We developed a decision analytic model to evaluate the decision to have gastric bypass surgery for the treatment of morbid obesity. The optimal decision for individual patients varies depending upon the balance of risks between perioperative mortality, excess annual mortality associated with increasing BMI, and the efficacy of surgery; however, for the average morbidly obese patient, gastric bypass surgery increases life expectancy. Younger patients have lower surgical risk and longer time horizon over which to realize the benefits of surgery. For older patients, the gain is smaller, and for some gastric bypass surgery will decrease life expectancy.
The results of our base case are similar to the results of a previously published decision analysis by Pope and colleagues22
. Their analysis found that a 40 year-old woman with a BMI of 40 kg/m2
would gain 2.6 years of life expectancy with bariatric surgery and that the absolute life expectancy benefit is similar across age groups. However, we found, the absolute gain in life expectancy is inversely correlated with age.
There are three major differences between the two models which lead to somewhat different conclusions. First, the model by Pope and colleagues assumed that the increase in life expectancy was due to changes in BMI after surgery, placing the patient in a new BMI category with a lower mortality rate; while in our model we made no assumptions about weight loss, rather we used an efficacy term derived from a large prospective cohort study. Secondly, Pope and colleagues estimated the additional mortality associated with obesity by using a large prospective cancer trial and were unable to adjust for the effect of age on mortality. By using the National Health Interview Survey, we were able to use a nationally representative sample fully adjusted for age, gender and BMI across a broader continuum. Finally, the surgical morality risk used by Pope and colleagues was based upon case series and explored in sensitivity analyses. The surgical risk in our model is based upon a logistic regression model derived from the National Inpatient Survey that takes into account patient age and gender. These three factors allow our model to better examine a wide range of patient-specific scenarios and BMI categories.
There are several limitations to our analysis. Since the dataset from NIS is derived from administrative data, it does not include clinical variables, such as BMI, which may be important predictors of surgical mortality. However, the NIS dataset provides the best nationally representative sample of surgical mortality and until more complete datasets become available provides the most generalizable estimates of surgical mortality. A number of obesity-associated conditions may increase operative mortality and conversely may increase the benefits following successful surgery. However, data capturing this level of detail do not currently exist. When such data become available, models like ours will be able to make even more specific recommendations.
The data used to determine the efficacy of surgery, from the study by Adams et al., is from a single state, Utah, and is not from a randomized controlled trial. While this data is not nationally representative and involves selection bias, it is the largest study to date demonstrating the efficacy of gastric bypass surgery.
Another limitation of our analysis is that we did not model long-term complications following surgery, including the need for surgical revision. However, by using efficacy data published by Adams and colleagues, we indirectly accounted for long-term mortality due to surgery. The most common complications, anastomotic stricture, marginal ulcer and internal hernia, are rarely fatal and therefore have limited impact on life expectancy.
Because data describing longitudinal changes in quality of life over time due to changes in body weight are not yet available, we chose to use life expectancy alone as our outcome metric. Gastric bypass surgery has been shown to improve quality of life in the short-term23
. Until there are studies demonstrating the durability and stability of the quality of life improvements using preference based utilities suitable for use in a decision analysis, incorporating quality of life adjustments would bias the results of the model towards favoring gastric byapss.
We did not explicitly model deaths due to accident or suicide although it has been reported that patients having bariatric surgery may be at increased risk of these events14
. However, we included these deaths in the determination of the efficacy and thus biased the model further against bariatric surgery by systematically underestimating the effect of surgery on obesity related mortality.
It is likely that certain subgroups of patients with high mortality due to obesity associated conditions but with a BMI less than 35 may benefit from gastric bypass. Our sensitivity analyses demonstrated that, for women in particular, there are subgroups of patients with a BMI between 30 and 35 whose survival would improve with surgery. Further research needs to explore the benefit of bariatric surgery in subgroups of patients who may benefit outside of the current guidelines.
The decision analysis presented here is a step forward in understanding optimal patient selection but also highlights some of the areas for which better data are needed. Understanding more about how efficacy of bariatric surgery varies based upon patient characteristics is an important next step because the data necessary to accurately model these outcomes are not currently available. For example, there is currently data on the impact of bariatric surgery on the resolution of diabetes24
, but there are no data on the stability of this resolution over time or how this affects long-term mortality. Likewise, it would not be accurate to stratify operative mortality by obesity-associated condition in the decision analysis without including long term mortality projections. Including pre-surgical duration of obesity-associated conditions may also be important, especially in patients with diabetes mellitus: the presence of microvascular or macrovascular complications prior to surgery may impact both surgical risk and the efficacy of surgery.
In conclusion, while not all patients are guaranteed a good outcome, our model indicates that gastric bypass increases life expectancy for most patient subgroups; however, for those at high surgical risk or in whom efficacy of surgery is likely to be low, benefit will be minimal. We believe results of this analysis can be used to better inform both patients’ and physicians’ decisions regarding gastric bypass surgery.