Journal article

A five-drug class model using routinely available clinical features to optimise prescribing in type 2 diabetes: a prediction model development and validation study

Abstract:: Background: Data to support individualised choice of optimal glucose-lowering therapy are scarce for people with type 2 diabetes. We aimed to establish whether routinely available clinical features can be used to predict the relative glycaemic effectiveness of five glucose-lowering drug classes.

Methods: We developed and validated a five-drug class model to predict the relative glycaemic effectiveness, in terms of absolute 12-month glycated haemogl...
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Publication status:: Published

Peer review status:: Peer reviewed

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APA Style

Dennis, J. M., Young, K. G., Cardoso, P., Farmer, A., & Holman, R. R. (2025). A five-drug class model using routinely available clinical features to optimise prescribing in type 2 diabetes: a prediction model development and validation study. Lancet, 405(10480), 701–714.

MLA Style

Dennis, JM, et al. “A Five-Drug Class Model Using Routinely Available Clinical Features to Optimise Prescribing in Type 2 Diabetes: a Prediction Model Development and Validation Study.” Lancet, vol. 405, no. 10480, 2025, pp. 701–14.

Chicago Style

Dennis, JM, KG Young, P Cardoso, A Farmer, and RR Holman. 2025. “A Five-Drug Class Model Using Routinely Available Clinical Features to Optimise Prescribing in Type 2 Diabetes: a Prediction Model Development and Validation Study.” Lancet 405 (10480): 701–14.
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Files:: Dennis_et_al_2025_A_five-drug_class.pdf

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Publisher copy:: 10.1016/S0140-6736(24)02617-5

Authors

+ Dennis, JM More by this author

Role:: Author

+ Young, KG More by this author

Role:: Author

+ Cardoso, P More by this author

Role:: Author

+ Farmer, A More by this author

Institution:: University of Oxford
Division:: MSD
Department:: Primary Care Health Sciences
Role:: Author

+ Holman, RR More by this author

Institution:: University of Oxford
Division:: MSD
Department:: RDM
Role:: Author

et al.

Contributors

+ MASTERMIND consortium

Role:: Contributor

Publisher:: Elsevier
Journal:: Lancet More from this journal
Volume:: 405
Issue:: 10480
Pages:: 701-714
Publication date:: 2025-02-25
Acceptance date:: 2024-11-27
DOI:: 10.1016/S0140-6736(24)02617-5
EISSN:: 1474-547X
ISSN:: 0140-6736

Language:: English
Pubs id:: 2067346
Local pid:: pubs:2067346
Deposit date:: 2024-11-28
ARK identifier:: ark:/29072/ora_3a1a764fec89400c9421e606c536be89

Terms of use

Copyright holder:: Dennis et al.
Copyright date:: 2025
Rights statement:: © 2025 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.

Licence:: CC Attribution (CC BY)

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