Abstract
Background
Body Mass Index (BMI) is often used to characterize patient risk in bariatric surgery. Its relationship with other risk factors has not been well characterized.
Objectives
To evaluate the relationship between BMI and demographic/clinical characteristics of patients undergoing bariatric surgery.
Setting
10 clinical centers in the United States between March 1, 2005, and March 26, 2007.
Methods
The Longitudinal Assessment of Bariatric Surgery-1 (LABS-1) is a study of 30-day outcomes in patients undergoing bariatric procedures. The sample for this study includes participants with a BMI ≥40 kg/m2 and no history of bariatric procedures. This analysis examined relationships between BMI strata and several demographic/clinical characteristics.
Results
Of 2559 patients (23% male, 10% Black/African-American, 9% age≥ 60 years) with a BMI ≥40 kg/m2, 29% had a BMI of 50–<60 kg/m2, and 12% had a BMI ≥60 kg/m2. The percentage of males and Black/African-Americans increased with higher BMI category and the percentage of older patients (age≥ 60 years) decreased. Patients with higher BMI were more likely to have a history of several comorbid conditions (hypertension, diabetes, congestive heart failure, asthma, poor functional status, sleep apnea, pulmonary hypertension, venothromboembolism, or venous edema with ulcerations) than patients with a BMI of 40–<50 kg/m2 after adjusting for age, race, sex and ethnicity.
Conclusion
Higher BMI was associated with several patient characteristics that have been linked to less weight loss, more adverse outcomes and increased healthcare utilization in prior studies. Outcomes analysis must consider the potential for confounding of BMI with demographic and clinical characteristics.
INTRODUCTION
Most of the data concerning outcomes of commonly performed bariatric surgical procedures come from small, single-center case series or limited datasets 1. Determining which patients are likely to have favorable, or poor, outcomes after surgery is challenging because potential predictor and outcome variables are not always collected in a standard fashion, because success can be judged in different, and not always, comparable ways, and because factors related to outcomes are inter-related. Gathering accurate data that are associated with outcomes in bariatric surgery and performing appropriate analyses are essential to identify those at high-risk of adverse outcome and those most likely to benefit from surgery. In addition to the scientific benefits that accrue from such investigations, better outcome predictors will help to improve the informed consent process.
Important adverse outcomes in bariatric surgery, such as perioperative death and reoperations, occur infrequently and appear to be occurring less often over time 2. Several factors may account for this trend including increased surgical experience, improved equipment, more procedures done in high volume centers 3, dedicated multidisciplinary bariatric teams, and patient selection. Determining which patients are at highest risk for these relatively infrequent events in a prospective study requires a standardized pre-surgical evaluation of patient characteristics and assessment of out-of-hospital outcomes in large cohorts. When factors associated with adverse outcomes (e.g., comorbidities) are also associated with other risk factors for those outcomes (e.g., body mass index [BMI]), an even larger cohort is required to have adequate statistical power to identify independent risk factors.
In the absence of large clinical cohorts, administrative and claims databases have been used to examine predictors of adverse outcomes. Many of these datasets include no clinical information beyond diagnostic and procedure coding and offer only the most basic descriptive information about the patient (sex, age and insurance status for example, but not weight, height, medications or severity of conditions). Furthermore, there is little standardization across hospital abstractors with respect to definitions of data submitted to administrative databases. Despite these limitations, administrative data have been used to identify several patient characteristics associated with increased risk of adverse outcome including older age 4, male sex 4 and Medicare/Medicaid status 4,5. Smaller clinical series 6–8 and analysis of a Veterans Administration hospital system registry 9 have confirmed these findings and demonstrated that patients with higher BMIs and more co-morbid conditions have increased perioperative risks.
Higher BMI seems to be an intuitive risk factor for adverse post-surgery outcomes, but it may be just a surrogate metric of risk, reflecting the risk of the variables associated with BMI. Recently, a stratification algorithm based on BMI>50, male sex, hypertension and risk of pulmonary embolus was proposed to identify those at highest risk for perioperative death 10. With only 31 events occurring over a 10-year time period, the adequacy of this single-center’s data to account for many of the inter-relationships among risk factors is problematic. Thus, the resulting stratification algorithm may not prove to predict accurately the risk of post-surgical mortality in the general bariatric population.
