Literature DB >> 30293932

Prediction of lowest nocturnal blood glucose level based on self-monitoring of blood glucose in Japanese patients with type 2 diabetes.

Kazunori Sakurai1, Yuko Kawai1, Masanori Yamazaki2, Mitsuhisa Komatsu1.   

Abstract

AIMS: Continuous glucose monitoring (CGM) is not available for all patients with type 2 diabetes (T2D) at risk of nocturnal hypoglycemia (NH). This study was performed to predict the lowest nocturnal blood glucose (LNBG) levels.
METHODS: An LNBG prediction formula was developed by multivariate analysis using the data including self-monitoring of blood glucose from a formula making (FM) group of 29 insulin-treated T2D patients with CGM. The validity of the formula was assessed by nonparametric regression analysis of actual and predicted values in a formula validation group consisting of 21 other insulin-treated patients. The clinical impact on prediction was evaluated using a Parkes error grid.
RESULTS: In the FM group with a median age of 64.0, the following formula was established: Predicted LNBG (mg/dL) = 127.4-0.836 × Age (y) + 0.119 × Self-monitored fasting blood glucose (mg/dL) + 0.717 × Basal insulin dose (U/day) (standard error of calibration 17.2 mg/dL). Based on the validation results, standard error of prediction was 31.0 mg/dL. All predicted values fell within zones A (no effect on clinical action) and B (little or no effect on clinical outcome) on the grid.
CONCLUSIONS: LNBG could be predicted, and may be helpful for NH prevention.
Copyright © 2018 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Continuous glucose monitoring; Hypoglycemia; Insulin; Self-monitoring of blood glucose; Type 2 diabetes

Mesh:

Substances:

Year:  2018        PMID: 30293932     DOI: 10.1016/j.jdiacomp.2018.09.007

Source DB:  PubMed          Journal:  J Diabetes Complications        ISSN: 1056-8727            Impact factor:   2.852


  2 in total

1.  Predicting and Preventing Nocturnal Hypoglycemia in Type 1 Diabetes Using Big Data Analytics and Decision Theoretic Analysis.

Authors:  Clara Mosquera-Lopez; Robert Dodier; Nichole S Tyler; Leah M Wilson; Joseph El Youssef; Jessica R Castle; Peter G Jacobs
Journal:  Diabetes Technol Ther       Date:  2020-05-14       Impact factor: 6.118

2.  Development of a Deep Learning Model for Dynamic Forecasting of Blood Glucose Level for Type 2 Diabetes Mellitus: Secondary Analysis of a Randomized Controlled Trial.

Authors:  Syed Hasib Akhter Faruqui; Yan Du; Rajitha Meka; Adel Alaeddini; Chengdong Li; Sara Shirinkam; Jing Wang
Journal:  JMIR Mhealth Uhealth       Date:  2019-11-01       Impact factor: 4.773

  2 in total

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