Is Obesity a Risk Factor for Mortality in Coronary Artery Bypass Surgery;
http://www.100md.com
《循环学杂志》
Providence Health System, Portland, Ore.
Abstract
Background— The published articles examining obesity and CABG surgery contain conflicting results about the role of body mass index (BMI) as a risk factor for in-hospital mortality.
Methods and Results— We studied 16 218 patients who underwent isolated CABG in the Providence Health System Cardiovascular Study Group database from 1997 to 2003. The effect of BMI on in-hospital mortality was assessed by logistic regression, with BMI group (underweight, normal, overweight, and 3 subgroups of obesity) as a categorical variable or transformations, including fractional polynomials, of BMI as a continuous variable. BMI was not a statistically significant risk factor for mortality in any of these assessments. However, using cumulative sum techniques, we found that the lowest risk-adjusted CABG in-hospital mortality was in the high-normal and that overweight BMI subgroup patients with lower or higher BMI had slightly increased mortality.
Conclusions— Body size is not a significant risk factor for CABG mortality, but the lowest mortality is found in the high-normal and overweight subgroups compared with obese and underweight.
Key Words: coronary disease ; mortality ; obesity ; risk factors ; surgery
Introduction
In the United States, obesity has risen at an epidemic rate during the past 20 years. Results from the 1999 to 2000 National Health and Nutrition Examination Survey indicate that an estimated 64% of US adults are either overweight or obese, defined as having a body mass index (BMI) 25 kg/m2.1 Obesity is also a major contributor to the risk of cardiovascular disease.2 These facts have resulted in an increase in the prevalence of obesity in the CABG surgery population in our country.
The literature dealing with obesity as a risk factor for in-hospital mortality after CABG contains conflicting results. Of 17 studies3–19 that dealt specifically with BMI as a risk factor, only the study with the largest sample size3 found obesity to be significant. Another study with a medium-sized sample4 found high BMI to be protective, and 3 studies found "underweight" (very low BMI) to be a risk factor.5–7 The remaining 12 studies did not find any effect of BMI.8–19
Motivated by these apparently contradictory findings, we used the Providence Health System Cardiovascular Study Group (PHS) database to investigate the role of BMI in CABG operative (in-hospital) mortality.
Methods
Clinical Material
January 1997 through December 2003, 16 232 patients underwent isolated CABG in 9 Providence Health System hospitals. All those hospitals with cardiac surgery programs participate in the PHS, which requires prospectively collecting data according to standard definitions and transmitting them to an independent coordinating center for merging into a common database. The 14 patients with incomplete data on height and/or weight were excluded, leaving 16 218 patients for study.
Statistical Methods
BMI, weight in kilograms divided by the square of height in meters (kg/m2), is the widely accepted way to estimate body fat and overall health risk from obesity or undernutrition. According to the National Institutes of Health (NIH),20 the normal BMI range is 18.5 to 24.9 kg/m2, 25 to 29.9 kg/m2 is overweight, and 30 kg/m2is obese. Obesity is further subdivided into mild (30.0 to 34.9 kg/m2), moderate (35.0 to 39.9 kg/m2), and extreme (40.0 kg/m2). A BMI <18.5 kg/m2 is considered underweight.
Demographic, historical, and perioperative variables known to affect mortality were selected for analysis. Comparisons of these variables between normal and other BMI subgroups were done through the use of ANOVA with Scheffé’s post hoc correction for continuous variables and through logistic regression with BMI subgroup as the only risk factor for dichotomous variables.
A previously developed PHS logistic regression model21 that was validated on recent patients was used for all patients to investigate the possible effect of body size on mortality. To assess the properties of the logistic regression models, the c statistic (area under the receiver-operating characteristic curve)22 was used to measure discrimination, and the Hosmer-Lemeshow 2 test statistic23 was used to measure calibration. As an additional verification of the PHS model, we performed the link test.24 The link test is a simple method to assess the overall model specification by computing a new regression model using as predictor variables the risk score from the original model plus its square (quadratic term). If the additional quadratic term is significant, there is evidence of model misspecification, indicating that there may be additional variables missing from the model. (However, this test does not guarantee that there are no missing terms; even if this term is not significant, there are still probably unknown risk factors missing that could be added to the model.)
The logistic regression model assumes that the logit (logarithm of the odds) of death is a linear function of the risk factor—in this case, BMI. However, it is often the case in biomedical models that a mathematical transformation (like the square or square root) of the risk factor provides a better fit. Thus, BMI was tested both as a continuous variable, including BMI itself and several transformations (BMI–1, BMI1/2, BMI2, BMI3, ln(BMI), BMI±BMI2), and as a categorical variable (BMI group). The components of BMI, height, and weight were tested, along with body surface area (BSA). As the ultimate extension of this exercise, the method of fractional polynomials25 (FPs) was used. This method considers, in an organized way, the logarithm plus several powers (–2, –1, –0.5, 0.5, 1, 2, 3) of the continuous variable (BMI), either singly or 2 at a time, as possible additions to the predictor variables already in the model. Then, the likelihood ratios from the resulting models are compared to select optimal transformation(s) of BMI. When the dependent (outcome) variable is dichotomous (death), it is difficult to appreciate its relationship to a continuous risk factor graphically. A cumulative sum (cusum) technique26 was used to overcome this difficulty and to examine the role of BMI at the individual patient level.
