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Original Contribution |

Survival Among High-Risk Patients After Bariatric Surgery FREE

Matthew L. Maciejewski, PhD; Edward H. Livingston, MD; Valerie A. Smith, MS; Andrew L. Kavee, MA; Leila C. Kahwati, MD, MPH; William G. Henderson, PhD, MPH; David E. Arterburn, MD, MPH
[+] Author Affiliations

Author Affiliations: Center for Health Services Research in Primary Care, Durham VA Medical Center, Durham, North Carolina (Dr Maciejewski, Ms Smith, and Mr Kavee); Division of General Internal Medicine, Department of Medicine, Duke University, Durham, North Carolina (Dr Maciejewski); Division of Gastrointestinal and Endocrine Surgery, University of Texas Southwestern Medical Center, and Veterans Administration North Texas Health Care System, Dallas, and Biomedical Engineering, University of Texas, Arlington (Dr Livingston); National Center for Health Promotion and Disease Prevention, Office of Patient Care Services, Department of Veterans Affairs, Durham, North Carolina (Dr Kahwati); Denver Veterans Affairs Medical Center, Denver, Colorado, Department of Biostatistics and Informatics, University of Colorado School of Public Health, Aurora, and Colorado Health Outcomes Program, University of Colorado, Aurora (Dr Henderson); and Group Health Research Institute, Seattle, Washington, and Department of Medicine, Division of General Internal Medicine, University of Washington, Seattle (Dr Arterburn).


JAMA. 2011;305(23):2419-2426. doi:10.1001/jama.2011.817.
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Published online

Context Existing evidence of the survival associated with bariatric surgery is based on cohort studies of predominantly younger women with a low inherent obesity-related mortality risk. The association of survival and bariatric surgery for older men is less clear.

Objective To determine whether bariatric surgery is associated with reduced mortality in a multisite cohort of predominantly older male patients who have a high baseline mortality rate.

Design, Setting, and Participants Retrospective cohort study of bariatric surgery programs in Veterans Affairs medical centers. Mortality was examined for 850 veterans who had bariatric surgery in January 2000 to December 2006 (mean age 49.5 years; SD 8.3; mean body mass index [BMI] 47.4; SD 7.8) and 41 244 nonsurgical controls (mean age 54.7 years, SD 10.2; mean BMI 42.0, SD 5.0) from the same 12 Veteran Integrated Service Networks; the mean follow-up was 6.7 years. Four Cox proportional hazards models were assessed: unadjusted and controlled for baseline covariates on unmatched and propensity-matched cohorts.

Main Outcome Measure All-cause mortality through December 2008.

Results Among patients who had bariatric surgery, the 1-, 2-, and 6-year crude mortality rates were, respectively, 1.5%, 2.2%, and 6.8% compared with 2.2%, 4.6%, and 15.2% for nonsurgical controls. In unadjusted Cox regression, bariatric surgery was associated with reduced mortality (hazard ratio [HR], 0.64; 95% confidence interval [CI], 0.51-0.80). After covariate adjustment, bariatric surgery remained associated with reduced mortality (HR, 0.80; 95% CI, 0.63-0.995). In analysis of 1694 propensity-matched patients, bariatric surgery was no longer significantly associated with reduced mortality in unadjusted (HR, 0.83; 95% CI, 0.61-1.14) and time-adjusted (HR, 0.94; 95% CI, 0.64-1.39) Cox regressions.

Conclusion In propensity score–adjusted analyses of older severely obese patients with high baseline mortality in Veterans Affairs medical centers, the use of bariatric surgery compared with usual care was not associated with decreased mortality during a mean 6.7 years of follow-up.

Figures in this Article

Obesity incidence has stabilized after decades of rapid increases,1 whereas the prevalence of patients with a BMI greater than 35 increased 39% between 2000 and 2005, the prevalence of severe obesity (BMI >40) increased 50%, and the prevalence of superobesity (BMI >50) increased 75%.2,3 Obesity is difficult to treat, and bariatric surgery is the most effective means to induce weight loss for the severely obese. Consequently, obesity surgery rates rapidly increased in tandem.4 Although bariatric surgery reliably induces weight loss, there are few long-term studies of survival related to surgically induced weight loss.5

Previous bariatric surgery survival studies examined outcomes in younger, primarily white and female populations whose obesity-related mortality risk is low6 and decreasing.710 These patients' lower mortality has required studies with large samples to detect any mortality differences.11,12 Obesity-related mortality is highest in men and minority patients who have high rates of comorbid diseases,13 and these patients would potentially benefit the most from bariatric surgery–induced weight loss.

To date, no study to our knowledge has examined the long-term survival of high-risk patients who underwent bariatric surgery. This study's purpose was to determine whether bariatric surgery is associated with reduced mortality in a multisite cohort of predominantly older male high-risk patients who were older than patients in most previous bariatric outcomes assessments. We compared survival through December 2008 between a cohort of veterans who had bariatric surgery in Veterans Affairs medical centers in 2000-2006 and a cohort of veterans who did not have bariatric surgery. This is the first study that we are aware of to compare survival associated with bariatric surgery in predominantly male cohorts of surgical patients and nonsurgical controls.

