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J Thorac Cardiovasc Surg 2003;126:2044-2051
© 2003 The American Association for Thoracic Surgery
Surgery for acquired cardiovascular disease |
a Department of Cardiothoracic Anesthesia (G-3), Cleveland, Ohio, USA
b Department of Biostatistics, The Cleveland Clinic Foundation, Cleveland, Ohio, USA
c Department of Anesthesia, Texas Heart Institute, Houston, Tex, USA
d Department of Thoracic and Cardiac Surgery, The Cleveland Clinic Foundation, Cleveland, Ohio, USA
Received for publication April 16, 2003; revisions received June 4, 2003; accepted for publication June 13, 2003.
* Address for reprints: Colleen Gorman Koch, MD, MS, Department of Cardiothoracic Anesthesia (G-3), The Cleveland Clinic Foundation, 9500 Euclid Ave, Cleveland, OH 44195, USA
kochc{at}ccf.org
| Abstract |
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METHODS: From January 1993 to June 2002, 15,597 patients underwent isolated coronary artery bypass grafting at a single institution. Multivariable logistic regression was used to develop a model of female gender.
RESULTS: Of 15,597 patients, 3596 (23%) were women. Eighteen variables were predictive of the female gender profile, including shorter stature, increased weight, more hypertension, insulin-treated diabetes mellitus, heart failure, and higher triglyceride and high-density lipoprotein cholesterol levels. Hematocrit, bilirubin, and creatinine values were lower in women compared with men.
CONCLUSIONS: The preoperative profiles of women and men undergoing coronary artery bypass grafting are dissimilar. Statistical modeling techniques provide a unique perspective on the preoperative profile of the female patient, who is known to be at a higher risk undergoing coronary artery bypass grafting.
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Statistical methods
The analysis identified a parsimonious set of characteristics that best differentiated men from women undergoing CABG, thus creating a gender profile.
Preliminary analysis
Initial exploratory analyses were performed by calculating summary statistics for each variable. The only variables to have a considerable amount of missing data were triglyceride, high-density lipoprotein (HDL), and low-density lipoprotein (LDL) cholesterol levels, with approximately 44% missing in each. These variables were examined further, but no obvious missing patterns were found. Thus, noninformative imputation was used, and missing value indicators were included in the final model. Univariable statistics were also initially computed to compare men and women using the Student t test for continuous variables and the chi-square test or Fisher's exact test statistics for categoric variables.
Before modeling the data, continuous and ordinal variables were plotted against the logit of being female to identify possible transformations necessary to linearize the relationship.
Multivariable analysis
Bootstrap aggregation (bagging) was used to identify variables and their candidate transformations.13 Coronary branches (mid, proximal, and distal) were excluded from the bagging analysis (discussed next). A total of 200 data sets of 15,597 were randomly selected with replacement and automatically analyzed by stepwise regression with an entry significance level of 0.10 and a significance level of 0.05 to stay in the model. Variables chosen in at least 50% of the models were considered for the final parsimonious model.
Aggregation step
To account for correlation among covariates, a cluster analysis was performed by investigating variables that may instinctively cluster together, such as height, weight, body surface area, and body mass index. Exploratory analysis was performed within clusters by plots and intermediate modeling to identify any interactions and distinguish which parameters and transformations have the most distinct relationship with the probability of being female.
Finally, a parsimonious model was determined with the selected covariates and missing value indicators for the variables where applicable. The model was further reduced by backward elimination of P values greater than .05. It is worth noting that although albumin appeared in 85% of the bootstrap models, it did not remain in the final model. Last, the coronary branches were successively added, yet none were found to significantly contribute to the quality of the model. All results were computed using SAS 8.2 software (SAS Institute Inc, Cary, NC).
| Results |
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| Discussion |
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Variables more commonly prevalent in women were not necessarily predictive of the female patient. Virtually all investigations report that, on average, female patients are significantly older than male patients undergoing CABG surgery. Although we found the prevalence of advanced age to be greater in women, we did not find advanced age to be a significant predictor of female gender in the final parsimonious model.
The preoperative profile for women is distinct from that of the male patient population. Our parsimonious model has high discriminatory ability to distinguish females from males among the demographic, angiographic, and laboratory values from the database. The 18 variables that were statistically predictive of female gender describe the female patient's physiologic "milieu," reflecting the patient's cardiovascular status, organ function, laboratory profile, and physical stature.
Prior studies have described greater disabling symptoms in women despite less extensive coronary artery disease.3,7 We also found that women dominated the New York Heart Association functional classification 3 and 4 categories; however, they had better preserved ejection fractions and less extensive coronary artery disease compared with men. The final parsimonious model did not identify higher New York Heart Association classification categories as a significantly predictive variable for female gender.
In our study, the preoperative lipid profiles for women were distinctly different from men. Women had higher triglyceride, HDL, and LDL cholesterol values. Both the HDL and triglyceride variables were included in the final parsimonious model as significant predictors of female gender. Kannel and Wilson14 mentioned a metabolic link of clustered coronary risk factors among women: dyslipidemia, hypertension, and glucose intolerance. Data from the Framingham Heart Study support that 80% of women with elevated serum cholesterol levels had 1 or more other major risk factors. An incremental increase in the ratio of total and HDL cholesterol "steeply" increased the risk of coronary heart diseaserelated events.14 Additional laboratory values that distinguished women from men were lower preoperative serum creatinine and bilirubin measurements and lower preoperative red blood cell volume and hematocrit values. Reduced preoperative red blood cell volume measurements in women may have important implications with regard to hemodilution from the cardiopulmonary bypass machine and the subsequent use of red blood cells.
One of the primary limitations of this study is that we can only comment on predictors of female gender based on our measured covariates. Unmeasured variables could impact the final model. Second, our study collected clinical variables and does not explain the purported causes or mechanism for the progression of coronary artery disease. The addition of genetic information to further profile patients may contribute to mechanistic causes in the future. Furthermore, our study was from a single institution and characterizes only those patients with heart disease presenting for coronary artery revascularization.
Gender deserves specific consideration in the broad realm of coronary artery disease. Women may have less risk of developing coronary artery disease earlier in their lives. Data from the Framingham Heart Study indicate that high ratios of total and HDL cholesterol, diabetes, and electrocardiographic evidence of left ventricular hypertrophy eliminate the female advantage.14 The impact of aggressive risk factor modification programs on perioperative outcome is unknown. The key to implementing risk factor modification programs involves an understanding of the high-risk population that the programs are designed to target. A preoperative profile of women such as defined by our model can specifically target patients for intervention.
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