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Original Investigation |

Variations in Coronary Procedure Utilization Depending on Body Mass Index FREE

William S. Yancy Jr, MD, MHS; Maren K. Olsen, PhD; Lesley H. Curtis, PhD; Kevin A. Schulman, MD; Michael S. Cuffe, MD; Eugene Z. Oddone, MD, MHS
[+] Author Affiliations

Author Affiliations: Center for Health Services Research in Primary Care, Veterans Affairs Medical Center (Drs Yancy, Olsen, and Oddone); Departments of Medicine (Drs Yancy, Curtis, Schulman, Cuffe, and Oddone) and Biostatistics and Bioinformatics (Dr Olsen), Duke University Medical Center; and Duke Clinical Research Institute (Drs Yancy, Curtis, Schulman, and Cuffe), Durham, NC.


Arch Intern Med. 2005;165(12):1381-1387. doi:10.1001/archinte.165.12.1381.
Text Size: A A A
Published online

Background  Increased body mass index (BMI) (calculated as weight in kilograms divided by the square of height in meters) is a risk factor for coronary heart disease and is associated with lower preventive services utilization. The relationship between BMI and utilization of diagnostic or therapeutic procedures for coronary heart disease has not been examined.

Methods  We evaluated 109 664 Medicare patients who were hospitalized for acute myocardial infarction in a nongovernmental acute care hospital between 1994 and 1996, were 65 years or older, and weighed 159 kg or less. We used logistic regression to examine the relationship of BMI with utilization of cardiac catheterization, percutaneous coronary intervention, and coronary artery bypass grafting while adjusting for patient and hospital characteristics.

Results  Participants had a mean age of 75.8 years; 53% were men and 90% were white. Individuals with a BMI of 25.0 to 35.0 had the highest rates of coronary procedure utilization. Compared with patients with a BMI of 25.0 to 29.9, those with a BMI of 35.0 to 39.9 had a reduced adjusted odds ratio (OR) of receiving coronary artery bypass grafting (OR, 0.88; 95% confidence interval [CI], 0.79-0.98), whereas patients with a BMI of 40.0 or greater had the lowest odds of receiving cardiac catheterization (OR, 0.82; 95% CI, 0.73-0.92), percutaneous coronary intervention (OR, 0.89; 95% CI, 0.77-1.03), and coronary artery bypass grafting (OR, 0.68; 95% CI, 0.57-0.82). Patients who did not receive coronary revascularization had higher mortality rates than those who did.

Conclusions  For patients hospitalized with acute myocardial infarction, those with a very high BMI were less likely to receive invasive coronary procedures. Future research should investigate reasons for these variations in coronary procedure utilization.

Figures in this Article

In the past 15 years, there has been extensive literature documenting that the use of coronary procedures can vary depending on patient characteristics, such as race and sex.1,2 Potential reasons for these variations include differences in index disease severity, in comorbid illness severity, in patient preferences, and in physician recommendations.2 Furthermore, disease prevalence, response to noninvasive therapies, and response to invasive therapies all may vary depending on patient characteristics. Physicians need to be aware of these variations to make proper therapeutic recommendations and to avoid the introduction of systematic bias that might compromise the care of individuals with certain characteristics. For these reasons, understanding disparities in health care remains a priority in health policy.

Little research has evaluated whether health care disparities exist in relation to a patient’s body weight. Recent literature35 examining outcomes after percutaneous coronary intervention (PCI) gives conflicting data regarding whether body weight is associated with either mortality or procedure complications, with the largest study3 showing increased risk for underweight and extremely obese patients. For cardiac surgery, obese patients are at higher risk for wound infections and at lower risk for needing a blood transfusion, but, similar to PCI, results are conflicting for mortality and other serious outcomes,68 with the largest study7 showing higher risk for severely obese patients. Few studies911 have examined variations in utilization of specific health care interventions as a function of body weight. Specifically, we did not identify any studies evaluating the relationship between body weight and the use of coronary procedures.