Study Design and Study Population

This evaluation is a retrospective cohort study of bariatric surgery patients and a cohort of nonsurgical, severely obese controls. As described previously,14,15 bariatric patients were identified from a database of major surgical procedures performed in Veterans Affairs medical centers maintained by the VA Surgical Quality Improvement Program (formerly known as the National Surgical Quality Improvement Program). Veterans in the program database were identified as having bariatric surgery in fiscal years 2000-2006, according to an algorithm of Current Procedural Terminology-4 codes (43842, 43843, 43846, 43847, and 43848) and International Classification of Diseases, Ninth Revision (ICD-9) codes (278.01, associated with Current Procedural Terminology-4 codes 43659, 43621, or 43633), with a sensitivity of 99.2% and specificity of 99.9%.16

We identified 892 patients who had bariatric procedures and excluded revisional procedures for 4 patients (Figure 1). Survival time through December 31, 2008, began with each veteran's first instance of observed BMI greater than or equal to 35 after January 1, 2000. Patients were excluded because of death dates before surgery (n = 4), presurgical BMIs less than 35 (n = 9), or missing presurgical comorbidity scores (n = 25). The final surgical cohort included 850 high-risk veterans who had bariatric surgery. We refer to these surgical patients as high risk according to the high proportion of men, older age, and higher BMI compared with that of cohorts in previous bariatric mortality assessments11 because other work has found that patients with these characteristics have higher rates of postsurgical mortality.12,17

Place holder to copy figure label and caption
Figure 1. Cohort of Surgical Patients
Graphic Jump Location

VA indicates Veterans Affairs; BMI, body mass index; and DCG, diagnostic cost group (a measure of comorbidity burden).

Nonsurgical controls were identified from a Veterans Affairs registry of 1.8 million veterans whose fiscal year 2000 height and weight data were extracted from Veterans Affairs electronic medical records of outpatient visits from 136 Veterans Affairs medical centers.18 We identified 98 545 veterans who had recorded BMIs between 29 and 80 and had never undergone bariatric surgery in a Veterans Affairs hospital (Figure 2). Control patients were excluded because of missing patient identifiers needed to link other Veterans Affairs databases (n = 563), deaths before fiscal year 2000 (n = 373), or incomplete data to construct patient covariates (n = 157). We also excluded nonsurgical controls with no BMI values greater than or equal to 35 (n = 4853), with missing baseline comorbidity scores (n = 5836), with age 80 years or older during the study period (n = 8509), or with clinical exclusions related to gastrointestinal cancer requiring surgery or peritoneal effusion/ascites (n = 299). To more closely match the availability of medical care resources between surgical and control groups, we excluded severely obese veterans who lived in Veterans Integrated Service Networks lacking a bariatric surgery center in 2000-2006 (n = 36 711). The final nonsurgical control cohort included 41 244 veterans with an initial eligible BMI in 2000-2006 and complete data on patient characteristics and survival status. This study was approved by the Surgical Quality Data Use Group of the VA Office of Patient Care Services; and the institutional review boards of the Durham, North Texas, and Denver Veterans Affairs Medical Centers and of the Group Health Research Institute.

Place holder to copy figure label and caption
Figure 2. Cohort of Nonsurgical Controls
Graphic Jump Location

BMI indicates body mass index; FY, fiscal year; VA, Veterans Affairs; and DCG, diagnostic cost group (a measure of comorbidity burden).

Survival Outcome

The primary outcome was all-cause mortality, defined as survival time from first BMI greater than or equal to 35 until death date or the end of the observation period (December 31, 2008). The maximum possible survival time was 9 years (January 1, 2000, to December 31, 2008). Trained VA Surgical Quality Improvement Program nurses tracked operative death and perioperative death up to 30 days after the operation with a standardized abstraction form by following the patient by chart review during hospitalization. The nurse then contacted the patient or family 30 days after the operation to identify any subsequent adverse events and validated and updated mortality by checking against a Veterans Affairs vital status database.19 Veterans Affairs death dates beyond 30 days for case patients and for all controls were obtained from 4 administrative data sets (Beneficiary Identification and Record Locator Subsystem Death File, Veterans Affairs Inpatient utilization files, Medicare Vital Status file, and Social Security Administration Death Master File).20

Statistical Analysis

Unadjusted differences between patients undergoing or not undergoing bariatric surgery were compared with χ2 tests for categorical variables and 2-tailed unpaired t tests for continuous variables in the unmatched cohorts, McNemar tests and paired t tests in the matched cohorts, and standardized differences to enable comparison of covariate imbalance between the matched and unmatched cohorts.21 The association between bariatric surgery and all-cause mortality was examined in the unmatched cohorts with crude mortality rate comparisons and unadjusted and multivariable Cox proportional hazards regression models.

Operations occurred at different points for the surgical cohort, with considerable delay (median duration = 539 days) between the “zero time” of first reported BMI greater than 35 and day of surgery; therefore, surgery was modeled as a time-dependent treatment to account for survivor treatment bias. Failure to account for this bias would generate biased hazard ratios (HRs) (<1) if the true treatment effect were null.22,23 The proportional hazards assumption was confirmed by inspection of log (−log[survival]) curves.

