The authors have declared that no competing interests exist.
The RouxenY Gastric Bypass (RYGB) has been one of the most popular surgeries in the USA for years. While many models have been made to investigate the factors that affect weight loss, these factors are still highly debated.
To create a model that predict performance of RYGB patients.
110 out of 344 patients who received a RYGB at a single institution between Jan 2010 and April 2014 were included in this study. Data was collected retrospectively. Patients were included if they had greater than 1 year follow up with at least three follow up points and could be modeled with r^{2}>0.95. All patients were one year beyond surgery, while 40 were completely lost to follow up, 104 at 1 month, 138 at 3 months, 188 at 6 months, and 225 at one year. 9 patients were not included because they did not meet the criteria of the study. Patients were divided into quartiles based on percentage excess weight loss (%EWL) at one year. Multivariate analysis was performed to determine the significant factors that influence patients being in the first quartile of weight loss (1760% %EWL).
Only males with a Body Mass Index (BMI) above 44 and females with a BMI above 64 were found to be predictive of patients being in the first quartile. Our model has Positive and Negative predictor values of 66% and 80% respectively with sensitivity and specificity of 29% and 95% respectively.
An model to predict %EWL was created, only gender and preoperative BMI were found to be significant factors. In general females have better outcomes with higher BMI’s than do males. This information should be discussed with patients when deciding a procedure. However, more studies are needed for validation of these results.
The RouxenY Gastric Bypass (RYGB) has been the most popular surgery for weight loss in the USA for many years
Some models out there do predict weight loss. The first one in this category was from Mor et al who showed that weight loss results of RYGB are determined from early postoperative visits
Other studies looked more into the psychological reasons for weight loss failure. These studies focused on looking at support systems in place with patients and strict psychological tests to determine how well patients will do postoperatively
In 2015 Wise et al used a newer innovation in artificial neural networks to make a workable preoperative prediction of weight loss model
344 patients underwent primary RYGB surgery at a single private practice institution between Jan 2010 and April 2014. Demographic data and comorbid conditions were collected. All revisional patients were excluded from this study. Patients with a BMI over 65 were excluded from the study. Data was gathered retrospectively on a prospectively kept database. Patients were diagnosed with type 2 diabetes, hypertension, or gastroesophageal reflux disease (GERD) if they were on medications for these diseases. Sleep apnea was only counted if the patient had the diagnosis from a sleep study. Severity of these diseases was not assessed.
To be included for evaluation patients had to have follow up greater than one year with at least three follow up points in the first year. This data allowed weight loss to be interpolated at specific time points through nonlinear regression. All patients needed an r² value of at least .95. (Simply, this means that at most 5% of the weight loss cannot be explained by simply time since the operation, but by extraneous variables).
Out of the 344 patients only 110 met the criteria for this study. Out of the 244 40 were completely lost to follow up, 104 at 1 month, 138 at 3 months, 188 at 6 months, 225 at 1 year. The 9 additional patients did not meet the other criteria of this study. Comparisons between the whole population and study population were performed for age, sex, weight, height, and body mass index (BMI). Comparisons are found on




N  110  344  
BMI (kg/m^{2})  49.4±11.7  47.4±7.9  .345 
Weight  302.8±83.4  293.1 ±63.2  .415 
Height (inches)  65.4±4.03  65.6±3.8  .601 
Age  44.1±12.5  45.2±12.9  .9 
Male/Female  13/97  68/276  .08 
From each patient’s weight at one year, percentage excess weight loss (%EWL) was calculated as the percentage of weight lost above the weight the patient would be if their BMI was 25. Patients were then divided into quartiles based on %EWL at one year.
Multiple logistic regression was run using each individual patient’s data to determine the significant variables that effect weight loss. Variables studied were gender, age, BMI, sleep apnea, diabetes, GERD, and hypertension. The Wald statistic was used in order to determine significance of each variable. A model was then made using multiple logistic regression with only the variables that significantly affect weight loss. The efficacy of the model was determined using a chisquared test, likelihood ratio, area under the receiver operator characteristic (ROC) curve and the HosmerLemeshow Statistic. The cut off value of the regression was optimized using a ROC curve. This was found using an index takes into account probability of patients ending up in the bottom quartile. All statistical analysis was done using SigmaPlot statistical software.
110 patients met the inclusion criteria. Of these patients 97 were female (86%). The preoperative demographics are shown in








