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Ann Thorac Surg 2006;82:2088
© 2006 The Society of Thoracic Surgeons
Cardiothoracic Surgical Unit, Papworth Hospital, Cambridge, CB3 8RE United Kingdom
(Email: sam.nashef{at}papworth.nhs.uk).
In this study, Mazzoni and colleagues [1] have examined preoperative risk scoring (using EuroSCORE in both its additive and logistic versions) in combination with postoperative variables using Sequential Organ Failure Assessment (SOFA). Their main aim was to determine whether these are associated with excess mortality and morbidity after hospital discharge. Perhaps, not surprisingly, they found that both are associated with worse outcomes.
The study and the methods are sound and elegantly demonstrate the strong association between risk prediction and subsequent likelihood of death and early readmission. The main weakness in the study is the "so what" question: sick patients before and after surgery are likely to do worse, and at the most basic level, this study simply confirms that fact, albeit scientifically and elegantly. We may question how knowledge of these findings can have an impact on the management of such patients, but it is possible that the simple and objective identification of patients likely to have adverse outcomes may provide the impetus for better prevention and treatment of such outcomes.
From the point of view of both humanitarians and health economists, such studies may eventually help us decide when continuing invasive and aggressive treatment becomes futile. Currently our risk models are not sufficiently refined to achieve that aim, but the work represented in this study is a step along that path. Furthermore, correct identification of patients who can be confidently predicted to have high rates of untoward events and readmissions may allow the development of better expectant and proactive treatment strategies after discharge from both the intensive care unit and the hospital.
Perhaps an interesting analogy is in the decision-making that precedes cardiac surgery. Those familiar with the logistic EuroSCORE model will know that it is possible for a cardiac surgical patient to have a score of 90% to 100%, meaning that the probability of surviving heart surgery is very thin indeed. Despite that, we have shown in work to be published this year [2] that some such patients do survive surgery and, more importantly, that their quality of life in the long term is comparable with that of cardiac surgical patients with far lower risk prediction. Perhaps this explains partly why many cardiac surgeons intuitively continue to strive against the odds to achieve survival for their very high-risk patients. It would be intriguing to know whether the addition of increasingly sophisticated postoperative risk stratification can modify this paradigm. This study suggests that this possibility may be on the horizon.
Why do we have risk models? There are several good reasons. One is to aid in surgical decision-making and its corollary, informed consent by the patient. Another is to provide a yardstick against which the quality of care can be assessed. A third is to identify patients, as in this study, who may benefit from targeted and proactive care in the expectation of a higher rate of problems, and thus to facilitate the desired aim of improving outcome for such patients. If improvement in risk modeling continues, is it possible that one day we shall reach the absurd extreme of having models so sophisticated that they can predict the future with unerring accuracy? If that happens, no patient will die after cardiac surgery, because the risk model will have identified the certainty of such an outcome and surgery will have been avoided. On the other hand, if that happens, continuous improvement in the quality of outcomes may well grind to a halt.
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