For other adverse outcomes, cerebrovascular accident, myocardial infarction, deep sternal infection, renal failure requiring dialysis, blood transfusion usage, postoperative length of stay >14 days, mechanical ventilation in an intensive care unit >24 hours, reoperation for cardiac reason, and postoperative coronary angiograph intervention were selected for analysis. We dichotomized the outcome variables and used logistic regression to determine the effect of BMI.
Statistical analyses were performed with SPSS version 11.0, SPSS Inc (logistic regression, ANOVA); Stata version 8.2, Stata Corp (fractional polynomials, link test, cusum); and S-PLUS version 6.1, Insightful Corp (graphics). A value of P<0.05 was considered significant.
Results
Patient Characteristics and Mortality
Obese patients were younger and more often female. Obese patients also had more diabetes, systemic hypertension, and pulmonary hypertension. Underweight patients were older, were more often female, and had more peripheral vascular disease, chronic obstructive pulmonary disease, congestive heart failure, myocardial infarction, ventricular arrhythmia, mitral insufficiency, left main disease, and New York Heart Association functional class III and IV.
Obese patients had less mortality; underweight patients had higher (unadjusted) mortality. Examination of the causes of death showed no obvious differences between BMI groups.
Risk-Adjusted Mortality
A previous CABG risk model developed using PHS patients operated on from January 1997 to June 200221 included 12 risk factors (identified in Table 1) but no variables related to body size. Before using this model, we performed a validation step by computing predictions for the new patients added since it was developed (July 2002 to December 2003). The old model fit the new patient data well; the c statistic was 0.811, and the Hosmer-Lemeshow statistic was 10.07 (P=0.26).
Thus, all 16 218 cases from January 1997 to December 2003 were combined for the present investigation. Of these, 219 patients (1.4%) did not have all of the risk factors required for the risk model; the remaining 15 999 were used to see whether the increased sample size (since the original model was developed) might help to identify a body size effect. The risk model fit the entire data set well; the c statistic was 0.806, and Hosmer-Lemeshow statistic was 3.77 (P=0.88). When the link test was used on the combined data set, the quadratic term was not significant (P=0.55), providing no evidence of incorrect model specification.
Predicted mortality decreased with increasing BMI (Figure 2). This means that in general the underweight patients are in worse condition considering all risk factors in the model and that obese patients are in relatively better condition. Observed mortality (Figure 2) follows that predicted by the model (without body size variables) fairly well, except for a somewhat lower risk from the mid-normal through the overweight group. Thus, after adjustment for other risk factors, there appears to be some unexplained risk of mortality in the underweight and obese subgroups compared with the middle BMI subgroup.
Morbidity
Discussion
BMI was associated with many patient characteristics, especially age and gender. Women had higher BMIs than men in the same age group. Younger patients had higher BMIs than elders. More women are in the underweight and obesity subgroups. Because age and gender are important risk factors for mortality, we must determine the relationship between BMI and mortality after adjusting for the effects of age, gender, and other risk factors.
Previous Studies
Of 13 general risk models for mortality after CABG from a recent literature search,27 only the 2 largest studies, from the Society of Thoracic Surgeons (STS) National Cardiac Surgery Database28 and from New York State,29 included obesity. None of them included underweight as a risk factor. Two of them, STS28 and Northern New England Cardiovascular Disease Study Group,30 included BSA. The number of risk factors varied greatly in these studies and correlated strongly with the number of patients studied (r=0.96).
Because the STS database has the most CABG patients, we further examined all their previous risk models. The STS has published CABG risk models for 9 different years, included in 5 articles.28,31–34 The number of risk factors increased with the number of cases available for analysis; only when the model had >30 factors did obesity enter the model. Interestingly, the models of 199532 and 199628 include both BSA and obesity, and the 1999 model31 has BSA only.
Apropos to the present investigation, we found 17 further studies that dealt specifically with obesity as a risk factor for mortality and morbidity.3–19 Only one study, the largest study using the STS database,3 found obesity to be significant for death, with ORs of 1.21 for BMI of 35 to 40 kg/m2 and 1.58 for BMI >40 kg/m2. One other study4 found it to be protective.
Thus, most CABG risk models and special investigations of obesity do not find obesity to be significant; only studies with a huge number of patients and dozens of other risk factors do. This is consistent with obesity having a small clinical effect; in general, the smaller the clinical size of an effect, the larger the number of patients needed to confirm it statistically.
We found that BMI is a risk factor for deep sternal infection, which is coincident with many other studies.3–6,8–12 We also have the same findings as other studies4–6,11–13 that obese patient have less postoperative bleeding and fewer need blood transfusions.
BMI Grouping
The direct comparison of results among these previous studies of BMI and CABG outcomes is problematic because of different definitions and groupings of BMI values. Many studies use the NIH BMI subgroups for analysis. However, NIH determined the overweight and obesity subgroups on the basis of the risks of natural morbidity (hypertension; dyslipidemia; type 2 diabetes; coronary heart disease; stroke; gallbladder disease; osteoarthritis; sleep apnea and respiratory problems; and endometrial, breast, prostate, and colon cancers). In addition, NIH determined the underweight category on the basis of the risks of undernutrition, osteoporosis, infertility, and impaired immunocompetence.20 Thus, using this categorization to analyze the operative risk of CABG may not be suitable. We tried to keep the BMI variable continuous and found, using the method of FPs and cusum, that the lowest mortality was in the high-normal and overweight subgroups, with BMI in the range 23 to 30 kg/m2 (Figure 3). Our study of mortality risk model for valve surgery also found that a BMI of 20 to 32 kg/m2 is a protective factor for death.35 Thus, when undergoing the physical insult of surgery, a little extra fat may be a protective factor. However, one must consider that in general, and especially after a CABG procedure, there is a negative effect on overall health, particularly coronary health, associated with being over one’s ideal body weight.
