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Eur J Cardiothorac Surg 2007;31:607-613. doi:10.1016/j.ejcts.2006.12.035
Copyright © 2007, European Association for Cardio-Thoracic Surgery. Published by Elsevier B.V. All rights reserved

A multi-centre additive and logistic risk model for in-hospital mortality following aortic valve replacement

^a Department of Cardiothoracic Surgery, The Cardiothoracic Centre-Liverpool, Thomas Drive, Liverpool L14 3PE, UK
^b Department of Clinical Governance, The Cardiothoracic Centre-Liverpool, Thomas Drive, Liverpool L14 3PE, UK
^c Department of Cardiothoracic Surgery, Blackpool Victoria Hospital, UK
^d Department of Cardiothoracic Surgery, Manchester Royal Infirmary, UK
^e Department of Cardiothoracic Surgery, South Manchester University Hospital, UK

Received 10 September 2006; received in revised form 18 December 2006; accepted 19 December 2006.

^_* Corresponding author. Tel.: +44 151 228 1616; fax: +44 151 288 2371. (Email: manojkud{at}hotmail.com).

Objective: To develop a multivariate prediction model for in-hospital mortality following aortic valve replacement. Methods: Retrospective analysis of prospectively collected data on 4550 consecutive patients undergoing aortic valve replacement between 1 April 1997 and 31 March 2004 at four hospitals. A multivariate logistic regression analysis was undertaken, using the forward stepwise technique, to identify independent risk factors for in-hospital mortality. The area under the receiver operating characteristic (ROC) curve was calculated to assess the performance of the model. The statistical model was internally validated using the technique of bootstrap resampling, which involved creating 100 random samples, with replacement, of 70% of the entire dataset. The model was also validated on 816 consecutive patients undergoing aortic valve replacement between 1 April 2004 and 31 March 2005 from the same four hospitals. Results: Two hundred and seven (4.6%) in-hospital deaths occurred. Independent variables identified with in-hospital mortality are shown with relevant co-efficient values and p-values as follows: (1) age 70–75 years: 0.7046, p < 0.001; (2) age 75–85 years: 1.1714, p < 0.001; (3) age > 85 years: 2.0339, p < 0.001; (4) renal dysfunction: 1.2307, p < 0.001; (5) New York Heart Association class IV: 0.5782, p = 0.003; (6) hypertension: 0.4203, p = 0.006; (7) atrial fibrillation: 0.604, p = 0.002; (8) ejection fraction < 30%: 0.571, p = 0.012; (9) previous cardiac surgery: 0.9193, p < 0.001; (10) non-elective surgery: 0.5735, p < 0.001; (11) cardiogenic shock: 1.1291, p = 0.009; (12) concomitant CABG: 0.6436, p < 0.001. Intercept: –4.8092. A simplified additive scoring system was also developed. The ROC curve was 0.78, indicating a good discrimination power. Bootstrapping demonstrated that estimates were stable with an average ROC curve of 0.76, with a standard deviation of 0.025. Validation on 2004–2005 data revealed a ROC curve of 0.78 and an expected mortality of 4.7% compared to the observed rate of 4.1%. Conclusions: We developed a contemporaneous multivariate prediction model for in-hospital mortality following aortic valve replacement. This tool can be used in day-to-day practice to calculate patient-specific risk by the logistic equation or a simple scoring system with an equivalent predicted risk.

Key Words: Aortic valve replacement • Mortality • Risk factors • Risk prediction

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