Background: Sleeve gastrectomy (SG) is a widely used and effective treatment for patients with obesity and comorbid metabolic abnormalities. No specialized tool is available to predict metabolic syndrome (MS) remission after SG. We aimed to present a nomogram that evaluated the probability of MS remission 1 year after SG.

Methods: Patients who underwent SG were enrolled. Of these patients, those with baseline MS were analyzed at the end of follow-up. They were divided into a training set and a validation set. Multivariate logistic regression analysis was performed to identify independent predictors of MS remission 1 year after SG, and these predictors were employed to create a nomogram. Receiver operating characteristic (ROC) curve analysis was used to evaluate discrimination ability. Calibration was performed with the Hosmer-Lemeshow goodness-of-fit test. The net benefits of the nomogram were evaluated using decision curve analysis (DCA).

Results: Three hundred and eighteen patients (94 males and 224 females) with a median age of 34.0 years were analyzed at the end of follow-up. They were divided into a training set and a validation set with 159 individuals each. A combination of age, preoperative high-density lipoprotein cholesterol (HDL-c), presence of elevated triglycerides (TG) and the glycated hemoglobin (HbA1c) level independently and accurately predicted MS remission. The nomogram included all four factors. The model showed moderate discrimination in the training and validation sets (Area under curve 0.800 and 0.727, respectively). The Hosmer-Lemeshow X2 values of the nomogram were 8.477 (P=0.388) for the training set and 5.361 (P=0.718) for the validation set, both indicating good calibration. Moreover, DCA showed substantial clinical benefits of the nomogram in both datasets.

Conclusions: Our simple nomogram could assist in predicting MS remission in Chinese patients with obesity 1 year after SG.

Disclosure

Y.Pan: None. X.Han: None. Y.Tu: None. P.Zhang: None. H.Yu: None. Y.Bao: None.

Funding

Clinical Research Plan of SHDC (2020CR1017B)

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