The association of obesity with utilization of coronary procedures is increasingly pertinent. The prevalence of obesity is rising, with recent studies classifying more than 65% of Americans as overweight or obese (body mass index [BMI] [calculated as weight in kilograms divided by the square of height in meters] ≥25).12 Because obesity is an independent risk factor for coronary heart disease (CHD) and contributes to the development of other risk factors for CHD, such as hypertension, diabetes mellitus, and hyperlipidemia, this increase in prevalence has profound implications regarding the diagnosis and treatment of CHD.13

The goal of this study is to determine whether increased body weight is associated with variations in coronary procedure utilization in patients hospitalized with acute myocardial infarction (AMI). Because these procedures may be perceived as being riskier as the patient’s body weight increases, we hypothesize that obese patients are less likely to receive coronary procedures compared with normal-weight patients.

PATIENT SAMPLE

The patient sample was obtained from the Cooperative Cardiovascular Project, a large cohort study initiated by the Health Care Financing Association with the aim of measuring the quality of health care for Medicare patients with AMI. The Cooperative Cardiovascular Project cohort included all Medicare patients discharged with a principal diagnosis of AMI (code 410 of the International Classification of Diseases, Ninth Revision, Clinical Modification,14 excluding a fifth digit of 2, which would indicate AMI in the 8 weeks preceding the index admission) from all nonfederal acute care hospitals during a specified 8-month period in each of 46 states between January 1, 1994, and February 21, 1996. Hospital bills (UB-92 claims) in the Medicare National Claims History File were used to identify patients. Alabama, Connecticut, Iowa, and Wisconsin were excluded because the sampling was modified in these states.

An AMI was defined as a creatine phosphokinase MB fraction greater than 0.05; a lactate dehydrogenase level exceeding 1.5 times the upper limit of normal, with a higher isoenzyme 1 level than isoenzyme 2 level; or the presence of 2 of the following conditions: chest pain, a doubling of the creatine phosphokinase level, or evidence of new MI on an electrocardiogram.15

Data concerning patient characteristics, presentation of illness, severity of illness, coexisting conditions, laboratory test results, treatment, and complications were abstracted from medical records at 2 abstraction centers. The average agreement between abstractors was 95%.16 Starting with a sample of 206 986 patients, the following deletions were made sequentially: cases that did not meet the clinical criteria for AMI (n = 27 509), hospital admissions other than the index admission (n = 13 433), invalid ZIP codes of residences (n = 2), persons treated outside the 50 United States (n = 1316), and persons with a terminal illness (n = 598). This yielded a net sample of 164 128.

STATISTICAL ANALYSIS

Patients were classified into groups depending on BMI and according to the classification system of the National Institutes of Health.17 Patients who weighed more than 159 kg (n = 34) were excluded because many catheterization tables that were in use during the study period were restricted at that weight cutoff value. Patients who were classified as underweight (BMI <18.5; n = 5649) were excluded because our hypothesis involved patients who were above normal weight and because underweight patients have reduced survival due to known or unknown comorbid illness. Finally, we excluded patients who were hospitalized in a facility that did not offer the cardiac procedure in question, either cardiac catheterization (n = 48 781) or open heart surgery (n = 82 595). Therefore, the final sample size was 109 664 patients for the cardiac catheterization and PCI models and 75 850 patients for the coronary artery bypass grafting (CABG) model.

The primary outcome variable was utilization of a cardiac procedure during the initial AMI hospitalization. Patients in this sample were seen at 1972 unique hospitals. Using generalized estimating equations,18 adjusting for clustering of patients within hospitals did not significantly alter model results; therefore, separate logistic regressions were performed using each of the 3 coronary procedures—cardiac catheterization, PCI, or CABG—as the outcome variable. Each BMI classification was represented by an indicator variable in the models. The BMI classifications of overweight (25.0-29.9) and obesity class 1 (30.0-34.9) had the highest rates of procedure use. Overweight was chosen as the referent category because of its higher prevalence.

