1. Bariatric Surgery Weight Loss Prediction Tool
Pavan Kumar Chundi, Analyst (Quality & Transformation), James M. Anderson Center For Health Systems Excellence
Introduction
Bariatric surgery (weight-loss surgery) is a treatment for severe obesity in which specialists modify the stomach and its connections to help teens lose weight. For severely obese adolescents who have not had success with behavioral and nutritional approaches to weight loss, bariatric surgery is another effective tool for weight management [1].
The purpose of this study is to build a robust interactive prediction tool for:
Patients and families to better understand how they can lose weight post Bariatric Surgery.
Clinicians to motivate the patients by comparing their weight loss with that of the average at their follow up visit and also help plan early interventions for better care management.
Keywords: Prediction Model, Piecewise Linear Regression, Bariatric Surgery, SAS, R, Tableau
With the data set being a monotone missing pattern, there is more flexibility with the imputation techniques used. In this case, a Regression model[2] was chosen.
A Piecewise Linear Regression model was built using SAS.
5 different regression models were created i.e. one for each time stamp.
Acknowledgements
Thomas Inge, MD, PhD, Surgical Director, Surgical Weight Loss Program for Teens
Linda Kollar, MSN, APRN, CNP, Bariatric Clinical Director, Surgical Weight Loss Program for Teens
Denise L White, PhD, Director QI Analytics
James Papp, Director External Reporting
Cincinnati Children’s Marketing and Web services Team (website graphic tool)
Kate Flynn, Data Systems (Administrative support)
Conclusion
Piecewise Linear Regression modeling approach has proved to be a statistically valid method based on the values from R2 and Adj.R2.
Prediction Tool published on Cincinnati Children’s website and can be accessed at .
Tool currently being used in the clinic at follow up visits to educate the patients on weight management.
Prediction model to be reviewed annually with plan to include 4 year and 5 year time stamps.
Prediction Tool Web Interface
Model 1
6 Months Weight
6 months weight = b0 + b1* Presurgeryweight + b2* AgeAtSurgery + b3* Gender
Model 2
12 Months Weight
12 months weight = b0 + b1* 6 months weight*
*(includes the missing values from the Model 1)
Model 3
18 Months Weight
18 months weight = b0 + b1*12 months weight*
*(includes the missing values from the Model 2)
Model 4
2Year Weight
2Year weight = b0 + b1*18 months weight*
*(includes the missing values from the Model 3)
Model 5
3 Year Weight
3Year weight = b0 + b1* 2Year weight*
*(includes the missing values from the Model 4)
Method
Bariatric Surgery data and data for all subsequent visits post surgery was extracted and formatted to get the required time stamps. An exploratory data analysis was done is SAS.
Along with the inputs from the clinical team, literature was reviewed to obtain a comprehensive list of factors that could effect the weight loss over time.
Upon review, the data followed a monotone missing pattern (i.e. if a patient failed to show up a certain visit then the patient fails to show up at all the other subsequent visit).
Before applying any kind of imputation technique, a review of the patient medical record was performed to track weight for the required time stamp. At each time stamp, a three month time interval was used to check to see if the patient weight was recorded elsewhere in the hospital.
Comments
“Amazing tool.  Bravo Pavan. I would like to “pilot” this concept of monitoring in real time and using the app as a tool to motivate!” – Thomas Inge, MD, PhD
“I want to tell you again that this tool has been very helpful for our patients in clinic. It really helps them to get a sense of what they should expect…the majority say they want to do better than average, will be interesting to see what happens.”- Linda Kollar, MSN
Figure 2: Fit Diagnostics for 6 Months Weight (SAS OUTPUT)
Cross validation data and graphs to be added.
References
Surgical Weight Loss Program for Teens. (n.d.). Retrieved January 7, 2016,
Yuan, Y. C. (2010). Multiple imputation for missing data: Concepts and new development (Version 9.0). SAS Institute Inc, Rockville, MD, 49.
Robjhyndmancom. (2010, 4 October 2010). Why every statistician should know about cross-validation. [Weblog]. Retrieved 22 February 2016, from
Results
Piecewise regression model for each time period of interest.
A k-fold-cross-validation technique in R was used to validate the performance of the models, where the sample is randomly partitioned into k-sub samples and one partition is left out at each iteration. In this study k=5.
Cross-validation is primarily a way of measuring the predictive performance of a statistical model [3].
Example of Regression Model results for 6 Months weight.
Figure 1: Exploratory Data Analysis of Weight tracked at Pre-surgery and at regular intervals post surgery in Tableau.
Figure 3: K-fold Cross Validation plot in R