Abstract
ObjectivesGlycaemic control in children and adolescents with type 1 diabetes mellitus can be challenging, complex and influenced by many factors. This study aimed to identify patient characteristics that were predictive of satisfactory glycaemic control in the paediatric population using a logistic regression mixed-effects (population) modelling approach.MethodsThe data were obtained from 288 patients aged between 1 and 22 years old recorded retrospectively over 3 years (1852 HbA1c observations). HbA1c status was categorised as ‘satisfactory’ or ‘unsatisfactory’ glycaemic control, using an a priori cut-off value of HbA1c ≥ 9% (75 mmol/mol), as used routinely by the hospital’s endocrine paediatricians. Patients’ characteristics were tested as covariates in the model as potential predictors of glycaemic control.ResultsThere were three patient characteristics identified as having a significant influence on glycaemic control: HbA1c measurement at the beginning of the observation period (Odds Ratio (OR) = 0.30 per 1% HbA1c increase, 95% confidence interval (CI) = 0.20–0.41); Age (OR = 0.88 per year increase, 95% CI = 0.80–0.94), and fractional disease duration (disease duration/age, OR = 0.80 per 0.10 increase, 95% CI = 0.66–0.93) were collectively identified as factors contributing significantly to lower the probability of satisfactory glycaemic control.ConclusionsThe study outcomes may prove useful for identifying paediatric patients at risk of having unsatisfactory glycaemic control, and who could require more extensive monitoring, support, or targeted interventions.
Highlights
Appropriate glycaemic control in children and adolescents with type 1 diabetes mellitus (T1DM) is crucial for reducing potentially serious adverse effects such as ketoacidosis, nephropathy, microangiopathy, neuropathy and diabetic retinopathy in later stages of the disease [1]
There were three patient characteristics identified as having a significant influence on glycaemic control: haemoglobin A1c (HbA1c) measurement at the beginning of the observation period (Odds Ratio (OR) = 0.30 per 1% HbA1c increase, 95% confidence interval (CI) = 0.20–0.41); Age (OR = 0.88 per year increase, 95% CI = 0.80–0.94), and fractional disease duration were collectively identified as factors contributing significantly to lower the probability of satisfactory glycaemic control
The study outcomes may prove useful for identifying paediatric patients at risk of having unsatisfactory glycaemic control, and who could require more extensive monitoring, support, or targeted interventions
Summary
Appropriate glycaemic control in children and adolescents with type 1 diabetes mellitus (T1DM) is crucial for reducing potentially serious adverse effects such as ketoacidosis, nephropathy, microangiopathy, neuropathy and diabetic retinopathy in later stages of the disease [1]. Due to the complex nature of the endocrine system and its pathology, and the sources of variability that influence glucose control we hypothesized that multivariate mixed-effects, logistic regression modelling of routinely recorded clinical data would provide a potentially useful means of identifying patient characteristics which influence glycaemic control. Logistic regression modelling is a commonly used method of analysis for estimating the probability ( via the Odds Ratio [OR]), of a variable on categorical response data [8] Such categorical data are frequently ‘information poor’ such that in the present study the recorded response (dependent variable) can be 1 of only 2 possibilities (‘satisfactory’ or ‘unsatisfactory’ glycaemic control). The specific aim of this study was to apply such an approach to screen and quantify characteristics recorded routinely in the paediatric endocrine clinic in order to develop a predictive population model which could be applied to improve glycaemic control in young children and adolescents with T1DM
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