The purpose of this report is to evaluate the relationships between BMI and other potentially important risk factors for adverse outcome in patients undergoing bariatric surgery. Identifying the extent to which BMI is associated with other risk factors is expected to be important in accurately predicting outcomes for patients considering bariatric surgery. The hypothesis being tested was that the most commonly considered risk factor, higher BMI, was associated with other risk factors commonly related to less weight loss, adverse outcomes and increased healthcare utilization (e.g., male sex, older age, Black/African-American race, Hispanic ethnicity, and comorbid conditions).
METHODS
Participants
The Longitudinal Assessment of Bariatric Surgery (LABS) is a prospective, multi-center observational cohort study 11. LABS contains multiple components; LABS-1 is a 30-day safety study in consecutive patients 18 years or older undergoing bariatric surgical procedures by LABS certified surgeons at: University of Pittsburgh Medical Center (Pennsylvania), Columbia-Presbyterian Hospital or Cornell University (New York), East Carolina Medical Center (North Carolina), the MeritCare Health Systems through the Neuropsychiatric Research Institute (North Dakota), Sacramento Bariatric (California), University of Washington Medical Center or Virginia Mason Medical Center (Washington), and Oregon Health and Sciences University or Legacy Good Samaritan Hospital (Oregon). The Data Coordinating Center is at the University of Pittsburgh, Graduate School of Public Health. The LABS-1 protocol and consent form were approved by the Institutional Review Board at each institution.
This analysis included 2,559 LABS-1 participants with a BMI of at least 40 kg/m2 and no previous bariatric procedure, who underwent a bariatric surgical procedure between March 1, 2005-March 26, 2007. Patients with a BMI below 40 kg/m2 were excluded from this analysis because many insurers require presence of one or more co-morbidities in these patients. In addition, patients who had had a previous bariatric procedure were excluded because they represented a more heterogeneous group of patients undergoing a wider variety of procedures.
Data definitions
The LABS-1 pre-operative evaluation was completed through in-person interviews, physical evaluation and chart review. Standardized protocols were used to measure weight and height within 30 days prior to surgery. Within 90 days prior to surgery LABS-certified data collectors assessed history of comorbidities (hypertension, diabetes mellitus, congestive heart failure, asthma, functional status as measured by ability to walk unassisted for 200 feet, deep vein thrombosis/pulmonary embolism (DVT/PE), sleep apnea, ischemic heart disease, pulmonary hypertension, and venous edema with ulcerations) and indications of severity (e.g. use of CPAP or BiPAP machine for sleep apnea) based on patient self-report and chart review. The data point venous edema with ulcerations was added to the LABS-1 pre-operative evaluation after study initiation and was therefore missing in the first 359 participants. Details of the LABS-1 pre-operative, operative and post-operative data collection forms and definitions include demographic, clinical, laboratory, anthropomorphic, and adverse outcome components are published 11. Data were entered twice using a web-based data entry system developed, distributed and maintained by the Data Coordinating Center.
Data analysis
Age was grouped as less than 30, 30–<40, 40–<50, 50–<60, 60+ years. Demographic and clinical data were tabulated by BMI strata (40–<50 kg/m2, 50–<60 kg/m2 and ≥60 kg/m2). The Mantel-Haenszel test for trend 12 was used to test for a relationship between BMI strata and each of several demographic and clinical characteristics. In addition, a series of logistic regression models were used to estimate the odds of having specific comorbid conditions for the higher BMI strata (BMI 50–<60 kg/m2 and BMI ≥60 kg/m2) compared to BMI 40–<50 kg/m2 adjusting for age, sex, race and ethnicity.
RESULTS
Demographic and clinical characteristics of the total sample and by BMI stratum are presented in Table 1. The sample was 23% male, 10% Black/African-American, 7% Hispanic and 9% at least 60 years of age. Over half (58%) of the patients had a BMI between 40–<50 kg/m2, 29% had a BMI between 50–<60 kg/m2 and 12% had a BMI ≥60 kg/m2. The proportion of males and of Blacks/African-Americans increased with each successive BMI category. However, there was not a significant trend in ethnicity (i.e. proportions Hispanic) by BMI group, and there was a significant inverse relationship between age and BMI, such that the proportion of older patients (age ≥60 years) was smaller in the higher BMI groups than in the lower BMI groups.
