Multinational patterns of second line antihyperglycaemic drug initiation across cardiovascular risk groups: federated pharmacoepidemiological evaluation in LEGEND-T2DM

Rohan Khera; Lovedeep Singh Dhingra; Arya Aminorroaya; Kelly Li; Jin J Zhou; Faaizah Arshad; Clair Blacketer; Mary G Bowring; Fan Bu; Michael Cook; David A Dorr; Talita Duarte-Salles; Scott L DuVall; Thomas Falconer; Tina E French; Elizabeth E Hanchrow; Scott Horban; Wallis Cy Lau; Jing Li; Yuntian Liu; Yuan Lu; Kenneth Kc Man; Michael E Matheny; Nestoras Mathioudakis; Michael F McLemore; Evan Minty; Daniel R Morales; Paul Nagy; Akihiko Nishimura; Anna Ostropolets; Andrea Pistillo; Jose D Posada; Nicole Pratt; Carlen Reyes; Joseph S Ross; Sarah Seager; Nigam Shah; Katherine Simon; Eric Yf Wan; Jianxiao Yang; Can Yin; Seng Chan You; Martijn J Schuemie; Patrick B Ryan; George Hripcsak; Harlan Krumholz; Marc A Suchard

doi:10.1136/bmjmed-2023-000651

Multinational patterns of second line antihyperglycaemic drug initiation across cardiovascular risk groups: federated pharmacoepidemiological evaluation in LEGEND-T2DM

BMJ Med. 2023 Oct 6;2(1):e000651. doi: 10.1136/bmjmed-2023-000651. eCollection 2023.

Authors

Rohan Khera^{1

2

3}, Lovedeep Singh Dhingra¹, Arya Aminorroaya¹, Kelly Li⁴, Jin J Zhou^{4

5}, Faaizah Arshad⁴, Clair Blacketer⁶, Mary G Bowring⁷, Fan Bu⁴, Michael Cook⁷, David A Dorr⁸, Talita Duarte-Salles⁹, Scott L DuVall^{10

11}, Thomas Falconer¹², Tina E French^{13

14}, Elizabeth E Hanchrow^{13

14}, Scott Horban¹⁵, Wallis Cy Lau^{16

17

18

19}, Jing Li²⁰, Yuntian Liu^{1

2}, Yuan Lu¹, Kenneth Kc Man^{16

17

18

19}, Michael E Matheny^{13

14}, Nestoras Mathioudakis²¹, Michael F McLemore^{13

14}, Evan Minty²², Daniel R Morales¹⁵, Paul Nagy^{23

24}, Akihiko Nishimura²⁵, Anna Ostropolets²⁶, Andrea Pistillo⁹, Jose D Posada²⁷, Nicole Pratt²⁸, Carlen Reyes⁹, Joseph S Ross^{2

29

30}, Sarah Seager²⁰, Nigam Shah^{31

32

33}, Katherine Simon^{13

14}, Eric Yf Wan^{18

19

34}, Jianxiao Yang³⁵, Can Yin²⁰, Seng Chan You^{36

37}, Martijn J Schuemie³⁸, Patrick B Ryan²⁶, George Hripcsak²⁶, Harlan Krumholz^{1

