1. Determinants of poor glycaemic control and proteinuria in patients with type 2 diabetes: a retrospective analysis of general practice records in Ireland.
- Author
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Sullivan, Maria, O'Connor, Raymond, and Hannigan, Ailish
- Subjects
CONFIDENCE intervals ,GLYCEMIC control ,FAMILY medicine ,MULTIPLE regression analysis ,AGE distribution ,MULTIPLE organ failure ,RETROSPECTIVE studies ,MANN Whitney U Test ,TYPE 2 diabetes ,PRIMARY health care ,RISK assessment ,T-test (Statistics) ,PROTEINURIA ,DESCRIPTIVE statistics ,MEDICAL records ,CHI-squared test ,LOGISTIC regression analysis ,ODDS ratio ,DATA analysis software ,SECONDARY analysis ,DISEASE risk factors ,DISEASE complications - Abstract
Background: Analysis of general practice records can address the information gap on the epidemiology of type 2 diabetes (T2DM) in Ireland, informing practice and the development of interventions in primary care. The aim of this study was to identify patients with poor glycaemic control, risk factors for complications and evidence of end organ damage in a large multi-practice study and to profile their characteristics. Methods: Patients with T2DM were identified using disease coding in Health One practice management software in 41 general practices. Patients' demographics and clinical data were extracted. Rates of poor glycaemic control (glycated haemoglobin > 58 mmol/mol) and albumin creatinine ratio > 3 mg/mmol were calculated. A multilevel logistic regression analysis using both patient and practice variables was conducted. Results: Data was collected from 3188 patients of whom 29% (95% CI 28 to 31%) had poor glycaemic control, which was associated with younger age, higher BMI and higher total cholesterol. Only 42% of patients (n = 1332) had albumin creatinine ratio measured with 42% (95% CI 40 to 45%) of these having values > 3 mg/mmol. Older age groups, men, those with hypertension, eGFR < 60 ml/min/1.73m
2 and poor glycaemic control were most associated with higher values of albumin creatinine ratio. Conclusions: Analysing this large multi-practice dataset gives important information on the prevalence and characteristics of diabetic patients who are most at risk of poor outcomes. It highlights that recording of some data could be improved. [ABSTRACT FROM AUTHOR]- Published
- 2024
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