Table 1.
Demographic and clinical characteristics by BMI (kg/m2) group
| BMI (kg/m2) Group | |||||
|---|---|---|---|---|---|
| Total | 40 to < 50 | 50 to < 60 | 60+ | ||
| Demographics | (N=2559) | (N=1490) | (N=751) | (N=318) | p value* |
| Patient age, % | |||||
| <30 | 11.7 | 11.4 | 13.2 | 9.7 | .0254 |
| 30–39 | 26.0 | 25.8 | 24.2 | 31.1 | |
| 40–49 | 29.8 | 28.5 | 31.6 | 31.4 | |
| 50–59 | 24.0 | 24.4 | 23.3 | 23.3 | |
| 60+ | 8.6 | 9.9 | 7.7 | 4.4 | |
| Sex, % | |||||
| Male | 22.7 | 20.3 | 25.3 | 28.0 | .0004 |
| Race, % | |||||
| Black/African American # missing |
9.7 39 |
7.9 26 |
11.1 9 |
15.3 4 |
<.0001 |
| Ethnicity, % | |||||
| Hispanic | 7.2 | 7.7 | 5.9 | 7.9 | .5340 |
| History of Comorbid Conditions | |||||
| Hypertension, % | 54.6 | 51.7 | 58.3 | 58.8 | .0018 |
| Diabetes mellitus, % | 33.3 | 31.0 | 34.2 | 41.8 | .0003 |
| Congestive heart failure, % | 2.6 | 1.4 | 3.6 | 5.7 | <.0001 |
| Asthma, % | 24.0 | 21.2 | 26.0 | 32.7 | <.0001 |
| History of intubation, % | 7.2 | 5.9 | 6.2 | 12.8 | .0447 |
| Functional Status, % | |||||
| Able to walk 200 ft unassisted | 92.5 | 96.3 | 91.3 | 77.0 | <.0001 |
| Assistance need to walk 200 ft.** | 5.4 | 3.1 | 6.9 | 12.3 | |
| Unable to walk 200 ft.** | 2.2 | 0.6 | 1.7 | 10.7 | |
| # missing | 1 | 1 | 0 | 0 | |
| History of Comorbid Conditions | (N=2559) | (N=1490) | (N=751) | (N=318) | |
| DVT/PE, % | 4.1 | 3.0 | 4.4 | 8.5 | <.0001 |
| Sleep apnea, % | 48.3 | 41.5 | 53.7 | 67.6 | <.0001 |
| Use of CPAP/BiPAP, % | 80.3 | 76.2 | 83.5 | 86.0 | .0002 |
| Supplemental oxygen dependent, % | 3.9 | 2.2 | 4.2 | 7.7 | .0007 |
| Ischemic heart disease, % | 4.2 | 4.0 | 4.3 | 5.0 | .4461 |
| Pulmonary hypertension, % | 1.6 | 0.7 | 2.0 | 4.4 | <.0001 |
| Venous edema with ulcerations, % | 4.6 | 1.8 | 6.3 | 14.1 | <.0001 |
| # missing | 359 | 198 | 112 | 49 | |
| Number of comorbid conditions, % | |||||
| None | 17.4 | 21.8 | 13.6 | 6.3 | <.0001 |
| 1 | 27.9 | 30.2 | 26.0 | 22.0 | |
| 2 | 26.5 | 25.4 | 29.6 | 24.2 | |
| 3 | 16.0 | 14.1 | 17.0 | 22.3 | |
| 4 or more | 12.2 | 8.6 | 13.8 | 25.2 | |
Mantel-Haenszel test for trend
These categories combined was considered to be a comorbidity
Overall, there was a substantial burden of comorbid conditions in the study group with 83% reporting a history of at least one comorbid condition. The most prevalent conditions were: hypertension (55%), obstructive sleep apnea (48%), diabetes mellitus (33%) and asthma (24%). The inability to walk 200 feet unassisted was reported by 8%, and the other conditions examined (congestive heart failure, ischemic heart disease, pulmonary hypertension, DVT/PE and venous edema with ulcerations) each occurred in less than 5% of the sample. Patients with a higher BMI were significantly more likely to have a history of hypertension, diabetes, congenital heart failure, asthma, endotracheal intubation for asthma, poor functional status, DVT/PE, sleep apnea, CPAP use for sleep apnea, oxygen use for sleep apnea, pulmonary hypertension, and venous edema, compared to patients with lower BMI (Table 1). In addition, patients with a higher BMI were more likely than those with lower BMI to have more co-morbid conditions. For example, 94% of those with a BMI ≥60 kg/m2 had at least one co-morbid condition compared to 86% of those with a BMI between 50–<60 kg/m2 and 78% with a BMI between 40–<50 kg/m2) and the proportions of participants with 4 or more comorbid conditions increased from 9% to 14% to 25% for the three BMI categories, respectively.