For multivariable analyses, independent variables that might clinically affect the decision to use coronary procedures were included in the models. Patient characteristics included age (continuous), sex, race (white or nonwhite), and cigarette smoking history (current or not current). Patient socioeconomic status was represented by the median household income for the patient’s ZIP code area (continuous). Medical history was represented by indicator variables for history of severe chronic illness (human immunodeficiency virus positive or AIDS, immunosuppression, liver failure or cirrhosis, metastatic cancer, lymphoma, or leukemia), multiple other clinical conditions (Table 1), and serum creatinine level at presentation (continuous). Previous cardiac intervention was represented by indicator variables for previous PCI or CABG. Severity of AMI variables included the presence of anterior MI, the presence of ST-segment elevation on electrocardiography, the duration of chest pain at presentation (continuous), and peak creatine phosphokinase level (continuous). Hospital variables included the prehospitalization setting of the patient (transfer from another acute care hospital/emergency department or other), the discharge disposition of the patient (transfer to another acute care hospital or other), hospital ownership (for-profit, not-for-profit, or government), and hospital teaching status (nonteaching, resident physician–bed ratio ≤0.1, or resident physician–bed ratio >0.1).

Table Graphic Jump LocationTable 1. Descriptive Characteristics of the Study Patients and Admitting Hospitals*

Several patients in the sample were missing measurements for height (n = 14 339; 13% of the total sample), weight (n = 7828; 7%), or both (n = 16 614; 15%), and fewer patients were missing data for other predictor variables. Rather than exclude these patients, we used Markov chain Monte Carlo methods to multiply impute values for missing variables under a multivariate normal model.19 Each of 10 imputed data sets was analyzed using the logistic regression models; we then combined parameter estimates and standard errors according to the rules developed by Rubin.20 All models provided good discriminative ability (c indexes, 0.72-0.78).21

Next we sought to evaluate whether variations in coronary procedure use translated into variations in 1-year mortality rates, depending on patient BMI. In this observational data set, the relationship between procedure use and survival is confounded by several other variables, for example, patients who received a procedure were systematically different than those who did not, and several of these variables were also related to mortality. A direct comparison of mortality rates for procedure use vs no procedure use may be biased owing to these systematic differences. To adjust for this, we applied the following methods.

Two separate logistic regression models were developed. One model predicted mortality in patients who received PCI, and the other model predicted mortality in those who did not receive PCI.22 The following independent variables were included in the models in addition to those in the coronary procedure utilization models: the presence of cardiac arrest that required cardiopulmonary resuscitation in the 6 hours before arrival at the hospital, the presence of shock or congestive heart failure at the time of arrival, the presence of bleeding in the 48 hours before arrival, chest pain lasting longer than 60 minutes after arrival, and the use of thrombolytics, β-blockers, or aspirin during hospitalization.

The estimated coefficients from the 2 models were then applied to the entire sample, yielding 2 predicted probabilities of mortality for each patient. Each set of predicted mortality rates was averaged within each BMI category. The difference between each of the 2 rates represents the average causal treatment difference within the BMI category. We then applied these steps to the 10 imputed data sets and combined estimates. Bootstrap resampling was used to estimate standard errors of the difference in mortality rates.23 These procedures were then repeated for CABG. For the CABG analyses, obesity class 2 and obesity class 3 were combined owing to a small number of events. For all analyses, P = .05 was used as the level of significance. Analyses were performed using statistical software (SAS version 8.02; SAS Institute Inc, Cary, NC).

The patient sample had a mean ± SD age of 75.8 ± 7.2 years; 53.2% were men and 90.4% were white (Table 1). The 2 highest BMI classifications were predominantly composed of women (Table 1). In addition, persons in the lower BMI classifications were more likely to be older or to smoke cigarettes. The proportion of patients with specific comorbid illnesses also varied by BMI classification.

