We thank Dr Lown et al and Dr Miceli et al for their comments about our article. 1 They raise several different and interesting points.It is of course possible that the selection of a limited number of variables for the risk model may better benefit from other more sophisticated statistical approaches. However, even if our model is apparently simplistic, it seems to work better than other complex models. We agree that an area under curve value of 0.744 is probably inadequate for clinical purposes, but this is the value reached by the widely used EuroSCORE in many clinical settings. 2 Actually, in the validation series, the area under curve value of the ACEF score (age, creatinine, ejection fraction) was higher than 0.8. 2 As stated in our article, it is a matter of terminology. We could say that the ACEF and the EuroSCORE are equally good or equally bad models. To answer the authors' question, we did sensitivity analyses in a subgroup of patients at low, medium, and high risk, and the worst accuracy was achieved in the high-risk patients (EuroSCORE Ͼ5). However, it should be considered that the ACEF is built for elective patients. When addressing the whole range of cardiac surgery population, more variables are needed to improve the accuracy of the model, up to 5 for medium-risk patients and even 12 in high-risk patients. 2 Dichotomization of continuous variables is always arbitrary in statistics but is a common practice in the development of risk scores. The important point is to dichotomise according to a method that may guarantee the best specificity and sensitivity of the identified cutoff value. We did dichotomise the serum creatinine value (as in the great majority of the existing scores) using the sensitivity and specificity values of the receiver operating characteristics curve coordinates and the Youden index. We agree that every kind of preoperative renal function impairment may strongly affect the operative mortality, and we cannot exclude that using serum creatinine as a continuous variable (as we did for age and ejection fraction) may improve the accuracy of the model, however at the expenses of more complex calculations.We agree with Lown et al about the limitations of area under curve-based systems and the risk of overfitted models, availability of data, and subjectivity, and these last reasons led us to propose a parsimonious model. Conversely, we are not sure that simple models may be more adequate for acute settings and complex models for elective settings. Actually, our experience is that when acute risk conditions intervene, more factors are needed to stratify the risk. 2 It was demonstrated that when severe acute conditions are present, the current scores are inadequate to stratify mortality risk. 3 When developing this model, we considered many potential factors that could be independently associated with operative mortality after cardiac surgery. Among these factors are those mentioned by Miceli et al (gender, chronic obstructive pulmonary disease, operations other than isolated coronary a...