In multivariable analyses, the adjusted odds of having a history of hypertension, congestive heart failure, asthma, poor functional status, sleep apnea, pulmonary hypertension, or venous edema were each significantly higher (ranging from 1.4 to 13.5 times greater) for participants in the 50–>60 kg/m2 or ≥60 kg/m2 BMI groups compared to participants with a BMI less than 50 kg/m2 (Table 2). In other words, BMI was associated with these comorbidities independent of age, sex, race and ethnicity. In addition, the adjusted odds of having a history of diabetes and DVT/PE were significantly higher in patients whose BMI was ≥60 kg/m2 compared to patients in the lowest BMI stratum. The adjusted odds of reporting a history of ischemic heart disease was not significantly higher in either BMI group.
Table 2.
Unadjusted and adjusted odds ratios (OR)s and 95% confidence intervals (CI)s of having specific comorbid conditions for the higher BMI stratums (BMI 50–<60 kg/m2 and BMI 60+ kg/m2) compared to BMI 40–<50 kg/m2.
| Co-morbidity | BMI group | Unadjusted OR (95% CI) | *Adjusted OR (95% CI) |
|---|---|---|---|
| Hypertension | 50–<60 kg/m2 | 1.31 (1.09, 1.56) | 1.37 (1.13, 1.66) |
| 60 + kg/m2 | 1.33 (1.04, 1.70) | 1.42 (1.09, 1.85) | |
| Diabetes Mellitus | 50–<60 kg/m2 | 1.16 (.96, 1.40) | 1.20 (.98, 1.45) |
| 60 + kg/m2 | 1.60 (1.25, 2.05) | 1.73 (1.34, 2.24) | |
| Congestive Heart Failure | 50–<60 kg/m2 | 2.61 (1.47, 4.65) | 2.79 (1.55, 5.01) |
| 60 + kg/m2 | 4.20 (2.21, 7.97) | 4.96 (2.56, 9.62) | |
| Asthma | 50–<60 kg/m2 | 1.30 (1.06, 1.60) | 1.36 (1.11, 1.68) |
| 60 + kg/m2 | 1.81 (1.39, 2.36) | 1.92 (1.46, 2.51) | |
| Poor Functional Status** | 50–<60 kg/m2 | 2.47 (1.71, 3.58) | 3.03 (2.05, 4.47) |
| 60 + kg/m2 | 7.77 (5.34, 11.31) | 13.49 (8.83, 20.61) | |
| DVT/PE*** | 50–<60 kg/m2 | 1.48 (.93, 2.33) | 1.55 (.98, 2.47) |
| 60 + kg/m2 | 2.98 (1.82, 4.88) | 3.34 (2.02, 5.53) | |
| Sleep Apnea | 50–<60 kg/m2 | 1.63 (1.37, 1.94) | 1.69 (1.41, 2.03) |
| 60 + kg/m2 | 2.94 (2.27, 3.80) | 3.24 (2.48, 4.23) | |
| Ischemic Heart Disease | 50–<60 kg/m2 | 1.06 (.68, 1.64) | 1.15 (.73, 1.82) |
| 60 + kg/m2 | 1.26 (.72, 2.22) | 1.62 (.89, 2.95) | |
| Pulmonary Hypertension | 50–<60 kg/m2 | 2.74 (1.25, 6.00) | 2.80 (1.27, 6.15) |
| 60 + kg/m2 | 6.19 (2.79, 3.77) | 6.78 (3.01, 15.28) | |
| Venous Edema | 50–<60 kg/m2 | 3.68 (2.19, 6.21) | 3.69 (2.18, 6.24) |
| 60 + kg/m2 | 9.08 (5.31, 15.52) | 9.49 (5.51, 16.36) |
Adjusted for age, sex, race, and ethnicity
Inability to walk at least 200 ft. unassisted
Deep Vein Thrombosis or Pulmonary Embolism
With the exception of asthma, the adjusted odds of having a history of each comorbidity examined was significantly higher for older people (ranging from 1.5 to 3.1 times greater for each 10 year increase in age). The adjusted odds of having hypertension, diabetes mellitus, congestive heart failure, sleep apnea, ischemic heart disease, and venous edema were significantly higher for males compared to females (ranging from 1.3 to 3.1 times greater). However, the adjusted odds of reporting a history of asthma and poor functional status was significantly lower for males compared to females (odds rations of 0.5 and 0.6, respectively). Sex was not significantly related to history of DVT/PE or pulmonary hypertension. In addition, the odds of hypertension was significantly higher in Blacks/African-Americans compared to others (1.5 times greater), while the odds of diagnosed sleep apnea was significantly lower (0.7 times higher) in Blacks/African-Americans. No other comorbidities were significantly related to race in multivariable analyses.
