Background: Although type 2 diabetes (T2D) remission after gastric cancer surgery has been reported, little is known about the predictors of postoperative T2D remission.
Methods: This study used data from a nationwide cohort provided by the National Health Insurance Service in Korea. We developed a diabetes prediction (DP) score, which predicted postoperative T2D remissions using a logistic regression model based on preoperative variables. We applied machine-learning algorithms [random forest, XGboost, and least absolute shrinkage and selection operator (LASSO) regression] and compared their predictive performances with those of the DP score.
Results: The DP score comprised five parameters: baseline body mass index (< 25 or ≥ 25 kg/m2), surgical procedures (subtotal or total gastrectomy), age (< 65 or ≥ 65 years), fasting plasma glucose levels (≤ 130 or > 130 mg/dL), and antidiabetic medications (combination therapy including sulfonylureas, combination therapy not including sulfonylureas, single sulfonylurea, or single non-sulfonylurea]). The DP score showed a clinically useful predictive performance for T2D remission at 3 years after surgery [training cohort: area under the receiver operating characteristics (AUROC) 0.73, 95% confidence interval (CI), 0.71-0.75; validation cohort: AUROC 0.72, 95% CI 0.69-0.75], which was comparable to that of the machine-learning models (random forest: AUROC 0.71, 95% CI 0.68-0.74; XGboost: AUROC 0.70, 95% CI 0.67-0.73; LASSO regression: AUROC 0.75, 95% CI 0.73-0.78 in the validation cohort). It also predicted the T2D remission at 6 and 9 years after surgery.
Conclusions: The DP score is a useful scoring system for predicting T2D remission after gastric cancer surgery.
Keywords: Gastrectomy; Gastric cancer; Prediction; Type 2 diabetes.
© 2021. The International Gastric Cancer Association and The Japanese Gastric Cancer Association.