Cardiovasc Prev Pharmacother.  2021 Oct;3(4):106-114. 10.36011/cpp.2021.3.e14.

Development of a Predictive Model for Glycated Hemoglobin Values and Analysis of the Factors Affecting It

Affiliations
  • 1Division of Endocrinology and Metabolism, Department of Internal Medicine, Soonchunhyang University College of Medicine, Soonchunhyang University Seoul Hospital, Seoul, Korea
  • 2Division of Endocrinology and Metabolism, Department of Internal Medicine, Korea University College of Medicine, Seoul, Korea
  • 3Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Korea
  • 4Department of Internal Medicine, Gachon University Gil Medical Center, Gachon University College of Medicine, Incheon, Korea
  • 5Division of Nephrology, Soonchunhyang University College of Medicine, Soonchunhyang University Seoul Hospital, Seoul, Korea
  • 6Division of Endocrinology and Metabolism, Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
  • 7BK21 FOUR R&E Center for Learning Health Systems, Korea University, Seoul, Korea

Abstract

Background
Glycated hemoglobin (HbA1c), which reflects the patient's blood sugar level, can only be measured in a hospital setting. Therefore, we developed a model predicting HbA1c using personal information and self-monitoring of blood glucose (SMBG) data solely obtained by a patient.
Methods
Leave-one-out cross-validation (LOOCV) was performed at two university hospitals. After measuring the baseline HbA1c level before SMBG (Pre_HbA1c), the SMBG was recorded over a 3-month period. Based on these data, an HbA1c prediction model was developed, and the actual HbA1c value was measured after 3 months. The HbA1c values of the prediction model and actual HbA1c values were compared. Personal information was used in addition to SMBG data to develop the HbA1c predictive model.
Results
Thirty model training sessions and evaluations were conducted using LOOCV. The average mean absolute error of the 30 models was 0.659 (range, 0.005–2.654). Pre_HbA1c had the greatest influence on HbA1c prediction after 3 months, followed by post-breakfast blood glucose level, oral hypoglycemic agent use, fasting glucose level, height, and weight, while insulin use had a limited effect on HbA1c values.
Conclusions
The patient's SMBG data and personal information strongly influenced the HbA1c predictive model. In the future, it will be necessary to develop sophisticated predictive models using large samples for stable SMBG in patients.

Keyword

Blood glucose self-monitoring; Diabetes mellitus; Glycated hemoglobin A

Figure

  • Figure 1. Plot of the predicted and observational glycated hemoglobin data. Solid blue line represents the “line of best fit” for the point.HbA1c = glycated hemoglobin.

  • Figure 2. The contributors to model predictions.BMI = body mass index; HbA1c = glycated hemoglobin; OHA = oral hypoglycemic agents.


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