DISCUSSION
Despite the importance of developing risk prediction strategies for bariatric surgery, the available data on risk have been limited and the considerable inter-relationships of the group of candidate predictive variables have not been well described. This analysis of 2559 patients within the LABS cohort identifies associations between BMI and several factors previously reported to be associated with surgical outcomes. This study confirms that the burden of comorbid conditions for patients undergoing bariatric surgery is considerable. Unlike studies that use administrative data and identify a relatively small number of comorbid conditions in patients having bariatric surgery 13–15, this study, which used rigorous research methods and clearly defined data points assessed via self-report and patient charts, found that 83% of patients had at least one comorbid condition and over half had two or more comorbid conditions.
For nearly all of the comorbid conditions examined, the odds of having that condition increased with increasing BMI stratum compared to the lowest BMI stratum after controlling for other putative risk factors for comorbidity (sex, age, race, and ethnicity). In several reported case series, including more limited assessments of health conditions and medication use, higher BMI has been linked to worse outcome 6,8,9. While higher BMI may confer some risk of adverse outcome simply because of technical difficulties related to operative technique and recovery, higher BMI may also exert its effect on patient risk through conditions associated with higher BMI (i.e. hypertension, diabetes mellitus, congestive heart failure, asthma, poor functional status, DVT/PE, sleep apnea, pulmonary hypertension, and venous edema). Due to relatively small sample size, infrequency of events and limits of claims-based datasets, prior studies evaluating BMI and risk have been unable to sort out which factors linked to higher BMI account for this finding, and it is therefore possible that BMI is serving as a surrogate metric of risk. Controlling for potentially confounding variables is important in assessing risk of adverse outcomes considering the higher prevalence of comorbid conditions in this population.
Several studies have suggested that male patients 4,13,15,16 have more adverse outcomes, increased healthcare utilization, and less weight loss than female patients following bariatric surgery. In addition, a few studies have found that Black/African-American patients lose less weight compared to others 17–19. However, these studies have not adequately controlled for the relationship between race and sex and other factors such as BMI. In contrast, a retrospective case series found the relationship between male sex and morbidity persisted even after adjusting for patient weight, suggesting that factors other than weight links males and poor outcome 20. A recent study also identified male sex as a risk factor for major complications, even after controlling for patient age, type of surgery, history of diabetes, BMI, and surgeon experience 21. In the present analysis we found that the proportions of males and of Black/African-American patients were higher as BMI category increased. It is possible that there are differences in the timing of the decision to undergo on operation by both sex and race (i.e. males and of Black/African-American wait until they are more obese before considering bariatric surgery). However, in this analysis we could not tease out the factors, such as socioeconomic status and access to healthcare versus disease burden, that affect this decision process. We did find the odds of having several co-morbidities (i.e. hypertension, diabetes mellitus, congestive heart failure, sleep apnea, ischemic heart disease, and venous edema) was significantly higher for males compared to females, even after adjusting for BMI, which may reflect true differences in disease burden between the sexes and/or differences in the timing of the decision to undergo an operation. Since LABS1 relies on self-report of health conditions and receipt of healthcare interventions (such as polysomnography and CPAP machines) the findings of less obstructive sleep apnea in Black/African-American patients may also relate to access to care/diagnosis issues rather than a lack of pathology. No matter the reason for these differences, incorporating race and sex into risk stratification models requires that BMI and co-morbidities also be considered.
