Development and Validation of a Nocturnal Hypoglycaemia Risk Model for Patients With Type 2 Diabetes Mellitus.

IF 2 4区 医学 Q2 NURSING
Nursing Open Pub Date : 2024-10-01 DOI:10.1002/nop2.70055
Chen Gong, Tingting Cai, Ying Wang, Xuelian Xiong, Yunfeng Zhou, Tingting Zhou, Qi Sun, Huiqun Huang
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引用次数: 0

Abstract

Aim: To develop and test different machine learning algorithms for predicting nocturnal hypoglycaemia in patients with type 2 diabetes mellitus.

Design: A retrospective study.

Methods: We collected data from dynamic blood glucose monitoring of patients with T2DM admitted to the Department of Endocrinology and Metabolism at a hospital in Shanghai, China, from November 2020 to January 2022. Patients undergone the continuous glucose monitoring (CGM) for ≥ 24 h were included in this study. Logistic regression, random forest and light gradient boosting machine algorithms were employed, and the models were validated and compared using AUC, accuracy, specificity, recall rate, precision, F1 score and the Kolmogorov-Smirnov test.

Results: A total of 4015 continuous glucose-monitoring data points from 440 patients were included, and 28 variables were selected to build the risk prediction model. The 440 patients had an average age of 62.7 years. Approximately 48.2% of the patients were female and 51.8% were male. Nocturnal hypoglycaemia appeared in 573 (14.30%) of 4015 continuous glucose monitoring data. The light gradient boosting machine model demonstrated the highest predictive performances: AUC (0.869), specificity (0.802), accuracy (0.801), precision (0.409), recall rate (0.797), F1 score (0.255) and Kolmogorov (0.603). The selected predictive factors included time below the target glucose range, duration of diabetes, insulin use before bed and dynamic blood glucose monitoring parameters from the previous day.

Patient or public contribution: No Patient or Public Contribution.

2 型糖尿病患者夜间低血糖风险模型的开发与验证。
目的:开发并测试用于预测 2 型糖尿病患者夜间低血糖的不同机器学习算法:设计:回顾性研究:我们收集了2020年11月至2022年1月期间中国上海一家医院内分泌与代谢科收治的T2DM患者的动态血糖监测数据。本研究纳入了接受连续血糖监测(CGM)≥ 24 小时的患者。采用逻辑回归、随机森林和轻梯度提升机算法,并使用AUC、准确率、特异性、召回率、精确度、F1得分和Kolmogorov-Smirnov检验对模型进行验证和比较:共纳入了 440 名患者的 4015 个连续血糖监测数据点,并选择了 28 个变量来建立风险预测模型。440 名患者的平均年龄为 62.7 岁。约 48.2% 的患者为女性,51.8% 为男性。在 4015 个连续血糖监测数据中,有 573 个(14.30%)出现夜间低血糖。轻梯度增强机模型的预测性能最高:AUC (0.869)、特异性 (0.802)、准确性 (0.801)、精确性 (0.409)、召回率 (0.797)、F1 分数 (0.255) 和 Kolmogorov (0.603)。选定的预测因素包括低于目标血糖范围的时间、糖尿病病程、睡前使用胰岛素和前一天的动态血糖监测参数:无患者或公众贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Nursing Open
Nursing Open Nursing-General Nursing
CiteScore
3.60
自引率
4.30%
发文量
298
审稿时长
17 weeks
期刊介绍: Nursing Open is a peer reviewed open access journal that welcomes articles on all aspects of nursing and midwifery practice, research, education and policy. We aim to publish articles that contribute to the art and science of nursing and which have a positive impact on health either locally, nationally, regionally or globally
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