| アブストラクト | 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. |
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| ジャーナル名 | Diabetes, obesity & metabolism |
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| Pubmed追加日 | 2019/8/31 |
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| 投稿者 | Shields, Beverley M; Hattersley, Andrew T; Farmer, Andrew J |
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| 組織名 | Institute of Biomedical and Clinical Science, University of Exeter Medical;School, University of Exeter, Exeter, UK.;Nuffield Department of Primary Care Health Sciences, University of Oxford,;Oxford, UK. |
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| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/31468676/ |
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