In a multivariable Cox model, we controlled for baseline age, sex, self-reported race, marital status, BMI, comorbidity burden measured by the diagnostic cost group score, and Veterans Integrated Service Network of residence. Baseline BMI was constructed from data contained in the corporate data warehouse vital status files, the registry file, and VA Surgical Quality Improvement Program files on the surgical cases but relied primarily on corporate data warehouse values after exclusion of weight values 3 SDs above or below the presurgical or postsurgical mean for each patient. The diagnostic cost group score aggregates inpatient and outpatient diagnoses in the year before baseline, with scores greater than 1.0 implying above-average expected expenditures and scores less than 1.0 implying below-average expected expenditures. Diagnostic cost group scores were as predictive of veterans' 1-year mortality as other comorbidity scores2426 and were highly predictive of mortality,14 use, and expenditures15 in bariatric surgery.

In a third analysis, we accounted for the nonequivalence (eg, selection bias) of the nonsurgical control cohort via propensity score matching with logistic regression, which has been shown to be less biased than propensity adjustment or weighting in survival analysis.27 A propensity score represents the predicted probability that a given patient will undergo bariatric surgery, and patients who had bariatric surgery procedures were matched to controls with a greedy algorithm.28 The propensity score model included interaction of age, age squared, diagnostic cost group, BMI, BMI squared, BMI cubed, sex, race, marital status, and Veterans Integrated Service Networks, as well as numerous 2-way interactions,29 and had a concordance index of 0.85.

Despite the large (n = 41 244) sample of controls, we conducted one-to-one matching to avoid the possible bias of many-to-one matching.30 Each surgical case patient was matched to a single nonsurgical control if their predicted propensity scores were identical to 8 digits. If such a match was not found, the case patient was matched to a control on the basis of a 7-, 6-, 5-, 4-, 3-, 2-, or 1-digit match. This process matched 847 surgical case patients (of 850 possible; 99.6%) to 847 nonsurgical controls, and covariate balance between matched surgical case patients and nonsurgical controls was assessed via McNemar tests, 2-tailed paired t tests, and standardized differences. We then conducted an unadjusted Cox regression stratified on the matched pairs to account for the lack of independence between cohorts induced by matching and a Cox regression adjusted for differences in the year indicating zero time. Alternative propensity score analyses from many-to-one matching generated similar results, so we present the 1:1 matching results here.

The a priori level of statistical significance was set at P < .05 for all analyses, which were 2-tailed and performed with SAS, version 9.2 (SAS Institute Inc, Cary, North Carolina).

Patient Characteristics in Unmatched Cohorts

The unmatched cohort of 850 veterans who underwent bariatric surgery in 2000-2006 was significantly younger (49.5 vs 54.7 years; P < .001), had higher BMIs (47.4 vs 42.0; P < .001), and had greater comorbidity burden as measured by diagnostic cost group score (0.60 vs 0.47; P < .001) than the 41 244 veterans in the nonsurgical control cohort (Table 1). The surgical patients were more likely to be superobese (BMI ≥50; 31.3% vs 7.0%; P < .001) and white (77.9% vs 67.8%; P < .001) but less likely to be aged 65 years or older (2.1% vs 20.7%; P < .001), men (73.9% vs 91.7%; P < .001), or married (52.1% vs 57.7%; P = .001). The first observation of a BMI greater than or equal to 35 that defined the start time for tracking survival was earlier for nonsurgical controls than case patients (P < .001). As a result, the surgical case patients were observed 1 year less (6.69 years vs 7.62 years; P < .001) than the nonsurgical controls; nearly 85% of the controls had their first recorded BMI greater than 35 in 2000 compared with only 40% of surgical case patients. The mean follow-up time was 6.7 years.

Table Graphic Jump LocationTable 1. Baseline Characteristics of Unmatched Bariatric Surgical Patients and Nonsurgical Controls
Association of Bariatric Surgery and Mortality in Unmatched Cohorts

Eleven of 850 surgical case patients (1.29%) died within 30 days of surgery. The surgical case patients had lower crude mortality rates than the nonsurgical controls (1 year, 1.5% vs 2.2%, P = .17; 2 year, 2.2% vs 4.6%, P < .001; 6 year, 6.8% vs 15.2%, P < .001). In an unadjusted Cox regression, bariatric surgery was associated with reduced mortality (HR, 0.64; 95% confidence interval [CI], 0.51-0.80; P < .001). Given the significant differences between surgical case patients and nonsurgical controls, these crude and unadjusted results cannot represent the unbiased association of treatment and survival. After adjustment for baseline covariates (eTable), bariatric surgery remained associated with reduced mortality but to a lesser extent (adjusted HR, 0.80; 95% CI, 0.63-0.995; P = .045).

Patient Characteristics in Matched Cohorts and Association of Bariatric Surgery and Mortality

Propensity score matching resulted in well-balanced cohorts of surgical case patients (n = 847) and nonsurgical controls (n = 847), which were similar in all observed characteristics except year of study entry (Table 2), improving covariate balance from the unmatched cohorts (Table 1). Eleven of the 847 surgical case patients (1.30%) died within 30 days of surgery. Crude mortality differed between matched case patients and controls only at 6 years (1 year, 1.5% vs 2.0%, P = .58; 2 year, 2.2% vs 3.5%, P = .14; 6 year, 6.7% vs 12.8%, P < .001). In Cox regressions using the 1694 propensity-matched patients, bariatric surgery was no longer associated with reduced mortality in unadjusted analysis (HR, 0.83; 95% CI, 0.61-1.14) and in analysis adjusting for differences in start times (HR, 0.94; 95% CI, 0.64-1.39) (Table 3).