Patients with a normal BMI (18.5-24.9) and obesity class 3 (BMI ≥40.0) had the lowest raw utilization rates for diagnostic and therapeutic coronary procedures (Table 2 provides frequencies and Table 3 gives unadjusted odds ratios). Utilization of cardiac procedures according to BMI class varied the most for cardiac catheterization; nearly 50% of all patients received cardiac catheterization, with a 13% difference between the lowest and highest utilization BMI classes (Table 2). Approximately 18% of the patients received PCI, with an absolute variation of almost 5%, and 15% of patients received CABG, with an absolute variation of 5%.

Table Graphic Jump LocationTable 2. Frequency of Coronary Procedures by BMI Classification
Table Graphic Jump LocationTable 3. Unadjusted and Adjusted Odds Ratios for Utilization of Coronary Procedures*

In the adjusted analyses, there was also an inverted U relationship between coronary procedure use and BMI (Table 3 and the Figure). As in the unadjusted analyses, normal-weight patients had lower odds than the referent BMI classification of receiving each of the coronary procedures. However, the relationship was not as strong after adjusting for known predictors of coronary procedure use. On the upper end of the BMI classification system, individuals with obesity class 2 (BMI of 35.0-39.9) were less likely to receive CABG (odds ratio, 0.88; 95% confidence interval, 0.79-0.98) than the referent patients. Meanwhile, individuals with obesity class 3 were less likely to receive cardiac catheterization (odds ratio, 0.82; 95% confidence interval, 0.73-0.92) and CABG (odds ratio, 0.68; 95% confidence interval, 0.57-0.82) than the referent patients. In contrast to the relationship seen in patients with a normal BMI, adjusting for possible confounding characteristics strengthened the relationship between severe obesity and lower coronary procedure use. Odds ratios for individuals with obesity class 3 were of comparable magnitude to the lower odds of procedure use for patients of nonwhite race and female sex (Table 3). In addition, patients with diabetes mellitus were less likely than nondiabetic patients to receive cardiac catheterization or PCI but as likely to receive CABG.

Place holder to copy figure label and caption
Figure.

Adjusted odds ratios for the utilization of cardiac catheterization (A), percutaneous coronary intervention (B), and coronary artery bypass grafting (C) according to body mass index (BMI) (calculated as weight in kilograms divided by the square of height in meters). Error bars represent 95% confidence intervals.

Graphic Jump Location

Mortality rates after AMI also seemed to vary by BMI classification (Table 4). Compared with patients who did not receive a procedure, mortality rates were lower for patients who received PCI or CABG regardless of BMI class. The average causal treatment difference, however, was greater for the lower BMI classes than for the higher BMI classes. For example, normal-weight patients who did not receive PCI had an adjusted mortality rate of 0.37 compared with 0.29 for those who received PCI, resulting in an average causal treatment difference of 0.08. In contrast, the average causal treatment difference for patients with obesity class 3 was 0.03. The trend was similar for the CABG analyses.

Table Graphic Jump LocationTable 4. Adjusted* 1-Year Mortality Rates by BMI

As a result of the higher prevalence of CHD, obese persons are more likely than normal-weight persons to require diagnostic and therapeutic coronary procedures.13 These procedures, however, may be limited at high body weights because manufacturers’ recommendations restrict patient body weight to less than 159 kg on many cardiac catheterization tables. Regardless of equipment weight capacity, physicians might be hesitant to perform diagnostic catheterization for patients who are severely obese owing to perceptions about increased morbidity, technical difficulty, and issues of constrained therapeutic options. Because of their high CHD risk status, however, obese persons may derive sufficient benefit from these procedures to outweigh perceived risks. Furthermore, if diagnostic tests are not performed to document CAD, it is possible that obese persons might not receive maximally appropriate therapy. Finally, visual documentation of CAD by cardiac catheterization might also supply motivation for patients to change unhealthy behaviors.24

