Background: Heart valve surgery has an associated in-hospital mortality rate of 4% to 8%. This study aims to develop a simple risk model to predict the risk of in-hospital mortality for patients undergoing heart valve surgery to provide information to patients and clinicians and to facilitate institutional comparisons.
Methods and results: Data on 32,839 patients were obtained from the Society of Cardiothoracic Surgeons of Great Britain and Ireland on patients who underwent heart valve surgery between April 1995 and March 2003. Data from the first 5 years (n=16,679) were used to develop the model; its performance was evaluated on the remaining data (n=16,160). The risk model presented here is based on the combined data. The overall in-hospital mortality was 6.4%. The risk model included, in order of importance (all P<0.01), operative priority, age, renal failure, operation sequence, ejection fraction, concomitant tricuspid valve surgery, type of valve operation, concomitant CABG surgery, body mass index, preoperative arrhythmias, diabetes, gender, and hypertension. The risk model exhibited good predictive ability (Hosmer-Lemeshow test, P=0.78) and discriminated between high- and low-risk patients reasonably well (receiver-operating characteristics curve area, 0.77).
Conclusions: This is the first risk model that predicts in-hospital mortality for aortic and/or mitral heart valve patients with or without concomitant CABG. Based on a large national database of heart valve patients, this model has been evaluated successfully on patients who had valve surgery during a subsequent time period. It is simple to use, includes routinely collected variables, and provides a useful tool for patient advice and institutional comparisons.