ArticlePloS one2017
Prediction of glycaemic control in young children and adolescents with type 1 diabetes mellitus using mixed-effects logistic regression modelling.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Mixed-effects neural network modelling to predict longitudinal trends in fasting plasma glucose.BMC medical research methodology · 2024Article
- SuPreme Study: a protocol to study the neuroprotective potential of sulfate among very/extremely preterm infants.BMJ open · 2023Article
- Personality, Coping and Developmental Conditions in Female Adolescents and Young Adults with Type 1 Diabetes: Influence on Metabolic Control and Quality of Life.Frontiers in psychiatry · 2021Article
- Food insecurity is associated with high risk glycemic control and higher health care utilization among youth and young adults with type 1 diabetes.Diabetes research and clinical practice · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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