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Eur J Cardiothorac Surg 2006;29:697-698
© 2006 Elsevier Science NL
Cardiac Surgery Department, Leuven University Hospital Gasthuisberg, Leuven, B-3000, Belgium
* Corresponding author. Tel.: +32 16 344 339; fax: +32 16344616. (Email: Paul.Sergeant{at}uz.kuleuven.ac.be).
Kang et al. [1] have created an interesting manuscript, of interest not only to experts in quality control or congenital surgery but also to all of us wanting cardiac surgery to sustain in this challenging world.
Indeed we are active in labour-intensive environments where cost-containment and cost-control challenge any use of human or material resource; irrelevant if this challenge is imposed by hospital administrations or self-imposed. The second challenge is the increased expectation of total absence of periprocedural risk as well by patient as society. Their perception of absence of risk is substantiated in legal persecutions and the concomitant explosion of insurance protection. Our third fundamental challenge is the improved results of nonsurgical interventional but also of noninterventional therapies.
This impacts the mandatory processes of evolution of our profession. John Kirklin proposed in the second part of the 20th century his concept of incremental improvements. Surgeons should try to improve gradually every step or detail of their medical production process. There was very little risk of underperformance in this minimal-difference evolution, because Kirklin added the registration of the surgical process descriptive and outcome variables. The proof of benefit of these improvements was very precise if one used the Blackstone methodologies but also very time-consuming. The visualisation of the effects of these incremental changes was very dissociated in time, sometimes months or even years.
In the 21st century, the industry has moved away from stability and structure as its main competitive force towards disruptive innovation. These disruptive innovations are realised not for the innovations themselves but for the possibility of exponential improvements. The surgical community has to innovate similarly in a disruptive fashion to obtain also exponential improvements, either in annihilating early risk, improving late benefit, reducing resource consumption, preferably all three combined.
We are therefore in need of rapid systems of quality control, corrected for variability, allowing real time process control, because disruptive routines have the possibility of disruptive worse results. In addition, we will have to understand the limitations of our tools.
The first element in these rapid systems is the scoring of variability. The authors use the RACHS system [2], but most systems can be used, as well for adult as for congenital cardiac surgery. The limitations of these models are multiple: the variability of the original dataset, the richness of their descriptive variables, their relationships with socio-economic environments, their questionable stability over time, finally an outcome's interval covering hospital mortality but not the complete periprocedural risk. Units with exceptional expertise can even use their own models and use the correct outcome's interval, as there are 3 months for CABG, 6 months for valve surgery and even longer for congenital therapies. Within some countries, formal agreements have been made on adult cardiac surgery systems, often without understanding their limitations. The K.U. Leuven uses self-developed complex systems with scientifically correct observation intervals but also outside systems with incorrect intervals for intra- and interdepartmental performance monitoring of cardiac surgery.
The second element is the use of a graphical display. The authors have chosen for the VLAD system [3]. This system with high graphical power is a very easy to understand. It allows the use of any observation interval, as stated earlier, and can be created in any spreadsheet program. Many national systems use it to visualise interdepartmental differences. This is perfectly possible since their study samples are usually large enough. Indeed the limitations of this display were the absence of uncertainty intervals and the use in reduced samples. From a conceptual perspective one can understand that the minimal sample should have a cumulative risk of one, but these limitations were never formalised, since the practical use would probably demand much larger samples. Personal exploration about the use of this method in individual units has identified that most units failed to construct the simple spreadsheet structures. Only expert units and national monitoring systems actively applied this system. The K.U. Leuven used the system extensively for intradepartmental quality monitoring by year but also by individual surgeon, even published them on the intranet. It is a perfect method for observing not only the integration of a new surgeon in a high-performance experienced team but also the stability of the performance in surgeons with gradually reducing surgical activity. We refined the graphical displays to interact with our demands. The system has been our core guide in the disruptive re-engineering of CABG towards an OPCAB approach in all patients in October 1999. We needed to have real time performance monitoring to avoid the possibility of disruptive worsening of our result. From the first series of 100 patients the impact became obvious with an impressive positive angle of the VLAD line, indicative of a totally different relation of this new procedure with the patient variability. This angle has been stable after several thousand patients operated according to these totally different concepts.
The authors have added a third step to the previous ones, visualising variation due to chance or due to statistical significance. This deletes one of the previously cited limitations but places new demands on the authors since they will have to provide downloadable preprogrammed spreadsheets if they want their technique to be used by individual nonexpert units. Any reduction of risk is of clinical relevance for the patient but science is driven by the laws of mathematics and therefore the more performing a system becomes, the more it can be used and who knows the easier be improved.
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