Table Graphic Jump LocationTable 2. Baseline Characteristics of Propensity-Matched Bariatric Surgical Patients and Nonsurgical Controls
Table Graphic Jump LocationTable 3. Results From Unadjusted and Adjusted Cox Proportional Hazards Models on Unmatched and Matched Cohorts of Surgical Patients and Nonsurgical Controls

Older predominantly male patients with obesity-related comorbid conditions have high mortality and could benefit the most from aggressive obesity treatment. In a propensity-matched cohort of obese high-risk primarily male patients, bariatric surgery was not significantly associated with survival during a mean of 6.7 years of follow-up. These results are in contrast to those of several previous observational studies, which can be explained by important differences in the populations examined, the duration of follow-up, and the extent of covariate adjustment.

One reason bariatric surgery was not associated with survival in this study might be the higher perioperative mortality observed for Roux-en-Y gastric bypass in veteran cohorts.16,31 The 30-day mortality rates of 1.3% observed in this study is 4-fold higher than that reported in the recent multisite Longitudinal Assessment of Bariatric Surgery study, which included a younger, predominantly female cohort.9 Roux-en-Y operations are inherently more difficult in large male patients and are known to be associated with higher mortality rates than for female patients, who have anatomy more favorable for performing complex gastric surgery.17 Our previous research on this cohort demonstrated less than expected benefit in other outcomes as well. We observed less medication discontinuation32 than that in studies of lower-risk, mostly female diabetic patients33 and found that health care expenditures were not improved by bariatric surgery15 within 3 years after surgery. Operations with lower perioperative event rates, such as laparoscopic adjustable banding procedures or gastric sleeve resections, could prove more beneficial. There are no reliable long-term studies of these operations in high-risk cohorts that would suggest that these operations result in lower mortality than gastric bypass procedures.

Our conclusion of no association of bariatric surgery with survival in this high-risk predominantly male cohort contrasts with that of several previous studies, which have differed in cohort characteristics, control identification, and comorbidity adjustment. In a comparison in a cohort of 154 patients with diabetes who underwent Roux-en-Y and 78 obese diabetic patients evaluated for bariatric surgery at the same center,34 unadjusted survival was greater (91%) after a mean follow-up of 9 years in the surgical group compared with the nonsurgical group (72%) at 6 years. The higher mortality observed in the nonsurgical group may be explained by the larger number of minority patients (50% vs 23%), who are known to have greater obesity-related mortality than whites,13 and by the lack of comorbidity adjustment. Our unadjusted cohort resembles these patients, but in our rigorous propensity matching analysis, the significant association of bariatric surgery observed in our unmatched analyses was eliminated.

Christou et al35 examined survival for a cohort of 1035 bariatric surgery patients compared with 5746 control patients identified as obese by having an ICD-9 entry of obesity (278.00) or morbid obesity (278.01) in their medical record and no codes for obesity surgery. This analysis yielded lower mortality (0.68% vs 6.17%) for the bariatric surgery cohort compared with the controls during a 5-year follow-up period. The control group was matched 6:1 on patient age, sex, and duration of follow-up, but no clinical information was available for comorbidity adjustment. Flum and Dellinger36 performed an observational study of bariatric patients and controls identified from diagnostic codes in hospital discharge data from Washington State. Bariatric surgery was associated with improved survival (HR, 1.59; 95% CI, 1.49-1.72) after adjustment by age, sex, and Charlson comorbidity score. Both of these studies relied on ICD-9 codes to identify severely obese control patients, which likely identified a more chronically ill and superobese population and likely failed to identify many less severely obese patients who would also have been eligible for bariatric surgery and have served as a valid contrast to the bariatric patients.

Adams et al11 compared survival outcomes for 7925 patients who underwent gastric bypass surgery in Utah with that of 7925 individuals identified as obese from self-reported height and weight information in driver's license applications. Patients were matched on BMI, age, and sex but lacked clinical information for comorbidity adjustment. A modest difference in unadjusted mortality was observed: After 7.1 years of follow-up, the surgical group experienced 2.7% mortality compared with 4.1% in the controls (adjusted HR, 0.60; 95% CI, 0.45-0.67). The average age was much younger11 than in this Veterans Affairs surgical cohort (39.5 vs 49.5) and the proportion of women was much higher11 (84% vs 26%). Demographic differences and lack of comorbidity adjustment may be important factors explaining discrepancies between the results obtained by Adams et al11 and by us.

The only published study examining survival after bariatric surgery that compared groups with high-quality clinical data was the Swedish Obese Subjects (SOS) Study.12 Clinical information was available for 2027 patients who had bariatric surgery compared with 2037 who elected not to pursue bariatric surgery. These cohorts were followed up for a mean of 11 years and matched according to 18 clinical variables. Bariatric surgery was associated with better survival (adjusted HR, 0.76; 95% CI, 0.59-0.99), according to a 1.3% absolute mortality difference. The Swedish analysis is not comparable to ours because of the different mortality risk in the control groups. The 11-year mortality in the control group was 6.3% in the SOS Study, whereas the 7-year unadjusted mortality rate in the control group was 22.4% in our study. Additionally, two-thirds of the SOS cohort underwent vertical banded gastroplasty, an operation no longer performed in the United States, limiting the generalizability of that study to modern bariatric practice.