2

30}, Marc A Suchard^{4

10

39

40}

Affiliations

¹ Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, USA.
² Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT, USA.
³ Section of Health Informatics, Department of Biostatistics, Yale University School of Public Health, New Haven, CT, USA.
⁴ Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA.
⁵ Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
⁶ Observational Health Data Analytics, Janssen Research and Development, Titusville, NJ, USA.
⁷ Johns Hopkins University School of Medicine, Baltimore, MD, USA.
⁸ Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University School of Medicine, Portland, OR, USA.
⁹ Real-World Epidemiology Research Group, Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain.
¹⁰ Veterans Affairs Informatics and Computing Infrastructure, United States Department of Veterans Affairs, Salt Lake City, UT, USA.
¹¹ The University of Utah School of Medicine, Salt Lake City, UT, USA.
¹² Department of Biomedical Informatics, Columbia University, New York, NY, USA.
¹³ Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA.
¹⁴ Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
¹⁵ Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, UK.
¹⁶ Research Department of Practice and Policy, School of Pharmacy, University College London, London, UK.
¹⁷ Centre for Medicines Optimisation Research and Education, University College London Hospitals NHS Foundation Trust, London, UK.
¹⁸ Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China.
¹⁹ Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong, China.
²⁰ Data Transformation, Analytics, and Artificial Intelligence, Real World Solutions, IQVIA Inc, Durham, NC, USA.
²¹ Division of Endocrinology, Diabetes, and Metabolism, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
²² Faculty of Medicine, O'Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada.
²³ Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
²⁴ Division of Health Science Informatics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA.
²⁵ Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA.
²⁶ Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA.
²⁷ Systems Engineering and Computing, School of Engineering, Universidad del Norte, Barranquilla, Colombia.
²⁸ Quality Use of Medicines and Pharmacy Research Centre, UniSA Clinical and Health Sciences, University of South Australia, Adelaide, SA, Australia.
²⁹ Section of General Medicine and National Clinician Scholars Program, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
³⁰ Department of Health Policy and Management, Yale University School of Public Health, New Haven, CT, USA.
³¹ Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA, USA.
³² Clinical Excellence Research Center, Stanford University School of Medicine, Stanford, CA, USA.
³³ Technology and Digital Solutions, Stanford Health Care, Stanford, CA, USA.
³⁴ Department of Family Medicine and Primary Care, School of Clinical Medicine, University of Hong Kong, Hong Kong, China.
³⁵ Department of Computational Medicine, University of California Los Angeles David Geffen School of Medicine, Los Angeles, CA, USA.
³⁶ Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea (aka South Korea).
³⁷ Institute for Innovation in Digital Healthcare, Yonsei University, Seoul, Republic of Korea (aka South Korea).
³⁸ Epidemiology, Office of the Chief Medical Officer, Johnson & Johnson, Titusville, NJ, USA.
³⁹ Department of Biomathematics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
⁴⁰ Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.

Abstract

Objective: To assess the uptake of second line antihyperglycaemic drugs among patients with type 2 diabetes mellitus who are receiving metformin.

Design: Federated pharmacoepidemiological evaluation in LEGEND-T2DM.

Setting: 10 US and seven non-US electronic health record and administrative claims databases in the Observational Health Data Sciences and Informatics network in eight countries from 2011 to the end of 2021.

Participants: 4.8 million patients (≥18 years) across US and non-US based databases with type 2 diabetes mellitus who had received metformin monotherapy and had initiated second line treatments.

Exposure: The exposure used to evaluate each database was calendar year trends, with the years in the study that were specific to each cohort.

Main outcomes measures: The outcome was the incidence of second line antihyperglycaemic drug use (ie, glucagon-like peptide-1 receptor agonists, sodium-glucose cotransporter-2 inhibitors, dipeptidyl peptidase-4 inhibitors, and sulfonylureas) among individuals who were already receiving treatment with metformin. The relative drug class level uptake across cardiovascular risk groups was also evaluated.

Results: 4.6 million patients were identified in US databases, 61 382 from Spain, 32 442 from Germany, 25 173 from the UK, 13 270 from France, 5580 from Scotland, 4614 from Hong Kong, and 2322 from Australia. During 2011-21, the combined proportional initiation of the cardioprotective antihyperglycaemic drugs (glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors) increased across all data sources, with the combined initiation of these drugs as second line drugs in 2021 ranging from 35.2% to 68.2% in the US databases, 15.4% in France, 34.7% in Spain, 50.1% in Germany, and 54.8% in Scotland. From 2016 to 2021, in some US and non-US databases, uptake of glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors increased more significantly among populations with no cardiovascular disease compared with patients with established cardiovascular disease. No data source provided evidence of a greater increase in the uptake of these two drug classes in populations with cardiovascular disease compared with no cardiovascular disease.

Conclusions: Despite the increase in overall uptake of cardioprotective antihyperglycaemic drugs as second line treatments for type 2 diabetes mellitus, their uptake was lower in patients with cardiovascular disease than in people with no cardiovascular disease over the past decade. A strategy is needed to ensure that medication use is concordant with guideline recommendations to improve outcomes of patients with type 2 diabetes mellitus.

Keywords: Cardiology; Diabetes mellitus; Epidemiology; Pharmacology, clinical; Quality of health care.

Abstract

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