Study Limitations
Our study and most of the others used BMI to determine obesity. However, BMI has its limitations. BMI may not accurately reflect body fatness in people who are very short (<5 ft) and in older people who tend to lose muscle mass as they age, people of Indian or Asian descent whose frame size is smaller, and athletes who have large amounts of muscle mass. A recent study found that in the same patient groups, the prevalence of obesity, defined by BMI, waist circumference, and waist circumference and waist-to-hip ratio, could vary 3-fold. It concluded that waist circumference is an easy method that reflects central obesity more accurately.36 Albumin level is also considered an index for obesity. Our database did not contain the variables needed to test these other methods of defining obesity.
This analysis depends in part on baseline mortality. Our results are similar to the STS national database, which reported on 559 000 patients operated on during 1997 to 2000,3 with an overall mortality of 2.6% compared with 2.2% in this study and a mortality of 2.6% in patients with BMI >35 kg/m2 compared with 2.3% in this study.
There were only 90 underweight patients (0.6%) in our study cohort and 7 deaths among them. This small number limits the power and precision for the study of this group. On the other hand, our risk model did not contain every conceivable risk factor, but only those supported by our data. The difference in mortality that we did see with underweight patients may have been due to other risk factors not in our model rather than the intrinsic small size itself.
Conclusions
Body size is not a significant risk factor for CABG mortality, but the lowest mortality is found in the high-normal and overweight subgroups (compared with obese and underweight).
Acknowledgments
The following Providence Health System hospitals provided essential collaboration and use of their cardiac surgery patient data: Alaska: Providence Anchorage Medical Center; Washington: Providence Everett Medical Center, Providence St Peter Hospital (Olympia), Providence Yakima Medical Center; Oregon: Providence Portland Medical Center, Providence St Vincent Medical Center (Portland); California: Providence St Joseph Medical Center (Burbank), Providence Holy Cross Medical Center (Mission Hills), Providence Little Company of Mary Hospital (Torrance).
References
Flegal KM, Carroll MD, Ogden CL, Johnson CL. Prevalence and trends in obesity among US adults, 1999–2000. JAMA. 2002; 288: 1723–1727.
Hubert HB, Feinleib M, McNamara PM, Castelli WP. Obesity as an independent risk factor for cardiovascular disease: a 26-year follow-up of participants in the Framingham Heart Study. Circulation. 1983; 67: 968–977.
Prabhakar G, Haan CK, Peterson ED, Coombs LP, Cruzzavala JL, Murray GF. The risks of moderate and extreme obesity for coronary artery bypass grafting outcomes: a study from the Society of Thoracic Surgeons’ database. Ann Thorac Surg. 2002; 74: 1125–1131.
Ivert T, Sandberg E, Hammar N. [Obesity doesn’t increase the risks of coronary surgery: results from a study of 7,248 patients who underwent the surgery]. Lakartidningen. 2000; 97: 972–974.
Schwann TA, Habib RH, Zacharias A, Parenteau GL, Riordan CJ, Durham SJ, Engoren M. Effects of body size on operative, intermediate, and long-term outcomes after coronary artery bypass operation. Ann Thorac Surg. 2001; 71: 521–531.
Engelman DT, Adams DH, Byrne JG, Aranki SF, Collins JJ Jr, Couper GS, Allred EN, Cohn LH, Rizzo RJ. Impact of body mass index and albumin on morbidity and mortality after cardiac surgery. J Thorac Cardiovasc Surg. 1999; 118: 866–873.
Reeves BC, Ascione R, Chamberlain MH, Angelini GD. Effect of body mass index on early outcomes in patients undergoing coronary artery bypass surgery. J Am Coll Cardiol. 2003; 42: 668–676.
Kuduvalli M, Grayson AD, Oo AY, Fabri BM, Rashid A. Risk of morbidity and in-hospital mortality in obese patients undergoing coronary artery bypass surgery. Eur J Cardiothorac Surg. 2002; 22: 787–793.
Moulton MJ, Creswell LL, Mackey ME, Cox JL, Rosenbloom M. Obesity is not a risk factor for significant adverse outcomes after cardiac surgery. Circulation. 1996; 94 (suppl II): II-87–II-92.
Koshal A, Hendry P, Raman SV, Keon WJ. Should obese patients not undergo coronary artery surgery; Can J Surg. 1985; 28: 331–334.
Kim J, Hammar N, Jakobsson K, Luepker RV, McGovern PG, Ivert T. Obesity and the risk of early and late mortality after coronary artery bypass graft surgery. Am Heart J. 2003; 146: 555–560.
Birkmeyer NJ, Charlesworth DC, Hernandez F, Leavitt BJ, Marrin CA, Morton JR, Olmstead EM, O’Connor GT. Obesity and risk of adverse outcomes associated with coronary artery bypass surgery: Northern New England Cardiovascular Disease Study Group. Circulation. 1998; 97: 1689–1694.