Our results highlight the importance of statistical adjustment and careful selection of surgical and nonsurgical cohorts, particularly during evaluation of bariatric surgery according to administrative data.37 Previous studies claiming a survival benefit for bariatric surgery had limited clinical information to conduct detailed risk adjustment or matching.10,11,35,36 The survival differences between the bariatric surgery and control groups were modest in most previous studies, so the beneficial effects of surgery may have been attenuated if adjustment for confounders had been possible. We demonstrated that risk adjustment with regression analysis resulted in a significant association of surgery and survival that was reduced when equivalence in baseline characteristics improved via propensity matching in this high-risk patient group.

These results suggest that bariatric surgery is not associated with reduced mortality at 6.7 years for older men with higher than average baseline risk for obesity-related complications and mortality compared with bariatric cohorts considered in previous evaluations.17 This analysis was based on a multisite sample, with data abstracted in a standardized process to ensure high-quality data and minimal measurement error. Additionally, comprehensive claims data enabled us to adjust for patient factors (race, BMI, comorbidity burden, marital status) related to receipt of bariatric surgery and mortality that are unavailable in many health systems. The well-validated diagnostic cost group risk score is used by the Centers for Medicare & Medicaid Services to risk-adjust payments made to Medicare managed care plans38 and has been shown to predict 1-year mortality14 and presurgical and postsurgical expenditures of this surgical cohort.15 Careful adjustment for risk differences is a critical need in bariatric evaluations,39 and the use of this risk adjustment measure represents a significant advance in reducing confounding in the mortality association of bariatric surgery.

Our study is subject to several limitations. First, we focused on a cohort of older, predominantly male, sicker patients, so results may not generalize to nonveteran, younger, female, or healthier populations. It is possible that bariatric surgery reduces mortality for younger patients and not for older male patients, but we did not have sufficient sample size to examine whether the association varied across subgroups. Second, our study does not include patients who underwent laparoscopic banding procedures; thus, we cannot generalize our results to such patients. Third, the sample size was restricted in the 1:1 propensity-matched analysis such that large CIs were generated, which cannot rule out that bariatric surgery provides clinically significant benefit or harm to some patients. However, 4:1 propensity-matched analysis yielded similar results. Future studies with larger samples should also consider stratification across clinically relevant patient factors to fully understand which subgroups benefit most from bariatric surgery. Fourth, these results do not account for unobserved confounding that may persist even after propensity score matching because this analysis was based on a quasi-experimental design from administrative data, not a randomized trial.37 The estimated treatment represents an association and not necessarily the causal effect of bariatric surgery on survival. We were able to effectively reduce imbalance in observed covariates via one-to-one propensity score matching, without a significant loss in surgical sample size. This finding was also robust to alternative propensity score approaches, which suggests we strengthened the internal validity of our results without sacrificing generalizability.

These results suggest that bariatric surgery is not associated with reduced mortality among older men within a few years after surgery, which is not entirely surprising; the survival benefit in the SOS Study was not observed until a median of 13 years of follow-up. Therefore, it is possible that we would also observe a protective benefit 10 or more years after surgery, and additional observation time may be required in this older predominantly male cohort. Even though bariatric surgery is not associated with reduced mortality among older male patients, many patients may still choose to undergo bariatric surgery, given the strong evidence for significant reductions in body weight and comorbidities and improved quality of life.

According to propensity score–adjusted analyses of a cohort of predominantly older men, bariatric surgery does not appear to be associated with survival during a mean of 6.7 years of follow-up.

Corresponding Author: Matthew L. Maciejewski, PhD, Center for Health Services Research in Primary Care (152), Durham VA Medical Center, 411 W Chapel Hill St, Ste 600, Durham, NC 27705 (matthew.maciejewski@va.gov).

Published Online: June 12, 2011. doi:10.1001/jama.2011.817

Author Contributions: Dr Maciejewski had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Maciejewski, Livingston, Kahwati, Henderson, Arterburn.

Acquisition of data: Maciejewski, Kavee, Henderson.

Analysis and interpretation of data: Maciejewski, Livingston, Smith, Kahwati, Henderson, Arterburn.

Drafting of the manuscript: Maciejewski, Livingston, Smith, Arterburn.

Critical revision of the manuscript for important intellectual content: Maciejewski, Livingston, Smith, Kahwati, Henderson, Arterburn.

Statistical analysis: Maciejewski, Smith, Kavee, Henderson.

Obtained funding: Maciejewski, Arterburn.

Administrative, technical, or material support: Maciejewski, Smith, Kavee, Kahwati, Henderson.

Study supervision: Maciejewski, Arterburn.

Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Maciejewski reports receiving consultation funds from Takeda Pharmaceuticals, Novartis, the Surgical Review Corporation, and the Research Data and Assistance Center at the University of Minnesota and owns stock in Amgen. Dr Livingston reports receiving consulting funds from Texas Instruments. Dr Arterburn reports receiving research funding and has received salary support as a medical editor for the not-for-profit (501[3]c) Foundation for Informed Medical Decision Making (http://www.fimdm.org), which develops content for patient education programs. The foundation has an arrangement with a for-profit company, Health Dialog, to coproduce and market these programs to health care organizations. Dr Arterburn was formerly with the Cincinnati Veterans Affairs and Dr Livingston was formerly with the Dallas Veterans Affairs. No other disclosures were reported.

Funding/Support: This research was supported by the Office of Research and Development, Health Services Research and Development Service, Department of Veterans Affairs (IIR 05-201, SHP 08-137). Dr Maciejewski was also supported by a Research Career Scientist award from the Department of Veterans Affairs (RCS 10-391).