In a large national sample of Medicare patients hospitalized for AMI, we found that the utilization of 3 coronary procedures varied substantially with BMI. Normal-weight patients were less likely than heavier patients to receive each of the 3 procedures. Overweight and class 1 obese individuals had the highest rates of procedure utilization rather than normal-weight individuals, who we expected would have the highest utilization rates. Rates of utilization decreased again in individuals with more severe obesity, with the lowest adjusted odds for all 3 procedures occurring in patients with obesity class 3. The disparity in procedure utilization seen in class 3 obese patients was similar to that seen in persons of nonwhite race and in women, 2 groups with well-described and extensively studied disparities in the utilization of coronary procedures.1,2 We examined the clinical consequences of these disparities by calculating the average treatment difference between patients who received PCI (or CABG) and those who did not. Patients who received these interventions had improved mortality rates across all BMI classes, but this difference was less prominent in the higher BMI classes.

Lower rates of utilization in normal-weight patients might be explained by their higher rates of comorbid illness. Procedures may not be considered in patients with significant comorbid illness because of the potential for complications or because of a poor baseline prognosis. The adjusted analyses demonstrated that much of the discrepancy in coronary procedure utilization seen in normal-weight persons could be explained by these patient characteristics.

Patients with obesity class 3 also had evidence of increased comorbid illness. However, the relationship between obesity class 3 and decreased procedure utilization was actually strengthened by adjusting for possible confounding patient- and hospital-related factors. Furthermore, because patients weighing more than 159 kg were excluded, body weight restrictions placed by catheterization equipment manufacturers do not explain this effect.

Type 2 diabetes mellitus is highly correlated with obesity. Therefore, we performed analyses to examine how the prevalence of diabetes mellitus affected the utilization of coronary procedures in each BMI classification. Because the benefit from CABG over PCI is considered to be greater in diabetic patients than in nondiabetic patients,25 one would expect diabetic patients to be less likely to receive PCI and at least as likely to receive CABG compared with nondiabetic patients, regardless of BMI. We found this to be true, but we also found that patients with diabetes mellitus were less likely than nondiabetic patients to receive cardiac catheterization. The lower rate of cardiac catheterization in diabetic patients is concerning because catheterization is required before beneficial revascularization with CABG.

Epidemiologic studies26 have shown that mean BMI increases with age until approximately 65 years, after which mean BMI begins to decline. Furthermore, the optimal BMI in terms of survival may increase slightly with age, and the relative risk of mortality by BMI may decrease with age.26,27 These relationships are important to consider when interpreting data from the Cooperative Cardiovascular Project. In our analyses, no individual was younger than 65 years, and the mean age of the overall sample was 76 years. Therefore, as expected, the proportion of the sample that is classified as obese is less than would be seen in a younger sample.

This leads to an important limitation of our study. In a younger sample than the Cooperative Cardiovascular Project cohort, the highest rate of procedure utilization would likely occur at a lower BMI because younger individuals who are underweight or normal weight are likely to be healthier than elderly patients in these BMI classifications. This fact, combined with the likelihood that a younger sample would have a higher prevalence of overweight and obesity, might result in disparities in procedure utilization of greater magnitude or at even earlier stages of obesity. Therefore, in a younger patient sample, one would expect that a higher proportion of individuals would be deprived of potentially beneficial procedures. Examination of these questions in a more recent cohort is also warranted given the increasing prevalence of obesity and the improvements in procedural techniques and equipment technology.

Another limitation is that our study is an analysis of retrospective cohort data. One can only speculate as to why patients of differing BMI receive coronary procedures at different rates, and data collection is limited to that which is recorded in the medical record. In addition, height, weight, or both measurements were missing in 15% of the sample. In our analyses, we used multiple imputation, which should minimize the effect of this nonrandom pattern of missing data. Finally, the superior 1-year survival rate in patients who received PCI or CABG compared with those who did not was less pronounced in patients with a greater BMI. Future research should investigate whether the risks of coronary procedures outweigh the benefits in very obese patients.