Ranucci M, Cazzaniga A, Soro G, Morricone L, Enrini R, Caviezel F. Obesity and coronary artery surgery. J Cardiothorac Vasc Anesth. 1999; 13: 280–284.
Potapov EV, Loebe M, Anker S, Stein J, Bondy S, Nasseri BA, Sodian R, Hausmann H, Hetzer R. Impact of body mass index on outcome in patients after coronary artery bypass grafting with and without valve surgery. Eur Heart J. 2003; 24: 1933–1941.
Rockx MA, Fox SA, Stitt LW, Lehnhardt KR, McKenzie FN, Quantz MA, Menkis AH, Novick RJ. Is obesity a predictor of mortality, morbidity and readmission after cardiac surgery; Can J Surg. 2004; 47: 34–38.
Ennker J, Schoeneich R, Schroder T, Schoeneich F, Ennker IC. [The impact of morbid obesity on the peri- and postoperative course after aortocoronary bypass surgery]. Dtsch Med Wochenschr. 2001; 126: 419–423.
Brandt M, Harder K, Walluscheck KP, Schottler J, Rahimi A, Moller F, Cremer J. Severe obesity does not adversely affect perioperative mortality and morbidity in coronary artery bypass surgery. Eur J Cardiothorac Surg. 2001; 19: 662–666.
Fasol R, Schindler M, Schumacher B, Schlaudraff K, Hannes W, Seitelberger R, Schlosser V. The influence of obesity on perioperative morbidity: retrospective study of 502 aortocoronary bypass operations. Thorac Cardiovasc Surg. 1992; 40: 126–129.
Prasad US, Walker WS, Sang CT, Campanella C, Cameron EW. Influence of obesity on the early and long term results of surgery for coronary artery disease. Eur J Cardiothorac Surg. 1991; 5: 67–73.
Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults: executive summary: Expert Panel on the Identification, Evaluation, and Treatment of Overweight in Adults. Am J Clin Nutr. 1998; 68: 899–917.
Wu Y, Grunkemeier GL, Handy JR Jr. Coronary artery bypass grafting: are risk models developed from on-pump surgery valid for off-pump surgery; J Thorac Cardiovasc Surg. 2004; 127: 174–178.
Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982; 143: 29–36.
Hosmer DW, Lemeshow S. A goodness-of-fit test for the multiple regression model. Commun Stat. 1980; A10: 1043–1069.
Pregibon D. Goodness-of-link tests for generalized linear models. Appl Stat. 1980; 29: 15–24.
Royston P, Ambler G, Sauerbrei W. The use of fractional polynomials to model continuous risk variables in epidemiology. Int J Epidemiol. 1999; 28: 964–974.
Royston P. The use of cusums and other techniques in modelling continuous covariates in logistic regression. Stat Med. 1992; 11: 1115–1129.
Grunkemeier GL, Zerr KJ, Jin R. Cardiac surgery report cards: making the grade. Ann Thorac Surg. 2001; 72: 1845–1848.
Shroyer AL, Plomondon ME, Grover FL, Edwards FH, Schwartz M, Bero J. The 1996 coronary artery bypass risk model: the Society of Thoracic Surgeons Adult Cardiac National Database. Ann Thorac Surg. 1999; 67: 1205–1208.
Hannan EL, Kilburn H Jr, Racz M, Shields E, Chassin MR. Improving the outcomes of coronary artery bypass surgery in New York State. JAMA. 1994; 271: 761–766.
O’Connor GT, Plume SK, Olmstead EM, Coffin LH, Morton JR, Maloney CT, Nowicki ER, Tryzelaar JF, Hernandez F, Adrian L. A regional prospective study of in-hospital mortality associated with coronary artery bypass grafting: the Northern New England Cardiovascular Disease Study Group. JAMA. 1991; 266: 803–809.
Shroyer AL, Coombs LP, Peterson ED, Eiken MC, DeLong ER, Chen A, Ferguson TB Jr, Grover FL, Edwards FH, Plomondon ME. The Society of Thoracic Surgeons: 30-day operative mortality and morbidity risk models. Ann Thorac Surg. 2003; 75: 1856–1865.
Shroyer AL, Grover FL, Edwards FH. 1995 coronary artery bypass risk model: the Society of Thoracic Surgeons Adult Cardiac National Database. Ann Thorac Surg. 1998; 65: 879–884.
Edwards FH, Grover FL, Shroyer AL, Schwartz M, Bero J, Clark RE. The Society of Thoracic Surgeons National Cardiac Surgery Database: current risk assessment. Ann Thorac Surg. 1997; 63: 903–908.
Edwards FH, Clark RE, Schwartz M. Coronary artery bypass grafting: the Society of Thoracic Surgeons National Database experience. Ann Thorac Surg. 1994; 57: 12–19.
Jin R, Grunkemeier GL, Starr A. Validation and refinement of mortality risk models for heart valve surgery. Ann Thorac Surg. In press.
Sonmez K, Akcakoyun M, Akcay A, Demir D, Duran NE, Gencbay M, Degertekin M, Turan F. Which method should be used to determine the obesity, in patients with coronary artery disease; (body mass index, waist circumference or waist-hip ratio). Int J Obes Relat Metab Disord. 2003; 27: 341–346.(Ruyun Jin, MD; Gary L. Gr)
Abstract
Background— The published articles examining obesity and CABG surgery contain conflicting results about the role of body mass index (BMI) as a risk factor for in-hospital mortality.