110  28  27  28  27 


46.7±7.9  51±8.1  49.7±7.3  45±7.3  40.9±4.6  <.001 

284.8±56.6  314.9±57.6  296.4±50.4  271.5±54.3  255.8±46.8  <.001 

65.3±3.9  65.6±5.2  64.7±3.4  65±3.4  66.1±3.5  .329 

45.1±12.9  46.6±11.5  42.8±13.4  45.2±13  45.6±14.1  .824 

13/97  8/20  0/27  3/25  2/25  .008 

75%±23%  47%±12%  67%±4%  82%±5%  104%±13%  <.001 

17%134%  17%60%  62%73%  74%88%  90%134%  

31 (28%)  11 (39%)  6 (22%)  7 (25%)  7 (26%)  .496 

43 (39%)  17 (61%)  8 (30%)  11 (39%)  7 (26%)  .037 

42 (38%)  10 (36%)  11 (41%)  11 (39%)  10 (37%)  .994 

33 (30%)  4 (14%)  11 (41%)  10 (36%)  8 (30%)  .201 

52 (47%)  17 (61%)  11 (41%)  14 (50%)  10 (37%)  .237 



Age  .646 
Gender  .006 
BMI  .002 
Diabetes  .298 
Sleep apnea  .622 
GERD  .638 
Hypertension  .154 
Hyperlipidemia  .23 
A model was obtained by running multiple logistic regression where success is being in the bottom quartile. The predictive model obtained by multiple logistical regression is if males are above a BMI of 63.7 and females are above a BMI of 44.1 they will not be able achieve an %EWL of 60% at 1 year. This model has a Positive and Negative predictor value of 66% and 80% respectively with a sensitivity and specificity of 29% and 95% respectively. Our model also has an area under the curve of .75 (
The purpose of this study was to use simple data collection points in order to come up with a mathematical model to predict RYGB outcomes. This equation presented in the discussion sections is easy to perform and requires no advance math skills and can be done on any simple calculator. Many varying models have been made to predict weight loss after surgery but they are complex and burdensome and our paper is unique because we have actually shown the reader the equation validating our thesis. We believe that as others apply the same methods to their own data sets that the model will become both more predictive and better refined. With a variety of procedures it is certain that there is not a procedure that fits for all. In order to help patients, most surgeons match objectives with likely outcomes. Most quote average %EWL, but this is vastly inaccurate. In order to be more accurate than average %EWL, we used multivariate analysis.
Our cohort had an average of 75% %EWL at one year. This is well within the range of published %EWL values at one year
One interesting thing about our model is that it can indirectly show that within limits that technical variances and preoperative patient behavior are not responsible for one year outcomes. Our model predicts accurately without having any of these factors. With this information more studies into predictive modeling should be made.
Most investigators have defined success of a bariatric surgery as greater than 50% %EWL
Many factors have been found to influence the outcome of the RYGB. These include race, initial BMI, hypertension, gender, height, age, diabetes, ghrelin, and initial weight
In light of these considerations we sought to make an easily used predictive model by only including things that should already be collected in any practice.
Our finding of only BMI and gender effecting weight loss is consistent in the literature and correlates with our previous work. In 2009 we also found that the only significant effectors of weight loss at one year were gender and BMI
However, we are not completely sure to as gender was found to be a significant factor. We can only postulate on the reasons. It may have to do with the fact that females tend to come in for follow ups on a more regular basis. With BMI the reasons are clearer. The postulated reason we have come up with is that the RYGB may not incorporate enough malabsorption to significantly help those who are super obese.
Our model shows that diabetes is not a significant factor in weight loss at 1 year. The literature has been unclear on this issue and we hope our study ultimately helps determine whether or not if affects weight loss after RYGB.
We also did not find GERD to be a factor which agrees with previously published literature for the RYGB
Hyperlipidemia was also not found to be a significant factor in weight loss at one year. Hyperlipidemia has not previously been shown to effect weight loss which agrees with our findings.
Literature is split on whether hypertension effects weight loss however in our study we found that it had no effect on eventual weight loss
Some may criticize our total follow up population of 110 patients out of 344. However, this does not affect our ability to examine the effects of different comorbidities on weight loss and their interaction as we are looking at weight loss as the output and 110 patients is easily enough statistically to draw weight loss conclusions from the variables presented. This is reflected in the high pvalues of the comorbidities analysis. Additionally, this is especially important if you believe that those who don’t follow up have worse outcomes than those that do. In the first study of its kind Hunter Mehaffey J et al demonstrated that those that are lost to follow up do not have any statistical difference in outcomes to those who regularly follow up
Only having 13 male patients in this study was a severe limitation. We included it in the analysis since it was pertinent and gave us high p values and we could not think of a reason to exclude them. Yet, we acknowledge the limited data set.
It is important to note that this model predicts only one female ending up in the bottom quartile of the 97 females in the study. It is not until the female patients exceed a BMI of 64 can we positively predict if they will be in the bottom quartile. While in the males, this happens at a relatively low BMI of 44. In our study 8 of the 13 males who were eligible ended up in the bottom quartile. This could represent selection bias towards males who failed followed up in our practice while those who succeeded did not. Ideally we would like to see a larger sample size to validate both our 2009 paper
The last important limitation of the paper is its low sensitivity. This is not surprising as our numbers were low and we hope as we acquire larger data sets we can raise this number but in order to not alarm patients whom we discuss our results with we elected to model our patients for the highest specificity possible and accept the low sensitivity as part of the model.
We have developed a model that can be used to help predict weight loss results after the RYGB. Only gender and preoperative BMI were found to be significant factors. In general females have better outcomes with higher BMI’s than do males. This model can be used preoperatively to allow surgeons to educate patients about weight loss goals and design better postoperative treatments preoperatively. This information should be discussed with patients when deciding a procedure.