Nomogram for Predicting 5-Year Diabetes Remission After Roux-en-Y Gastric Bypass Surgery in Chinese Patients with Obesity.

PURPOSE: Roux-en-Y gastric bypass (RYGB) is widely used as an effective treatment for type 2 diabetes (T2D) patients with obesity; long-term (> 5 years) diabetes remission (DR) rates have been less frequently reported. To date, there is no validated model to guide procedure selection based on long-term glucose control in patients with T2D. The purpose of this study is to establish a new tool for predicting long-term DR with a nomogram. MATERIALS AND METHODS: One hundred five individuals with complete preoperative information and undergoing RYGB in a university hospital were enrolled in this retrospective study. DR criteria after bariatric surgery were defined according to the 2009 ADA guideline. Fifteen individuals were lost to 5-year follow-up. Thus, 90 individuals were available and seen at the end of follow-up. The baseline and 5-year data of these 90 individuals were analyzed. Multivariate logistic regression analysis was performed to identify independent predictors for long-term DR, and these predictors were used to create a nomogram. RESULTS: Preoperative glycated hemoglobin (A1C) and diabetes duration were identified as independent influential factors that could be combined for precise prediction of long-term complete DR. We created a nomogram by using these 2 factors. The area under the curve was 0.78 (95% confidence interval 0.67-0.89). The Hosmer-Lemeshow X2 value of nomogram was 3.86 (P = 0.87) and indicated consistency between predicted and observed remission. CONCLUSION: Our nomogram was a novel and useful tool for accurate prediction of long-term DR after RYGB.

Comparison of Four Risk Prediction Models for Diabetes Remission after Roux-en-Y Gastric Bypass Surgery in Obese Chinese Patients with Type 2 Diabetes Mellitus.

BACKGROUND: Roux-en-Y gastric bypass (RYGB) is a major type of bariatric surgery. Various models have been established for facilitating clinical decision-making and predicting outcomes after RYGB; the ABCD, DiaRem, advanced-DiaRem (Ad-DiaRem), and DiaBetter scores are among the most commonly used risk prediction models. However, these models were primarily developed based on retrospective analyses of patients from Western countries at 1 year after RYGB. The present study was to assess the performance of these models and identify the optimal model, for predicting postoperative diabetes remission in diabetic Chinese patients. METHODS: The present study included a total of 253 RYGB patients; 214 completed a 1-year follow-up and 131 completed a 3-year follow-up. The assessments and comparisons of the predictive performance of the four models were based on both discrimination and calibration measures. Discrimination was assessed according to the area under the receiver operating characteristic curve (AUC), and calibration was evaluated by Hosmer-Lemeshow goodness-of-fit tests and predicted-to-observed ratios. RESULTS: One hundred thirteen (52.8%) in the 1-year follow-up group and 59 (45.0%) in the 3-year follow-up group achieved complete diabetes remission. Although all models showed similar discriminatory capacity and good calibration, the DiaBetter model exhibited the best predictive performance (1-year follow-up, AUC 0.760, 95% confidence interval [CI] 0.697-0.815, predicted-to-observed ratio 1.04; 3-year follow-up, AUC 0.804, 95% CI 0.726-0.868, predicted-to-observed ratio 0.95). CONCLUSIONS: The present results indicated that the DiaBetter model is the optimal model for predicting postoperative diabetes remission in diabetic Chinese individuals, due to its excellent predictive accuracy and ready availability for use in clinical practice.