In conclusion, we demonstrated that differing rates of coronary procedure utilization exist in patients of different BMI classes. In this patient sample, overweight and class 1 obese patients had the highest rates of utilization, whereas normal-weight and class 3 obese patients had significantly lower rates of utilization in comparison. Whereas increased morbidity may partially explain the lower utilization rates in patients with a normal BMI, it is unclear why severely obese patients have lower utilization rates. Moreover, unless it can be shown that the risks of these procedures outweigh the benefits in patients with severe obesity, initiatives may be necessary to prevent these variations in health care utilization. Finally, given the relationships between sex and BMI and between race and BMI, future research looking at health care disparities in women and racial minorities should consider BMI a confounder.

Correspondence: William S. Yancy, Jr, MD, MHS, Center for Health Services Research in Primary Care (152), Durham Veterans Affairs Medical Center, 508 Fulton St, Durham, NC 27705 (yancy006@mc.duke.edu).

Accepted for Publication: January 31, 2004.

Financial Disclosure: None.

Funding/Support: This study was supported by contract 500-96-P623 sponsored by the Delmarva Foundation for Medical Care Inc and the Centers for Medicare and Medicaid Services (formerly the Health Care Financing Administration), US Department of Health and Human Services, both in Baltimore, Md; and by Health Services Research Career Development Award RCD 02-183-1 from the Department of Veterans Affairs, Washington, DC (Dr Yancy).

Disclaimer: The contents of this publication do not necessarily reflect the views of the US Department of Health and Human Services, and neither does mention of trade names, commercial products, or organizations imply endorsement by the US government. The authors assume full responsibility for the accuracy and completeness of the ideas presented herein.

Additional Information: This article is a direct result of the Health Care Quality Improvement Program initiated by the Centers for Medicare and Medicaid Services, which has encouraged the identification of quality improvement projects derived from analysis of patterns of care.

