Dataset Information

Identifying routine clinical predictors of non-adherence to second-line therapies in type 2 diabetes: A retrospective cohort analysis in a large primary care database.

ABSTRACT:

Aims

To investigate whether combinations of routinely available clinical features can predict which patients are likely to be non-adherent to diabetes medication.

Materials and methods

A total of 67?882 patients with prescription records for their first and second oral glucose-lowering therapies were identified from electronic healthcare records (Clinical Practice Research Datalink). Non-adherence was defined as a medical possession ratio (MPR) ?80%. Potential predictors were examined, including age at diagnosis, sex, body mass index, duration of diabetes, glycated haemoglobin, Charlson index and other recent prescriptions.

Results

Routine clinical features were poor at predicting non-adherence to the first diabetes therapy (c-statistic = 0.601 for all in combined model). Non-adherence to the second drug was better predicted for all combined factors (c-statistic =0.715) but this improvement was predominantly a result of including adherence to the first drug (c-statistic =0.695 for this alone). Patients with an MPR ?80% for their first drug were 3.6 times (95% confidence interval 3.3,3.8) more likely to be non-adherent to their second drug (32% vs. 9%).

Conclusions

Although certain clinical features were associated with poor adherence, their performance for predicting who is likely to be non-adherent, even when combined, was weak. The strongest predictor of adherence to second-line therapy was adherence to the first therapy. Examining previous prescription records could offer a practical way for clinicians to identify potentially non-adherent patients and is an area warranting further research.

SUBMITTER: Shields BM

PROVIDER: S-EPMC6916179 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Publications

Identifying routine clinical predictors of non-adherence to second-line therapies in type 2 diabetes: A retrospective cohort analysis in a large primary care database.

Shields Beverley M BM Hattersley Andrew T AT Farmer Andrew J AJ

Diabetes, obesity & metabolism 20191007 1

<h4>Aims</h4>To investigate whether combinations of routinely available clinical features can predict which patients are likely to be non-adherent to diabetes medication.<h4>Materials and methods</h4>A total of 67 882 patients with prescription records for their first and second oral glucose-lowering therapies were identified from electronic healthcare records (Clinical Practice Research Datalink). Non-adherence was defined as a medical possession ratio (MPR) ≤80%. Potential predictors were exam ...[more]

PMID: 31468676

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Dataset Information

Identifying routine clinical predictors of non-adherence to second-line therapies in type 2 diabetes: A retrospective cohort analysis in a large primary care database.

Aims

Materials and methods

Results

Conclusions

Publications

Identifying routine clinical predictors of non-adherence to second-line therapies in type 2 diabetes: A retrospective cohort analysis in a large primary care database.

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