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J Thorac Cardiovasc Surg 2006;132:12-19
© 2006 The American Association for Thoracic Surgery

Surgery for Acquired Cardiovascular Disease

Risk factor identification and mortality prediction in cardiac surgery using artificial neural networks

Johan Nilsson, MD, PhD a , * , Mattias Ohlsson, PhD b , Lars Thulin, MD, PhD a , Peter Höglund, MD, PhD c , Samer A.M. Nashef, FRCS d , Johan Brandt, MD, PhD a

a Department of Cardiothoracic Surgery, Lund University, Lund, Sweden
b Department of Theoretical Physics, Lund University, Lund, Sweden
c Competence Centre for Clinical Research, Lund University Hospital, Lund, Sweden
d Papworth Hospital, Cambridge, UK

Received for publication September 1, 2005; revisions received December 19, 2005; accepted for publication December 29, 2005.

* Address for reprints: Dr Johan Nilsson, Department of Cardiothoracic Surgery, Heart and Lung Center, Lund University Hospital, SE 221 85 LUND, Sweden. (Email: johan.nilsson{at}thorax.lu.se).

OBJECTIVE: The artificial neural network model is a nonlinear technology useful for complex pattern recognition problems. This study aimed to develop a method to select risk variables and predict mortality after cardiac surgery by using artificial neural networks.

METHODS: Prospectively collected data from 18,362 patients undergoing cardiac surgery at 128 European institutions in 1995 (the European System for Cardiac Operative Risk Evaluation database) were used. Models to predict the operative mortality were constructed using artificial neural networks. For calibration a sixfold cross-validation technique was used, and for testing a fourfold cross-testing was performed. Risk variables were ranked and minimized in number by calibrated artificial neural networks. Mortality prediction with 95% confidence limits for each patient was obtained by the bootstrap technique. The area under the receiver operating characteristics curve was used as a quantitative measure of the ability to distinguish between survivors and nonsurvivors. Subgroup analysis of surgical operation categories was performed. The results were compared with those from logistic European System for Cardiac Operative Risk Evaluation analysis.

RESULTS: The operative mortality was 4.9%. Artificial neural networks selected 34 of the total 72 risk variables as relevant for mortality prediction. The receiver operating characteristics area for artificial neural networks (0.81) was larger than the logistic European System for Cardiac Operative Risk Evaluation model (0.79; P = .0001). For different surgical operation categories, there were no differences in the discriminatory power for the artificial neural networks (P = .15) but significant differences were found for the logistic European System for Cardiac Operative Risk Evaluation (P = .0072).

CONCLUSIONS: Risk factors in a ranked order contributing to the mortality prediction were identified. A minimal set of risk variables achieving a superior mortality prediction was defined. The artificial neural network model is applicable independent of the cardiac surgical procedure.

Abbreviations and Acronyms ANN = artificial neural network; CABG = coronary artery bypass grafting; CI = confidence interval; ROC = receiver operating characteristics; SD = standard deviation


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