Role of the Sponsor: The Health Services Research and Development Service, Department of Veterans Affairs had no role in the design, conduct, collection, management, analysis, or interpretation of the data; or in the preparation, review, or approval of the manuscript.

Disclaimer: The opinions expressed are those of the authors and not necessarily those of the Department of Veterans Affairs, the US government, Duke University, the University of Texas Southwestern Medical Center, the University of Texas, the University of Colorado, the Group Health Research Institute, or the University of Washington. Dr Livingston, a JAMA Contributing Editor, did not participate in the review of or decision to publish this article.

Additional Contributions: We acknowledge helpful comments from Daniel Almirall, PhD, University of Michigan, Ann Arbor; Robert (Skip) Woolson, PhD, Center for Health Services Research in Primary Care, Durham VA Medical Center, and Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina; Eugene Oddone, MD, MS, Center for Health Services Research in Primary Care, Durham VA Medical Center, and Division of General Internal Medicine, Department of Medicine, Duke University, Durham, North Carolina; and Seth Eisen, MD, Department of Veterans Affairs, Office of Research and Development, Washington, DC. No one received financial compensation for his/her contributions. We thank the VA Surgical Quality Data Use Group for its role as scientific advisor and for the critical review of data use and analysis presented in this article.

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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(5):625-633
PubMed   |  Link to Article
Livingston EH, Huerta S, Arthur D, Lee S, De Shields S, Heber D. Male gender is a predictor of morbidity and age a predictor of mortality for patients undergoing gastric bypass surgery.  Ann Surg. 2002;236(5):576-582
PubMed   |  Link to Article
Das SR, Kinsinger LS, Yancy WS Jr,  et al.  Obesity prevalence among veterans at Veterans Affairs medical facilities.  Am J Prev Med. 2005;28(3):291-294
PubMed   |  Link to Article
Davis CL, Pierce JR, Henderson W,  et al.  Assessment of the reliability of data collected for the Department of Veterans Affairs National Surgical Quality Improvement Program.  J Am Coll Surg. 2007;204(4):550-560
PubMed   |  Link to Article
Sohn MW, Arnold N, Maynard C, Hynes DM. Accuracy and completeness of mortality data in the Department of Veterans Affairs.  Popul Health Metr. 2006;4:2
PubMed   |  Link to Article
Austin PC. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples.  Stat Med. 2009;28(25):3083-3107
PubMed   |  Link to Article
Austin PC, Platt RW. Survivor treatment bias, treatment selection bias, and propensity scores in observational research.  J Clin Epidemiol. 2010;63(2):136-138
PubMed   |  Link to Article
Beyersmann J, Wolkewitz M, Schumacher M. The impact of time-dependent bias in proportional hazards modelling.  Stat Med. 2008;27(30):6439-6454
PubMed   |  Link to Article
Maciejewski ML, Liu CF, Derleth A, McDonell M, Anderson S, Fihn SD. The performance of administrative and self-reported measures for risk adjustment of Veterans Affairs expenditures.  Health Serv Res. 2005;40(3):887-904
PubMed   |  Link to Article
Maciejewski ML, Liu CF, Fihn SD. Performance of comorbidity, risk adjustment, and functional status measures in expenditure prediction for patients with diabetes.  Diabetes Care. 2009;32(1):75-80
PubMed   |  Link to Article
Fan VS, Maciejewski ML, Liu CF, McDonell M, Fihn SD. Comparison of risk adjustment measures based on self-report, administrative data, and pharmacy records to predict clinical outcomes.  Health Serv Outcomes Res Methodol. 2006;6(1-2):21-36
Link to Article
Austin PC. The performance of different propensity-score methods for estimating relative risks.  J Clin Epidemiol. 2008;61(6):537-545
PubMed   |  Link to Article
Parsons LS. Reducing bias in a propensity score matched-pair sample using greedy matching techniques. Paper presented at: Proceedings of the 26th Annual SAS Users Group International Conference; April 2001; Cary, NC
Stukel TA, Fisher ES, Wennberg DE, Alter DA, Gottlieb DJ, Vermeulen MJ. Analysis of observational studies in the presence of treatment selection bias: effects of invasive cardiac management on AMI survival using propensity score and instrumental variable methods.  JAMA. 2007;297(3):278-285
PubMed   |  Link to Article
Austin PC. Statistical criteria for selecting the optimal number of untreated subjects matched to each treated subject when using many-to-one matching on the propensity score.  Am J Epidemiol. 2010;172(9):1092-1097
PubMed   |  Link to Article
Lautz DB, Jackson TD, Clancy KA,  et al.  Bariatric operations in Veterans Affairs and selected university medical centers: results of the Patient Safety in Surgery study.  J Am Coll Surg. 2007;204(6):1261-1272
PubMed   |  Link to Article
Maciejewski ML, Livingston EH, Kahwati LC, Henderson WG, Kavee AL, Arterburn DE. Discontinuation of diabetes and lipid-lowering medications after bariatric surgery at Veterans Affairs medical centers.  Surg Obes Relat Dis. 2010;6(6):601-607
PubMed   |  Link to Article
Schauer PR, Burguera B, Ikramuddin S,  et al.  Effect of laparoscopic Roux-en Y gastric bypass on type 2 diabetes mellitus.  Ann Surg. 2003;238(4):467-484
PubMed
MacDonald KG Jr, Long SD, Swanson MS,  et al.  The gastric bypass operation reduces the progression and mortality of non–insulin-dependent diabetes mellitus.  J Gastrointest Surg. 1997;1(3):213-220
PubMed   |  Link to Article
Christou NV, Sampalis JS, Liberman M,  et al.  Surgery decreases long-term mortality, morbidity, and health care use in morbidly obese patients.  Ann Surg. 2004;240(3):416-423
PubMed   |  Link to Article
Flum DR, Dellinger EP. Impact of gastric bypass operation on survival: a population-based analysis.  J Am Coll Surg. 2004;199(4):543-551
PubMed   |  Link to Article
Flum DR. Administrative data analyses in bariatric surgery—limits of the technique.  Surg Obes Relat Dis. 2006;2(2):78-81
PubMed   |  Link to Article
Pope GC, Kautter J, Ellis RP,  et al.  Risk adjustment of Medicare capitation payments using the CMS-HCC model.  Health Care Financ Rev. 2004;25(4):119-141
PubMed
Hutter MM, Jones DB, Riley SM Jr,  et al.  Best practice updates for weight loss surgery data collection.  Obesity (Silver Spring). 2009;17(5):924-928
PubMed   |  Link to Article

