Journal Article

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

Wong, Jenna, Li, Dongdong, Toh, Sengwee, Li, Xiaojuan, Messenger-Jones, Elizabeth, Arterburn, David, Wang, Rui

2024

Overview

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. 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. 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 (R 0.862, 95% confidence interval [CI] 0.815-0.898) and calibration. The benchmark algorithm had good performance (R 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. 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.

• Publisher: Dove Medical Press Limited,Taylor & Francis Ltd,Dove Press,Dove,Dove Medical Press
• Subject: administrative claims / Algorithms / Analysis / bariatric surgery / Body mass index / Codes / Comorbidity / comparative effectiveness research / confounding variable / Diabetes / Documentation / Electronic health records / Electronic records / Evaluation / Gastrointestinal surgery / Hospital patients / Machine learning / Medical records / Obesity / Original Research / Patients / Pharmacy / Regression analysis / supervised machine learning / Surgery / Surgical outcomes / Type 2 diabetes / Warehouse stores / Weight control