Methods and Results— We studied 16 218 patients who underwent isolated CABG in the Providence Health System Cardiovascular Study Group database from 1997 to 2003. The effect of BMI on in-hospital mortality was assessed by logistic regression, with BMI group (underweight, normal, overweight, and 3 subgroups of obesity) as a categorical variable or transformations, including fractional polynomials, of BMI as a continuous variable. BMI was not a statistically significant risk factor for mortality in any of these assessments. However, using cumulative sum techniques, we found that the lowest risk-adjusted CABG in-hospital mortality was in the high-normal and that overweight BMI subgroup patients with lower or higher BMI had slightly increased mortality.
Conclusions— Body size is not a significant risk factor for CABG mortality, but the lowest mortality is found in the high-normal and overweight subgroups compared with obese and underweight.
Key Words: coronary disease ; mortality ; obesity ; risk factors ; surgery
Introduction
In the United States, obesity has risen at an epidemic rate during the past 20 years. Results from the 1999 to 2000 National Health and Nutrition Examination Survey indicate that an estimated 64% of US adults are either overweight or obese, defined as having a body mass index (BMI) 25 kg/m2.1 Obesity is also a major contributor to the risk of cardiovascular disease.2 These facts have resulted in an increase in the prevalence of obesity in the CABG surgery population in our country.
The literature dealing with obesity as a risk factor for in-hospital mortality after CABG contains conflicting results. Of 17 studies3–19 that dealt specifically with BMI as a risk factor, only the study with the largest sample size3 found obesity to be significant. Another study with a medium-sized sample4 found high BMI to be protective, and 3 studies found "underweight" (very low BMI) to be a risk factor.5–7 The remaining 12 studies did not find any effect of BMI.8–19
Motivated by these apparently contradictory findings, we used the Providence Health System Cardiovascular Study Group (PHS) database to investigate the role of BMI in CABG operative (in-hospital) mortality.
Methods
Clinical Material
January 1997 through December 2003, 16 232 patients underwent isolated CABG in 9 Providence Health System hospitals. All those hospitals with cardiac surgery programs participate in the PHS, which requires prospectively collecting data according to standard definitions and transmitting them to an independent coordinating center for merging into a common database. The 14 patients with incomplete data on height and/or weight were excluded, leaving 16 218 patients for study.
Statistical Methods
BMI, weight in kilograms divided by the square of height in meters (kg/m2), is the widely accepted way to estimate body fat and overall health risk from obesity or undernutrition. According to the National Institutes of Health (NIH),20 the normal BMI range is 18.5 to 24.9 kg/m2, 25 to 29.9 kg/m2 is overweight, and 30 kg/m2is obese. Obesity is further subdivided into mild (30.0 to 34.9 kg/m2), moderate (35.0 to 39.9 kg/m2), and extreme (40.0 kg/m2). A BMI <18.5 kg/m2 is considered underweight.
Demographic, historical, and perioperative variables known to affect mortality were selected for analysis. Comparisons of these variables between normal and other BMI subgroups were done through the use of ANOVA with Scheffé’s post hoc correction for continuous variables and through logistic regression with BMI subgroup as the only risk factor for dichotomous variables.
A previously developed PHS logistic regression model21 that was validated on recent patients was used for all patients to investigate the possible effect of body size on mortality. To assess the properties of the logistic regression models, the c statistic (area under the receiver-operating characteristic curve)22 was used to measure discrimination, and the Hosmer-Lemeshow 2 test statistic23 was used to measure calibration. As an additional verification of the PHS model, we performed the link test.24 The link test is a simple method to assess the overall model specification by computing a new regression model using as predictor variables the risk score from the original model plus its square (quadratic term). If the additional quadratic term is significant, there is evidence of model misspecification, indicating that there may be additional variables missing from the model. (However, this test does not guarantee that there are no missing terms; even if this term is not significant, there are still probably unknown risk factors missing that could be added to the model.)
The logistic regression model assumes that the logit (logarithm of the odds) of death is a linear function of the risk factor—in this case, BMI. However, it is often the case in biomedical models that a mathematical transformation (like the square or square root) of the risk factor provides a better fit. Thus, BMI was tested both as a continuous variable, including BMI itself and several transformations (BMI–1, BMI1/2, BMI2, BMI3, ln(BMI), BMI±BMI2), and as a categorical variable (BMI group). The components of BMI, height, and weight were tested, along with body surface area (BSA). As the ultimate extension of this exercise, the method of fractional polynomials25 (FPs) was used. This method considers, in an organized way, the logarithm plus several powers (–2, –1, –0.5, 0.5, 1, 2, 3) of the continuous variable (BMI), either singly or 2 at a time, as possible additions to the predictor variables already in the model. Then, the likelihood ratios from the resulting models are compared to select optimal transformation(s) of BMI. When the dependent (outcome) variable is dichotomous (death), it is difficult to appreciate its relationship to a continuous risk factor graphically. A cumulative sum (cusum) technique26 was used to overcome this difficulty and to examine the role of BMI at the individual patient level.
