Original article: cardiovascular
Multivariable prediction of in-hospital mortality associated with aortic and mitral valve surgery in Northern New England

https://doi.org/10.1016/j.athoracsur.2003.12.035Get rights and content

Abstract

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 8,943 heart valve surgery patients aged 30 years and older. There were 5,793 cases of aortic valve replacement and 3,150 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 χ2 [8 df] = 11.88, p = 0.157, and for the mitral model it was χ2 [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.

Section snippets

Data

The Northern New England Cardiovascular Disease Study Group (NNECDSG) prospective valve surgery registry includes data from eight medical centers in northern New England. Data were collected on 11,539 consecutive aortic and mitral valve surgery patients aged 30 years and older during the study period from July 1991 through December 2001. Due the small numbers of isolated tricuspid or pulmonic valve cases and diverse multiple valve procedures including surgery on the aorta, 2,596 cases were not

Aortic cases

The average age of patients undergoing aortic valve replacement was 69.5 years, and the mean body surface area (BSA) was 1.94 m2. The statistically significant univariate predictors of in-hospital mortality included: age, BSA, prior cardiac surgery, diabetes, prior stroke, history of coronary artery disease (CAD), serum creatinine greater than or equal to 1.3 mg/dL, New York Heart Association (NYHA) class IV, congestive heart failure (CHF) history, preoperative atrial fibrillation, ejection

Comment

In this prospective study of in-hospital mortality after heart valve surgery in northern New England from July 1989 to June 2001, multivariable risk prediction models were developed separately for aortic and mitral valve procedures and were employed to create a clinical tool for predicting preoperative patient mortality risk. We were guided in our choice of variables by our own experience in developing a prediction model for mortality after CABG surgery [22], as well as by previously reported

Conclusion

We present two statistical models for aortic and mitral valve operations that encompass the majority of heart valve operations in our region. Both models were highly statistically significant with good discrimination and excellent goodness-of-fit. The results of our study are in general agreement with those of others as far as the important independent predictors of in-hospital mortality. The models include independent predictor variables that are readily available before operation. The

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    Copyright © 2004 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.