Multivariable prediction of in-hospital mortality associated with aortic and mitral valve surgery in Northern New England
| Autor: | Donato Sisto, Reed D. Quinn, Gerald T. O'Connor, Bruce J. Leavitt, Nancy J. O. Birkmeyer, John H. Sanders, David C. Charlesworth, Robert A. Clough, Paul N Uhlig, Ronald W Weintraub, Elaine M. Olmstead, Edward R. Nowicki, Lawrence J. Dacey |
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| Rok vydání: | 2004 |
| Předmět: |
Adult
Male Pulmonary and Respiratory Medicine Aortic valve medicine.medical_specialty medicine.medical_treatment Coronary artery disease Aortic valve replacement Valve replacement New England Risk Factors Internal medicine Mitral valve Humans Medicine Hospital Mortality Cardiac Surgical Procedures Coronary Artery Bypass Aged Aged 80 and over Heart Valve Prosthesis Implantation Analysis of Variance Models Statistical business.industry Mortality rate Atrial fibrillation Middle Aged medicine.disease Surgery Logistic Models medicine.anatomical_structure ROC Curve Aortic Valve Area Under Curve Heart failure Multivariate Analysis cardiovascular system Cardiology Mitral Valve Female Cardiology and Cardiovascular Medicine business |
| Zdroj: | The Annals of Thoracic Surgery. 77:1966-1977 |
| ISSN: | 0003-4975 |
| DOI: | 10.1016/j.athoracsur.2003.12.035 |
| Popis: | 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 χ 2 [8 df] = 11.88, p = 0.157, and for the mitral model it was χ 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. |
| Databáze: | OpenAIRE |
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