Dataset Information

Prediction of glycaemic control in young children and adolescents with type 1 diabetes mellitus using mixed-effects logistic regression modelling.

ABSTRACT:

Objectives

Glycaemic 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.

Methods

The 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.

Results

There 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.

Conclusions

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.

SUBMITTER: van Esdonk MJ

PROVIDER: S-EPMC5540397 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Publications

Prediction of glycaemic control in young children and adolescents with type 1 diabetes mellitus using mixed-effects logistic regression modelling.

van Esdonk Michiel Joost MJ Tai Bonnie B Cotterill Andrew A Charles Bruce B Hennig Stefanie S

PloS one 20170802 8

<h4>Objectives</h4>Glycaemic 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.<h4>Methods</h4>The data were obtained from 288 patients aged between 1 and 22 years old recorded retrospectively over 3 years (1852 H ...[more]

PMID: 28767734

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Dataset Information

Prediction of glycaemic control in young children and adolescents with type 1 diabetes mellitus using mixed-effects logistic regression modelling.

Objectives

Methods

Results

Conclusions

Publications

Prediction of glycaemic control in young children and adolescents with type 1 diabetes mellitus using mixed-effects logistic regression modelling.

Similar Datasets

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