Interestingly, although older age has been associated with an increased likelihood of adverse outcomes in many studies 4,15,22,23, in this study the highest BMI strata included the smallest proportion of older patients. Patient selection may contribute to this finding such that surgeons who believe older patients and heavier patients are at greatest risk may be less likely to select patients who are both the heaviest (BMI ≥60 kg/m2) and oldest (≥60 years) to minimize risk of poor outcome. Additional explanations are that the heaviest patients may not wait as many years to undergo surgery, patients may lose weight in later years, and the heaviest patients may not live to be as old. While outcomes analysis is still needed to fully understand risk, our data suggest that older age may not exert its effect on risk through the mechanism of increased BMI.
In conclusion, this analysis reveals associations between BMI and factors commonly considered to be important in risk assessment including sex, race, age and comorbid conditions. Ongoing and future LABS consortium studies will attempt to determine the relative contributions of these factors on the risks and benefits of bariatric surgical procedures.
LABS Study Acknowledgements
This clinical study was a cooperative agreement funded by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). Grant numbers: DCC -U01 DK066557; Columbia-Presbyterian - U01-DK66667; University of Washington - U01-DK66568 (in collaboration with GCRC, Grant M01RR-00037); Neuropsychiatric Research Institute - U01-DK66471; East Carolina University – U01-DK66526; University of Pittsburgh Medical Center – U01-DK66585; Oregon Health & Science University – U01-DK66555.
LABS personnel contributing to the study include:
Columbia Univeristy Medical Center, New York, NY: Paul D. Berk, MD (Principal Investigator), Marc Bessler, MD (Co-Investigator), Dan Davis, DO (Co-Investigator), W. Barry Inabnet, MD (Co-Investigator), Amna Daud, MD, MPH (Coordinator), Munira Kassam (Data Manager) the Cornell University Medical Center, New York, NY: Michel Gagner, MD (Co-Investigator), Alfons Pomp, MD (Co-Investigator), Greg Dakin, MD (Co-Investigator), Faith Ebel (Coordinator), Gladys Strain, PhD (Coordinator), Alex Broseus (Data Manager) East Carolina Medical Center, Greenville, NC: William Chapman, MD, FACS, Lynis Dohm, PhD, Dwan Finch, RN, Kenneth MacDonald, MD, FACS, Walter Pories, MD, FACS Neuropsychiatric Research Institute, Fargo, ND: James E. Mitchell, MD (Principal Investigator), Michael Howell, MD (Investigator), Tim Monson, MD (Investigator), Luis Garcia, MD (Investigator), Kathy Lancaster, BA (Research Coordinator), Justin Boseck, BA, Jamie Roth, MA (Research Assistants) Oregon Health & Science University: Bruce M. Wolfe, MD (Principal Investigator), Clifford Deveney, MD (Co-Investigator), Jonathan Purnell, MD (Co-Investigator), Donald McConnell, MD (Surgical Investigator), Robert O’Rourke, MD (Surgical Investigator), Stefanie Green, Chad Sorenson, Milena Petrovic (Research Coordinators), Robyn Lee (Data Manager) Legacy Good Samaritan Hospital, Portland, OR: Emma Patterson, MD (Co-Investigator), Dennis Hong, MD (Co-Investigator), Jay Jan, MD (Co-Investigator), Amy Free (Research Coordinator) Sacramento Bariatric Medical Associates, Sacramento, CA: Iselin Austrheim-Smith, CCRP (Research Coordinator), Laura Machado, MD (Surgical Investigator) University of Pittsburgh Medical Center, Pittsburgh, PA: Anita P. Courcoulas, MD, MPH, FACS (Principal Investigator), David E. Kelley, MD (Co-Investigator), Lewis H. Kuller, MD, DrPH (Co-Investigator), William Gourash, MSN, CRNP (Research Coordinator) University of Washington, Seattle, WA: