Department of Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China.
School of Nursing, Sun Yat-sen University, Guangzhou 510085, China.
J Diabetes Res. 2020 Sep 19;2020:7292108. doi: 10.1155/2020/7292108. eCollection 2020.
To develop a simple hypoglycemic prediction model to evaluate the risk of hypoglycemia during hospitalization in patients with type 2 diabetes treated with intensive insulin therapy.
We performed a cross-sectional chart review study utilizing the electronic database of the Third Affiliated Hospital of Sun Yat-sen University, and included 257 patients with type 2 diabetes undergoing intensive insulin therapy in the Department of Endocrinology and Metabolism. Logistic regression analysis was used to derive the clinical prediction rule with hypoglycemia (blood glucose ≤ 3.9 mmol/L) as the main result, and internal verification was performed.
In the derivation cohort, the incidence of hypoglycemia was 51%. The final model selected included three variables: fasting insulin, fasting blood glucose, and total treatment time. The area under the curve (AUC) of this model was 0.666 (95% CI: 0.594-0.738, < 0.001).
The model's hypoglycemia prediction and the actual occurrence are in good agreement. The variable data was easy to obtain and the evaluation method was simple, which could provide a reference for the prevention and treatment of hypoglycemia and screen patients with a high risk of hypoglycemia.
开发一种简单的低血糖预测模型，以评估接受强化胰岛素治疗的 2 型糖尿病患者住院期间发生低血糖的风险。
我们进行了一项横断面图表回顾研究，利用中山大学附属第三医院的电子数据库，纳入了内分泌代谢科 257 例接受强化胰岛素治疗的 2 型糖尿病患者。使用逻辑回归分析得出以低血糖（血糖≤3.9mmol/L）为主要结果的临床预测规则，并进行内部验证。
在推导队列中，低血糖的发生率为 51%。最终模型选择包括三个变量：空腹胰岛素、空腹血糖和总治疗时间。该模型的曲线下面积（AUC）为 0.666（95%CI：0.594-0.738，<0.001）。
模型对低血糖的预测与实际发生情况吻合较好。变量数据易于获取，评估方法简单，可为低血糖的防治和筛选低血糖高危患者提供参考。