Figures

Place holder to copy figure label and caption
Figure 1. Cohort of Surgical Patients
Graphic Jump Location

VA indicates Veterans Affairs; BMI, body mass index; and DCG, diagnostic cost group (a measure of comorbidity burden).

Place holder to copy figure label and caption
Figure 2. Cohort of Nonsurgical Controls
Graphic Jump Location

BMI indicates body mass index; FY, fiscal year; VA, Veterans Affairs; and DCG, diagnostic cost group (a measure of comorbidity burden).

Tables

Table Graphic Jump LocationTable 1. Baseline Characteristics of Unmatched Bariatric Surgical Patients and Nonsurgical Controls
Table Graphic Jump LocationTable 2. Baseline Characteristics of Propensity-Matched Bariatric Surgical Patients and Nonsurgical Controls
Table Graphic Jump LocationTable 3. Results From Unadjusted and Adjusted Cox Proportional Hazards Models on Unmatched and Matched Cohorts of Surgical Patients and Nonsurgical Controls

References

Flegal KM, Carroll MD, Ogden CL, Curtin LR. Prevalence and trends in obesity among US adults, 1999-2008.  JAMA. 2010;303(3):235-241
PubMed   |  Link to Article
Sturm R. Increases in morbid obesity in the USA: 2000-2005.  Public Health. 2007;121(7):492-496
PubMed   |  Link to Article
Ogden CL, Carroll MD, Curtin LR, McDowell MA, Tabak CJ, Flegal KM. Prevalence of overweight and obesity in the United States, 1999-2004.  JAMA. 2006;295(13):1549-1555
PubMed   |  Link to Article
Livingston EH. The incidence of bariatric surgery has plateaued in the US.  Am J Surg. 2010;200(3):378-385
PubMed   |  Link to Article
Veterans Administration.  Management of obesity and overweight. http://www.healthquality.va.gov/obesity_clinical_practice_guideline.asp. Accessed February 21, 2011
Livingston EH, Ko CY. Effect of diabetes and hypertension on obesity-related mortality.  Surgery. 2005;137(1):16-25
PubMed   |  Link to Article
Flegal KM, Graubard BI, Williamson DF, Gail MH. Excess deaths associated with underweight, overweight, and obesity.  JAMA. 2005;293(15):1861-1867
PubMed   |  Link to Article
Buchwald H, Estok R, Fahrbach K, Banel D, Sledge I. Trends in mortality in bariatric surgery: a systematic review and meta-analysis.  Surgery. 2007;142(4):621-635
PubMed   |  Link to Article
Flum DR, Belle SH, King WC,  et al; Longitudinal Assessment of Bariatric Surgery (LABS) Consortium.  Perioperative safety in the longitudinal assessment of bariatric surgery.  N Engl J Med. 2009;361(5):445-454
PubMed   |  Link to Article
Flum DR, Salem L, Elrod JA, Dellinger EP, Cheadle A, Chan L. Early mortality among Medicare beneficiaries undergoing bariatric surgical procedures.  JAMA. 2005;294(15):1903-1908
PubMed   |  Link to Article
Adams TD, Gress RE, Smith SC,  et al.  Long-term mortality after gastric bypass surgery.  N Engl J Med. 2007;357(8):753-761
PubMed   |  Link to Article
Sjöström L, Narbro K, Sjöström CD,  et al; Swedish Obese Subjects Study.  Effects of bariatric surgery on mortality in Swedish obese subjects.  N Engl J Med. 2007;357(8):741-752
PubMed   |  Link to Article
Fontaine KR, Redden DT, Wang C, Westfall AO, Allison DB. Years of life lost due to obesity.  JAMA. 2003;289(2):187-193
PubMed   |  Link to Article
Arterburn D, Livingston EH, Schifftner T, Kahwati LC, Henderson WG, Maciejewski ML. Predictors of long-term mortality after bariatric surgery performed in Veterans Affairs medical centers.  Arch Surg. 2009;144(10):914-920
PubMed   |  Link to Article
Maciejewski ML, Smith VA, Livingston EH,  et al.  Health care utilization and expenditure changes associated with bariatric surgery.  Med Care. 2010;48(11):989-998
PubMed   |  Link to Article
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(5):625-633
PubMed   |  Link to Article
Livingston EH, Huerta S, Arthur D, Lee S, De Shields S, Heber D. Male gender is a predictor of morbidity and age a predictor of mortality for patients undergoing gastric bypass surgery.  Ann Surg. 2002;236(5):576-582
PubMed   |  Link to Article
Das SR, Kinsinger LS, Yancy WS Jr,  et al.  Obesity prevalence among veterans at Veterans Affairs medical facilities.  Am J Prev Med. 2005;28(3):291-294
PubMed   |  Link to Article
Davis CL, Pierce JR, Henderson W,  et al.  Assessment of the reliability of data collected for the Department of Veterans Affairs National Surgical Quality Improvement Program.  J Am Coll Surg. 2007;204(4):550-560
