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

Autor:	Gong, Chen, Cai, Tingting, Wang, Ying, Xiong, Xuelian, Zhou, Yunfeng, Zhou, Tingting, Sun, Qi, Huang, Huiqun
Předmět:	RISK assessment RANDOM forest algorithms PREDICTION models EXERCISE RECEIVER operating characteristic curves RESEARCH funding LOGISTIC regression analysis RETROSPECTIVE studies DESCRIPTIVE statistics MANN Whitney U Test LONGITUDINAL method BLOOD sugar TYPE 2 diabetes DATA analysis software HYPOGLYCEMIA SENSITIVITY & specificity (Statistics) BLOOD sugar monitoring DIET DISEASE risk factors
Zdroj:	Nursing Open; Oct2024, Vol. 11 Issue 10, p1-11, 11p
Abstrakt:	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. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
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