My Weight Loss Journey

Background The Michigan Bariatric Surgery Collaborative (MBSC) has developed an app that calculates outcomes, to include predicted weight loss and venous thromboembolism rates. The app is called Weigh the Odds. This app provides an important method for discussing outcomes with patients. Objectives We sought to determine the validity of the MBSC outcomes calculator in a Hispanic population. Setting Community practice in Texas. Methods A community practice with a large percentage of Hispanic patients was used to validate the MBSC calculator. The correlation coefficient was calculated. A chi-square test was used to compare quantitative variables between the operation types. Lin's concordance correlation along with the Bland-Altman plot were performed to estimate the level of agreement between actual and expected weights at 1 year. Results Two hundred seventy-six patients met the eligibility criteria and were included in the study. The average body mass index (BMI) at the time of initial bariatric surgery was 45.2 kg/m2 (standard deviation [SD], 6.68). One hundred seventy-seven patients underwent sleeve gastrectomy (SG), and 99 patients underwent Roux-en-Y gastric bypass (RYGB) as their initial bariatric operation. The mean actual weight at 1 year for the entire cohort was 90.8 kg (SD, 19.6), while the mean expected weight was 89.2 kg (SD, 16.9), leading to a mean difference of 1.6 kg. The concordance correlation coefficient of .80 (95% CI: .76-.84, P < .001) demonstrates a strong agreement between actual and expected weights at 1 year. There was a lower correlation coefficient for RYGB patients. Conclusion The concordance correlation coefficient demonstrated a strong agreement between actual and expected weights at 1 year. These findings validate the MBSC outcomes calculator in a Hispanic population.

Background Risk-prediction tools can support doctor–patient (shared) decision making in clinical practice by providing information on complication risks for different types of bariatric surgery. However, external validation is imperative to ensure the generalizability of predictions in a new patient population. Objective To perform an external validation of the risk-prediction model for serious complications from the Michigan Bariatric Surgery Collaborative (MBSC) for Dutch bariatric patients using the nationwide Dutch Audit for Treatment of Obesity (DATO). Setting Population-based study, including all 18 hospitals performing bariatric surgery in the Netherlands. Methods All patients registered in the DATO undergoing bariatric surgery between 2015 and 2020 were included as the validation cohort. Serious complications included, among others, abdominal abscess, bowel obstruction, leak, and bleeding. Three risk-prediction models were validated: (1) the original MBSC model from 2011, (2) the original MBSC model including the same variables but updated to more recent patients (2015–2020), and (3) the current MBSC model. The following predictors from the MBSC model were available in the DATO: age, sex, procedure type, cardiovascular disease, and pulmonary disease. Model performance was determined using the area under the curve (AUC) to assess discrimination (i.e., the ability to distinguish patients with events from those without events) and a graphical plot to assess calibration (i.e., whether the predicted absolute risk for patients was similar to the observed prevalence of the outcome). Results The DATO validation cohort included 51,291 patients. Overall, 986 patients (1.92%) experienced serious complications. The original MBSC model, which was extended with the predictors "GERD (yes/no)," "OSAS (yes/no)," "hypertension (yes/no)," and "renal disease (yes/no)," showed the best validation results. This model had a good calibration and an AUC of .602 compared with an AUC of .65 and moderate to good calibration in the Michigan model. Conclusion The DATO prediction model has good calibration but moderate discrimination. To be used in clinical practice, good calibration is essential to accurately predict individual risks in a real-world setting. Therefore, this model could provide valuable information for bariatric surgeons as part of shared decision making in daily practice.