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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: 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 w...

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Autores principales: Shields, Beverley M., Hattersley, Andrew T., Farmer, Andrew J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Blackwell Publishing Ltd 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6916179/
https://www.ncbi.nlm.nih.gov/pubmed/31468676
http://dx.doi.org/10.1111/dom.13865
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author Shields, Beverley M.
Hattersley, Andrew T.
Farmer, Andrew J.
author_facet Shields, Beverley M.
Hattersley, Andrew T.
Farmer, Andrew J.
author_sort Shields, Beverley M.
collection PubMed
description 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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spelling pubmed-69161792019-12-17 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. Hattersley, Andrew T. Farmer, Andrew J. Diabetes Obes Metab Original Articles 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. Blackwell Publishing Ltd 2019-10-07 2020-01 /pmc/articles/PMC6916179/ /pubmed/31468676 http://dx.doi.org/10.1111/dom.13865 Text en © 2019 The Authors. Diabetes, Obesity and Metabolism published by John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Articles
Shields, Beverley M.
Hattersley, Andrew T.
Farmer, Andrew J.
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
title 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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_short 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
title_sort 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
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6916179/
https://www.ncbi.nlm.nih.gov/pubmed/31468676
http://dx.doi.org/10.1111/dom.13865
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