Predicting serious complication risks after bariatric surgery External validation of the MBSC risk prediction model using the DATO


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);
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 (1.92%) patients
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
AUC of 0.602, compared with an AUC of 0.65 and moderate-good calibration in the Michigan
model.

Conclusion

The DATO prediction model has a 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.



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