Krumholz  HMDouglas  PSLauer  MSPasternak  RC Selection of patients for coronary angiography and coronary revascularization early after myocardial infarction: is there evidence for a gender bias? Ann Intern Med 1992;116785- 790
PubMed Link to Article
Kressin  NRPetersen  LA Racial differences in the use of invasive cardiovascular procedures: review of the literature and prescription for future research. Ann Intern Med 2001;135352- 366
PubMed Link to Article
Minutello  RMChou  ETHong  MK  et al.  Impact of body mass index on in-hospital outcomes following percutaneous coronary intervention (report from the New York State Angioplasty Registry). Am J Cardiol 2004;931229- 1232
PubMed Link to Article
Powell  BDLennon  RJLerman  A  et al.  Association of body mass index with outcome after percutaneous coronary intervention. Am J Cardiol 2003;91472- 476
PubMed Link to Article
Gurm  HSBrennan  DMBooth  JTcheng  JELincoff  AMTopol  EJ Impact of body mass index on outcome after percutaneous coronary intervention (the obesity paradox). Am J Cardiol 2002;9042- 45
PubMed Link to Article
Potapov  EVLoebe  MAnker  S  et al.  Impact of body mass index on outcome in patients after coronary artery bypass grafting with and without valve surgery. Eur Heart J 2003;241933- 1941
PubMed Link to Article
Prabhakar  GHaan  CKPeterson  EDCoombs  LPCruzzavala  JLMurray  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;741125- 1130
PubMed Link to Article
Birkmeyer  NJCharlesworth  DCHernandez  F  et al. Northern New England Cardiovascular Disease Study Group, Obesity and risk of adverse outcomes associated with coronary artery bypass surgery. Circulation 1998;971689- 1694
PubMed Link to Article
Wee  CCMcCarthy  EPDavis  RBPhillips  RS Screening for cervical and breast cancer: is obesity an unrecognized barrier to preventive care? Ann Intern Med 2000;132697- 704
PubMed Link to Article
Fontaine  KRFaith  MSAllison  DBCheskin  LJ Body weight and health care among women in the general population. Arch Fam Med 1998;7381- 384
PubMed Link to Article
Lubitz  RMLitzelman  DKDittus  RSTierney  WM Is obesity a barrier to physician screening for cervical cancer? Am J Med 1995;98491- 496
PubMed Link to Article
Hedley  AAOgden  CLJohnson  CLCarroll  MDCurtin  LRFlegal  KM Prevalence of overweight and obesity among US children, adolescents, and adults, 1999-2002. JAMA 2004;2912847- 2850
PubMed Link to Article
Must  ASpadano  JCoakley  EHField  AEColditz  GDietz  WH The disease burden associated with overweight and obesity. JAMA 1999;2821523- 1529
PubMed Link to Article
Public Health Service HCFA, International Classification of Diseases, 9th Revision, Clinical Modification: ICD-9-CM. 4th ed. Washington, DC Health Care Financing Administration1980;
Rathore  SSBerger  AKWeinfurt  KP  et al.  Acute myocardial infarction complicated by atrial fibrillation in the elderly: prevalence and outcomes. Circulation 2000;101969- 974
PubMed Link to Article
Marciniak  TAEllerbeck  EFRadford  MJ  et al.  Improving the quality of care for Medicare patients with acute myocardial infarction: results from the Cooperative Cardiovascular Project. JAMA 1998;2791351- 1357
PubMed Link to Article
NHLBI Obesity Education Initiative, Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults: The Evidence Report.  Bethesda, Md US Dept of Health and Human Services, Public Health Service, National Institutes of Health, National Heart, Lung, and Blood Institute1998;
Liang  KYZeger  SL Longitudinal data analysis using generalized linear models. Biometrika 1986;7313- 22
Link to Article
Schafer  JL Analysis of Incomplete Multivariate Data.  London, England Chapman & Hall1997;
Rubin  DB Multiple Imputation for Nonresponse in Surveys.  New York, NY John Wiley & Sons Inc1987;
Harrell  FELee  KLMark  DB Multivariate prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 1996;15361- 387
PubMed Link to Article
Chen  PYTsiatis  AA Causal inference on the difference of the restricted mean lifetime between two groups. Biometrics 2001;571030- 1038
PubMed Link to Article
Efron  BTibshirani  RJ An Introduction to the Bootstrap.  New York, NY Chapman & Hall1993;
Frid  DOckene  ISOckene  JK  et al.  Severity of angiographically proven coronary artery disease predicts smoking cessation. Am J Prev Med 1991;7131- 135
PubMed
Brooks  RCDetre  KM Clinical trials of revascularization therapy in diabetics. Curr Opin Cardiol 2000;15287- 292
PubMed Link to Article
Stevens  JCai  JPamuk  ERWilliamson  DFMichael  JTWood  JL The effect of age on the association between body-mass index and mortality. N Engl J Med 1998;3381- 7
PubMed Link to Article
Calle  EEThun  MJPetrelli  JMRodriquez  CHeath  CWJ Body-mass index and mortality in a prospective cohort of U.S. adults. N Engl J Med 1999;3411097- 1105
PubMed Link to Article

Figures

Place holder to copy figure label and caption
Figure.

Adjusted odds ratios for the utilization of cardiac catheterization (A), percutaneous coronary intervention (B), and coronary artery bypass grafting (C) according to body mass index (BMI) (calculated as weight in kilograms divided by the square of height in meters). Error bars represent 95% confidence intervals.

