开发用于识别 1 型糖尿病患者高血糖期间酮体升高的机器学习模型。

IF 5.7 2区 医学 Q1 ENDOCRINOLOGY & METABOLISM
Diabetes technology & therapeutics Pub Date : 2024-06-01 Epub Date: 2024-03-08 DOI:10.1089/dia.2023.0531
Simon Lebech Cichosz, Clara Bender
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引用次数: 0

摘要

目的:糖尿病酮症酸中毒(DKA)是一种因缺乏胰岛素导致血浆葡萄糖升高和代谢性酸中毒而危及生命的严重疾病。及早发现 DKA 对开始治疗、减少并发症和死亡风险非常重要。本研究的目的是开发和测试预测模型,对高血糖时出现酮体升高的风险发出警报。研究方法我们对 138 名 1 型糖尿病患者的数据进行了分析,这些患者的酮体测量值和连续血糖监测 (CGM) 数据的佩戴时间超过 30,000 天。我们采用了一种有监督的二元分类机器学习方法来识别酮体水平的升高(≥0.6 mmol/L)。数据材料在患者层面随机分为 70%/30%(训练/测试)数据集。比较了逻辑回归(LR)和随机森林(RF)分类器。结果在纳入的患者中,有 913 份酮体样本符合建模条件,包括 273 份酮体水平≥0.6 mmol/L 的事件样本。RF分类器的接收操作特征曲线下面积为0.836(置信区间[CI] 90%,0.783-0.886),LR分类器的接收操作特征曲线下面积为0.710(CI 90%,0.646-0.77)。结论本研究采用的识别 1 型糖尿病患者酮体水平升高的新方法表明,CGM 可以成为早期预测有发生 DKA 风险的患者的宝贵资源。未来的研究还需要对结果进行验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development of Machine Learning Models for the Identification of Elevated Ketone Bodies During Hyperglycemia in Patients with Type 1 Diabetes.

Aims: Diabetic ketoacidosis (DKA) is a serious life-threatening condition caused by a lack of insulin, which leads to elevated plasma glucose and metabolic acidosis. Early identification of developing DKA is important to start treatment and minimize complications and risk of death. The aim of the present study is to develop and test prediction model(s) that gives an alarm about their risk of developing elevated ketone bodies during hyperglycemia. Methods: We analyzed data from 138 type 1 diabetes patients with measurements of ketone bodies and continuous glucose monitoring (CGM) data from over 30,000 days of wear time. We utilized a supervised binary classification machine learning approach to identify elevated levels of ketone bodies (≥0.6 mmol/L). Data material was randomly divided at patient level in 70%/30% (training/test) dataset. Logistic regression (LR) and random forest (RF) classifier were compared. Results: Among included patients, 913 ketone samples were eligible for modeling, including 273 event samples with ketone levels ≥0.6 mmol/L. An area under the receiver operating characteristic curve from the RF classifier was 0.836 (confidence interval [CI] 90%, 0.783-0.886) and 0.710 (CI 90%, 0.646-0.77) for the LR classifier. Conclusions: The novel approach for identifying elevated ketone levels in patients with type 1 diabetes utilized in this study indicates that CGM could be a valuable resource for the early prediction of patients at risk of developing DKA. Future studies are needed to validate the results.

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来源期刊
Diabetes technology & therapeutics
Diabetes technology & therapeutics 医学-内分泌学与代谢
CiteScore
10.60
自引率
14.80%
发文量
145
审稿时长
3-8 weeks
期刊介绍: Diabetes Technology & Therapeutics is the only peer-reviewed journal providing healthcare professionals with information on new devices, drugs, drug delivery systems, and software for managing patients with diabetes. This leading international journal delivers practical information and comprehensive coverage of cutting-edge technologies and therapeutics in the field, and each issue highlights new pharmacological and device developments to optimize patient care.
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