预测 2 型糖尿病患者在接受速效胰岛素治疗后 HbA1c 达不到目标值的情况:在临床试验数据中使用机器学习框架。

IF 4.1 Q2 ENDOCRINOLOGY & METABOLISM Journal of Diabetes Science and Technology Pub Date : 2024-09-20 DOI:10.1177/19322968241280096
Carsten Wridt Stoltenberg, Stine Hangaard, Ole Hejlesen, Thomas Kronborg, Peter Vestergaard, Morten Hasselstrøm Jensen
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引用次数: 0

摘要

背景和目的:血糖控制对 2 型糖尿病患者至关重要。然而,只有大约一半的患者能达到 HbA1c≤7% 的目标。找出那些可能难以达到这一目标的患者可能很有价值,因为他们需要额外的支持。因此,本研究的目的是建立一个模型来预测那些在使用速效胰岛素后无法达到 HbA1c 目标值的 2 型糖尿病患者:研究使用了一项随机对照试验(NCT01819129)中的数据,该试验的参与者均为开始使用速效胰岛素的 2 型糖尿病患者。数据包括人口统计学、临床实验室值、自我监测血糖(SMBG)、健康相关生活质量(SF-36)和身体测量。为预测未达标者的 HbA1c 目标,我们开发了一种逻辑回归方法。前向特征选择输入了 196 个潜在特征。为了评估性能,采用了 20 次分层 5 倍交叉验证和接收者操作特征曲线下面积(AUROC):结果:在纳入的 467 名参与者中,有 98 人(21%)未达到 HbA1c ≤7% 的目标值。前向选择确定了 7 个特征:基线 HbA1c(%)、基线前连续 3 天所有餐次的餐后 SMBG 平均值(mmol/L)、性别、尿液中无酮体、基线白蛋白(g/dL)、基线低密度脂蛋白胆固醇(mmol/L)和尿液中的微量蛋白。该模型的AUROC为0.745 [95% CI = 0.734, 0.756]:该模型能够预测未达到 HbA1c 目标值的患者,效果良好,有可能及早识别出需要额外支持以达到血糖控制的 2 型糖尿病患者。
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Prediction of People With Type 2 Diabetes Not Achieving HbA1c Target After Initiation of Fast-Acting Insulin Therapy: Using Machine Learning Framework on Clinical Trial Data.

Background and aims: Glycemic control is crucial for people with type 2 diabetes. However, only about half achieve the advocated HbA1c target of ≤7%. Identifying those who will probably struggle to reach this target may be valuable as they require additional support. Thus, the aim of this study was to develop a model to predict people with type 2 diabetes not achieving HbA1c target after initiating fast-acting insulin.

Methods: Data from a randomized controlled trial (NCT01819129) of participants with type 2 diabetes initiating fast-acting insulin were used. Data included demographics, clinical laboratory values, self-monitored blood glucose (SMBG), health-related quality of life (SF-36), and body measurements. A logistic regression was developed to predict HbA1c target nonachievers. A potential of 196 features was input for a forward feature selection. To assess the performance, a 20-repeated stratified 5-fold cross-validation with area under the receiver operating characteristics curve (AUROC) was used.

Results: Out of the 467 included participants, 98 (21%) did not achieve HbA1c target of ≤7%. The forward selection identified 7 features: baseline HbA1c (%), mean postprandial SMBG at all meals 3 consecutive days before baseline (mmol/L), sex, no ketones in urine, baseline albumin (g/dL), baseline low-density lipoprotein cholesterol (mmol/L), and traces of protein in urine. The model had an AUROC of 0.745 [95% CI = 0.734, 0.756].

Conclusions: The model was able to predict those who did not achieve HbA1c target with promising performance, potentially enabling early identification of people with type 2 diabetes who require additional support to reach glycemic control.

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来源期刊
Journal of Diabetes Science and Technology
Journal of Diabetes Science and Technology Medicine-Internal Medicine
CiteScore
7.50
自引率
12.00%
发文量
148
期刊介绍: The Journal of Diabetes Science and Technology (JDST) is a bi-monthly, peer-reviewed scientific journal published by the Diabetes Technology Society. JDST covers scientific and clinical aspects of diabetes technology including glucose monitoring, insulin and metabolic peptide delivery, the artificial pancreas, digital health, precision medicine, social media, cybersecurity, software for modeling, physiologic monitoring, technology for managing obesity, and diagnostic tests of glycation. The journal also covers the development and use of mobile applications and wireless communication, as well as bioengineered tools such as MEMS, new biomaterials, and nanotechnology to develop new sensors. Articles in JDST cover both basic research and clinical applications of technologies being developed to help people with diabetes.
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