scholarly journals Adherence to Nice Guidance For Initiating Glp-1 Mimetics Among Patients With Type 2 Diabetes In Primary Care In England And Wales - An Evaluation Using The Clinical Practice Research Datalink (Cprd)

2014 ◽  
Vol 17 (7) ◽  
pp. A362
Author(s):  
K. Jameson ◽  
K. D'Oca ◽  
T. Murray-Thomas ◽  
C. O'Regan ◽  
P. Leigh
Keyword(s):  
Type 2 Diabetes ◽  
Primary Care ◽  
Clinical Practice ◽  
Practice Research ◽  
Clinical Practice Research Datalink ◽  
England And Wales ◽  
Nice Guidance

scholarly journals Cohort profile for the MASTERMIND study: using the Clinical Practice Research Datalink (CPRD) to investigate stratification of response to treatment in patients with type 2 diabetes

BMJ Open ◽  
2017 ◽  
Vol 7 (10) ◽  
pp. e017989 ◽  
Author(s):  
Lauren R Rodgers ◽  
Michael N Weedon ◽  
William E Henley ◽  
Andrew T Hattersley ◽  
Beverley M Shields
Keyword(s):  
Type 2 Diabetes ◽  
Clinical Practice ◽  
Treatment Response ◽  
Response To Treatment ◽  
Practice Research ◽  
Clinical Practice Research Datalink ◽  
Data Quality Control ◽  
Data Set ◽  
The Impact

PurposeThis is a retrospective cohort study using observational data from anonymised primary care records. We identify and extract all patients with type 2 diabetes and associated clinical data from the Clinical Practice Research Datalink (CPRD) to inform models of disease progression and stratification of treatment.ParticipantsData were extracted from CPRD on 8 August 2016. The initial data set contained all patients (n=313 485) in the database who had received a type 2 diabetes medication. Criteria were applied to identify and exclude those with type 1 diabetes, polycystic ovarian syndrome or other forms of diabetes (n=40 204), and for data quality control (n=12). We identified 251 338 patients for inclusion in future analyses of diabetes progression and treatment response.Findings to dateFor 6-month response to treatment, measured by change in glycated haemoglobin (HbA1c), we have 91 765 patients with 119 785 treatment response episodes. The greatest impact on reduction of HbA1c occurs with first-line and second-line treatments, metformin and sulfonylurea. Patients moving to third-line treatments tend to have greater weights and higher body mass index. We have investigated the impact of non-adherence to commonly used glucose-lowering medications on HbA1c. For baseline-adjusted HbA1c change over 1 year, non-adherent patients had lower HbA1c reductions than adherent patients, with mean and 95% CI of −4.4 (−4.7 to −4.0) mmol/mol (−0.40 (−0.43 to −0.37) %).Future plansFindings from studies using these data will help inform future treatment plans and guidelines. Additional data are added with updates from CPRD. This will increase the numbers of patients on newer medications and add more data on those already receiving treatment. There are several ongoing studies investigating different hypotheses regarding differential response to treatment and progression of diabetes. For side effects, links to Hospital Episode Statistics data, where severe events such as hypoglycaemia will be recorded, will also be explored.

scholarly journals Prediction of type 2 diabetes risk in people with non-diabetic hyperglycaemia: model derivation and validation using UK primary care data

BMJ Open ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. e037937
Author(s):  
Briana Coles ◽  
Kamlesh Khunti ◽  
Sarah Booth ◽  
Francesco Zaccardi ◽  
Melanie J Davies ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Primary Care ◽  
Prognostic Models ◽  
Predictor Variables ◽  
Validation Dataset ◽  
Practice Research ◽  
Clinical Practice Research Datalink ◽  
Primary Care Data

ObjectiveUsing primary care data, develop and validate sex-specific prognostic models that estimate the 10-year risk of people with non-diabetic hyperglycaemia developing type 2 diabetes.DesignRetrospective cohort study.SettingPrimary care.Participants154 705 adult patients with non-diabetic hyperglycaemia.Primary outcomeDevelopment of type 2 diabetes.MethodsThis study used data routinely collected in UK primary care from general practices contributing to the Clinical Practice Research Datalink. Patients were split into development (n=109 077) and validation datasets (n=45 628). Potential predictor variables, including demographic and lifestyle factors, medical and family history, prescribed medications and clinical measures, were included in survival models following the imputation of missing data. Measures of calibration at 10 years and discrimination were determined using the validation dataset.ResultsIn the development dataset, 9332 patients developed type 2 diabetes during 293 238 person-years of follow-up (31.8 (95% CI 31.2 to 32.5) per 1000 person-years). In the validation dataset, 3783 patients developed type 2 diabetes during 115 113 person-years of follow-up (32.9 (95% CI 31.8 to 33.9) per 1000 person-years). The final prognostic models comprised 14 and 16 predictor variables for males and females, respectively. Both models had good calibration and high levels of discrimination. The performance statistics for the male model were: Harrell’s C statistic of 0.700 in the development and 0.701 in the validation dataset, with a calibration slope of 0.974 (95% CI 0.905 to 1.042) in the validation dataset. For the female model, Harrell’s C statistics were 0.720 and 0.718, respectively, while the calibration slope was 0.994 (95% CI 0.931 to 1.057) in the validation dataset.ConclusionThese models could be used in primary care to identify those with non-diabetic hyperglycaemia most at risk of developing type 2 diabetes for targeted referral to the National Health Service Diabetes Prevention Programme.

Sign in / Sign up

Export Citation Format

Share Document