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在1型糖尿病中提升体的早期检测使用胰岛素和葡萄糖动力学跨年龄组:模型开发研究
1Department of Health Science and Technology, Aalborg University, Selma Lagerløfs Vej 249, Aalborg, 9260, Denmark, 45 99403809.
JMIR diabetes
|April 10, 2025
概括
持续的葡萄糖监测 (CGM) 和胰岛素数据可以预测1型糖尿病 (T1D) 患者的体升高. 这种机器学习方法有助于在儿童和成年人中早期发现糖尿病酸性脂肪酸症的风险.
科学领域:
- 内分泌学 在内分泌学.
- 人工智能在医学中的应用
- 糖尿病管理 糖尿病管理
背景情况:
- 糖尿病酸症 (DKA) 是1型糖尿病 (T1D) 的严重并发症.
- 低于最佳的糖尿病管理,包括不经常的子监测,在儿童和青少年T1D患者中很常见.
- 早期发现DKA风险对于预防严重后果至关重要.
研究的目的:
- 调查持续葡萄糖监测 (CGM) 和胰岛素数据对T1D中体升高的预测能力.
- 开发一种机器学习模型,用于使用闭环系统预测儿科和成人T1D患者的高水平.
主要方法:
- 利用监督二进制分类机器学习与特征工程来预测升高的体 (>0.6 mmol/L).
- 整合了Dexcom G6 CGM,iLet生物胰腺胰岛素输送和血糖自我监测的数据.
- 开发了一个极端梯度增强模型,使用来自259名参与者 (年龄在6-79岁) 的数据,超过49000个监测日.
主要成果:
- 在整合CGM,胰岛素和葡萄糖数据时,预测模型实现了0.82的曲线下接收器运行特征面积 (ROC-AUC).
- 来自CGM的特征显示出强大的预测性能 (ROC-AUC 0.75-0.76).
- 该模型成功地在1768个符合条件的样本中,在383个样本中识别了升高的体.
结论:
- CGM和胰岛素数据为T1D中体升高的早期预测提供了一个有希望的方法.
- 开发的预测模型显示了在儿童和成人T1D群体中应用的潜力.
- 这种方法可以增强DKA风险评估和管理策略.
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