BACKGROUND: Predicting risk for aortic and mitral valve surgery is important both for informed consent of patients and objective review of surgical outcomes. Development of reliable prediction rules requires large data sets with appropriate risk factors that are available before surgery. METHODS: Data from eight Northern New England Medical Centers in the period January 1991 through December 2001 were analyzed on 8943 heart valve surgery patients aged 30 years and older. There were 5793 cases of aortic valve replacement and 3150 cases of mitral valve surgery (repair or replacement). Logistic regression was used to examine the relationship between risk factors and in-hospital mortality. RESULTS: In the multivariable analysis, 11 variables in the aortic model (older age, lower body surface area, prior cardiac operation, elevated creatinine, prior stroke, New York Heart Association [NYHA] class IV, congestive heart failure [CHF], atrial fibrillation, acuity, year of surgery, and concomitant coronary artery bypass grafting) and 10 variables in the mitral model (female sex, older age, diabetes, coronary artery disease, prior cerebrovascular accident, elevated creatinine, NYHA class IV, CHF, acuity, and valve replacement) remained independent predictors of the outcome. The mathematical models were highly significant predictors of the outcome, in-hospital mortality, and the results are in general agreement with those of others. The area under the receiver operating characteristic curve for the aortic model was 0.75 (95% confidence interval [CI], 0.72 to 0.77), and for the mitral model, 0.79 (95% CI, 0.76 to 0.81). The goodness-of-fit statistic for the aortic model was chi(2) [8 df] = 11.88, p = 0.157, and for the mitral model it was chi(2) [8 df] = 5.45, p = 0.708. CONCLUSIONS: We present results and methods for use in day-to-day practice to calculate patient-specific in-hospital mortality after aortic and mitral valve surgery, by the logistic equation for each model or a simple scoring system with a look-up table for mortality rate.
BACKGROUND: Predicting risk for aortic and mitral valve surgery is important both for informed consent of patients and objective review of surgical outcomes. Development of reliable prediction rules requires large data sets with appropriate risk factors that are available before surgery. METHODS: Data from eight Northern New England Medical Centers in the period January 1991 through December 2001 were analyzed on 8943 heart valve surgery patients aged 30 years and older. There were 5793 cases of aortic valve replacement and 3150 cases of mitral valve surgery (repair or replacement). Logistic regression was used to examine the relationship between risk factors and in-hospital mortality. RESULTS: In the multivariable analysis, 11 variables in the aortic model (older age, lower body surface area, prior cardiac operation, elevated creatinine, prior stroke, New York Heart Association [NYHA] class IV, congestive heart failure [CHF], atrial fibrillation, acuity, year of surgery, and concomitant coronary artery bypass grafting) and 10 variables in the mitral model (female sex, older age, diabetes, coronary artery disease, prior cerebrovascular accident, elevated creatinine, NYHA class IV, CHF, acuity, and valve replacement) remained independent predictors of the outcome. The mathematical models were highly significant predictors of the outcome, in-hospital mortality, and the results are in general agreement with those of others. The area under the receiver operating characteristic curve for the aortic model was 0.75 (95% confidence interval [CI], 0.72 to 0.77), and for the mitral model, 0.79 (95% CI, 0.76 to 0.81). The goodness-of-fit statistic for the aortic model was chi(2) [8 df] = 11.88, p = 0.157, and for the mitral model it was chi(2) [8 df] = 5.45, p = 0.708. CONCLUSIONS: We present results and methods for use in day-to-day practice to calculate patient-specific in-hospital mortality after aortic and mitral valve surgery, by the logistic equation for each model or a simple scoring system with a look-up table for mortality rate.
Authors: Benoit de Varennes; Kevin Lachapelle; Renzo Cecere; Patrick Ergina; Dominique Shum-Tim; Christo Tchervenkov; John Sampalis Journal: Can J Cardiol Date: 2007-11 Impact factor: 5.223
Authors: Christine S Zuern; Christian Eick; Konstantinos Rizas; Cosmina Stoleriu; Barbara Woernle; Stephen Wildhirt; Christian Herdeg; Ulrich Stock; Meinrad Gawaz; Axel Bauer Journal: Clin Res Cardiol Date: 2011-10-05 Impact factor: 5.460
Authors: Sarah E Henrickson Parker; Aaron A Laviana; Rishi K Wadhera; Douglas A Wiegmann; Thoralf M Sundt Journal: World J Surg Date: 2010-02 Impact factor: 3.352