such plateau was observed for surgeon B.ConclusionOn documenting these learning curves and discussion of the reporting methods used, we concluded that the most informative outcome measure, with the least potential observer bias was T2 positive margins. Whether as a single measure or in combination with others, this has potential for use as an objective outcome representative of improvement in a surgeon’s skill over time.

Highlights

Radical prostatectomy (RP) surgical outcome data is increasingly being reported in terms of a learning curve, as influenced by surgeonspecific factors [1]
For laparoscopic RP (LRP) there is variation in the number of cases estimated to be required for competency: 51 cases according to complication rates; 110 cases according to blood loss, operative time and positive surgical margin (PSM) rate [3]; and 250 according to recurrence and PSM [7, 8]
Surgeon A operated between April 2003 and June 2009 and surgeon B between October 2008 and October 2012.The observation period and frequency of surgeries was different between the two surgeons as this reflects the uptake of radical prostatectomy in the UK with time

Summary

Introduction

Radical prostatectomy (RP) surgical outcome data is increasingly being reported in terms of a learning curve, as influenced by surgeonspecific factors [1]. To date, learning curves in urology have demonstrated changes in outcomes—such as operative time, blood loss, LOS, positive surgical margin (PSM) rate, complications, and cancer recurrence—to be predictive of a surgeon’s experience [3,4,5,6,7]. The influence of the learning curve on these reports, and the extent to which this is influenced by case mix will be of paramount consideration in documenting and interpreting a surgeon’s data with application to surgical training and clinical practice. Most surgeons do not have facilities for collecting patient reported outcomes, though this is expected to change with the demand for published outcomes increasing. The current British radical prostatectomy data set relies on a surgeon’s input data even though much of which is incomplete and difficult to verify [10, 11]

Methods

Results

Discussion

Conclusion