Yao Huilan, Yuan Shijin, Pan Hongying, Hong Sisi, Huang Chen, Zhao Linfang, Yuan Hongdi, Mei Lei, Zheng Yinghong, Liu Xiaolong, Lu Weina
Nursing Department, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Department of Medical Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Front Endocrinol (Lausanne). 2025 Jul 7;16:1590780. doi: 10.3389/fendo.2025.1590780. eCollection 2025.
To identify factors influencing hypoglycemia in patients with type 2 diabetes mellitus (T2DM) following gastrointestinal tumor surgery and construct a predictive model for assessing hypoglycemia risk.
We retrospectively collected data on 1280 patients with T2DM who underwent gastrointestinal tumor surgery and divided them into two groups-one for model building (n = 982) and another for validation (n = 298). We used multivariate logistic regression to develop a predictive model for hypoglycemia following gastrointestinal tumor surgery. The model's predictive performance was evaluated using the area under the receiver operating characteristic (ROC) curve, and its generalization ability was evaluated using the bootstrap test and the five-fold cross-validation test.
We identified hypoglycemia following gastrointestinal tumor surgery in 124 of 982 (12.6%) T2DM patients in the developmental cohort. Finally, five predictors, including duration of diabetes, operation duration, preoperative fasting time, preoperative hypoglycemic regimen (subcutaneous insulin injection), and glucose fluctuation on the day of surgery, were integrated into the predictive model. The performance of the hypoglycemia risk prediction model for patients with T2DM undergoing gastrointestinal tumor surgery was comprehensively evaluated. The model demonstrated an area under the ROC curve (AUC) of 0.837 (95% CI: 0.792-0.882), indicating a strong discriminative ability. Internal validation via five-fold cross-validation with bootstrap resampling revealed close approximation of the calibration curve to the ideal line, refining high consistency between predicted probabilities and actual hypoglycemia occurrence. Decision curve analysis (DCA) further supported its clinical utility, indicating value in clinical decision making for hypoglycemia risk stratification and preventive intervention selection.
The developed model exhibits high discriminative ability and good calibration. Following visualization (e.g., nomogram), it provides a clinical tool for healthcare providers to stratify hypoglycemia risk in T2DM patients undergoing gastrointestinal tumor surgery, enabling personalized perioperative glucose management and informed decision making to improve patient outcomes.
确定影响2型糖尿病（T2DM）患者胃肠道肿瘤手术后低血糖的因素，并构建评估低血糖风险的预测模型。
我们回顾性收集了1280例接受胃肠道肿瘤手术的T2DM患者的数据，并将他们分为两组，一组用于模型构建（n = 982），另一组用于验证（n = 298）。我们使用多因素逻辑回归来建立胃肠道肿瘤手术后低血糖的预测模型。使用受试者操作特征（ROC）曲线下面积评估模型的预测性能，并使用自助法检验和五折交叉验证检验评估其泛化能力。
在开发队列的982例T2DM患者中，我们发现124例（12.6%）在胃肠道肿瘤手术后出现低血糖。最后，将糖尿病病程、手术时长、术前禁食时间、术前降糖方案（皮下注射胰岛素）和手术日血糖波动这五个预测因素纳入预测模型。对接受胃肠道肿瘤手术的T2DM患者低血糖风险预测模型的性能进行了综合评估。该模型的ROC曲线下面积（AUC）为0.837（95%CI：0.792 - 0.882），表明具有较强的判别能力。通过五折交叉验证和自助重采样进行的内部验证显示校准曲线与理想线非常接近，预测概率与实际低血糖发生之间具有高度一致性。决策曲线分析（DCA）进一步支持了其临床实用性，表明在低血糖风险分层和预防性干预选择的临床决策中具有价值。
所开发的模型具有较高的判别能力和良好的校准。经过可视化（如列线图）后，它为医疗保健提供者提供了一种临床工具，用于对接受胃肠道肿瘤手术的T2DM患者的低血糖风险进行分层，从而实现个性化的围手术期血糖管理和明智的决策，以改善患者预后。
