Validating Risk Prediction Models of Diabetes Remission After Sleeve Gastrectomy
Many risk prediction models of diabetes remission after bariatric and metabolic surgery have been proposed. Most models have been created using Roux-en-Y gastric bypass cohorts. However, validation of these models in sleeve gastrectomy (SG) is limited. The objective of our study is to validate the performance of risk prediction models of diabetes remission in obese patients with diabetes who underwent SG.
This retrospective cohort study included 128 patients who underwent SG with at least 1 year follow-up from Dec 2011 to Sep 2016 as the validation cohort. A literature review revealed total 11 models with 2 categories (scoring system and logistic regression), which were validated by our study dataset. Discrimination was evaluated by area under the receiver operating characteristic (AUC) while calibration by Hosmer–Lemeshow test and predicted versus observed remission ratio.
At 1 year after surgery, 71.9% diabetes remission (HbA1c < 6.0 off medication) and 61.4% excess weight loss were observed. Individual metabolic surgery, ABCD, DiaRem, Advanced-DiaRem, DiaBetter, Ana et al., and Dixon et al. models showed excellent discrimination power (AUC > 0.8). In calibration, all models overestimated diabetes remission from 5 to 30% but did not lose their goodness of fit.
This is the first comprehensive external validation of current risk prediction models of diabetes remission at 1 year after SG. Seven models showed excellent predicting power, and scoring models were recommended more because of their easy utility.
KeywordsDiabetes remission Risk prediction models External validation Sleeve gastrectomy Bariatric surgery Metabolic surgery
This manuscript was edited by Wallace Academic Editing.
Compliance with Ethical Standards
Conflict of Interest
The authors declare that they have no conflict of interest.
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