Comparison of various prediction models in the effect of laparoscopic sleeve gastrectomy on type 2 diabetes mellitus in the Chinese population 5 years after surgery

Background: The effect of bariatric surgery on type 2 diabetes mellitus (T2DM) control can be assessed based on predictive models of T2DM remission. Various models have been externally verified internationally. However, long-term validated results after laparoscopic sleeve gastrectomy (LSG) surgery are lacking. The best model for the Chinese population is also unknown. Methods: We retrospectively analyzed Chinese population data 5 years after LSG at Beijing Shijitan Hospital in China between March 2009 and December 2016. The independent t-test, Mann-Whitney U test, and chi-squared test were used to compare characteristics between T2DM remission and non-remission groups. We evaluated the predictive efficacy of each model for long-term T2DM remission after LSG by calculating the area under the curve (AUC), sensitivity, specificity, Youden index, positive predictive value (PPV), negative predictive value (NPV), and predicted-to-observed ratio, and performed calibration using Hosmer-Lemeshow test for 11 prediction models. Results: We enrolled 108 patients, including 44 (40.7%) men, with a mean age of 35.5 years. The mean body mass index was 40.3 +/- 9.1 kg/m(2), the percentage of excess weight loss (%EWL) was (75.9 +/- 30.4)%, and the percentage of total weight loss (%TWL) was (29.1 +/- 10.6)%. The mean glycated hemoglobin A1c (HbA1c) level was (7.3 +/- 1.8)% preoperatively and decreased to (5.9 +/- 1.0)% 5 years after LSG. The 5-year postoperative complete and partial remission rates of T2DM were 50.9% [55/108] and 27.8% [30/108], respectively. Six models, i.e., "ABCD", individualized metabolic surgery (IMS), advanced-DiaRem, DiaBetter, Dixon et al's regression model, and Panunzi et al's regression model, showed a good discrimination ability (all AUC >0.8). The "ABCD" (sensitivity, 74%; specificity, 80%; AUC, 0.82 [95% confidence interval [CI]: 0.74-0.89]), IMS (sensitivity, 78%; specificity, 84%; AUC, 0.82 [95% CI: 0.73-0.89]), and Panunzi et al's regression models (sensitivity, 78%; specificity, 91%; AUC, 0.86 [95% CI: 0.78-0.92]) showed good discernibility. In the Hosmer-Lemeshow goodness-of-fit test, except for DiaRem (P <0.01), DiaBetter (P <0.01), Hayes et al (P = 0.03), Park et al (P = 0.02), and Ramos-Levi et al's (P <0.01) models, all models had a satifactory fit results (P >0.05). The P values of calibration results of the "ABCD" and IMS were 0.07 and 0.14, respectively. The predicted-to-observed ratios of the "ABCD" and IMS were 0.87 and 0.89, respectively. Conclusion: The prediction model IMS was recommended for clinical use because of excellent predictive performance, good statistical test results, and simple and practical design features.