David E. Cummings, MD (Co-Investigator), E. Patchen Dellinger, MD (Surgical Investigator), David R. Flum, MD MPH (Principal Investigator), Brant Oelschlager, MD (Surgical Investigator), Allison Devlin, MS (Research Coordinator), Jo Ann Broeckel Elrod, PhD (Research Manager), Sarah Hammond (Administrative Coordinator), Kris Kowdley, MD, (Co-Investigator) Virginia Mason Medical Center, Seattle, WA: Jeffrey Hunter, MD (Surgical Investigator), Ravi Moonka, MD (Surgical Investigator), Richard Thirlby, MD (Surgical Investigator), Matthew Roubicek, RN, Stephen Geary, RN, Olivia A. Seibenick, CCRC (Research Coordinator) Data Coordinating Center, Graduate School of Public Health at the University of Pittsburgh, Pittsburgh, PA: Steven H. Belle, PhD, MScHyg (Principal Investigator), Kevin Kip, PhD (Co-Investigator), Wendy King, PhD (Co-Investigator), Faith Selzer, PhD (Co-Investigator), Laurie Koozer, BA (Project Coordinator), Debbie Martin, BA (Data Manager), Rocco Mercurio, MBA (Data Manager), Michelle Caporali, BA (Data Manager), National Institute of Diabetes and Digestive and Kidney Diseases: Carolyn W. Miles, PhD (Project Scientist), Susan Z. Yanovski, MD (Scientific Advisor), Myrlene A. Staten, MD (Scientific Advisor), Mary Horlick, MD (Scientific Advisor), and Patricia R. Robuck, PhD, MPH (Scientific Advisor) National Cancer Institute: David E. Kleiner, MD, PhD (Central Pathologist)
Contributor Information
Steven H. Belle, DCC, University of Pittsburgh.
William Chapman, East Carolina Medical Center.
Anita P. Courcoulas, University of Pittsburgh Medical Center.
David R. Flum, University of Washington.
Michel Gagner, Cornell University Medical Center.
William B. Inabnet, Columbia University Medical Center.
Wendy C. King, DCC, University of Pittsburgh.
James E. Mitchell, Neuropsychiatric Research Institute.
Emma J. Patterson, Legacy Good Samaritan Hospital.
Richard Thirlby, Virginia Mason Medical Center.
Bruce M. Wolfe, Oregon Health & Science University.
Susan Z. Yanovski, National Institute of Diabetes and Digestive and Kidney Diseases.
References
- 1.Buchwald H, Avidor Y, Braunwald E, et al. Bariatric surgery: a systematic review and meta-analysis. JAMA. 2004;292:1724–1737. doi: 10.1001/jama.292.14.1724. [DOI] [PubMed] [Google Scholar]
- 2.Encinosa WE. Bariatric surgery utilization and outcomes in 1998 and 2004. AHRQ brief 2007. 2007 Ref Type: Report. [PubMed] [Google Scholar]
- 3.Birkmeyer NJ, Wei Y, Goldfaden A, Birkmeyer JD. Characteristics of hospitals performing bariatric surgery. JAMA. 2006;295:282–284. doi: 10.1001/jama.295.3.282-c. [DOI] [PubMed] [Google Scholar]
- 4.Flum DR, Salem L, Elrod JA, et al. Early mortality among Medicare beneficiaries undergoing bariatric surgical procedures. JAMA. 2005;294:1903–1908. doi: 10.1001/jama.294.15.1903. [DOI] [PubMed] [Google Scholar]
- 5.Carbonnel AM, Lincourt AE, Matthews BD, et al. National study of the effect of patient and hospital characteristics on bariatric surgery outcomes. Am Surg. 2005;71:308–314. [PubMed] [Google Scholar]
- 6.Ballantyne GH, Svahn J, Capella JF, et al. Predictors of prolonged hospital stay following open and laparoscopic gastric bypass for morbid obesity: body mass index, length of surgery, sleep apnea, asthma, and the metabolic syndrome. Obes Surg. 2004;14:1042–1050. doi: 10.1381/0960892041975460. [DOI] [PubMed] [Google Scholar]