For other adverse outcomes, cerebrovascular accident, myocardial infarction, deep sternal infection, renal failure requiring dialysis, blood transfusion usage, postoperative length of stay >14 days, mechanical ventilation in an intensive care unit >24 hours, reoperation for cardiac reason, and postoperative coronary angiograph intervention were selected for analysis. We dichotomized the outcome variables and used logistic regression to determine the effect of BMI.
Statistical analyses were performed with SPSS version 11.0, SPSS Inc (logistic regression, ANOVA); Stata version 8.2, Stata Corp (fractional polynomials, link test, cusum); and S-PLUS version 6.1, Insightful Corp (graphics). A value of P<0.05 was considered significant.
Results
Patient Characteristics and Mortality
Obese patients were younger and more often female. Obese patients also had more diabetes, systemic hypertension, and pulmonary hypertension. Underweight patients were older, were more often female, and had more peripheral vascular disease, chronic obstructive pulmonary disease, congestive heart failure, myocardial infarction, ventricular arrhythmia, mitral insufficiency, left main disease, and New York Heart Association functional class III and IV.
Obese patients had less mortality; underweight patients had higher (unadjusted) mortality. Examination of the causes of death showed no obvious differences between BMI groups.
Risk-Adjusted Mortality
A previous CABG risk model developed using PHS patients operated on from January 1997 to June 200221 included 12 risk factors (identified in Table 1) but no variables related to body size. Before using this model, we performed a validation step by computing predictions for the new patients added since it was developed (July 2002 to December 2003). The old model fit the new patient data well; the c statistic was 0.811, and the Hosmer-Lemeshow statistic was 10.07 (P=0.26).
Thus, all 16 218 cases from January 1997 to December 2003 were combined for the present investigation. Of these, 219 patients (1.4%) did not have all of the risk factors required for the risk model; the remaining 15 999 were used to see whether the increased sample size (since the original model was developed) might help to identify a body size effect. The risk model fit the entire data set well; the c statistic was 0.806, and Hosmer-Lemeshow statistic was 3.77 (P=0.88). When the link test was used on the combined data set, the quadratic term was not significant (P=0.55), providing no evidence of incorrect model specification.
Predicted mortality decreased with increasing BMI (Figure 2). This means that in general the underweight patients are in worse condition considering all risk factors in the model and that obese patients are in relatively better condition. Observed mortality (Figure 2) follows that predicted by the model (without body size variables) fairly well, except for a somewhat lower risk from the mid-normal through the overweight group. Thus, after adjustment for other risk factors, there appears to be some unexplained risk of mortality in the underweight and obese subgroups compared with the middle BMI subgroup.
Morbidity
Discussion
BMI was associated with many patient characteristics, especially age and gender. Women had higher BMIs than men in the same age group. Younger patients had higher BMIs than elders. More women are in the underweight and obesity subgroups. Because age and gender are important risk factors for mortality, we must determine the relationship between BMI and mortality after adjusting for the effects of age, gender, and other risk factors.
Previous Studies
Of 13 general risk models for mortality after CABG from a recent literature search,27 only the 2 largest studies, from the Society of Thoracic Surgeons (STS) National Cardiac Surgery Database28 and from New York State,29 included obesity. None of them included underweight as a risk factor. Two of them, STS28 and Northern New England Cardiovascular Disease Study Group,30 included BSA. The number of risk factors varied greatly in these studies and correlated strongly with the number of patients studied (r=0.96).
Because the STS database has the most CABG patients, we further examined all their previous risk models. The STS has published CABG risk models for 9 different years, included in 5 articles.28,31–34 The number of risk factors increased with the number of cases available for analysis; only when the model had >30 factors did obesity enter the model. Interestingly, the models of 199532 and 199628 include both BSA and obesity, and the 1999 model31 has BSA only.
Apropos to the present investigation, we found 17 further studies that dealt specifically with obesity as a risk factor for mortality and morbidity.3–19 Only one study, the largest study using the STS database,3 found obesity to be significant for death, with ORs of 1.21 for BMI of 35 to 40 kg/m2 and 1.58 for BMI >40 kg/m2. One other study4 found it to be protective.
Thus, most CABG risk models and special investigations of obesity do not find obesity to be significant; only studies with a huge number of patients and dozens of other risk factors do. This is consistent with obesity having a small clinical effect; in general, the smaller the clinical size of an effect, the larger the number of patients needed to confirm it statistically.
We found that BMI is a risk factor for deep sternal infection, which is coincident with many other studies.3–6,8–12 We also have the same findings as other studies4–6,11–13 that obese patient have less postoperative bleeding and fewer need blood transfusions.
BMI Grouping
The direct comparison of results among these previous studies of BMI and CABG outcomes is problematic because of different definitions and groupings of BMI values. Many studies use the NIH BMI subgroups for analysis. However, NIH determined the overweight and obesity subgroups on the basis of the risks of natural morbidity (hypertension; dyslipidemia; type 2 diabetes; coronary heart disease; stroke; gallbladder disease; osteoarthritis; sleep apnea and respiratory problems; and endometrial, breast, prostate, and colon cancers). In addition, NIH determined the underweight category on the basis of the risks of undernutrition, osteoporosis, infertility, and impaired immunocompetence.20 Thus, using this categorization to analyze the operative risk of CABG may not be suitable. We tried to keep the BMI variable continuous and found, using the method of FPs and cusum, that the lowest mortality was in the high-normal and overweight subgroups, with BMI in the range 23 to 30 kg/m2 (Figure 3). Our study of mortality risk model for valve surgery also found that a BMI of 20 to 32 kg/m2 is a protective factor for death.35 Thus, when undergoing the physical insult of surgery, a little extra fat may be a protective factor. However, one must consider that in general, and especially after a CABG procedure, there is a negative effect on overall health, particularly coronary health, associated with being over one’s ideal body weight.
