Predicting serious complication risks after bariatric surgery: external validation of the Michigan Bariatric Surgery Collaborative risk prediction model using the Dutch Audit for Treatment of Obesity

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.