- 7.Fernandez AZ, DeMaria EJ, Tichansky DS, et al. Multivariate analysis of risk factors for death following gastric bypass for treatment of morbid obesity. Ann Surg. 2004;239:698–702. doi: 10.1097/01.sla.0000124295.41578.ab. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Flancbaum L, Belsley S. Factors affecting morbidity and mortality of Roux-en-Y gastric bypass for clinically severe obesity: an analysis of 1,000 consecutive open cases by a single surgeon. J Gastrointest Surg. 2007;11:500–507. doi: 10.1007/s11605-007-0117-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Livingston EH, Arterburn D, Schifftner TL, Henderson WG, DePalma RG. National Surgical Quality Improvement Program analysis of bariatric operations: modifiable risk factors contribute to bariatric surgical adverse outcomes. J Am Coll Surg. 2006;203:625–633. doi: 10.1016/j.jamcollsurg.2006.07.006. [DOI] [PubMed] [Google Scholar]
- 10.DeMaria EJ, Portenier D, Wolfe L. Obesity surgery mortality risk score: proposal for a clinically useful score to predict mortality risk in patients undergoing gastric bypass. Surg Obes Relat Dis. 2007;3:134–140. doi: 10.1016/j.soard.2007.01.005. [DOI] [PubMed] [Google Scholar]
- 11.Belle SH, Berk PD, Courcoulas AP, et al. Safety and efficacy of bariatric surgery: Longitudinal Assessment of Bariatric Surgery. Surg Obes Relat Dis. 2007;3:116–126. doi: 10.1016/j.soard.2007.01.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Rosner B. Fundamentals of Biostatistics. Belmont, CA: Wadsworth; 1995. [Google Scholar]
- 13.Flum DR, Dellinger EP. Impact of gastric bypass operation on survival: a population-based analysis. J Am Coll Surg. 2004;199:543–551. doi: 10.1016/j.jamcollsurg.2004.06.014. [DOI] [PubMed] [Google Scholar]
- 14.Santry HP, Gillen DL, Lauderdale DS. Trends in bariatric surgical procedures. JAMA. 2005;294:1909–1917. doi: 10.1001/jama.294.15.1909. [DOI] [PubMed] [Google Scholar]
- 15.Zingmond DS, McGory ML, Ko CY. Hospitalization before and after gastric bypass surgery. JAMA. 2005;294:1918–1924. doi: 10.1001/jama.294.15.1918. [DOI] [PubMed] [Google Scholar]
- 16.Zizza CA, Herring AH, Stevens J, Carey TS. Bariatric surgeries in North Carolina, 1990 to 2001: a gender comparison. Obes Res. 2003;11:1519–1525. doi: 10.1038/oby.2003.203. [DOI] [PubMed] [Google Scholar]
- 17.Buffington CK, Marema RT. Ethnic differences in obesity and surgical weight loss between African-American and Caucasian females. Obes Surg. 2006;16:159–165. doi: 10.1381/096089206775565258. [DOI] [PubMed] [Google Scholar]
- 18.Lufti R, Torquati A, Sekhar N, Richards WO. Predictors of success after laparoscopic gastric bypass: a multivariate analysis of socioeconomic factors. Surg Endosc. 2006;20:864–867. doi: 10.1007/s00464-005-0115-8. [DOI] [PubMed] [Google Scholar]
- 19.Parikh M, Lo H, Chang C, et al. Comparison of outcomes after laparoscopic adjustable gastric banding in African-Americans and whites. Surg Obes Relat Dis. 2006;2:607–610. doi: 10.1016/j.soard.2006.08.012. [DOI] [PubMed] [Google Scholar]
- 20.Livingston EH, Huerta S, Arthur D, et al. Male gender is a predictor of morbidity and age a predictor of mortality for patients undergoing bariatric surgery. Ann Surg. 2002;236:576–582. doi: 10.1097/00000658-200211000-00007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Jan JC, Hong D, Bardaro SJ, et al. Comparative study between laparoscopic adjustable gastric banding and laparoscopic gastric bypass: single-institution, 5-year experience in bariatric surgery. Surg Obes Relat Dis. 2007;3:42–50. doi: 10.1016/j.soard.2006.11.005. [DOI] [PubMed] [Google Scholar]
- 22.Livingston EH, Langert J. The impact of age and Medicare status on bariatric surgical outcomes. Arch Surg. 2006;141:1115–1120. doi: 10.1001/archsurg.141.11.1115. [DOI] [PubMed] [Google Scholar]
- 23.Pope GD, Birkmeyer JD, Finlayson SR. National trends in utilization and in-hospital outcomes of bariatric surgery. J Gastrointest Surg. 2002;6:855–860. doi: 10.1016/s1091-255x(02)00085-9. [DOI] [PubMed] [Google Scholar]