A multivariate prediction model incorporating 12 clinical risk factors accurately predicted in-hospital mortality following aortic valve replacement, achieving an ROC curve area of 0.78.
Observational (n=5,366)
Yes
Can a multivariate prediction model accurately predict in-hospital mortality following aortic valve replacement?
A newly developed and validated multi-centre additive and logistic risk model accurately predicts in-hospital mortality following aortic valve replacement, offering a practical tool for patient-specific risk calculation.
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, p85 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.
Kuduvalli et al. (Wed,) conducted a observational in Aortic valve replacement (n=5,366). Clinical risk factors was evaluated on In-hospital mortality. A multivariate prediction model incorporating 12 clinical risk factors accurately predicted in-hospital mortality following aortic valve replacement, achieving an ROC curve area of 0.78.