Risk-adjusted outcome prediction is mainly important in two separate fields. The first is quality monitoring: measuring actual versus predicted mortality in an institution allows assessment of the clinical surgical and anaesthesia performance while adjusting for the risk profile of the patients. Without risk stratification, surgeons and hospitals treating high-risk patients will appear to have worse results than others. This may prejudice referral patterns, affect the allocation of resources and even discourage the treatment of high-risk patients. The second field is that of informed consent and clinical decision-making. Risk-adjusted predicted mortality should form an important part of patient and surgeon decisions on whether or not to proceed with surgery. Clearly, no 'perfect' model can be produced as some aspects of mortality will always be related to risk factors not included in the model (e.g. the quality of the distal coronary artery vessels in coronary artery surgery) or due to chance happenings not related to preoperative patient characteristics (such as surgical error). An individual patient will either survive or die after cardiac surgery. Clearly, no scoring system will predict the specific outcome for every patient. However, risk stratification will inform patients and clinicians of the likely risk of death for a group of patients with a similar risk profile undergoing the proposed operation. This information is useful and should form part of the basis on which the patient and surgeon decide whether to proceed.