Study Limitations
Our study and most of the others used BMI to determine obesity. However, BMI has its limitations. BMI may not accurately reflect body fatness in people who are very short (<5 ft) and in older people who tend to lose muscle mass as they age, people of Indian or Asian descent whose frame size is smaller, and athletes who have large amounts of muscle mass. A recent study found that in the same patient groups, the prevalence of obesity, defined by BMI, waist circumference, and waist circumference and waist-to-hip ratio, could vary 3-fold. It concluded that waist circumference is an easy method that reflects central obesity more accurately.36 Albumin level is also considered an index for obesity. Our database did not contain the variables needed to test these other methods of defining obesity.
This analysis depends in part on baseline mortality. Our results are similar to the STS national database, which reported on 559 000 patients operated on during 1997 to 2000,3 with an overall mortality of 2.6% compared with 2.2% in this study and a mortality of 2.6% in patients with BMI >35 kg/m2 compared with 2.3% in this study.
There were only 90 underweight patients (0.6%) in our study cohort and 7 deaths among them. This small number limits the power and precision for the study of this group. On the other hand, our risk model did not contain every conceivable risk factor, but only those supported by our data. The difference in mortality that we did see with underweight patients may have been due to other risk factors not in our model rather than the intrinsic small size itself.
Conclusions
Body size is not a significant risk factor for CABG mortality, but the lowest mortality is found in the high-normal and overweight subgroups (compared with obese and underweight).
Acknowledgments
The following Providence Health System hospitals provided essential collaboration and use of their cardiac surgery patient data: Alaska: Providence Anchorage Medical Center; Washington: Providence Everett Medical Center, Providence St Peter Hospital (Olympia), Providence Yakima Medical Center; Oregon: Providence Portland Medical Center, Providence St Vincent Medical Center (Portland); California: Providence St Joseph Medical Center (Burbank), Providence Holy Cross Medical Center (Mission Hills), Providence Little Company of Mary Hospital (Torrance).
References
Flegal KM, Carroll MD, Ogden CL, Johnson CL. Prevalence and trends in obesity among US adults, 1999–2000. JAMA. 2002; 288: 1723–1727.
Hubert HB, Feinleib M, McNamara PM, Castelli WP. Obesity as an independent risk factor for cardiovascular disease: a 26-year follow-up of participants in the Framingham Heart Study. Circulation. 1983; 67: 968–977.
Prabhakar G, Haan CK, Peterson ED, Coombs LP, Cruzzavala JL, Murray GF. The risks of moderate and extreme obesity for coronary artery bypass grafting outcomes: a study from the Society of Thoracic Surgeons’ database. Ann Thorac Surg. 2002; 74: 1125–1131.
Ivert T, Sandberg E, Hammar N. [Obesity doesn’t increase the risks of coronary surgery: results from a study of 7,248 patients who underwent the surgery]. Lakartidningen. 2000; 97: 972–974.
Schwann TA, Habib RH, Zacharias A, Parenteau GL, Riordan CJ, Durham SJ, Engoren M. Effects of body size on operative, intermediate, and long-term outcomes after coronary artery bypass operation. Ann Thorac Surg. 2001; 71: 521–531.
Engelman DT, Adams DH, Byrne JG, Aranki SF, Collins JJ Jr, Couper GS, Allred EN, Cohn LH, Rizzo RJ. Impact of body mass index and albumin on morbidity and mortality after cardiac surgery. J Thorac Cardiovasc Surg. 1999; 118: 866–873.
Reeves BC, Ascione R, Chamberlain MH, Angelini GD. Effect of body mass index on early outcomes in patients undergoing coronary artery bypass surgery. J Am Coll Cardiol. 2003; 42: 668–676.
Kuduvalli M, Grayson AD, Oo AY, Fabri BM, Rashid A. Risk of morbidity and in-hospital mortality in obese patients undergoing coronary artery bypass surgery. Eur J Cardiothorac Surg. 2002; 22: 787–793.
Moulton MJ, Creswell LL, Mackey ME, Cox JL, Rosenbloom M. Obesity is not a risk factor for significant adverse outcomes after cardiac surgery. Circulation. 1996; 94 (suppl II): II-87–II-92.
Koshal A, Hendry P, Raman SV, Keon WJ. Should obese patients not undergo coronary artery surgery; Can J Surg. 1985; 28: 331–334.
Kim J, Hammar N, Jakobsson K, Luepker RV, McGovern PG, Ivert T. Obesity and the risk of early and late mortality after coronary artery bypass graft surgery. Am Heart J. 2003; 146: 555–560.
Birkmeyer NJ, Charlesworth DC, Hernandez F, Leavitt BJ, Marrin CA, Morton JR, Olmstead EM, O’Connor GT. Obesity and risk of adverse outcomes associated with coronary artery bypass surgery: Northern New England Cardiovascular Disease Study Group. Circulation. 1998; 97: 1689–1694.
