Characterization of bariatric surgery and outcomes using administrative claims data in the research network of a nationwide commercial health plan

Using Claims Data to Predict Pre-Operative BMI Among Bariatric Surgery Patients: Development of the BMI Before Bariatric Surgery Scoring System (B3S3)

Background

Lack of body mass index (BMI) measurements limits the utility of claims data for bariatric surgery research, but pre-operative BMI may be imputed due to existence of weight-related diagnosis codes and BMI-related reimbursement requirements. We used a machine learning pipeline to create a claims-based scoring system to predict pre-operative BMI, as documented in the electronic health record (EHR), among patients undergoing a new bariatric surgery.

Methods

Using the Optum Labs Data Warehouse, containing linked de-identified claims and EHR data for commercial or Medicare Advantage enrollees, we identified adults undergoing a new bariatric surgery between January 2011 and June 2018 with a BMI measurement in linked EHR data ≤30 days before the index surgery (n=3226). We constructed predictors from claims data and applied a machine learning pipeline to create a scoring system for pre-operative BMI, the B3S3. We evaluated the B3S3 and a simple linear regression model (benchmark) in test patients whose index surgery occurred concurrent (2011-2017) or prospective (2018) to the training data.

Results

The machine learning pipeline yielded a final scoring system that included weight-related diagnosis codes, age, and number of days hospitalized and distinct drugs dispensed in the past 6 months. In concurrent test data, the B3S3 had excellent performance (R2 0.862, 95% confidence interval [CI] 0.815-0.898) and calibration. The benchmark algorithm had good performance (R2 0.750, 95% CI 0.686-0.799) and calibration but both aspects were inferior to the B3S3. Findings in prospective test data were similar.

Conclusion

The B3S3 is an accessible tool that researchers can use with claims data to obtain granular and accurate predicted values of pre-operative BMI, which may enhance confounding control and investigation of effect modification by baseline obesity levels in bariatric surgery studies utilizing claims data.

Seventy years of bariatric surgery: A systematic mapping review of randomized controlled trials

While research publications on bariatric surgery (BS) have grown significantly over the past decade, there is no mapping of the existing body of evidence on this field of research. We performed a systematic review followed by a mapping of randomized controlled trials (RCTs) in BS for people with obesity. From January 2020 to December 2020, we performed a systematic review of RCTs evaluating BS, versus another surgical procedure, or versus a medical control group, through a search of Embase and PubMed. There was no restriction on outcomes for study selection. A total of 114 RCTs were included, most (73.7%) of which were based on a comparison with Roux-en-Y gastric bypass (RYGB) and conducted between 2010 and 2020. Only 15% of the trials were multicenter and few (3.5%) were international. The median number of patients enrolled was 61 (interquartile range [IQR]: 47.3-100). Follow-up time was 1 to 2 years in 36% and 22.8% of the trials, respectively. Weight loss was the most studied criterion (87% of RCTs), followed by obesity-related diseases, and medical and surgical complications (73%, 54%, and 47% of RCTs, respectively). Nutritional deficiency frequency, body composition, and mental health were little studied (20%, 18% and 5% of RCTs, respectively). Our literature review revealed that much research in BS is wasted because of replication of RCTs on subjects for which there is already body of evidence, with small populations and follow-up times mostly below 2 years. Yet several research questions remain unaddressed, and there are few long-term trials. Future studies should take into account the experience of the past 70 years of research in this field.