Predicting survival after surgery: a matter of life and death

The quantification of risk is now embedded in our approach to the assessment of the high-risk surgical patient, although this has not always been the case. The past fifty years have seen a dramatic change in approach [1]. In 1957, Dripps and colleagues suggested that the quantification of peri-operative risk was not possible, due to the myriad factors involved [2]. Ten years later, the 1967 edition of the American College of Surgeons publication Manual of Preoperative and Postoperative Management distinguished between good-, intermediateand poor-risk patients [3]. The 1977 publication of the Goldman Cardiovascular Risk scoring system [4] ushered in the approach whereby a number of factors are selected using statistical methods and combined to give a quantitative estimate of perioperative risk. The publication of the Cardiac Risk Index marked a move to a probabilistic approach that sought to quantify risk but acknowledged that the outcome for an individual patient could not be predicted with certainty. In their review of the changes in approach to the definition of surgical risk, Neuman and Bosk reported that by 1987, the Goldman Risk Index had been cited 224 times [1]. The decades since have seen the development of a substantial body of work based on this approach, with the development of general risk prediction tools such as the Lee Cardiovascular Risk Score, the POSSUM score and surgery-specific tools such as the Glasgow Aneurysm Score [5–7]. All of these focus on the risk of complications or mortality in the immediate postoperative period. In this edition of Anaesthesia, Carlisle and colleagues present a validation study of a clinical risk prediction tool that predicts not the likelihood of major complications and death, but the patient’s chances of survival, based on data from patients undergoing aortic surgery in four centres [8]. Carlisle has previously drawn attention to the fact that patients’ concerns about outcome extend ‘beyond one lunar orbit’: people do not undergo elective surgery in order to gain only an additional 30 days of life but to improve their long-term survival and wellbeing [9]. He has constructed a clinical risk prediction tool that takes an actuarial approach based on the UK Office for National Statistics (ONS) Life Tables. For a person of a given age and sex, the ONS tables give an estimate of mortality rate and life expectancy. The tool produced by Carlisle modifies this estimate to take in to account the presence of various clinical risk factors, renal function, and the patient’s exercise capacity. The tool provides an estimate of the likelihood of death with and without surgery, both in the next month and for longer periods. The model allows an estimate to be made of the extent to which an operation may shorten or lengthen the patient’s life. Thus, it is potentially able to inform care more than tools that offer only an estimate of the percentage of patients who will suffer morbidity or mortality following surgery. The study of outcome from aortic aneurysm repair is particularly apposite, since this is a prophylactic procedure done not to cure a disease such as cancer but to prevent aneurysm rupture. The patient will not be better immediately after surgery and may be considerably worse. Clinical decisionmaking is rendered more complex by the fact that there is more than one surgical option, and patients may be offered either open or endovascular repair, which have differing shortand long-term risk profiles. Therefore, questions of long-term survival with and without intervention are thrown into sharp relief. This risk calculator presents a new paradigm for balancing the risks and benefits of surgery, and raises questions over the role of exercise testing in pre-operative risk assessment, the appropriateness of modifying lifetable mortality estimates with data from other risk