PubMed   |  Link to Article
Sohn MW, Arnold N, Maynard C, Hynes DM. Accuracy and completeness of mortality data in the Department of Veterans Affairs.  Popul Health Metr. 2006;4:2
PubMed   |  Link to Article
Austin PC. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples.  Stat Med. 2009;28(25):3083-3107
PubMed   |  Link to Article
Austin PC, Platt RW. Survivor treatment bias, treatment selection bias, and propensity scores in observational research.  J Clin Epidemiol. 2010;63(2):136-138
PubMed   |  Link to Article
Beyersmann J, Wolkewitz M, Schumacher M. The impact of time-dependent bias in proportional hazards modelling.  Stat Med. 2008;27(30):6439-6454
PubMed   |  Link to Article
Maciejewski ML, Liu CF, Derleth A, McDonell M, Anderson S, Fihn SD. The performance of administrative and self-reported measures for risk adjustment of Veterans Affairs expenditures.  Health Serv Res. 2005;40(3):887-904
PubMed   |  Link to Article
Maciejewski ML, Liu CF, Fihn SD. Performance of comorbidity, risk adjustment, and functional status measures in expenditure prediction for patients with diabetes.  Diabetes Care. 2009;32(1):75-80
PubMed   |  Link to Article
Fan VS, Maciejewski ML, Liu CF, McDonell M, Fihn SD. Comparison of risk adjustment measures based on self-report, administrative data, and pharmacy records to predict clinical outcomes.  Health Serv Outcomes Res Methodol. 2006;6(1-2):21-36
Link to Article
Austin PC. The performance of different propensity-score methods for estimating relative risks.  J Clin Epidemiol. 2008;61(6):537-545
PubMed   |  Link to Article
Parsons LS. Reducing bias in a propensity score matched-pair sample using greedy matching techniques. Paper presented at: Proceedings of the 26th Annual SAS Users Group International Conference; April 2001; Cary, NC
Stukel TA, Fisher ES, Wennberg DE, Alter DA, Gottlieb DJ, Vermeulen MJ. Analysis of observational studies in the presence of treatment selection bias: effects of invasive cardiac management on AMI survival using propensity score and instrumental variable methods.  JAMA. 2007;297(3):278-285
PubMed   |  Link to Article
Austin PC. Statistical criteria for selecting the optimal number of untreated subjects matched to each treated subject when using many-to-one matching on the propensity score.  Am J Epidemiol. 2010;172(9):1092-1097
PubMed   |  Link to Article
Lautz DB, Jackson TD, Clancy KA,  et al.  Bariatric operations in Veterans Affairs and selected university medical centers: results of the Patient Safety in Surgery study.  J Am Coll Surg. 2007;204(6):1261-1272
PubMed   |  Link to Article
Maciejewski ML, Livingston EH, Kahwati LC, Henderson WG, Kavee AL, Arterburn DE. Discontinuation of diabetes and lipid-lowering medications after bariatric surgery at Veterans Affairs medical centers.  Surg Obes Relat Dis. 2010;6(6):601-607
PubMed   |  Link to Article
Schauer PR, Burguera B, Ikramuddin S,  et al.  Effect of laparoscopic Roux-en Y gastric bypass on type 2 diabetes mellitus.  Ann Surg. 2003;238(4):467-484
PubMed
MacDonald KG Jr, Long SD, Swanson MS,  et al.  The gastric bypass operation reduces the progression and mortality of non–insulin-dependent diabetes mellitus.  J Gastrointest Surg. 1997;1(3):213-220
PubMed   |  Link to Article
Christou NV, Sampalis JS, Liberman M,  et al.  Surgery decreases long-term mortality, morbidity, and health care use in morbidly obese patients.  Ann Surg. 2004;240(3):416-423
PubMed   |  Link to Article
Flum DR, Dellinger EP. Impact of gastric bypass operation on survival: a population-based analysis.  J Am Coll Surg. 2004;199(4):543-551
PubMed   |  Link to Article
Flum DR. Administrative data analyses in bariatric surgery—limits of the technique.  Surg Obes Relat Dis. 2006;2(2):78-81
PubMed   |  Link to Article
Pope GC, Kautter J, Ellis RP,  et al.  Risk adjustment of Medicare capitation payments using the CMS-HCC model.  Health Care Financ Rev. 2004;25(4):119-141
PubMed
Hutter MM, Jones DB, Riley SM Jr,  et al.  Best practice updates for weight loss surgery data collection.  Obesity (Silver Spring). 2009;17(5):924-928
PubMed   |  Link to Article

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