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1. Descriptive Characteristics of the Study Patients and Admitting Hospitals*
Table Graphic Jump LocationTable 2. Frequency of Coronary Procedures by BMI Classification
Table Graphic Jump LocationTable 3. Unadjusted and Adjusted Odds Ratios for Utilization of Coronary Procedures*
Table Graphic Jump LocationTable 4. Adjusted* 1-Year Mortality Rates by BMI

References

Krumholz  HMDouglas  PSLauer  MSPasternak  RC Selection of patients for coronary angiography and coronary revascularization early after myocardial infarction: is there evidence for a gender bias? Ann Intern Med 1992;116785- 790
PubMed Link to Article
Kressin  NRPetersen  LA Racial differences in the use of invasive cardiovascular procedures: review of the literature and prescription for future research. Ann Intern Med 2001;135352- 366
PubMed Link to Article
Minutello  RMChou  ETHong  MK  et al.  Impact of body mass index on in-hospital outcomes following percutaneous coronary intervention (report from the New York State Angioplasty Registry). Am J Cardiol 2004;931229- 1232
PubMed Link to Article
Powell  BDLennon  RJLerman  A  et al.  Association of body mass index with outcome after percutaneous coronary intervention. Am J Cardiol 2003;91472- 476
PubMed Link to Article
Gurm  HSBrennan  DMBooth  JTcheng  JELincoff  AMTopol  EJ Impact of body mass index on outcome after percutaneous coronary intervention (the obesity paradox). Am J Cardiol 2002;9042- 45
PubMed Link to Article
Potapov  EVLoebe  MAnker  S  et al.  Impact of body mass index on outcome in patients after coronary artery bypass grafting with and without valve surgery. Eur Heart J 2003;241933- 1941
PubMed Link to Article
Prabhakar  GHaan  CKPeterson  EDCoombs  LPCruzzavala  JLMurray  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;741125- 1130
PubMed Link to Article
Birkmeyer  NJCharlesworth  DCHernandez  F  et al. Northern New England Cardiovascular Disease Study Group, Obesity and risk of adverse outcomes associated with coronary artery bypass surgery. Circulation 1998;971689- 1694
PubMed Link to Article
Wee  CCMcCarthy  EPDavis  RBPhillips  RS Screening for cervical and breast cancer: is obesity an unrecognized barrier to preventive care? Ann Intern Med 2000;132697- 704
PubMed Link to Article
Fontaine  KRFaith  MSAllison  DBCheskin  LJ Body weight and health care among women in the general population. Arch Fam Med 1998;7381- 384
PubMed Link to Article
Lubitz  RMLitzelman  DKDittus  RSTierney  WM Is obesity a barrier to physician screening for cervical cancer? Am J Med 1995;98491- 496
PubMed Link to Article
Hedley  AAOgden  CLJohnson  CLCarroll  MDCurtin  LRFlegal  KM Prevalence of overweight and obesity among US children, adolescents, and adults, 1999-2002. JAMA 2004;2912847- 2850
PubMed Link to Article
Must  ASpadano  JCoakley  EHField  AEColditz  GDietz  WH The disease burden associated with overweight and obesity. JAMA 1999;2821523- 1529
PubMed Link to Article
Public Health Service HCFA, International Classification of Diseases, 9th Revision, Clinical Modification: ICD-9-CM. 4th ed. Washington, DC Health Care Financing Administration1980;
Rathore  SSBerger  AKWeinfurt  KP  et al.  Acute myocardial infarction complicated by atrial fibrillation in the elderly: prevalence and outcomes. Circulation 2000;101969- 974
PubMed Link to Article
Marciniak  TAEllerbeck  EFRadford  MJ  et al.  Improving the quality of care for Medicare patients with acute myocardial infarction: results from the Cooperative Cardiovascular Project. JAMA 1998;2791351- 1357
PubMed Link to Article
NHLBI Obesity Education Initiative, Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults: The Evidence Report.  Bethesda, Md US Dept of Health and Human Services, Public Health Service, National Institutes of Health, National Heart, Lung, and Blood Institute1998;
Liang  KYZeger  SL Longitudinal data analysis using generalized linear models. Biometrika 1986;7313- 22
Link to Article
Schafer  JL Analysis of Incomplete Multivariate Data.  London, England Chapman & Hall1997;
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