Ranucci M, Cazzaniga A, Soro G, Morricone L, Enrini R, Caviezel F. Obesity and coronary artery surgery. J Cardiothorac Vasc Anesth. 1999; 13: 280–284.
Potapov EV, Loebe M, Anker S, Stein J, Bondy S, Nasseri BA, Sodian R, Hausmann H, Hetzer R. Impact of body mass index on outcome in patients after coronary artery bypass grafting with and without valve surgery. Eur Heart J. 2003; 24: 1933–1941.
Rockx MA, Fox SA, Stitt LW, Lehnhardt KR, McKenzie FN, Quantz MA, Menkis AH, Novick RJ. Is obesity a predictor of mortality, morbidity and readmission after cardiac surgery; Can J Surg. 2004; 47: 34–38.
Ennker J, Schoeneich R, Schroder T, Schoeneich F, Ennker IC. [The impact of morbid obesity on the peri- and postoperative course after aortocoronary bypass surgery]. Dtsch Med Wochenschr. 2001; 126: 419–423.
Brandt M, Harder K, Walluscheck KP, Schottler J, Rahimi A, Moller F, Cremer J. Severe obesity does not adversely affect perioperative mortality and morbidity in coronary artery bypass surgery. Eur J Cardiothorac Surg. 2001; 19: 662–666.
Fasol R, Schindler M, Schumacher B, Schlaudraff K, Hannes W, Seitelberger R, Schlosser V. The influence of obesity on perioperative morbidity: retrospective study of 502 aortocoronary bypass operations. Thorac Cardiovasc Surg. 1992; 40: 126–129.
Prasad US, Walker WS, Sang CT, Campanella C, Cameron EW. Influence of obesity on the early and long term results of surgery for coronary artery disease. Eur J Cardiothorac Surg. 1991; 5: 67–73.
Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults: executive summary: Expert Panel on the Identification, Evaluation, and Treatment of Overweight in Adults. Am J Clin Nutr. 1998; 68: 899–917.
Wu Y, Grunkemeier GL, Handy JR Jr. Coronary artery bypass grafting: are risk models developed from on-pump surgery valid for off-pump surgery; J Thorac Cardiovasc Surg. 2004; 127: 174–178.
Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982; 143: 29–36.
Hosmer DW, Lemeshow S. A goodness-of-fit test for the multiple regression model. Commun Stat. 1980; A10: 1043–1069.
Pregibon D. Goodness-of-link tests for generalized linear models. Appl Stat. 1980; 29: 15–24.
Royston P, Ambler G, Sauerbrei W. The use of fractional polynomials to model continuous risk variables in epidemiology. Int J Epidemiol. 1999; 28: 964–974.
Royston P. The use of cusums and other techniques in modelling continuous covariates in logistic regression. Stat Med. 1992; 11: 1115–1129.
Grunkemeier GL, Zerr KJ, Jin R. Cardiac surgery report cards: making the grade. Ann Thorac Surg. 2001; 72: 1845–1848.
Shroyer AL, Plomondon ME, Grover FL, Edwards FH, Schwartz M, Bero J. The 1996 coronary artery bypass risk model: the Society of Thoracic Surgeons Adult Cardiac National Database. Ann Thorac Surg. 1999; 67: 1205–1208.
Hannan EL, Kilburn H Jr, Racz M, Shields E, Chassin MR. Improving the outcomes of coronary artery bypass surgery in New York State. JAMA. 1994; 271: 761–766.
O’Connor GT, Plume SK, Olmstead EM, Coffin LH, Morton JR, Maloney CT, Nowicki ER, Tryzelaar JF, Hernandez F, Adrian L. A regional prospective study of in-hospital mortality associated with coronary artery bypass grafting: the Northern New England Cardiovascular Disease Study Group. JAMA. 1991; 266: 803–809.
Shroyer AL, Coombs LP, Peterson ED, Eiken MC, DeLong ER, Chen A, Ferguson TB Jr, Grover FL, Edwards FH, Plomondon ME. The Society of Thoracic Surgeons: 30-day operative mortality and morbidity risk models. Ann Thorac Surg. 2003; 75: 1856–1865.
Shroyer AL, Grover FL, Edwards FH. 1995 coronary artery bypass risk model: the Society of Thoracic Surgeons Adult Cardiac National Database. Ann Thorac Surg. 1998; 65: 879–884.
Edwards FH, Grover FL, Shroyer AL, Schwartz M, Bero J, Clark RE. The Society of Thoracic Surgeons National Cardiac Surgery Database: current risk assessment. Ann Thorac Surg. 1997; 63: 903–908.
Edwards FH, Clark RE, Schwartz M. Coronary artery bypass grafting: the Society of Thoracic Surgeons National Database experience. Ann Thorac Surg. 1994; 57: 12–19.
Jin R, Grunkemeier GL, Starr A. Validation and refinement of mortality risk models for heart valve surgery. Ann Thorac Surg. In press.
Sonmez K, Akcakoyun M, Akcay A, Demir D, Duran NE, Gencbay M, Degertekin M, Turan F. Which method should be used to determine the obesity, in patients with coronary artery disease; (body mass index, waist circumference or waist-hip ratio). Int J Obes Relat Metab Disord. 2003; 27: 341–346.(Ruyun Jin, MD; Gary L. Gr)